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I, 7710 u- .u. t n r v v. 1! 1.! 7127. hfii. h; u k in? .2 '18 O2 \2 r... lt‘fl.‘ krv‘l‘latabzzl 2 . 2 2.622. 1 ‘20. .2”ka .l’i 8.5.x INJN. 22L." .$.2|\L¢LJ. 2.. .2 .qeamnzh 22.n....2....:.2..3..2.vt£..2 I. 2.20.122; 3......2urn.2.a....43.22.22.222..- ._ . . . 2.2.0422)... 22 2%.... 2 1.: 0:20.21; Vic.“ $22241.w\.a2|-2\.i|.. 2.1.4.2A 2 2 . . v . 2. , . . . .2 2... . . . . .22.. .28., , ... 2. . h1.2\~ 2....2222C h I \ 2 ‘ 1| l.‘ . 2" \\\\ .l 29111.22. 2 1:24.,OOIAAficn‘ .Lo 3 - .2.s2..1.t.t..u..... .2. ..-2.A2..22...22 I... .. ., 2.22.9.5: .22....22 121...}...2! 290‘ ..... ‘taa ‘5‘ ~22 LIBRARY Michigan State University This is to certify that the thesis entitled SIMULATION AND FEASIBILITY STUDY OF SOLAR WATER HEATING FOR THE FOOD PROCESSING INDUSTRY IN THE MIDWESTERN UNITED STATES presented by STEVEN MYRON THOMAS has been accepted towards fulfillment of the requirements for Masters of Science Agricultural degree in _ . Engineer1ng % Major professor Date lO-ll-77/my 0-7 639 SIMULATION AND FEASIBILITY STUDY OF SCLAR WATER HEATING FOR THE FOCD PMESSING INDUSTRY IN THE MIDWESTERN UNITED STATES By Steven Myron Thomas A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Agricultural Engineering Department 1977 ABSTRACT SIMULATICN AND FEASIBILITY STUDY OF SOLAR WATER HEATING FOR THE FOOD PROCESSING INDUSTRY IN THE MIDWESTERN UNITED STATES By Steven Myron Thomas The feasibility of solar water heating applications in the food processing industry has been studied. Warm water usage surveys were made for three plant sizes; small, medium, and large, for representa- tive plants in the dairy, meat and fruit and vegetable processing industries in the midwestern United States. A computer model, TRNSYS, was used to simulate a solar water heating system. Insolation simula— tion models were tested for predicting solar insolation data for the average year. The long—term performance for each plant solar water heating system was determined. An economic comparison was made for solar energy with electricity, fuel oil, and natural gas, to determine the current economic feasibility. Economic feasibility results indicate a significant solar energy contribution can be made by replacing up to 90 percent of the electric and 20 percent of the fossil fuel energy consumption for 11081: plants .ppmva, M25. a4 new MaJor Professor /[ /[ K / /7 7 Approved : . De artment Chairman over a 20—year payback period. ACKNOWLEDGEMENTS The author wishes to thank the Energy Research and Development Administration (ERDA) and the United States Department of Agriculture (USDA) for their financial support of this study. Sincere thanks and appreciation is extended to Dr. F. W. Bakker— Arkema (Agricultural Engineering Department) for his professional leadership, encouragement , and personal contribution to the progress and development of the program. The character, confidence and professional spirit of Dr. Bakker—Arkema was of primary importance to the author in the completion of this study. Appreciation is also extended to Mr. A. L. Rippen (Food Science and Human Nutrition Department) for his helpful guidance and fellowship during the course of the study. Mr. R. Patterson (Agricultural Engineering Department) is also acknowledged for his contribution to the completion of this study. Special thanks are due to Ms. Shari Cisco for her timely and professional typing of this manuscript. The Agricultural Engineering faculty and fellow students also contributed significantly to the overall learning experience. Many thanks to these people for their intellectual and personal fellowship. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS. LIST OF TABLES LIST OF FIGURES . LIST OF SYMBOLS . 1. 2. INTRODUCTION OBJECTIVES . REVIEW OF LITERATURE 3 3 3 03030303 0300 (900 Computer Mbdels .1 .2 TRNSYS Program. 3 Insolation Data 3 3.1 General availability 3.3.2 local availability . 3 3.3 Insolation models weather Data Availability . Principles of Solar Radiation . 4 .5 Principles of Heat Transfer 6 7 Solar Component Design and Technology . 3.7.1 Solar collectors 3.7.2 Other components Physical Solar Water Heating Systems Warm Water Usage . 3.9.1 Dairy plants 3. 9. 2 Meat plants . . 3. 9. 3 Fruit and vegetable plants . .10 Economic Analysis . 3.10.1 Life cycle costing method 3.10.2 Cost effectiveness method 3 10.4 Cbnventional energy costs Page 11 vi viii 38 38 4O MEI‘IDHDIOGY. 4.1 Insolation Test Models 1 Control model 2 Control model with constant temperature. 3 ASHRAE model using daily totals. . 4 ASHRAE model using weekly averages . 5 Whillierimodel . . . 6 Transmissivity model ibvbrbbbsbtb J. .l. .l. .1. .1. .l. 2 Energy Demand loads . .3 Physical Solar Water Heating system Design 4 SystemiMbdeling Herb-Lb 1 Simulation considerations 2 Card reader. 3 Radiation processor. .4 Flat plate collector .5 Controller . . 6 7 8 9 rbvbrbbbihvbhhibhb bbhbbbhpfibbbvbrk Heat exchanger . Pumps . . Storage tank . Auxiliary heater 4 5 Design Parameters . 4.5.1 Radiation processor 4.5.2 Collector 4 5.3 Heat exchanger . 4 5.4 Tank pump . . 4.5.5 Collector pump . 4.5.6 Storage tank 4.5.7 Auxiliary heater 4.6 Simulation Periods .6. Insolation modeling - Group A Hourly averages — Group B Storage tank tests - Group C . Collector fluid flow rate tests — Group D Dairy plants — Group E . . Meat plants — Group F . Fruit and vegetable plants — Group G ASHRAE averages - Group H .>.b.b.nie.>.n.a bohbmibao'mp 6 6 6 6 6 6 6 4.7 Description of Simulation Results . 4.7.1 Simulation outputs . . . 4. 7. 2 Description of performance results . 4.8 Projection of Results . iv Page 41 41 88 88 I 100 . 102 lO. 4 8 1 Method. . . . . . . 4.8.2 Dairy and meat plants . . 4 8 3 Fruit and vegetable plants. Conventional energy costs . Solar system capital investments Solar energy costs. . Cost effectiveness using capital investment analysis SIMULATION RESUIHS AND DISCUSSION . Insolation Models — Group A Hourly Average Models — Group B Tank Simulation Results - Group C. Collector Fluid Flow Rate Test Results — Group D . Processing Plant Results and Projections . 5 1 5.2 5.3 Storage 5 4 5 5 01010101 5. .5 5. 5. 1 Sample simulation results . 2 Dairy plants — Group E 3 Meat plants — Group F . 4 Fruit and vegetable plants - Group G FEASIBILITY RESUDTS AND DISCUSSION. 6.1 Dairy Plant Feasibility 6. 2 Meat Plant Feasibility 6. 3 Fruit and vegetable Plant Feasibility. SUMMARY CONCLUSIONS SUGGESTIONS FCEtFIHURE RESEARCH REFERENCES. APPENDIX A — APPENDIX B — APPENDIX C — APPENDIX D — APPENDIX E — APPENDIX F — APPENDIX G - APPENDIX H — APPENDIX I - APPENDIX J — APPENDIX K — ASHRAE Insolation Model Ratios Daylength Table for East Lansing . Whillier Insolation Nbdel Ratios . . Atmospheric Transmissivity Insolation Nbdel Ratios . Average Ambient Air Temperature used in Average Year ASHRAE Weekly Insolation Mbdel weekly Averages of Daily Insolation for all Test Locations Inogram SOLAR . TRNSYS Control Card Deck . Dairy Plant Snmulation Results Meat Plant Shmulation Results. . Fruit and Vegetable Plant Simulation Results V Page 102 102 103 104 104 105 105 106 107 107 115 116 119 119 119 124 131 135 144 144 152 157 162 166 168 170 175 176 177 179 180 181 186 191 221 A A! ‘1“ Table 0) $0010 I—' NH gs (90040301 11 12 mtbibibsbbbbfibbbbhbtbibbbwww LIST OF TABLES Energy Consumption of the Dairy, Meat, and Fruit and Vegetable Processing Industries. . Dairy Plant Warm Water Usage . Meat Plant Warm Water Usage Fruit and Vegetable Plant Warm Water Usage Summary of Simulation Parameters and Their SI Units Starting Day of Simulations Summary of Collector Areas Chosen for Testing. legend for Simulation Identification . Group A Simulations: Insolation Test Models . Group B Simulations: Hourly Average Tests Group C Simulations: Storage Tank Tests . Group D Simulations: Collector Flow Rate Tests Group E Simulations: Dairy Plants Group F Simulations: Meat Plants. Group G Simulations: .Fruit and Vegetable Plants . Group H Simulations: ASHRAE Averages. Group A Simulation Results for Spring. Group A Simulation Results for Summer. Group A Simulation Results for Fall Group A Simulation Results for Winter. vi Page .29 .30 .33 .35 .59 .60 .63 .69 .74 .76 .77 .79 .80 .85 .89 . 108 . 109 . 110 . 111 I \\\\\‘:‘ 41;) Table 0101010101 LOOOKIQU‘I ()1 l-' 0 Group A Simulation Results Summary. Group B Simulation Results for Hourly Averages. Storage Tank Design Simulation Results. Collector Flow Rate Simulation Results. Small Dairy Plant Simulation Results for September 1974 — East Lansing . Summary of September Simulation Results for Dairy Plants Page . 112 . 117 . 117 . 120 . 123 . 125 Summary of Annual Simulation Results for Medium Dairy Plant 127 Annual Projected Performance of Solar Water Heating System for Dairy Plants . . . . long Term Annual Performance of Solar Water Heating System for Dairy Plants at all Test locations Summary of Septerber Simulation Results for Meat Plants Annual Projected Performance of Solar Water Heating System for Meat Plants long Term Annual Performance of Solar Water Heating System for Meat Plant at all Test locations . Summary of September Simulation Results for Fruit and Vegetable Plants. . of Yearly Simulation Results for Fruit and Vegetable Plants. . . Annual Projected Performance of Solar Water Heating System for Small Fruit and Vegetable Plants. Annual Projected Performance of Solar Water Heating System for Medium Fruit and Vegetable Plant. Annual Projected Performance of Solar Water Heating System for large Fruit and Vegetable Plant . . Long Term Armual Performance of Solar Water Heating System for Fruit and Vegetable Plants at all Test locations. Summary of Economic Results of Solar Water Heating and Conventional Energy Sources . . 129 . 130 . 132 . 133 .134 . 136 . 137 . 139 . 140 . 142 . 143 F. Figure 0639630151101 l-' 03 15 LIST OF FIGURES Spectral Distribution of Solar Radiation. Description of Sun-Earth Orientation Angles . Principles of Flat Plate Collector Design Basic Elements of Solar Water Heating System. Dairy Processing Plant Water Demand Schedules Meat Processing Plant Water Demand Schedules. Fruit and Vegetable Processing Plant Water Demand Schedules . . Subroutine and Information Flow Diagram of the TRNSYS Solar Water Heating Model . Insolation Model Test Results Tank Volume Parametric Test Results . Collector Fluid Flow Rate Parametric Test Results Dairy Plant Solar Water Heater Performance Curves Dairy Plant Solar Water Heater Energy Cost Comparison Dairy Plant Solar Water Heater Capital Investment comparison. Meat Plant Solar Water Heater Performance Curves. Meat Plant Solar Water Heater Energy Cost Comparison. Meat Plant Solar Water Heater Capital Investment Comparison. . . . Fruit and Vegetable Plant Solar Water Heater Performance Curves . . . . . . . . . . viii Page 17 19 21 27 31 37 . 113 . 118 . 121 . 145 . 147 . 151 . 153 .154 .156 .158 Figure Page 6.8 Fruit and vegetable Plant Solar Water Heater Energy Cost Comparison. . . . . . 159 6.9 Fruit and Vegetable Plant Solar Water Heater Capital Investment Comparison . . . . 161 bee IVKDL LIST OF SYMBOLS surface area, m2 collector perimeter, m specific heat, kJ/kg C annual cost of solar energy system, $/year total initial investment for solar energy system, $ Capital Recovery Factor, $/$/year collector efficiency, % collector geometric efficiency factor, decimal convection heat transfer coefficient, kJ/hr m2 C back loss coefficient, kJ/hr m2 C edge loss coefficient, kJ/hr m C local hour angle measured from solar noon. degrees annual interest rate, % effective interest rate, % annual rate of fuel price increase, % thermal conductivity, kJ/hr m C thickness of insulation, m latitude, degrees mass flow rate, kg/hr mass flow through collector, kg present value of a sum of money, 33 initial amount of money or savings, 35 (2.2.9.8090 QODL QI‘ANK QIUI‘AL R Rh 3* RE RAUI’OI‘AL SOLAR rate of energy or heat transfer, kJ/hr back loss of collector, kJ/hr edge loss of collector, kJ/hr top loss of collector, kJ/hr auxiliary energy, kJ energy collected by collector, kJ energy lost by storage tank, kJ energy passing through heat exchanger, kJ energy delivered to load by storage tank, kJ sum of QAUX and QTANK, kJ measured daily horizontal insolation, ly/day measured hourly horizontal insolation, 1y/hr extraterrestrial daily horizontal radiation, 1y/day extraterrestrial hourly horizontal radiation, 1y/hr total radiation striking collector surface, kJ energy delivered to load by collector, % SOLAthotal energy delivered to load by collector, % of CHORAL SYSCP T; average period of daily collector operation, hrs/day time period, years temperature, C ambient air temperature, C mean collector plate temperature, C energy loss coefficient, kJ/m2 hr C overall heat transfer coefficient, kJ/hr C overall collector loss coefficient, kJ/hr m2 C top loss coefficient of collector, kJ/hr m2 C future value of a sum of money, $ xi solar altitude angle, degrees collector tilt angle from horizontal, degrees solar declination, degrees surface enittance, decimal Stefan—Boltzran constant, kJ/hr m2 K solar azimuth, degrees collector efficiency, % atmospheric transmissivity, and total collector transmittance, decimal xii 1. INTRODUCTION Recent years have imposed upon our society a new era of energy awareness. The energy conservationists along with world inflation and changes in international markets have made businessmen, consumers, and legislators realize the urgent need to look to the future and establish a long needed national energy policy dealing with the conservation of current energy sources and further research and development of current and alternate energy sources. Partially as a result of this increased awareness, more research is being conducted in all areas of fossil energy production and utilization. Because of the magnitude of this effort, the urgency of the need, and economic considerations, researchers are considering all areas of energy consumption including the agriculture industry which represents a small percentage of the total consumption. Energy consumption patterns are very diverse, therefore we must look into every facet and reevaluate the relative costs of resources and products. Small percentages when added up can make a significant contribution to decreasing the overall energy consumption, and therefore concern must be given for every per- centage point which can be gained through the use of alternate energy sources or conservation. The agriculture industry represents 12 to 20 percent (Stout, 1975) of the total energy use in the United States. Currently researchers are looking into using more alternate sources of energy to satisfy certain 2 energy demands in agriculture such as drying of grain crops, fruits, and vegetables, heating livestock housing, and using solar heater water for process cooking, heating of food stuffs , and peripheral functions such as cleaning machinery, product sterilization, and general washing. This study deals specifically with the possibility of using solar energy as an alternate energy source to replace conventional fossil fuel energy in supplying hot water for processing and cleaning operations in the food industry. For this study the food industry is divided into three general areas, namely the dairy, meat, and fruit and vegetable industries . A small , medium, and large processing plant representative from each of the three areas was selected and surveyed to determine their hot water energy consumption . These plants were taken as representative plants for the midwestern United States and analyzed for solar energy utilization potential at three locations; East Lansing (Michigan), Indianapolis (Indiana), and Columbia (Missouri). Solar energy as an alternate energy source has certain natural use restrictions which make the system difficult to design and utilize to its fullest capability. A primary concern is its variability and uncertainty of collect ion , thus making it undependable and necessitating some type of storage or back up energy supply to make it reliable to satisfy the food industry. Because the utilization of solar energy is inherently dependent upon the weather and system interact ions of storage, collection , and usage patterns, the design of such a system requires many calculations to determine its performance. In View of the complexity of this type of analysis a computer simulation lends itself as a viable tool to aid in the design of such a system and to determine its overall use potential. 3 Such a computer model called TRNSYS was chosen to fulfill the requirements. The TRNSYS program is capable of simulating the desired systems as a function of time, using actual weather data and hot water derand informa- tion as inputs. Since it is a transient simulation, calculations are performed at a specified time step over the simulations period. Because of this the accuracy of the simulation is directly related to the accuracy of the measured input weather and insolation data, and concern must be given to the type of data to use. Hourly weather and insolation data is recommended by the TRNSYS authors. Unfortunately, hourly insolation data suitable for use with TRNSYS is scarce and in most cases only available for a short period of time. Since the performance of any solar installations depends largely on location it would be desirable to be able to use data measured for one location, or use available records of daily or weekly values of insolation and temperature to predict appropriate hourly data. This study will investigate the extent of current weather data availability and test other insolation and weather models to determine the best model to predict the long term performance of the solar water heating systers in supplying the demands of the food processing plants. When the simulations are completed, basic economic analysis will illustrate the degree to which these solar water heating systems can be economically incorporated into commercial food processing plants. 2. OBJECTIVES The overall objective of this study is to study the economic and engineering feasibility of heating process water for the food processing industry with solar energy. Specifically, the objectives are: 1. Use the Transient Simulation Program (TRNSYS) to test different types of insolation and temperature data models and determine the applicability, for solar system design, of these models to generate hourly insolat ion and temperature data, using currently available data, to use in areas where actual measured hourly data is insufficient. Develop solar water heating design parameters and a system configuration applicable to the food processing industry. Use hot water usage surveys and the solar water heating simula- tion model to predict the long term contribution of solar energy to the total energy demand for each processing plant , system size, and geographic location. Use the simulation results to investigate the real potential for saving fossil fuel and the economic feasiblity of using solar energy at the present state of technology and economics for heating water for food processing plants. 3. REVIEW OF LITERATURE The solar energy industry is becoming diversified into all areas of energy usage ranging from high temperature steam power generation installations to low temperature drying of agricultural crops (Daniels and Duffie, 1955), not to mention the natural energy conversion performed by plants using photosynthesis . The research and publications resulting from this growing industry is increasing at a fast rate. This study is concerned only with the low temperature application of solar energy for water heating. This review will consider only the simulation models, data inputs and system component designs directly relating to the solar water heating feasibility corresponding to demands from the food processing industry. 3 . 1 Computer Models The design and performance analysis of any solar energy system requires many energy balance and transfer calculations . Because solar radiation is a dynamic occurrence , the behavior of the system continues to change over time requiring more calculations to determine its transient performance. A solar energy installation is a system of integrated components such as a collector, heat exchanger, storage tank, pumps, and controls . Mathematical models have been developed for each of these components, consisting of energy balances and sets of algebraic 6 and/or differential equations . Because of the complexity and magnitude of solving these integrated models , the application of modern high speed computers is necessary to determine the long term system performance. With this capability, the analysis of the transient responses of integrated solar energy systems is now possible. Several attempts have been made at developing reliable programs for this purpose. Ramsey (1975) has developed a flat plate collector computer program for heating liquids and has studied the non-uniform temperature characteristics of the collector and the flow distributions for different collector arrays . Kays and london (1958) presented relat ion- ships which describe the performance of different types of heat exchangers . A few programs have been campleted capable of simulating complete systems . Edenburn ( 1973) developed a specialized "Systems Analysis Computer Program" designed to model the total energy requirements of an entire cammmity. This model is too specific for general use. Graven (1974) discussed several programs and their status such as the Post Office Program, Transient Simulation Program - TRNSYS, and the Jet Propulsion Laboratory program. The Post Office Program deals with building loads and includes no treatment of collector design or storage components. The TRNSYS program contains over 20 routines to model the transient performance of different types of integrated solar energy systems including collectors, storage tanks, and auxiliary heaters. According to Graven (1974) the scope of this model is generally limited to solar energy applications and not applicable to general building loads and other types of energy supplies. The Jet Propulsion program is limited to solar water heating only, thus not generally applicable. Graven (1974) concluded that many programs are still in 7 the process of development and that no single readily available model is generally accepted. Edenburn and Grandjean (1975) and Edenburn (1975) discussed an "Energy System SiImilation Computer Program" called SOISYS developed by Sandia Laboratories. This program is capable of simulating the transient performance of energy systems cemposed of 21 component subroutines . 'Ihe TRNSYS and SOISYS programs are both capable of simulating solar water heating systems needed for this study. The TRNSYS program was chosen based on its availability, ease of operation, and the results presented by other researchers on its performance and accuracy . 3.2 TRNSYS Program The development and operation of TRNSYS is completely described by Klein M. (1974). Instruction is given for connecting the desired components and determining the appropriate parameters . TRNSY S then performs the necessary simultaneous solutions of algebraic and differen- tial equation over a specified time step to determine the system variables. Duffie and Beckman (1974) presented a detailed discussion of the procedures used by TRNSYS , individual component model descriptions , and methods for determining component parameters based on actual component design and application . Modeling considerations and recommenda- tions are also discussed and examples presented to illustrate the behavior of solar systers. TRNSYS was used by Oonk, Beclman, and Duffie (1975) to model residential heating and cooling performance of the Colorado State University house. Klein 'et a1. (1975) used TRNSYS to simulate a solar 8 water heating system for different system configurations and radiation data inputs. Different runs were made over a.one-month period to observe the performance of each system configuration. An 8 percent loss was observed in the amount of solar energy supplied to the load when a heat exchanger component was included in the model. A simulation compar- ison of input radiation data was made using hourly measured insolation data and hourly msan insolat ion data based on monthly insolation normals for the same period. The hourly mean insolation data simulation results indicated a 5 to 25 percent increase in performance over the hourly msmsmred insolation data.simulation. It was suggested that a probable cause of this result was suggested as being a muting effect by the average data on the effects and irregularity of cloud.cover on collector performance. No conclusion was drawn concerning the type of insolation data whidh should be used. Gutierrez §t_al. (1974) used TRNSYS in studying the effects of auxiliary energy supply, load type, and storage capacity variations on total system performance; constant collector design parameters, based on current design practices, were used during this study . It was concluded that a three-layered stratified storage tank gave the best results with respect to accuracy and.compute time compared to a higher degree of stratification; also, that the best.method of adding auxiliary heat to the warmxwater is directly to the line coming from the storage tank. The best and worst times of water reroval were also examined by the authors, with the most favorable time occurring early afternoon and the least favorable occurring just before sunrise. 9 3 . 3 Insolat ion Data TRNSYS requires input data values of solar radiation and ambient air temperature at constant time intervals for the duration of the simulation period. The time interval is generally one hour. However, in this study the time interval will be examined for its effect on system performance. Since two data values are needed at each time interval, a distinction will be made wherein: solar radiation data will be referred to as insolation data defined as the total amount of solar radiation received at the surface of the earth (Kreider and Kreith, 1975) , while all atmospheric conditions including air temperatures and wind speed will be referred to as weather data. 3 . 3 . 1 General availability Presently, there are 67 collection sites in the United States which measure daily total insolation (Solar Radiation . . ., 1976). Of these, 29 also provide hourly insolat ion values . The information collected by these stat ions is gathered and tabulated at the National Climatic Center in Ashville, North Carolina. The period of these collection records range as far back as 1952. Baker and Klink (1975) discussed the quality of this data. Discrepancies of 1p to 10 percent were accredited to calibration variations, age, and type of absorber surface of the recording instruments. Due to lack of funds and standard- ization among the different stations the accuracy and reliability of these records is generally questionable. In Septerber 1972 the National Climatic Center ceased publication of radiation data as requested by the 10 National Weather Service because the errors incorporated were estimated to range from 5 to 30 percent (Solar Radiation . . . , 1976).. The National Oceanic and Atmospheric Administrat ion has proposed that a new standardized network of collection stations be established. If and when this occurs, further radiation data will become available for use in solar system design . 3 . 3 . 2 Local availability This study is concerned with solar energy utilization in the midwestern United States . Three locations were chosen , based on the availability of data, geographic location, and the location suitability of food processing plants, in which to study feasibilities. The loca- tions chosen were East Lansing (Michigan), Columbia (Missouri), and Indianapolis (Indiana). All three locations have average daily insola- tion data available for weekly periods over a period of at least 13 years (Baker and Klink, 1975). Hourly insolation data is also available for 13 years (1946—1958) at the Columbia station and one year (1974-1975) at the East Lansing station. Hourly data is currently not available for the Indianapolis station. Since proper design requires that system performance is predictable for both good and bad insolat ion years , more representative and complete data on an hourly basis is desirable. 3. 3 . 3 Insolat ion models Extensive work has been done developing models to predict solar insolation necessary for calculating heating and cooling loads of 11 buildings. Although it is impossible to accurately predict future insolation, certain models predict insolation to the degree necessary for certain design problems . Three models have been proposed for simulating hourly horizontal solar insolat ion at a given location . The.American Society of'Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) has developed tables which can be used to predict clear day hourly insolation based.on solar time for different times of the year at different latitudes (ASHRAE, 1974). The purpose of these tables is to yield maximum design values of insolation for collector design. Clear day conditions occur infrequently. Actual insolation will be influenced by cloud cover, dust, water vapor, and other factors which affect the atmospheric transmissivity as described by Fritz (1957) and Threlkeld and Jordan ( 1957). The use of this insolation model in TRNSYS involves a modification to account for non—clear day operation. A set of standard curves proposed by Whillier (1956) and further expanded.by Liu and.JOrdan (1960) uses daily insolation totals to determine hourly insolation values based on solar time. This second mrtkfl.was developed.and tested in South Africa and gives reasonable accuracy for other locations . Duffie and Beckman (1974) recommended this model to estimate hourly insolation values for input to TRNSYS. Williams, Ioomis, and Carter (1974) have developed a Fortran computer program based on the'Whillier curves for calculating hourly insolation values given total daily insolation, location, daylength, and time of year. A third model to be tested consists of using daily transmissivities together with a.program.that calculates extraterrestrial radiation (Fumival, et a1., 1969). Equation (3.1) shows the relationship which 12 exists between measured daily horizontal insolation (R), the total horizontal extraterrestrial radiation (R*) , and the atmospheric transmissivity (T) (Baker and Haines, 1969); 1' = R/R* (3. 1) or for hourly insolation values: Rh = TX R*h (3.2) where Rh is hourly horizontal insolat ion and R"‘h hourly horizontal extraterrestrial radiation . Thomas (1977) discussed the behavior of transmissivity for a one-year (1974-75) period in East Lansing and gives weekly averages of daily transmissivity. All three models can be used to predict insolation for an "average" year based on weekly averages of daily total insolation or daily transmissivity values using 13 year average data contained in Baker and Haines (1969) . Other models for predicting daily insolation have been proposed. Fritz (1957), Moon (1940), Sadler (1974), Threlkeld and Jordan (1957), and Liu and Jordan (1960) discussed the effects of air moisture, dust, air mass thickness , wavelength , location with respect to industrial centers, etc. , and other atmospheric properties upon the atmospheric transmissivity and thus the insolation. Much of this work involves solar constant influences on insolation and is fundamental to the basic understanding of solar insolation characteristics. The ASHRAE model includes many of these findings. Baker and Haines (1969) conducted a study on finding the correla— tion between the amount of sunshine received per day and the total daily insolation. This study correlated sunshine to insolation to about 0.92. SLmshine duration periods did not give an indication of radiation intensity according to Baker and Haines (1969) thus resulting in some error. Also s1mshine records are not widely available for use with such 13 a model . Thomas (1977) developed linear models to predict hourly insolat ion from hourly inputs of cloud cover, cloud elevation , atmospheric transmissivity, and extraterrestrial radiation. This type of model appears to be quite reliable and applicable to use in a simulation except that hourly cloud information is equally difficult to obtain as the measured insolation itself. 3.4 Weather Data Availability Necessary inputs to a simulation also include air temperatures and wind speeds. Although wind speed information is not used in this simulation study it should be incorporated for certain collector designs (Klein M” 1975). Air temperatures are of significant importance . Hourly ambient air temperatures are generally included with records of hourly insolation data. local Climatological Data contains data on dry and wet bulb temperatures, wind speed, and cloud cover at three hour intervals . These values may be interpolated to produce hourly values with little error if temperature data is needed for combination with existing insolation data (Linvill, 1977). This was done by the author for one year at East Lansing. Daily average, maximum, and minimum terperatures are also readily available from local weather stations . Annual mean monthly temperatures are available for the three test locations for use in conjunction with long-term average insolat ion data . East Lansing values are obtainable from the Michigan Department of Agriculture 1 (1974), Indianapolis values from U. S. Department of Commerce (1964), and Columbia values from U. S. Department of Commerce (1968) . 14 3.5 Principles of Heat Transfer Tb evaluate the behavior and feasibility of a solar energy system a basic understanding of the modes of heat transfer is necessary. Consideration will be given to three modes of heat transfer, namely conduction, convection, and radiation. A driving force is necessary for heat transfer to occur. The magnitude of the driving force is determined by the temperatures of the bodies involved. From the Second law of Thermodynamics energy always tends toward a state of greater entropy and for this discussion corresponds to heat transfer from the body with the higher temperature to a body at a lower terperature (Jenkins and Perkins, 1970). Conductive heat transfer occurs by molecular actions of vibration or rotation (Kreider and Kreith, 197 5) . The equation describing one dimensional conductive heat transfer is given as: ._ 3T Q _ _kA 336 (3.3) where Q is the heat transfer rate, k the thermal conductivity, A the da_'r area perpendicular to heat f low _, an 8x the terperature gradient in the material. Convective heat transfer occurs by the motion of a fluid. The describing equation is: Q = hA AT (3.4) where h is the heat transfer coefficient, and AT the temperature difference between the surface and the fluid. 15 Radiative heat transfer occurs by means of electro—magnetic radiation and is described by: Q=erT‘* (3.5) where O is the Stefan-Boltzmann constant _, 8 the surface enittance, and T the absolute temperature of the radiating body. A fourth equation is needed to describe the energy reroved by a transport fluid. Equation (3.6) relates the mass flow rate 111, specific heat of the fluid c, and the temperature change of the fluid AT, to the rate of energy reroval Q. Q = mc AT (3.6) These relationships may be used to construct energy balances on components to determine their performance as a function of the physical properties of the component and the temperatures at which it is operating. Basic heat transfer texts such as Holman (1972), Kreith (1973), or - Kreider and Kreith (1975) may be referred to for a more detailed - discussion of these principles. 3.6 Principles of Solar Radiation The amount of energy reaching the earth fran the sun is called the solar constant, and varies 1.5 percent over the year (Moon, 1940). The degree of variation for most cases is insignificant for design purposes. The accepted value of the solar constant measured normal to 16 the sun rays outside of the earth atmosphere is 1353 J/s/m2 (ASHRAE, 1974). The energy which reaches the surface of the earth is dependent upon :many factors including: sun-earth orientation, atmospheric properties, and time of day. Figure 3.1 illustrates the spectral distribution characteristics of solar radiation, shows the distribution if the sun were radiating as a black body, the actual solar radiation distribution Outside the atmosphere , and the spectral distribution of the radiation which reaches the earth's surface. As solar radiation passes through the atmosphere, a percentagecflfit is absorbed by water vapor, dust, and gas molecules (Fritz, 1957). As a result, the magnitude of the direct solar (or beam) radiation is decreased and.the amount of diffuse or sky radiation is increased. Since the amount of diffuse radiation is largely a function of scattering, sky emissivity, and cloud conditions, it is best described by actual measured data. The performance of a solar collector is dependent upon the relative amounts of direct and diffuse radiation. Duffie and Beckman (1974) described empirical relationships distinguishing diffuse radiation originating near the sun for clear days from that of widely scattered diffuse radiation occurring on very cloudy or hazy days. When radiation strikes a material it may be transmitted, reflected, absorbed, or a combination of these (Siegel and Howell, 1972). When choosing materials for collector covers and absorbing plates considera- tion must be given to these properties and how they may influence collector performance. The transmittance of a material is the ratio of energy transmitted to the energy incident. The reflectance of a material is the ratio of reflected energy to the incident energy. Similarly l7 2000 .. BLACK BODY RADIAle BXI‘IMERRESTRIAL SOLAR RADLILI'ION mm. SIT'Y \ RADIATION REACHING mm mans SURFACE (W/Imfiflfl) 1000 -J \\ 0 I l o 1 2 'wavmrmcm (mm) Figure 3.1 Spectral Distribution of Solar Radiation . l8 absorptance is the ratio of energy absorbed to the amount of energy incident upon the surface. The sum total of these is numerically equal to unity. Fhfittance is a property describing the radiating characteristics of a body and is defined as the ratio of energy emfitted by a material to the amount emitted if it were a black body. Each of these properties is a function of the wavelength and incident angle of the incident energy. When considering collector covers; a low reflectance, emittance, and absorptance material is desirable for all wavelengths and incident angles, while a high transmittance is desirable for wavelengths and incident angles which allow the greatest amount of radiation to pass through. Desirable properties of the collector absorber plate are: low reflectance at all wavelengths and incident angles, zero transmittance, high absorptance for all incident angles and at wavelengths which most incident energy occurs, and low emittance at all angles and at wavelengths corresponding to the greatest intensities of the spectral distribution curve at the plate temperature. A surface in which the absorptance and emittance are not equal is called a selective surface (Kreider and Kreith, 1975), and has a greater potential for use in solar collectors. Materials with these properties are difficult to obtain and generally expensive. Figure 3.2 identifies the angles which are important in detennining the amount of radiation available at a particular location. The alti— tude angle (a) which the incoming beam radiation makes with the horizontal surface is given by (Kreider and Kreith, 1975): sina = sinL sin6 + cosL cosé cosHs (3.7) 7 9—4335?" 19 w W N ///,,Z€?///, 65% Figure 3.2 Description of Sun—Earth Orientation Angles. 20 where L is the latitude, 6 the solar declination, and HS the local solar hour angle measured from solar noon. Equation (3.7) can be used to determine the location and elevation of the sun for any time of year, thus allowing for the calculation of collector tilt angles. Most collectors are tilted from the horizontal in order to minhmize the angle of incidence of insolation, thus allowinglnathmn insolation to pass through the glazing to the absorber plate. 3.7 Solar Component Design and Technology 3.7.1 Solar collectors The basic principles of flat plate solar collector design are illustrated in Figure 3.3. ASHRAE (1974) illustrated fourteen common collector water and air heater designs. The number of covers depends upon the climate and the specific use for the collector. Kreider and Kreith (1975) reccmmended double glass glazing for most northern clhmate solar water heater installations. Other glazing materials such as plexiglas, polyvinyl flouride (Tedlar), polyethylene, and others are somethmes used. The choice of such materials depends upon desired performance, costs, life expectancy, maintenance, etc. Radiation passes through the transparent covers striking the absorber plate. As the radiation strikes the absorber plate, energy is absorbed causing the temperature to increase and heat transfer to occur from the plate to some transport fluid passing through the collector. The absorber plate is covered by some material that has a high absorbtivity and in certain designs, may be a selective surface. Proper collector DISLCLATICI‘I Figure 3.3 Principles of Flat Plate Collector Design. 22 design must take into consideration heat losses by conduction, convec- tion, and radiation, inefficiencies in transmittance and absorptance of the incident energy, the cost of materials, and the relative trade off of each. There are many commercially available collectors of good quality which have a reasonable long life expectancy. Costs for these collectors range from 100 to 270 dollars per square meter (Solar Research, 1977 and Owens—Illinois, 1977). Several design parameters are important when determining collector performance. Total transmittance describes the amount of insolation passing through the cover plates which strikes the absorber plate after accounting for reflection and absorption losses. Duffie and Beckman (1974) described the dependance of transmittance on cover thickness, extinction coefficient, number of covers, cover spacing, and incidence angle of the inccmfing radiation. Based on these relationships Duffie and Beckman (1974) presented figures for the determination of collector transmittance. Collector plate absorptance is given for several plate coating materials in Table 5.5.1 of Duffie and Beckman (1974). This parameter describes the ability of the collector to collect and retain the incoming radiation. Collector absorptance values range from 0.8 to 0.95 depending upon the coating material. The overall energy loss coefficient (U1) is an important parameter influencing the collector performance. Klein (1974) presents a method for calculating this value as a function of top (Qt)’ edge (Q8), and back (0%) heat losses: U1 = (Qt + Qb + Qe)/A (Tp — Ta) (3.8) 23 where Ul is the overall energy loss coefficient, A the collector area, T p the mean collector plate temperature, and Ta the ambient air terpera— ture. Klein (1974) proposes the use of equations (3.9), (3.10), and (3.11) to determine the top, back, and edge heat losses, respectively. Qt = UtA (Tp - Ta) (3.9) The top loss coefficient Ut in equation (3.9) accounts for radiation and convection losses from the top surface of the absorber plate and is related to the number of cover plates, wind speed, tilt angle, mean plate temperature, and emissivities of the absorber plate and covers. Whillier (1967) presented relationships to calculate the top loss coefficient for several collector designs. Duffie and Beckman (1974) presented curves (Figure 7.4.4 of Duffie and Beckman, 1974) based on an erpirical relationship developed by Klein (1974), which can be used to determine the top loss coefficient given the mean collector plate terperature, number of covers, ambient air terperature, plate emissivity and wind speed. Heat loss from the back side of the collector absorber plate may be obtained fran: Qb = A (Tp - Ta)/(m/k + l/hb) , (3.10) Where it is the insulation thickness, k the thermal conductivity of the insulation, and hb the convection coefficient between the bottom of the insulation and the ambient air. Edge losses may be determined by: Q8 = he (Ap) (Tp — Ta) (3.11) where he is the convection coefficient between the edge surface and the ambient air, and Ap the collector perimeter. The actual perfonmance of a collector may be described by the geometric efficiency factor F' (Duffie and Beckman, 1974). This factor is a ratio of the heat transfer resistance between the absorber plate and the ambient air and the heat transfer resistance from the fluid to the ambient air. The collector efficiency factor remains constant for a given collector design and flow rate. Figure 7.5.4 developed by Duffie and Beckman (1974) give values for the geometric efficiency factor given tube spacing, plate conductivity and thickness, overall loss coefficient, and the heat transfer coefficient between the collecting fluid and the inside of the tubes. The collector efficiency (n) is a term helpful to observe actual collector performance over a simulation period. Kreider and Kreith (1975) describe collector efficiency as the ratio of energy output to the total incident radiant energy. This efficiency is a function of the collector plate temperature and the relative period of operation used to calculate it. Care should be used when comparing efficiencies of different collectors so that equal time periods are used. 3.7.2 Other components Other components such as heat exchangers, storage tanks, and auxiliary heaters are generally of conventional design. Costs and performance vary according to materials of construction. These components require energy balances to describe their performance over time. Holman (1972) and Kays and London (1958) described the basic theory 25 for heat exchanger design. Heat exchanger capacity is described by the overall heat transfer coefficient UA. The choice of this design parameter depends upon the heat transfer rate required by the system. Preliminary calculations may be used to estimate the heat exchanger capacity needed for a certain installation. A counterflow type heat exchanger is recommended for applications involving small temperature differentials, as in solar energy systems, because small temperature differences between the wanm inlet fluid and the cold exit fluid are characteristically low, thus allowing for mathun heat transfer. Storage tanks may be divided into segments for modeling with the temperatures at each segment described by a set of differential equations. In order to construct energy balances on these tank segments, the tank losses must be accounted for. An energy loss coefficient (U) for an insulated tank can be determined from Holman (1972). The capacity of the auxiliary heater may be detenmined from equation (3.6). The capacity of the heater should be great enough to supply 100 percent of the demand. For simulation testing this criteria should be assumed. In actual design of the physical system the heater capacity may be sized allowing for a minimum energy contribution from the solar collectors and a factor of safety. 3.8 Physical Solar Water Heating Systems A solar water heating system is a combination of various components designed to collect incident direct and/or diffuse solar insolation and convey this energy, by means of a transport fluid, to a place of utiliza— tion and/or storage for future utilization. Clearly such an installation 26 is dependent upon each component in order to give best performance. Lof (1977) emphasized that although individual components are proven to perform under certain conditions, the overall performance and reliability of a system depends upon the proper sizing and combination of related carponents. Solar water heating is a direct use of solar energy which has been practiced most extensively in the last two decades. Kreider and Kreith (1975) predicted water heating will be the first wide use of solar energy during this period because of the high use factor and low initial cost. The basic elements of a solar water heater, illustrated in Figure 3.4, are a flat plate collector, storage tank, pump, controller, and an auxiliary heater. For operation in freezing climates an antifreeze solution may be used in the collector with a heat exchanger used to transfer the energy to the water. Duffie and Beckman (1974) stated typical collector dimensions are 1.2 by 1.2 meters with multiple units being connected in a single installation. Common absorber plates are copper or steel with tubes thermally bonded to the plate allowing for the passage of the collector fluid. The absorber plate and glazing covers are installed in a frame with 5 to 10 centimeters of insulation on the back. Storage tanks should be well insulated. Twenty centimeters of mineral wool insulation is recommended by Duffie and Beckman (1974). Auxiliary energy may be added in three ways as discussed by Gutierrez et_al. (1974): directly to the tank, to the water leaving the tank, or directly to the supply water, bypassing the tank. The authors concluded the second method resulted in most efficient operation. Sizing of system components depends upon the type of load, energy 27 COLLECTOR AUXHJARY E::: H AT R ( E E r__ COUNTER— FLOW HEAT EXCHANGER f:> HG PUMPS , STORAGE TANK THERMOSTKHC \\£ONTROLLER Figure 3.4 Basic Elements of Solar Water Heating System 28 costs, etc. (Duffie and Beckman, 1974). Kreider and Kreith (1975) presented the following general guidelines for residential installations: 36.8 square meters of collector to supply 100 kilograms of hot water, a storage tank capacity should allow a two—day supply, and a collector fluid flow rate of 40 kilograms per square meter of collector per hour. Guidelines for industrial applications may vary. 3 . 9 Warm Water Usage The food processing industry consmmes 9.5 x 1017 Joules annually (Reding and Shepard, 1975) . The energy consumption for the dairy, meat, and fruit and vegetable industries is given in Table 3.1 ( U. S. Department of Commerce, 1974). Recognizing that the warm water use in these processing plants is a significant percentage of this consump— tion, a potential exists for saving fossil fuel energy by the replacement with solar energy . Representative processing plants, one each of small, medium, and large from each industry were surveyed by Dansbury (1977) for their warm water consumption. His findings are shown in Tables 3.2, 3.3, and 3.4 and Figures 3.5, 3.6, and 3.7. 3.9.1 Dairy plants Dairy plants were chosen with an erphasis on fluid milk processing operations . Water usage information obtained from all plants consisted of warm water used for cleaning operations relating to fluid milk pro— cessing operations. Table 3.2 shows the volumes and temperatures required. 29 N.om e.o_ m.om o.sm m.me owaocmFm ~.Ne e.me w.me a.ms a.em was taranaz m.m e.m _.o_ m.o m.m ~moo w.m w.© m.m s.m P.e mpmsewmmm m.o m.o m.m _.w P.m mmom_P_omwo «Fees: o._ a.N _.N N._ w._ menace; Apnoea co NV com: smcmcm ac case N.om m.om m.me_ «.mm w.m_ s._©m 0mm— m._N o.No m.om_ o.em m.s~ N.mmm Fem— m.mw o.s_ N.omm smmF hex m_o_v com: s Loam o.F m.o w.o m3~a> beacons co “smegma _mo0p s.emr _apoo w.om_ N.mNF Aa coco A_mm_v _o:a someeotza co m3~m> .uom x__z acw~mmti mcwccmu mcaxoea .mmmmzmm ewz~i fleece mpamommc> ecu pasta one: scene >zem3ozH .Aesmp .mcccssoo co ucmsotmamo .m.:v mmwtomzecH mcwmmeOLa mpnmommm> new beats ecm .omcz .scrma ego co cowua53mcou smtmcm - _.m mFBme 30 Table 3.2 - Dairy Plant Warm Water Usage. Small: Average water temperature 65.53 C Annual demand schedule 52 weeks Processing days per week 3 - MWF Principle use cleaning Volume - daily l940 kg peak flow 692 kg/hr weekly 5820 kg 5 Energy demand - daily 4.28 X l0 kJ weekly l.285 x lo6 kJ annual 6.682 x 107 kJ Medium: . Average water temperature 66.ll C Annual demand schedule 52 weeks Processing days per week 5 - MTWTHF Principle use cleaning Volume - daily 6456 kg peak flow 7l7 kg/hr weekly 32,280 kg Energy demand - daily 1.44 X 105 kJ weekly 7.20 x lo6 kJ annual 3.747 X l08 kJ Large: Average water temperature 79.44 C Annual demand schedule 52 weeks Processing days per week 6 - MTWTHFS Principle use cleaning Volume - daily 25,000 kg peak flow l387 kg/hr weekly l50,000 kg Energy demand - daily 6.96 X 10 kJ weekly 4.l82 x lo7 kJ annual 2.l7 x lo9 kJ a) seam 800 p 700 a ..... El '1 1 600 ‘ =5 '9 .5 5 500 “ g . mm . now ms 400 .4 (kg/hr) 5 300 4 5 5 5 ‘1 l 200, a 5 i .5 128 - ----- ~' -' 100 O o b) mama 300 j 31 24 TIME (hr) 717 __ _____ ___5 . 700 q 5 5 (“l *‘1 l"—l .' || '55 5. 5 J .r ‘ u . 1'55. : l ; 5; t_ 3 500 — I. ,5 ,- : wma f3 535- nm '3 RATE 400 ~ ’5 .1: i (kg/hr) , 55 5 5 .5 5 300 a " 5, ,- .6 g P, .’_ j'. 200 j _.j l o “" - -...:+.-' 18 55 100 —4 0 A: 1600 144 5 168 .5 1400 :1. - - _ , 1200 -< ‘ WATER . '. FLO»! v: RATE 1000 -‘ ‘ ‘ (kg/hr) ‘. 'l '1' . 800 — - ; ._- .‘ .. ' l. '3 it, , 3 ' "i ’4' 600 _. 5 .55 5 '5 400 _ l \‘ {31-5 1’. ll!‘ , ' " l.‘ " é ' '5 ' 1' r ,3; > ‘ Hi") ’I‘ t i. ‘ zoo —4 ' .5; -. l 0 .n‘ I . ‘ t ' '. - l I T l l 1 Ti T l TIME (hr) Figure 3.5a,b,c Dairy Processing Plant Water Demand Schedules. 32 Usage patterns were determined based on plant scheduling, shifts, etc. Dairy plants exhibit a uniform cyclic demand over the entire year. Dansbury (1977 ) recarmended the weekly demand schedules shown in Figure 3.5 for use in computer simulation. 3.9.2 Meat plants The meat processing plant surveys reflect warm water used for cleaning and washing in slaughter and processing operations. Meat plants operate continuously over the year. Results similar to the dairy plants are given in Table 3.3 and Figure 3.6. 3.9.3 Fruit and vegetable plants The fruit and vegetable industry is widely diversified. The multitude of agricultural products processed by these plants presented irregular usage patterns throughout the year. The type of processing operations, whether canning or freezing also greatly influenced the warm water energy demand. A cross section of plant operation patterns is represented by the survey results of the three plants. The small plant is a canning plant which operated during must of the year according to Figure 3.7. Energy demnds shown in Table 3.4 include warm water usage for processing and cleaning operations . The medium plant is a freezing plant that operated 12 months a year. Although this plant produced more product than the small plant it was classified according to the relative energy demands of all three plants . 33 Table 3.3 - Meat Plant Warm Water Usage. . Small: Average water temperature 60.0 C Annual demand schedule 52 weeks Processing days per week 5 - MTWTHF Principle use cleaning Volume - daily 3,026 kg peak flow 504 kg/hr weekly l5,l30 k95 Energy demand - daily 5.98 X l06 kJ weekly 2.99 X l0 kJ annual l.56 X l08 kJ Medium: Average water temperature 7l.ll C Annual demand schedule 52 weeks Processing days per week 5 - MTNTHF Principle use cleaning Volume - daily 5,4l0 kg peak flow 902 kg/hr weekly 27,050 kg Energy demand - daily 1.32 x 106 kJ weekly 6.61 x 106 kJ annual 3.44 X l08 kJ Large: Average water temperature 7l.ll C Annual demand schedule 52 weeks Processing days per week 6 - MTWTHFS Principle use cleaning Volume - daily 42,000 kg peak flow l800 kg/hE weekly 2.52 X l0 kg Energy demand - daily l.03 X l07 kJ weekly 6.16 x 107 kJ annual 3.20 x 109 kJ 8) Stem 800 -5 700 "i 600 -1 WATER 500 -——-— — M H (kg/hr) 400 ‘4 300 -‘ 200 -4 100 - b) frlEDILM 1000 _. 902 ~ 7504 WATER FILM RATE (kmmm $00 —4 250... mm , r. r i r 1 1 24 48 72 THE (hfl _. -1”.- ---.-. -' \ .-.~"-rb« -' ‘4 -. ' .Bfwfl?.¢ ,5: -.-—u-— 144 168 c) LARGE 2000— 1800 144 mm: (kg/hr) 1000 .u 168 214 I I8 I 7’2 TIME (hr) 144 l 168 Figure 3.6a,b,c Meat Processing Plant'Water Demand Schedules. Table 3.4 Small: Type and products Average water temperature Annual demand schedule Period A 35 - Fruit and Vegetable Plant Warm Water Usage Processing days per week Principle use Volume - daily peak flow weekly Energy demand - daily Period 8 weekly total for period ProcesSing days per week Principle use Volume - daily peak flow weekly Energy demand - daily Period C weekly total for period Processing days per week Principle use Volume - daily peak flow weekly Energy demand - daily Period 0 weekly total for period Processing days per week Principle use Volume - daily peak flow weekly Energy demand - daily weekly total for period Total energy demand Canning: asparagus cherries green beans apples 84.8 C 36 days starting May l0 6 - MTWTHFS processing 25,600 kg 2,960 kg/ r 1.53 x 10 kg 7.72 x 106 kJ 4.63 x 107 kJ 2.387 x 108 kJ l8 days starting June 2l 6 - MTWTHFS processing 7,670 kg 947 kg/hr 46,000 kg 2.31 x 106 kJ 1.39 x 107 kJ 3.006 x 107 kJ 60 days starting August l 5 l/2 - MTWTHFS processing 76,680 kg 4,674 kg/hr 42l,700 k 2.3l X l0 8kJ l.27l X lo kJ 1.112 x 109 kJ 60 days starting October 1 6 - MTWTHFS processing 7,670 kg 947 kg/hr 44,000 kg 2.31 x 106 kJ 1.39 x 107 kJ 1.202 x 108 kJ 1.501 x 109 kJ 36 Table 3.4 (continued) Fruit and Vegetable Plant Warm Water Usage , Medium: Type and products Average water temperature Annual demand schedule Large: Period A Processing days per week Principle use Volume - daily peak flow weekly Energy demand - daily weekly total for period Period 8 Processing days per week Principle use Volume - daily peak flow weekly Energy demand - daily weekly total for period Total annual energy demand Type and products Average water temperature Annual demand schedule Processing days per week Volume - daily peak flow weekly Energy demand - daily weekly annual total freezing: asparagus cherries cabbage carrots squash apples 84.4 C All year excluding the month of August 6 — MTWTHFS processing 48,470 kg 2,9l0 kg/hr 29l,000 k l.46 X l0 kJ 8.76 x 107 kJ 4.14 x 109 kJ 30 days of August 6 - MTWTHFS processing 60,600 kg 3,670 kg/hr 364,000 kg 1.83 x 10 kJ 1.10 x 108 kJ 4.92 x 108 kJ 4.632 x 109 kJ canning: green beans 8 C 60 days starting August l 6 - MTWTHFS 507,000 kg 31,200 kg/Er 3.042 x 10 kg 1.382 x 108 kJ 8.29 x 108 kJ 8.28 x 109 kJ 3600- § =PERIOD 8,0 mm mm 2960. __ @=PERIOD c RATE 7 =PERIOD A (kg/ha / V C] 2400- / 1200— f 947-— . 722- A ,. i“ , 44 , 'o’oo' o—-—— ~ .29". 120 44 £68 b) MEDILM 4000 -« 3670 fl 3000— Daemon e 2910 _. WATER .=Psmoo A \ now 1. RATE (kg-{5 / 2000* 1000-— _3>:\\\\ 1 120 144 163 18,000“'- ‘““5 " 10,0011— 5 l l l l 0 24 48 TIME (hr) Figure 3.7a,b,c Fruit and Vegetable Processing Plant Water Demand Schedules. 38 The large plant, a canning plant, represents an extreme peak seasonal use pattern characteristic of many canning plants. This plant is specialized for processing one product for two months during the year as illustrated in Figure 3.7. 3 . 10 Economic Analysis Two methods of analyzing solar energy systems described by Kreider and Kreith (1975) may be used to determine the economic feasibility. Solar and conventional energy costs are significant factors in determining overall feasibility. 3.10.1 Life cycle costing method Life cycle costing is useful for observing the annual operation cost of an installation over its expected lifetime. The annual additional cost of a solar system can be calculated from: c,n = (ch, t01:) (CHE) (3.12) where Ch 15 the annual additlonal cost of solar system, Ch, tot the total additional initial investment in the solar system, and CRF the capital recovery factor obtained from tables (Table C.6, Kreider and Kreith, 1975). The CRF factor is a function of annual interest rate and expected life of the system. 39 3.10.2 Cost effectiveness method The method of cost effectiveness for solar systems illustrates the economic benefits of future increase in the cost of conventional energy sources. Equation (3.13) relates the future value of a present sum of money (Kreider and Kreith, 1975): X = p (1 + i )t’ (3.13) ann where X is the value of a future sum, P the present value of the sum, iann the armual interest rate, and t the period of years. Equation (3.14) gives the present worth (P) of an initial amount of money (PO) paid annually for a period of t'years where the annual payment is increasing at an annual rate 3'. __ . t’—l . . t’ P - PO (1 + leff) /leff(l + leff) (3.14) The effective interest rate (ieff) is given by: ieff = (1 + iann)/(1 + j) — 1 (3.15) Using these models the costs of solar energy utilization and conventional fuel sources may be compared. 3.10.3 Solar system costs Collector component cost estimates were presented in 1975 dollars 40 by Kreider and Kreith (1975). A double glass cover, selective surface collectorcostslAS dollars per square meter including materials and a 35 percent overhead. Approximate costs of other system components were also shown as: 0.12 dollars per kilogram of water stored and 5.5 dollars per square meter of collector for pumps, piping, etc. A constant cost for controls and miscellaneous items should also be included. 3.10.4 Conventional energy costs The present cost of energy to the consumer is dependent upon the geographic location, type of fuel, and method of conversion. Ckmmon conversion efficiencies presented by Fryling (1966) are necessary for estimating the energy costs. Current price ranges for three energy sources were chosen after discussion with commercial suppliers of oil, gas, and electricity: 3.5 to 4.5 cents per kilowatt hour for elec- tricity, 0.10—0.13 dollars per kilogram for #2 fuel oil, and 3.20—4.25 dollars per 1000 cubic meters for natural gas. 4. METHOIDIDGY 4.1 Insolation Test Models The lack of insolation data necessary for solar energy simulation design at the chosen test locations has necessitated the construction of an insolation model for determining hourly insolation and air tempera— ture data for computer simulation. Five models for predicting hourly insolation and air temperature data were chosen for comparison with a control model which uses actual measured values of hourly insolation and air temperature. All five models use a constant air temperature obtained by averaging the hourly air temperatures over the 336—hour simulation period. For non—daylight periods for all test models, insolation was assumed zero with the ambient air temperature remaining constant. The five models are: the control model with constant temperature, the ASHRAE model using daily total insolation, the ASHRAE model using weekly averages of daily total insolation, the Whillier model, and the Transmissivity model. The model which best compares with the control model will be used for simulating long—term performance of the solar water heating system. It was also desired to observe the effect of averaging insolation and air temperature data over periods greater than one hour. Hourly measured data was averaged for three and six hour periods and sirmrlated together with the original hourly data for a period of 1000 hours. Although three, six and other average insolation data, excluding 24 hour 41 42 averages (daily totals), are not readily available, the objective was twofold. First to examine the behavior of average data on the simula— tion results, and second, to observe the potential for interpolating daily total insolat ion data into three or six hour averages. It should be stated that measured hourly insolation values are hourly averages of instantaneous insolation. The best possible method would be to use instantaneous data for input to a simulation. Since TRNSYS calculates transient responses over a designated time step, the input data need not be averaged over a period less than this time step in order to maintain the desired accuracy. TRNSYS uses a linear interpolation routine to calculate insolation at each time step. The error incurred by using hourly data compared to average data interpolated over the time step is thus minimized. 4 . l . 1 Control model The control model was used for comparing the simulation results of the other insolation models. This model uses actual measured values of hourly insolation and air temperature for 1974 at the East Lansing test location, based on Eastern Standard Time. This data is also used to construct daily total insolation and the average air temperature for the period used in the other test models. 4.1.2 Control model with constant temperature The purpose of this model was to determine the influence of air temperatures on system performance. Since the other insolation models 43 use constant average air temperatures for the simulation period, the effect of constant average air temperature must be distinguished from that of actual air temperatures. The same temperature used with this model was used for all test models. The insolation data used for this model was the same hourly measured values used with the control model. 4.1.3 ASHRAE model using daily totals Chapter 59 of ASHRAE (1974) contains tables of clear day insolation values based on solar time, for a given day and latitude. These tables were constructed to give maximum design values for designing a solar energy system. Hourly insolation values in this table are the same for morning and afternoon. The procedure consists of using the total and hourly insolation values taken on a horizontal surface at 40 degrees north latitude, interpolated according to the time of year of the control simulation to calculate a ratio of hourly insolation to daily total insolation for each daylight hour (Appendix A). Daily total insolation values were obtained by summing the hourly insolation values for each day of the simulation period. These totals were then multiplied by the ratios of hourly insolation and total insolation to obtain the simulated hourly values. The resulting data corresponds to solar time with the maximum value occurring between 11:30-12:30 during solar noon. Since the morning and afternoon ratios are the same, a symmetric insolation curve results, which is not realistic in the strictest sense since atmospheric conditions influence radiation intensities. The use of actual daily totals with this model tends to compensate for the clear day condi— tions on which these ratios are based. This method assumes the ratios 44 remain constant for all atmospheric conditions. The validity of this assumption for use with modeling is questionable and can best be determined by simulation and comparison with actual insolation data results. 4.1.4 ASHRAE model using weekly averagfi Weekly averages of daily total insolation data for 13 years in East Lansing are available from Baker and ICLink (1975). To use this data, a model to construct hourly values from these weekly averages is needed. This ASHRAE model is similar to the one previously described except it uses weekly averages of daily insolation instead of daily totals to calculate hourly insolation. The daily total insolation values used in the first ASHRAE model are averaged over weekly periods and used as inputs to test this model. This model using the same ratios given in Appendix A, may be used to predict the hourly insolation values for an "average" year for all three test locations. 4 .1 . 5 Whillier model Whillier (1956) developed a set of curves relating the ratio of hourly insolation to daily total insolation and daylength for all types of atmospheric conditions. These curves are based on solar time. The maximum ratio occurs during the hours before and after solar noon (11:00—12:00 or 12200—1200). Since daylengths are necessary for determining these ratios, a computer program named EDLAR (Furnival e_t_a_l;. , 1969) was used to obtain daylengths for the test periods. Appendix B contains a table of daylengths generated by this program for East lansing fu— «W 45 (1974). Using this table ratios were interpolated from the Whillier curves (Whillier, 1956) and given in Appendix C. Using this information and daily insolation for the test periods, a data set similar to the ASHRAE data, was constructed and used in the test simulations. The Whillier model also reflects the symmetry described for the ASHRAE models. Whillier (1956) showed that a difference exists between morning and afternoon insolation for areas exhibiting effects of industrial smoke or haze from mountain ranges. East Lansing exhibited none of these characteristics, and therefore any weighting of morning or afternoon insolation values was neglected. 4 . l . 6 Transmissivity model The Transmissivity model uses values of weekly atmospheric transmissivity and hourly extraterrestrial radiation to predict hourly insolation. Thomas (1977) analyzed the 1974—75 radiation data at East lansing and calculated weekly atmospheric transmissivity values based on hourly measured insolation and hourly extraterrestrial radiation data obtained from program SOLAR. These transmissivities are given in Appendix D. The mLAR program was modified to calculate hourly extra— terrestrial radiation corresponding to Eastern Standard Time. A listing of the modified program is given in Appendix E. A data file was then constructed for testing this model. The model may be extended to predict hourly insolation for the "average" year using data from Baker and Haines (1969). 46 4.2 Energy'IEmand Loads Dairy, meat, and fruit and vegetable plant surveys of warm water usage were conducted by Dansbury (1977). Tables 3.2, 3.3, and 3.4 illustrate the results of these surveys. Tb incorporate this information into the shmulation, TRNSYS requires a demand function indicating the water flow rates at different times of the day. Since all plants operated less than seven processing days per week and were different in their peak flows, a seven day cycle was used to establish the demami functions. Special consideration is given to the fruit and vegetable plants which operate in seasonal patterns. Information was obtained concerning the number of shifts per day, working days per week, and time spent for processing cleaning operations. From this infonmation, functions were developed describing the periods of wann water use. Figures 3.5, 3.6, and 3.7 show the scheduling used in the sumulations for the dairy, meat, and fruit and vegetable processing plants, respectively. These figures along with Tables 3.2, 3.3, and 3.4 adequately describe the amount, temperature, and distribution of water usage. Each simulation period begins according to these functions and cycles every seven days. These schedules primarily constitute a daily cycle corrected by accounting for the slack weekend period. Dairy and meat plants both exhibit a constant demand cycle during the year. Fruit and vegetable plants, as shown in Figure 3.7, present a vastly different situation compared to the dairy and meat plants. Unless the plant is widely diversified, as the medium plant was in this survey, the fruit and vegetable plants generally reflect a strong seasonal dependence for their processing and energy consumption patterns together with.much higher water flow rates and temperature requirements. 47 Since periods of highest product ion occur during periods of higher insola— tion, solar energy appears to be well suited to these plants. The economics of plants with only summer period energy demand depend upon finding a full-time use for or a storage system to collect the off season energy. Simulation ms were conducted on these plants in a slightly different manner. During the off production periods for the small and large plants the solar energy system was assumed to produce heat for space heating of warehouses and offices. Actual simulation runs for space heating were not performed. Estimates based on simulation runs made during processing were used to evaluate the total energy contribu- tion made by the solar system for economic considerations . 4.3 Physical Solar Water Heating System Design A simple solar water heating concept is employed in this study to determine the contributions that solar energy can make to decrease the fossil fuel energy consumption of the food industry. It should be recognized that new solar energy technology is constantly being developed in areas of collection, storage, and construction materials. The solar water heating system presented attempts to represent the state of the art for the design of flat plate collectors and other system components. The physical system considered is represented by Figure 3.4 . me to overall climatic conditions in the midwest region the system was designed for cold weather operation . South facing selective surface flat plate collectors with a variable tilt angle were assumed. The collector 48 fluid was chosen to be an ant i—freeze solution in order to prevent freezing damage to the collectors during periods of off operation. Double glazed, insulated collectors carpatible with cold weather opera- tion were chosen . The collector fluid is pumped through a closed loop consisting of a collector and counterf low heat exchanger. A second loop uses water to remove the energy from the heat exchanger for storage in a tank to be delivered to the load. The proposed storage tank is insulated and located inside of a building maintained at constant terperature. Water is pumped between the heat exchanger and storage tank by a separate pump . The tank capacity and pump capacities were chosen based on system parametric tests. The storage tank will hold approximately twice the daily demand volume for the plant. System operation is controlled by an on/off thermostatic controller which senses the difference between the collector plate temperature and storage tank temperature. The controller turns the pumps on when the collector temperature becomes greater than the storage tank terperature. Warm water delivered by the system leaves the storage tank and passes through an auxiliary heater which, if needed, raises the water to the desired temperature. Replacement water is supplied directly to the storage tank from the main water supply at a constant terperature. This system can be readily modeled by TRNSYS. Other system con— f igurations can also be devised depending upon specific plant requirement for warm water recycling, reIoval of heat exchanger , or inclusion of other components such as a heat pump. These other considerations will not be analyzed in the basic study of the processing plants. 49 4 .4 System Modeling With the basic solar water heater concept established, a matching computer simulation model was developed with TRNSYS. An illustration of the subroutines used and the pattern of information flow is given in Figure 4 . l . A sample control card deck and output results are given in Appendix F. Changes, made to the control card deck, when simulating different plants were accomplished by changing values on the parameter cards of selected routines. There are thirteen subroutines together with the main executive program used in these simulations. Seven of these units correspond to physical system components, while the other six involve the manipulation of input and output data necessary for modeling. Each routine, as shown in Figure 4.1, is identified by a unit number. This number is used internally by TRNSYS to identify the variables and allow for commlmicat ion and information flow between the executive program and each subroutine. Each input, output , and parameter is organized specifically for each subroutine and is identified in the TRNSYS manual. The function and purpose of the pertinent units will be discussed in detail. All of the routines are interconnected. Therefore, units will be described according to their approximate order of appearance . For complete discussion of the facilities and necessary considerations for using TRNSYS, the operation manual should be consulted (Klein, 1974). UNI T 9 CARD READER U N 1T 16 RADIATION UNITZ CONTROLLER UNITI COLLECTOR UN|T26 PLOTTER UN|T3 COL PUMP UNITS HEAT EXCH UNFT24 INTEGRATOR UN|T|5 TANK PUMP UNIT 35 PWNTER D=CONSTANT UNITS AUX HEATER Figure 4.1 Subroutine and Information Flow Diagram of the TRNSYS Solar Water Heating IVbdel. 51 4 . 4 . l SimulatiOn considerat iOns The execution procedure used by TRNSYS consists of calculating the variables of all the imits during a given time step and then proceeding to the next time step , repeating the process using the values calculated during the previous time step. Some variables are common to more than one unit . The overall behavior of the system is represented by a set of algebraic and/or differential equations. During a given time step, TRNSYS iterates among the units until the variables converge to within a specified tolerance from their value of the previous interation . The magnitude of the tolerance is set in the control card deck by the TOIERANCES card. The smaller the tolerance the more iterations necessary to achieve convergence , the more compute time required, and the more accurate the results. After a set number of iterations, if the tolerances are not satisfied, TRNSYS will execute the next time step with the values of the variables at the last iteration regardless of their con- vergence status. If this happens repeatedly, the simulation will terminate in error. The number of iterations allowed is set by the LIMITS card. The size of the time step has a significant effect upon the calculation effort, accuracy, and allowable tolerances. The choice of the time step and tolerances is dependent upon the type of system being simulated. Units containing sets of differential equations generally require a smaller time step and a larger tolerance than a system modeled by algebraic equations . The values used in this study were selected as a compromise between compute time and accuracy requirerents. A time step of 0.2 hours and a tolerance of 0.1 were used for all plant simulations. Preliminary work 52 used for selecting these values indicated that the stratified storage tank unit would not operate satisfactorily under these restraints. In light of the multitude of simulation runs to be made and the resulting computer time required, it was decided to exclude the use of a stratified storage tank. The result of this decision is that the output data represent a conservative behavior of the solar system with respect to the amount of energy delivered by the storage tank. To simulate the solar water heating system for 336 hours approxi- mately 30 seconds of CP time were required, at a cost of $2.50. For simulating an entire year of 8712 hours approximately 760 seconds of CP time were required at a cost of about $62.00. Stability requirements were generally satisfied. Ebccept ions occurred when an input data point was in error or during rapid changes in the input insolation data. For these cases the iteration limit would be exceeded for that time step and TRNSYS would execute to the next time step, and display a warning message. The occurrences were infrequent and introduced no significant error into the final results . 4 .4 . 2 Card reader Unit 9 is a card reader routine used to read input temperature and insolation data at a designated time interval from the input file. Since the input variable units of existing data files are degrees Fahrenheit and Langleys per hour, a unit conversion technique is used to convert these values to degrees Celcius and kilo~Joules per hour per square meter, respectively. This unit also performs a linear inter- polat ion on the given input data to provide appropriate values 53 corresponding to the specified simulation time step. This unit supplies input data of hourly ambient air temperature to the collector, unit 1, and.measured horizontal insolation to the solar radiation processor, unit 16. 4.4.3 Radiation processor Unit 16 is a solar radiation processing routine which converts the input total horizontal measured insolation to total radiation incident on a tilted collector surface. This routine may operate in one of several modes depending upon the type of input radiation data and the method of calculating amounts of direct and diffuse radiation. The techniques used are described in detail by Liu and Jordan (1960). For this study it is assumed that the diffuse radiation originated near the sun which is most accurate for clear day conditions. This unit supplies input insolation data to the collector, unit 1. 4.4.4 Flat plate collector The collector routine, unit 1, also has multiple modes of operation. In this study the collector loss coefficient (U1) and total transmittance (T) are assumed to remain constant. Other modes consider these parameters as functions of collector temperature and radiation incidence angle. All simulations use the same collector design parameters with exception of collector area. The collector unit requires four inputs. The first two, fluid inlet temperature and flow rate, come from the collector pump, unit 3. 54 The third input, ambient air temperature comes from the card reader, unit 9. The fourth input is total incident radiation striking the collector, and comes from the radiation processor, unit 16. From the information of temperatures, collector flow rate, insolation, and the collector loss coefficient an energy balance is made on the collector. This analysis yields a value for the net energy collected. This value is then used to calculate the collector fluid exit temperature. There are three outputs frtmlunit l. The first and second are fluid exit temperature and flow rate. These variables are used as inputs to the heat exchanger and thenmostatic controller. The third output is the total energy collected during the tame step; this variable is integrated and printed out periodically so collector perfonmance can be evaluated. 4.4.5 Controller The thermostatic controller, unit 2, detenmines when the collector system operates by sensing the temperature difference between the collector outlet fluid and the storage tank. In order to maintain system stability this unit contains a feedback hysteresis characteristic to eliminate the possibility of repeatedly switching the system on and off. The parameters needed are upper and lower dead band temperature differentials to control the degree of hysteresis. The output of this unit is a control variable, either on (1) or off (0), which is sent to the collector and heat exchanger pumps. The control output function is also used as an input to unit 2, thus acting as a feedback variable to 55 indicate the status during the previous time step. 4 .4 . 6 Heat exchanger Unit 5 is a zero capacitance heat exchanger model capable of modeling crossflow, parallel flow, counterflow or constant effective- ness heat exchangers . The counterf low mode was used in this system to allow for maximum heat transfer characteristic of this design. The overall heat transfer coefficient (UA) is used to calculate heat exchanger effectiveness for each time step. From this value, the fluid exit temperatures are determined from the flow rates and inlet temperatures . Inputs to this unit are the fluid temperature and mass flow rates from the collector, unit 1, and the tank pump, unit 13, respectively. Five outputs are utilized from this routine. The warm side fluid temperature and flow rate is input to the collector pump, unit 3. Cold side fluid terperature and flow rate pass directly to the storage tank, unit 4. The fifth output is the total heat transferred during the time step and is integrated and printed out in the results. 4.4.7 Pumps The system contains two pumps, one for the collector fluid, and another for the heat exchanger-storage tank loop . These routines are simple on/off components controlled by the controller, unit 2. Maximum mass flow rates are specified parameters, and whenever the pump is operating, this flow rate is used. There is no temperature change of the fluid in either pump when the fluid passes through. Temperature m 56 inputs and outputs are used only to maintain uniformity in the information flow process. Inputs to the collector pump come from the warm side of the heat exchanger, unit 5, and the controller, unit 2. Inputs to the tank pump, unit 13, are from the storage tank, unit 4, and the controller, unit 2. 4.4.8 Storage tank The storage tank component is identified as unit 4. This study assumes an unstratified storage tank. The routine has the capacity to model a stratified storage tank for determining water temperature at different heights in the tank. The modeling of such a storage tank involves the solving of a set of simultaneous differential equations. Because of stability problems caused by the choice of time step and the extra computer time required, it was decided to use an unstratified storage tank. Since the volumes of water used in processing plants are large, the accuracy of using a stratified model is suspected. Inputs to the tank are fluid telperature and flow rate from the heat exchanger, unit 5, and a constant temperature and variable flow rate of the replacement fluid as determined by a forcing function routine, unit 14. The tank is insulated and assumed to be full at all times. An energy loss coefficient (U) must be specified for determining tank losses. Tank volume and height are specified parameters. Given the loss coeffi— cient, the model calculates the area of the tank, assuming a cylindrical shape, to determine the total environmental heat loss for the time period. An energy balance is made on the tank and accounts for energy delivered 57 from the heat exchanger, delivered to the load, and lost by conduction from the tank to its surrormdings. The energy lost and the total energy delivered by the tank to the load are outputs of the unit and are inte- grated and included in the simulation results. Other outputs of this unit are the fluid inlet temperature and flow rate to the heat exchanger, unit 5, and fluid terperature and flow rate to the auxiliary heater, unit 6. 4 .4 . 9 Auxiliary heater An auxiliary heating ccmponent, unit 6, is included for determining the amount of energy needed to supply the total demand when the solar collector system cannot meet the load. This is an on/off component routine controlled by an internal thermostat set at the desired constant demand terperature. Inputs to the unit are water temperature and mass flow rate from the storage tank, unit 4. The auxiliary heater adds energy to the water to bring it up to the minimum supply temperature. If the inlet temperature is greater than the heater temperature setting, the outlet terperature is set equal to the inlet temperature. For these cases the water supplied to the load is warmer than necessary. This condition is not allowed for in the model, since the flow rates, controlled by the forcing function, determine the system energy demands. Allowances for this condition are made in the analysis and discussion. The outputs of unit 6 are fluid temperature, fluid flow rate and energy added to the water. The energy is integrated for the entire simulation period and given in the results. 58 4 . 5 Design Parameters Each unit has one or more parameters which need to be established prior to simulat ion . These parameters are discussed for each component in the system. In two cases, parametric test runs were conducted to determine the best parameter for the processing plant simulations. Table 4.1 gives a summary of the necessary parameters and their SI units. 4 . 5 . 1 Radiation processor The solar radiation processor, unit 16 , requires seven parameters . Mode is the first and is equal to 1. The second parameter is the day of the year at the start of the simulation. Table 4.2 illustrates the day of the year for which each simulation begins together with the approximate duration of the simulation . lat itude is the third para— meter. Values for latitude taken from Baker and Klink (1975) are 42° 42' for East Iansing, 38° 58' for Columbia, and 39° 44' for Indianapolis. Collector tilt angle, measured from the horizontal is the fourth para— meter. Kreider and Kreith (1975) ,5 generally recommend an annual optimum tilt angle for residential water heating, equal to the latitude. latitude plus 15 degrees is accepted as best for annual residential space heating. The design of this solar water heater was assumed to have the capability of varying the collector tilt. Since no accepted rule exists for best tilt angles to use at different periods, an assumption was made. The collector tilt angle was assumed to be optimum when calculated as the completent angle of the solar altitude calculated at 15 degrees from local solar noon averaged over the period of the simulation. 59 Table 4.l - Summary of Simulation Parameters and Their SI Units. Radiation Processor - Unit l6 Parameter: Collector - Unit l Parameter: Controller - Unit 2 Parameter: l 2 CON—J Pump - Units 3 and l3 l Parameter: Heat Exchanger - Unit Parameter: Storage Tank - Unit 4 Parameter: Auxiliary Heater - Unit 6 Parameter: #00 U'I-P-WN-J l 2 3 4 Mode Day of the year at beginning of simulation Latitude Collector tilt angle from horizontal Collector southern orientation Solar constant Ground reflectance Mode Collector surface area, A Collector geometric efficiency factor, F‘ Specific heat of collector fluid Collector plate absorptance Collector overall loss coefficient Total cover transmittance Stick control Upper dead band temperature Lower dead band temperature Maximum fluid mass flow rate Mode Overall heat transfer coefficient, UA Specific heat of warm fluid Specific heat of cold fluid Volume Tank height Specific heat of storage fluid Mass density of fluid Overall loss coefficient, U Maximum heating rate Minimum supply temperature Dead band temperature Specific heat of fluid integer integer degrees degrees degrees kJ/m2 hr decimal I i teger m3 decimal kJ/kg C decimal kJ/m2 hr c decimal integer C C kg/hr integer kJ/hr C kJ/kg C kJ/kg C m3 m kJ/kg C kg/m kJ/m2 hr C kJ/hr C C kJ/kg C 60 Table 4.2 - Starting Day of Simulations. Data Source Season Duration Day of Year Comments East Lansing, l974 spring 2160 60 medium dairy 500 l3l small F & V summer 2l60 l52 medium dairy 2l60 l72 small F & V 2l3 all F & V plants l965 fall 2l60 244 medium dairy 336 244 all plants 2l60 273 small F & V winter 2l60 334 medium dairy Columbia, l949, 52 spring 2l60 57 medium dairy 500 l3l small F & V summer 2l60 l46 medium dairy 2l60 l72 small F & V 2l3 all F & V plants fall 2160 238 medium dairy 336 244 all plants 2l60 273 small F & V winter 2l6O 330 medium dairy Average Year spring 2l60 60 medium dairy summer 2l60 l52 medium dairy fall 2l60 244 medium dairy winter 2l60 334 medium dairy 61 Equation (3.7 ) was used to calculate the altitude angle with H s equal to 15 and declination values interpolated from ASHRAE (1974). Ground reflectance was assumed to be zero. 4 . 5. 2 Collector The collector, unit 1, requires seven parameters. The first para- meter, collector mode is equal to l . The second parameter, collector area, measured in square meters, is of primary importance in the design of the appropriate system size for each food processing plant . This parameter is used to scale storage tank volume, pump flow rates, and heat exchanger capacity . The initial collector areas to be simulated were chosen based on preliminary simulations according to the total daily energy requirement of the processing plant . A method was needed for selecting proper collector areas to simulate for each processing plant. It was desired to make runs which would result in a solar contribution to the total demand, in the range of 30 to 80 percent . Several preliminary runs were used to determine an approximate ratio of collector area to energy supplied to use as guideline for selecting collector areas to simulate. The resulting ratios were: 245 square meters of collector per million kiloJoules would supply approximately 70 percent of the demand, and 62 square meters of collector per .- million kiloJoules would supply approximately 30 percent of the demand. Using these criteria, the following rule was used to establish the collector areas to be simulated: the largest initial area was determined by taking the total daily energy demand in millions of kJ and multiplying it by 245, the smaller area was determined by using a factor of 62. For some cases of the fruit and m 62 vegetable plants, a smaller collector area was chosen because of the limited use characteristics of the plant. Table 4.3 summarizes the collector areas used in the food processing plant simulations. The fourth parameter is the specific heat of the collector fluid. A value of 2.386 kJ/kg C for ethylene glycol at 20 degrees C was obtained from Holman (1972). The fifth parameter is the collector plate absorptance. A selective absorber surface approximating lampblack in epoxy was chosen based on Table 5.5.1 of Duffie and Beckman (1974). An absorptance of 0.9 was estimated for long term collector performance. The seventh parameter, cover transmittance, was taken from Figure 6.2.1 in Duffie and Beckman (1974) for a collector with two glass. covers exhibiting a thickness-extinction coefficient product of 0.0125 per sheet, at an average incidence angle of 20 degrees. The figure shows a transmittance of 0.833 allowing for reflection and absorption. This value was held constant during all simulation runs. The sixth parameter, collector loss coefficient (U1), was determined according to the method described by Klein (1974). This method, discussed in Chapter 3, uses equations (3.8), (3.9), (3.10), and (3.11). The top (Qt)’ edge (Qe), and back (Qb) heat losses are calculated for a given collector design. For this analysis a collector with the following properties was assumed: collector dimension of 2x2 meters, surface area (A) of 4 square meters, edge perimeter (Ap) of 8 meters, two glass covers, absorber plate emissivity (8p) of 0.95, average plate temperature (Tp) of 85 degrees C, ten centimeters of back insulation with a resistance of 0.697 kJ/mzhr C. The following ambient conditions were assumed: wind speed of 10 m/s and an ambient air temperature of 10 degrees C. Values for the edge and back heat transfer coefficients were taken as —_————wu Table 4.3 - Summary of Collector Areas Choosen for (collector area in square meters). Testing Scale: a b c d e Small Dairy 25 _ 40 65 lOO Medium Dairy 80 l40 200 330 Large Dairy 400 700 l000 l600 Small Meat 35 65 l00 l50 Medium Meat 80 l40 200 320 Large Meat 600 l050 l600 2400 Small F & V 670 l340 2340 3340 5365* Medium F & V 525 l050 T850 2650 4200* Large F & V 40l5 8030 l4050 20070 32l00* *for East Lansing location only 64 1.8 kJ/m hr C and 45.0 kJ/mzhr C, respectively, as recommended by Klein (1974) . This results in a collector top loss coefficient of 15.8 kJ/mzhr c using Figure 7.4.c from Duffie and Beckman (1974). Equation (3.9) was used to calculate the top heat loss. The edge ‘ loss (Q G) was calculated as 1080 kJ/hr from equation (3.11) and a back loss (Qb) of 21 kJ/hr from equation (3.10). A resulting overall loss coefficient of 20 kJ/mzhr C was calculated using equation (3.8). This value was assumed constant for all simulations. The collector efficiency factor, F' , the third parameter, is important for describing collector performance. The determination of this value is dependent upon detailed collector design such as: bond conductance, tube spacing, etc. Figures 7 .5.4b,c from Duffie and Beckman (1974), were used to choose a typical value. Using a collector loss coefficient of 20, a plate conductivity thickness product (k) of 0.4 and a tube spacing of 12 centimeters, an efficiency factor of 0.95 was estimated for use in all simulations. 4.5.3 ' 'Heat eXchanger The heat exchanger unit requires four parameters: mode , specific heats of the cold and warm fluids, and the overall heat transfer coefficient, UA. Mode 2, the counterflow type, was used. The specific heat of the cold side water was taken as 4.186 kJ/kg C, and 2.386 kJ/kg C for the warm collector fluid. The overall heat transfer coefficient for the exchanger was chosen based upon a design assumed for a given system capacity in a preliminary run. Preliminary analysis showed an approximate daily usage of 500,000 65 kJ / day for a small dairy plant . Assuming the collector operated 5 hours per day, a heat transfer rate in the heat exchanger has to be 100,000 kJ/hr . An average temperature difference across the heat exchanger of 5.6 degrees C, based on recommendations for the design of heat transfer equipment from Jennings (1970), was used to obtain an overall design heat transfer coefficient from the following relation: Q = UA A T _ (4,1) UA was found to be 19,000 kJ/hr C. This value was standardized with a collector area of 100 m2. The resulting ratio of 190 kJ/hr C per square meter of collector was used to determine the heat exchanger capacities for other processing plants. 4.5.4 Tank pump The tank-heat exchanger pump, unit 13, requires a maximum flow rate specification. The choice of this value relates to the determination of the heat exchanger capacity. In order to remeve 100,000 kJ/hr at 5.6 degrees C from the heat exchanger, the fluid flow rate is restricted by equation (3.6). The maximum flow rate calculated was 4540 kg/hr. This parameter was also standardized for a collector area of 100 m2. A ratio of 45.4 kg/hr per square meter of collector area was used to determine the proper pump size relative to the other units when simulating different size systems. 66 4 . 5. 5 Collector pump The collector pump, unit 3, also requires a flow rate specification. Initially in preliminary runs this value was set equal to the tank pump flow rate. This parameter was desired to be partially optimized for a system size of 100 m2 of collector with a load for the small dairy, in order to allow for actual operational characteristics of the system during simulation. Five collector pump flow rates were tested for collector performance, ranging from 1135 to 5675 kg/hr. From these results a collector pump flow rate to collector area ratio of 34 .ll kg/hr per square meter of collector was obtained and used to scale collector flow rates for the processing plant simulations. 4.5.6 Storage tank Five parameters: tank volume, tank height, specific heat of the storage fluid, mass density of fluid, and heat loss coefficient, are required for unstratified storage tank operation. For simulating different size systers a ratio of tank volume to collector area was needed for proper scaling. The tank volume ratio was chosen from parametric test simulations based on the tank size which applied the greatest percentage of energy to the load. Sizes, ranging from 1.89 to 11.36 ms, were tested. A final ratio of 0.03785 m3/m2 of collector was used for scaling processing plant simulations. The tank height specification was based on approximate dimensions of commercially available steel tanks. Where large tanks were needed 67 the tank height was assumed to be 4 meters. A height of 2 meters was chosen for the smaller tanks. These specifications were chosen somewhat arbitrarily since actual tank design depends upon available space, etc. , at each plant. The specific heat and mass density for water was specified as 4.186 kJ/kg C and 1000 kg/ma, respectively. The loss coefficient for the tank was taken constant for all simulations. Eight centimeters of fiber insulation was assumed with a resulting loss coefficient, including film resistance, of 6.96 kJ/mZC. This loss could be decreased by the addition of more insulation. 4 . 5 . 7 Auxiliary heater The auxiliary heater requires the specification of fluid specific heat, minimum temperature, and maximum heating rate. The temperature settings, corresponding to the type of processing plant being simulated, were obtained from Tables 3.2, 3.3, and 3.4. Using these temperatures and the maximum flow rate for each plant, the maximum heating rate was calculated using equation (3.6), assuming the auxiliary heater supplied 100 percent of the demand. This condition allows for total system reliability if the solar energy system fails. 4.6 Simulation Periods This study deals with simulating a solar water heating system for 2 parametric tests, 9 insolation model tests, 9 energy demand loads, 3 geographic locations, 4 seasons, 5 years, varying durations, and at 68 least 4 different collector areas for each demand load. Because of the number of simulations and complexity in identifying each simulation the following method was developed to accurately describe each simulation run. Each simulation is identified by a code containing information relating to the type of plant, time of simulation, etc. Table 4.4 gives the legend which describes the meaning of each category. Each code contains 8 symbols which identify the: group, type, location, year, season, model, period, and scale for each simulation. Runs are classified in one of eight groups labeled A through H. Group A includes all hourly insolation test model simulations. Group B contains the simulations performed on the hourly average test models. Group C contains parametric test simulations for storage tank size determination. Group D contains parametric tests simulations of collector :flow rate determination. Groups E, F, and G contain simulations for the three food processing plants: dairy, meat, and fruit and vegetable, respectively. Group H contains simulations for the "average" year. Each group is identified according to plant type. Three plant types are outlined: S for small, M for medium, and L for large. Not every type is specified for each group. This is also true for other classifications. For example, Group A simulations were only performed on a small dairy plant. There were no Group A simulations for a medium or large plant. The location identifier describes the geographic location corre- sponding to the data used in the simulation. For each type there are three possible location specifications: 1 represents East Lansing 69 Table 4.4 Legend for Simulation Identification. Group: Type: Location: Year: Season: Model: Immmonwb U) LON-4 DVOU'l-D thKO Z‘nmm H3>C:'U (DDUO‘UOZZF'K Insolution Test Models Hourly Average Tests Storage Tank Parametric Tests Collector Fluid Flow Rate Parametric Tests Dairy Processing Plants Meat Processing Plants Fruit and Vegetable Plants - "Average" Year Tests Small size plant Medium Large East Lansing (Michigan) Indianapolis (Indiana) Columbia (Missouri) - l949 - l952 - l965 - 1974 ”Average” year according to Baker and Klink (l975) - Spring beginning March l - Summer beginning June l - Fall beginning September l - Winter beginning November 30 Control model using measured hourly insolution data Control model using constant air temperature ASHRAE model using daily total insolution inputs ASHRAE model using weekly average daily insolution Whillier model Transmissity model l hour average model 3 hour average model 6 hour average model 70 Table 4.4 (Continued) Legend for Simulation Identification. Period: Scale: 336 hours l000 hours 2l60 hours etc. a - Smallest system size or parameter specification for the period ' next smallest etc. (D O. O 0" l - Largest system size or parameter specification 71 (Michigan), 2 for Indianapolis (Indiana), and 3 for Columbia (Missouri). The year classification indicates the year the input data was recorded. Corresponding to each location classification there may be five year specifications: 65 representing the year 1965, 74 for 1974, 49 for 1949, 52 for 1952, and A for the ”average” year. The season classification indicates the time of year the simulation takes place. There may be four season classifications for each year: SP representing spring, SU for summer, FA for fall, and WI for winter. The model specification indicates the origin of the input insolation data. There are 9 models, K through S, which may be used for each season: Model K is the basic control model which uses measured hourly values of insolation and air temperature, L is the control model which uses constant air temperatures, M is the ASHRAE model which uses daily total insolation, N is the ASHRAE model which uses weekly averages of daily total insolation, O is the Whillier model, P is the Transmissivity model, Q is the l—hour average model, R is the 3—hour average model, and S is the 6—hour average model. The duration of each simulation is identified by the period specification. The number of hours for each simulation is equal to the numerical value of the period specification. Most snmulations were 336, 1000, or 2160 hours long. Thus following each model specifi- cation the period is specified: 336,1000,2l60, etc. Finally, a description of system size is identified by the scale specification. This specification describes the parameter variations of collector area, collector flow rate, etc., occurring for each period. Up to five scale specifications may be given for each period as follows: a,b,c,d,e. Scale ”a" indicates the smallest and scale ”e" the largest 72 collector area, etc. , for each period. Each category is always specified for each simulation run to eli— minate confusion. Some repetition exists in the description of models, types, etc. For the purpose of completeness, this repetition is tolerated. All simulations use the same solar water heater configuration as discussed under the System Modeling section. Two examples are given to illustrate the use of this method. mample One: A:S:l:74:SP,SU:K,N:336:d This code describes 4 simulation runs: an insolation model test (A) for the small plant (S) at the East Lansing location (1) in 1974 (74) for two seasons, spring (SP) and summer (SU), for models using hourly measured insolation (K), and ASHRAE weekly model (N) for a simulation time period of 336 hours with a collector area of 100 m2 (scale (1). Example Tim: G:M:3:49:FA:K:336:a,b,c,d This code describes 4 simulation runs: a fruit and vegetable plant (G) of medium size (M), at the Columbia location (3), occurring in 1949 (49), during the fall (FA) using actual measured insolation data (K), for a period of 336 hours for four different collector sizes (a,b,c,d). 4.6.1 Insolation modeling '— Group A Four time periods, to observe seasonal influences, of 336 hours each 73 were used to test the validity of the 5 hourly insolation models with the control during 1974 at East Lansing, Michigan. The four periods started on March 1, June 1, September 1 and November 30. These test rms were performed for a small dairy demand with a higher heater terperature than shown in Table 3.2. A collector area of 100 m2 was used. Table 4.5 describes the parameters and total energy demand for each run in this group. 4.6.2 Hourly averages — Group B Groxp B simulations compare the effect of averaging insolation data on the simulation results. Three runs were made of 1000 hours each beginning March 1, 1974 at East lensing, Michigan. A demand function for a small dairy plant was used with a collector area of 80 m2. Table 4.6 illustrates the parameters used in these runs. 4.6.3 Storage tank tests - Group C Two time periods of 336 hours beginning June 1 and November 30 for 1974 at East Lansing, Michigan were used to evaluate storage tank parameter selection. Five storage tank sizes ranging from 1.892 to 11.36 m3 were tested. Table 4.7 illustrates the conditions used for these simulations. 4.6.4 Collector fluid flow rate tests-— Group D Simulations for optimizing collector flow rate were performed for the same conditions as the storage tank tests. The collector flow rates Table 4.5 Group A Simulations: 74 Insolation Test Models. Type: S Location: l Year: 74 Season: SP Model: K Period: 336 Scale: _Q Collector area1 l00.0 Tilt angie2 49.9 Ambient air temperature3 variable Collector fluid flow rate1 4540 Heat exchanger coefficient1 l90.000 Exchanger pump flow rate1 4540 Storage volume 7.57 Tank height1 6.0 Supply water temperature1 l0.0 Heater capacity1 l05,000 Heater temperature 68.33 Demand flow-weekly1 5820 Energy demand - total1 2.842 x l06 Models: L, M, N, 0, P Period: 336 Scale: ‘9 Ambient air temperature3 3.5 Season: SU Model: K Period: 366 Scale: .Q Tilt angie2 24.3 Ambient air temperature3 variable Season: SU Models: L, M, N, O, P Period: 336 Scale: 1d Ambient air temperature3 l9.l7 75 Table 4.5 Group A Simulations: Insolation Test Models (Continued). Season: FA Model: K Period: 336 Scale: d Tilt angie2 39.0 Ambient air temperature3 variable Season: FA Models: L, M, N, 0, P Period: 336 Scale: .9 Ambient air temperature3 l6 ll Season: NI Model: K Period: 336 Scale: 9 Tilt anglez 65.9 Ambient air temperature3 variable Season: WI Models: L, M, N, 0, P Period: 336 Scale: d Ambient air temperature3 -l.56 1Constant for group 2Constant for season 3Constant for model(s) 76 Table 4. 6 - Group B Simulations: Hourly Average Tests. TYpe: S - Small.Dairy location: 1 - East lansing Year: 74 Season: SP Models: Q; R: 5 Period: 1000 Scale: ‘a Collector area 80 Tilt angle 50 Ambient air temperature variable Cbllector fluid flow rate 4540 Heat exchanger coefficient 19,000 Exchanger pump flow rate 4540 Storage volume 9.46 Tank height 3.6 Supply water temperature 10.0 Heater capacity 105,000 Heater temperature 74.0 Demand flow - bi-daily 3240 Energy demand - bi-daily 8.68 x 105 Energy demand - Total 1.823 x 107 —— w. Season: WI Model: K Period: 336 Scale: a Tilt angle2 60.0 1Constant for group 2Constant for season 77 Table 4.7 - Group C Simulations: Storage Tank Tests. Type: S Location: L Year: 74 Season: SU Model: K Period: 336 Scale: 3 g g d g Collector area1 l00 Tilt angie2 30 Ambient air temperature1 variable Collector fluid flow rate] 4540 Heat exchanger coefficient1 l9,000 Exchanger pump flow rate1 4540 Storage volume 1.892 3.785 5.680 7.57 ll.36 Tank height] 6.0 Supply water temperature1 l0.0 Heater capcity l05,000 Heater temperature 68.33 Demand flow-weekly 5820 Energy demand-total] 2.842 x 105 78 tested ranged from 1135 to 5675 kg/hr in the summer and 1135 to 4540 kg/hr in the winter. Table 4.8 presents the specific conditions and periods for these runs. 4.6.5 Dairy plants — Group E A two—week period starting September 1 was chosen to sirmilate all dairy plants for two years (65,74) at East Lansing, Michigan and two years (49,52) at Columibia, Missouri. Indianapolis insolation data was not available for simulation. These simulations serve as a base for comparing the performance of different sizes and types of plants for a given year. Each plant was simulated for 4 collector sizes. The medium dairy plant at one collector size (140 m2) was chosen to simulate for an entire year (1974) at East Lansing, Michigan, and for 2 years (1949, 1952) at Columbia, Missouri. This is the only plant which was simulated for an entire year. Annual performance of all other plants was projected from the results of these annual runs. The annual runs were broken into 4 periods of 2160 or 2184 hours each. The purpose for this was to facilitate the changing of the collector tilt angle. Table 4.9 describes in detail the parameters and energy demands for each dairy plant simulation. 4.6.6 Meat plants — Group F All meat plants were simulated for 4 collector sizes at the two- week September period for 2 years (65,74) at East lensing, Michigan and 2 years (49,52) at Columbia, Missouri. The annual operation of these 79 Table 4.8 — Group 0 Simulations: Type: S Location: l Year: 74 Season: SU Model: K Period: 336 Scale: Collector areaI Tilt angle2 Ambient air temperature1 Collector fluid flow rate1 Heat exchanger coefficient1 Exchanger pump flow rate1 Storage volume Tank height1 Supply water temperature1 Heater capacity Heater temperature Demand flow-weekly1 Energy demand-total Season: WI Model: K Period: 336 Scale: 3 Tilt angle2 60.0 1Constant for group 2Constant for season —+: Collector Flow Rate Tests. l00 30 variable ll35 l9,000 4540 7.57 6.0 l0.0 l05,000 68.33 5820 2 842 x 106 fl 80 Table 4.9 -Gr0up E Simulations: Dairy Plants. Type: S Location: l Years: 65, 74 Season: FA Model: K Period: 336 Scale: 3 g g g Collector area2 25 40 65 100 Tilt ang1e3 39.0 Ambient air temperature1 variable Collector fluid flow rate2 853 l364 2217 3411 Heat Exchanger Coefficient2 4750 7600 l2,350 l9,000 Exchanger pump flow rate2 ll35 l8l6 295l 4540 Storage vo1ume2 0.946 1.514 2.460 3.785 Tank height2 2.0 Supply water temperature1 l2 78 Heater capacity2 l70,000 Heater temperature 65.53 Demand flow-weeklyz 5820 Energy demand—total2 2.57 x l06 Location: 3 Years: 49, 52 Season: FA Model: K Period: 336 Scale: _ Tilt ang1e3 37.5 Type: M Location: l Years: 65, 74 Season: Fa Model: K Period: 336 81 Table 4.9 - Group E Simulations: Scale: Collector area2 Tilt angle3 Collector fluid flow rate2 Heat exchanger coefficient2 Exchanger pump flow rate Storage volume Tank height2 Heater capacity Heater temperature Demand flow-weekly Energy demand-total4 Year: 74 Season: SP Model: K Period: 2184 Scale: Tilt angle5 Energy demand-total4 Season: SU Model: K Period: 2184 Scale: .9 Tilt angle5 24.3 Season: FA Model: K Period: 2160 Scale: 0 Tilt ang1e5 39.0 Season: WI Model: K Period: 2184 Scale: 0 Tilt angle5 65.9 9.367 x 10 Dairy Plants (Continued). 9. .9 .2 80 140 200 39.0 2730 4775 6822 15,200 26,600 38,000 3632 6356 9080 3.03 5.30 7.57 2.0 170,000 66.11 32,280 1.411 x 107 9. 49.9 7 9. 330 11,260 62,700 14,980 12.50 82 Table 4.9~ - Group E Simulations: Dairy Plants (Continued). Location: 3 Years: 49, 52 Season: SP Model: K Period: 2184 Scale: __ 1111; angles 33.8 Energy demand-total5 9.367 x 107 Season: SU. Model: K Period: 2184 Scale: 9 Tilt angle5 23.6 Energy demand-total5 9.367 x 107 Season: FA Mode1: K Period: 336 Scale: 0 Tilt ang1e6 37.5 Energy demand-total6 1.441 x 107 Period: 21847 Scale: 0 Tilt angle6 48.3 Energy demand-total 9.357 x 107 Season: NI Mode1: K Period: 2160 Scale: 0‘ Tilt ang1e5 61.3 Energy demand-totals 9.367 x 107 83 Table 44.9-- Group E Simulations: Dairy Plants (Continued), Type: L Location: 1 Years: 65, 74 Season: FA Model: K Period: 336 Scale: g_ b_ g_ _d Collector area2 400 700 1000 1600 Tilt angle3 39.0 Collector flow rate2 13,640 23,880 34,110 54,580 Heat exchanger coefficient2 76,000 133,000 190,000 304,000 Exchanger pump flow rate2 18,160 21,780 45,400 72,640 Storage voiume2 15.10 26.50 37.85 60.60 Tank height2 2.0 4.0 4.0 4.0 Heater capacity2 405,000 Heater temperature2 79.44 Demand flow-weekly2 149,850 Energy demand-total2 8.363 x 107 Location: 3 Years: 49, 52 Season: FA Mode1: K Period: 336 Scale: .3 Tilt angle3 37.5 IConstant for group 2Constant for type 3Constant for location 4Constant for year(s) 5Constant for season 6Constant for period 7late portion of 49 run had bad weather data and was truncated at 1320 hours 84 plants is similar to the dairy plants. The operation of the plant during the September simulation period is the same as for the rest of the year, similar to the dairy plants. The September simulations serve as a means of comparing the meat plants with the annual performance of the medium dairy plant. Table 4.10 shows the parameters used for these simulations . 4.6.7 Fruit and Vegetable plants — Group G Simulation periods for these plants required individual attention since each one varied in its periods of production. All plants were modeled according to their September demand for the same two-week periods as the meat and dairy plants for comparison. Also, each plant was modeled for most of one year's processing demand for 1974 at East Lansing, Michigan and 1952 at Columbia, Missouri. All vegetable plants required separate runs during the year because of their seasonal periods of operation as shown in Table 3.4. The small vegetable plant was simulated for the loads according to Figure 3.7 for 864 hours starting the 131th day for period A, 360 hours starting the 173rd day for period B, 432 hours starting the 214th day for period C, and 1440 hours starting the 274th day for period D. The medium plant exhibits only two different demands, with the largest occurring in August. This high demand load was used for a 432 hour simulation. The performance for the remainder of the year was drawn fran previous yearly dairy runs. The large vegetable plant exhibited a very high short duration demand. This period was simulated for 432 hours. Table 4.11 presents the specific E7___________________________________________________________________________________ 85 Table4 10 - Group F Simulations: Type: S Location: 1 65, 74 Season: FA Mode1: K Period: 336 Scale: Years: Collector area2 Tilt angle3 Ambient air temperature1 Collector fluid flow rate Heat exchanger coefficient2 Exchanger pump flow rate2 Storage volume Tank height2 Supply water temperature1 Heater capacity Heater temperature Demand flow-weekly Energy demand-total2 Location: 3 Years: 49, 52 Season: FA Mode1: K Period: 336 Scale: _ Tilt ang1e3 37.5 Type: M Location: 1 Years: 65, 74 Season: FA Model: K Period: 336 Meat Plants. 2 .2 E 35 65 100 39.0 variable 1194 2217 3411 6650 12,350 19,000 1589 2951 4540 1.32 2.46 3.785 2.0 2.0 2.0 12.78 100,000 60.0 15,130 5.98 x106 4 150 4687 28,500 6810 5.68 2.0 Table 4.10- Group F Simulations: Scale: Collector area2 Tilt angle3 Collector fluid flow rate2 Heat exchanger coefficient Exchanger pump flow rate2 Storage volume Tank heightz Heater capacity Heater temperature Demand flow-weekly2 Energy demand-total2 Location: 3 Years: 49, 52 Season: FA Mode1: K Period: 336 Scale: ._ Tilt ang1e3 37.5 Type: L Location: 1 Years: 65, 74 Season: FA Mode1: K Period: 336 Scale: Collector area2 Tilt angle3 Collector fluid flow rate2 Heat exchanger coefficient2 Exchanger pump flow rate2 Storage volume Meat Plants (Continued). 1 E 2 80 140 200 39.0 2730 4775 6833 15,200 26,600 38,000 3632 6356 9080 3.03 5.30 7.57 2.0 2.0 2.0 221,000 71.11 27.050 1.321 x 107 e e g 600 1050 1600 39.0 20,500 35,800 54,580 114,000 199,500 304,000 27,240 47,670 72,640 22.7 40.0 _60.6 d 320 10,920 60,800 1453 12.11 2.0 A 2400 81,860 456,000 109,000 90.1 87 Table 4.10 - Group F Simulations: Meat Plants (Continued). 2 Tank height 4.0 4.0 4.0 4.0 Heater capacity2 450,000 Heater temperature 71 11 Demand flow-weekly2 252,000 Energy demand - total2 1.231 x 108 Location: 3 Years: 49,52 Season: FA Model: K Period: 336 Scale: 1 Tilt ang1e3 37.5 1Constant for group 2Constant for type 3Constant for location 88 parameters and energy demand information for each simulation. 4.6.8 ASHRAE averarés - Group H The medium dairy plant, using the same collector size as previous annual simulations, was simulated for complete years at East lansing, (Michigan) Indianapolis (Indiana) and Columbia (Missouri) for the "average" year using the ASHRAE model for weekly averages of daily insolation, Model N. Weekly average values of daily insolation were obtained from Baker and Klink (1975) for use with this model. Monthly mean tempera— tures were obtained for the tests locations from the sources indicated in Chapter 3. The average simulation data files were constructed as described for the ASHRAE weekly average test model. Four simulation periods of 2160 hours each were made at each location. The results of these simulations were used to determine the long—term performance of solar water heaters for all processing plants. The complete description of these runs is given in Table 4.12. Appendices E and F contain temperature and insolation data, respectively, used to calculate data for the "average” year simulations. 4.7 Description of Simulation Results 4.7.1 Simulation outputs To observe the behavior and performance of the solar water heating simulation model, values such as the energy collected and delivered by the system need to be determined. The TRNSYS model prints out 8 89 war x mmm.m myopou 1 ecoscc augmcm cea.mma Ns_xooz-zo_4 oeana m.¢m _ Ncgzuogmasmu prmm: ooo.emm.m Naoaoeaao tonne: mm.m_ ~mgsucamaEmu Loom: >_aa:m o.m o.m o.m o.m o.N Nogawm; xcmh o.mom o.omp o.mm m.0m «.mm mosapo> mmwaowm om.em 8_.m_ No.0, amo.o Nao.m nae, xv N one; 36.4 gaza aomeaeoxm mp.op mvm.m m¢¢.¢ oem.m mum._fimop xv Nuccwowwewoo ammcocoxm one: om.m_ mm.—_ Nwm.m ~mm.¢ mmm.m Aeop xv Noun; rope u_:Pm LouomF—ou c—nm_nm> _wgzmewQEmu new acmwne< o.mm mmpmcm “Pap mmmm oemm ovmm camp oso macaw Louom__ou w u u n m ”wpocm omm “vowgma x “_muoz — ”covuooon m ”maxh .moce_a o_oanomo> use pasta - meo_oe_=e_m u azoaw -_~.e o_aap oi x NoN._ «.mm _m m w v o_ x Nm4.m m._m .m m e v o_ x wmm.m a.m~ m. a a o_ x amm.m 8.4N .m m m Pogo“ 1 ucmsmo Amnccm o_mee o__e "osmom oee_ x ”coaxed ”Pecos .Aea:c_ococv -__.a o_aea 91 mo_ x ~m¢.m «_moou 1 ecosmn augmcm _.wm eo_mee nine m. “mpmom «me ”nowgma No, x coo.m epaooo18=esoe sateen ~.om eo_m=e cpre a “mpoom com ”vowgma x ”_mcoz :m "cemmmm O, x Awm.m mpeooo 1 season sateen m.am mo_mca o._e n “oFocm sow ”oo_aoa x "Peso: am ”commmm mm “wa> m m.am mo_mee n__e ml ”cpoom mmm ”cowama x “Pcooz wawgoum N Aeop xv Noam; zope gang Lomcmcoxm op xv Nocmwoweecoo gmacmcoxm pom: N umpmcm omm “newnwa x "Pecos F ”cowumcon .m .m ml tn UI z Hoax» as x Nom._ _apoo - eeaaoe sateen o.ae eo_aee “Fee a ”mFMUm oee_ ”eoaaoa x ”38: m.mm mm—mcm “Fee a "cpoom omm ”weaned x "_muoz m “cowomcon mo_ x n—m.m mquou1ocmsmu amnwcm m._m mopmce o_we .m ”m—mom Nme ”vowaca x ”_mnoz 2m ”commmm em ”amm> .Avo32wucouv 1 F—4V epoch o.N o.mpmp oo.¢p oo.P© om.o_ oo—Nm .w mop x oom.m mpmuou1ucmEcc amnmcm m.Pm mmpmcm “Fee a ”cFmom mme "vo_;ma x ”_wvoz :m ”:Ommmm as ”me> wo_ x eam.m m_eooo-eeeEoo autos“ co_ x _eo.m Ns_xooz-zo_c eceeoo wk.- mmgspmnmascu gmbmc: NOF x m¢.w prwomamo mewm: o.~ o.N o.N o.~ Noemaoe 9:64 o.oo~ o.omm o.eom o.mmp mossFo> mmmgopm P_.m mm.o mo.m mmw._ “mop xv Noam; zo_a gang mecocuxm o_.wm os.mm om.mp mo.sAmo_ xv Nucwwowwamoo mecmsoxw pom: mw.o mm.e em.m onm.P Amop xv Nope; sopc Loucm—Pou o.mm mopaee o_ae omoom omoe_ omom m_o¢ macro Loucmppou v o a m nwpoom omm "vowaca x ”_mcoz qd “commmm ea .mo H.88, P “cowpmoon 4 Hoax» .Aomssflpcoovl Ha.v magma 95 vownma Low pcmumcoue commmm Low pampmcoom wax» Lee ucmpmcoum azonm Lem pcmpmcou_ mo_ x ooN.~ msaooo-e:eeoe seamen _.mm moamee have a “oFmom concoa x ”Paco: 2m “commwm mm ”gmm> m.am mo_aea asap 8 ”mpmom 8mm “noncoa x H:82 m ”cowumoon .Aomzcmucoov 1:6 game 96 Table14.12- Group H Simulations - ASHRAE Averages, Type: M Location: 1 Year: A Season: SP Mode1: N Period: 2160 Scale: Collector area1 Tilt angle2 Ambient air temperature1 Collector fluid flow rate1 Heat exchanger coefficient1 Exchanger pump flow rate1 Storage volume1 Tank height] Supply water temperature1 . 1 Heater capac1ty Heater temperature Demand flow — weekly1 Energy demand - total1 Season: SU Mode1: N Period: 2160 Scale: ._ Tilt ang1e2 24.3 Season: FA Mode1: N Period: 2160 Scale: ._ Tilt ang1e2 39.0 ‘b 140 49.9 constant monthly averages 4775 4 26,600 6356 5.3 2.0 12.78 170,000 61.11 32,380 9.367 x 107 97 Table 4.12 - Group H Simulations - ASHRAE Averages (Continued). Season: NI Mode1: N Period: 2160 . Scale: .9 Tilt angle2 65.9 Location: 2 Year: A Season: SP Model: N Period: 2160 Scale: __ Tilt angle2 34.5 Season: SU Mode1: N Period: 2160 Scale: __ Tilt angle2 24.2 Season: FA Mode1: N Period: 2160 Scale: .9 Tilt angle2 49.0 Season: WI Model: N Period: 2160 Scale: ._ Tilt angle2 60.3 Location: 3 Year: A Season: SP Mode1: N , Period: 2160 Scale: b_ Tilt angle2 33.8 98 Table 4.12 - Group H Simulations - ASHRAE Averages (Continued). Season: SU Model: N Period: 2160 Scale: [0' Tilt angle2 23.6 Season: FA Model: N Period: 2160 Scale: \JIU' 2 (.0 Tilt angle Season: WI Mode1: N Period: 2160 Scale: [0' 2 Tilt angle 61.3 1Constant for group 2Constant for season 99 integrated system variables in the final results. Six of these are total energy flows and two are total mass flows during the simulation . Each of the variables, unique to each run, are included in the tables of simulation results in Appendices I, J, and K. A sample table is presented in Chapter 5, Table 5.9. The total radiation striking the collector surface is identified as RADPOTAL. This variable is the output of the radiation processor. QOOL is the total energy collected by the collector after accounting for losses encountered in the collector . Q'I‘ANK represents the net energy reroved from the tank which is delivered to the load. QAUX is the total energy required by the auxiliary heater to heat the water coming from the storage tank to the required temperature. The sum of QI‘ANK and QAUX is given as QIOTAL. This value is the total energy delivered by the entire system. In most cases this value is equal to the design load specified for each plant. However, QIDTAL may be larger than this value due to collect ion periods which cause the tank temperature to exceed the demand temperature . For these cases the system delivers more energy than required. QENV is the total energy loss to the enviromnent by the storage tank. This value is only used to observe the increase in system losses at higher operating temperatures. The total energy passing through the heat exchanger is identified by QHX. This value is not used directly in the results analysis , however the overall effect of the heat exchanger may be observed by calculating the. system efficiency. The total water demand during the simulation is also printed out by the model. This value is not included in the results and was used only as a check to make sure the energy demand simulated was the same as the 100 desired demand. For all cases this value was the same as the design demand and can be observed in the tables describing the simulation para- meters and energy demands . The total mass flow through the collector is identified as NIDL. This value was obtained for determining the average daily operation of the collectors. Since the mass flow rate of the collector pump is constant when the pump is on, this total mass flow directly relates the length of time which the system collected energy. 4.7.2 Description 'Of performance 'reSultS The integrated values from the simulation results are used to calculate 6 values useful for observing the system performance all of which are shown in the tables of simulation results in Appendices I , J, and K. The overall collector efficiency, (DIEF, describes the collector performance during all periods of poor insolation and changing air temperatures. This value is determined by dividing Q(I)L, the total energy collected, by RADIUl‘AL, the total incident energy. The energy delivered to the deland by the solar collector is represented as SOLAR. This value is the percent calculated by dividing QTANK, the energy delivered by the collector to the load, by the total design loadfor the plant. This value assumes any oversupply, due to temporary high tank terperatures, is used as part of the design load. This is reasonable for most cases since the duration of the higher tank temperature lasts only until sufficient cool supply water lowers the temperature below the demand temperature. If a thermostatically 101 controlled mixing value were used to control the maximum temperature of the water supplied to the load this oversupply could easily be utilized, thus causing the auxiliary energy demand to decrease. For periods during the summer and where large collectors are used, this assumption is less accurate. For these cases, the effects of long cloudy periods, which require a significant auxiliary energy supply, wdll be overshadowed by the oversupply during peak insolation periods. This results in a larger error as the percent SOLAR approaches 100 percent. When the degree of oversupply is significant, the "SOLAthotal" value is calculated. This quantity indicates the percent of solar energy supplied with respect to QTOTAL. SOLAthotal is presented in the simula- tion results for cases where a significant deviation occurs from.the value of SOLAR. The average period of daily system operation is given by SYSOP in hours per day. This value is calculated by dividing the total collector flow, MOOL, by the number of days and the maximum flow rate of the collector pump. This value is an average for the simulation period and.does not indicate a maximum.or*mfinimmm1daily operation period. The percentage of collected energy (QOOL) lost by the storage tank to the environment is calculated as TANK LOSS. The average temperature of water supplied by the storage tank is represented in the simulation results as "Avg. Temp". This value is calculated by dividing the energy delivered by the tank, QEANK; by the mass flow'and.specific heat of the water. This results in an.average temperature increase of the water as it passes through the tank. The exit temperature is then determined by summing this temperature increase and the constant cold water supply temperature. 102 4.8 Projection of Results 4.8.1 Method The amount of energy delivered by the solar energy system to the demand load, SOLAR, is of interest for economic considerations . Perfor- mance projections for each plant are made only for this performance criteria. The method for determining the annual system performance was chosen based on preliminary simulation results. One reference plant is simulated for a short period and an annual period. The ratio of SOLAR for the short period to SOLAR for the annual period is considered a function of the year and is assumed constant for that year. Other plants are simulated for the same short period as the reference plant . The SOLAR for the short period of each plant is then multiplied by the ratio for the reference plant to determine annual performance. The annual demand characteristics for each plant are necessary for determining the base periods for comparison of performance. The dairy and meat plants are similar in their derands and thus allowed the annual performances of each plant to be determined from one reference plant simulation, while the fruit and vegetable plants required a slightly different method as described in the following sect ions . 4.8.2 Dairy and meat plants The dairy and meat plants exhibit similar annual derand schedules which enable a direct camparison between the plants during different times 103 of the year. All dairy and meat plants were simulated for 336 hours in Septelber. The medium dairy plant was simulated for the entire year at one collector area. A ratio of the Septerber to the annual performance was used to project the annual performance for each plant and collector area for that year using the Septetber simulation results. "Average" year simulation results for the medium dairy plant at East Lansing and Columbia, using the ASHRAE weekly insolation model were used to construct a ratio using the September results, to predict the average long—term performance of the dairy and meat plants. The average long-term results for Indianapolis were then interpolated from the average year simulation results for all three locations and the projections for each plant from East Lansing and Columbia. For East Lansing and Columbia, two years of September data were available for simulation. For determining the average long-term perfor- mance, the results of the September simulations for each loaction were averaged . 4.8.3 Fruit and vegetable plants Fruit and vegetable plants do not exhibit a constant annual derand schedule. September simulations similar to the dairy and meat plants were performed on the fruit and vegetable plants for means of comparison with the dairy and neat plant results. The medium fruit and vegetable plant results for Septerber were used to partially predict the annual performance. Each fruit and vegetable plant was simulated for their seasonal demand periods for one year using one collector area at East Lansing and Columbia. 104 These results were used to determine the annual performance of each plant by summing up the seasonal energy demands and the contributions made by the solar collector to each demand period. The average long-term per- formance proj ect- ions at East Lansing and Columbia were then calculated using the fruit and vegetable plant annual results and the medium dairy plant ratio of annual results for the same year and the average simulation results using the ASHRAE weekly model. 4 . 9 Economic Analysis 4 .9 . l ConVent ional energy ecsts The cost of solar energy was compared with conventional energy sources of electricity, fuel oil, and natural gas. All energy costs were described as the cost in dollars to supply one million kilo-Joules. Typical emery conversion efficiencies for industrial boilers were taken from Fryling (1966) . Electricity was assumed to have a conversion efficiency of 85 percent. The current price to residential consumers was chosen for this analysis as 3.32 cents per kilowatt-hour. The result is an energy cost for electricity of 10.85 $/MKJ. The current price of No. 2 fuel oil was found to be 45.9 cents per gallon . Using a convers ion efficiency of 80 percent and a heat content of 132,000 BTU per gallon a final cost of 4.12 $/MKJ was calculated. Natural gas was taken at a price of 3.2 dollars per 1000 cubic feet, a heat content of 1000 BTU per cubic foot , and a conversion efficiency of 76 percent to yield a cost of 3.98 $/MKJ. 105 4.9.2 SOlar system capital investments The following equation based on estimates from Kreider and Kreith (1975) was used to calculate the capital investment (CI in dollars) for constructing solar water heating systems: CI = 2000 + 150(A) + 120(V) (4.1) where A is the collector area in square meters and V the storage volume in cubic meters. The cost of collector and piping were estimated from Kreider and Kreith (1975). A constant value of 2000 dollars was estimated for fixed costs such as controls and thermostats. 4.9.3 Solar energy costs A life cycle cost technique was used to determine the annual operating cost of the solar energy system. A 20—year solar energy system life expectancy and an annual interest rate of 10 percent were used to determine a Capital Recovery Factor (CRF) of 0.1175. The annual operating cost was then calculated from equation (3.12) for each collector size. The annual operating cost for each solar water heating system was divided by the annual amount of energy supplied by the system to determine the cost of each million kilo-Joules for comparison with the cost of conventional energy sources . 106 4.9.4 Cost effectiveness using capital investment analysis The cost effectiveness of the solar water heating system is (bt ermined by comparing the capital investment of a solar emery system and the allowable investment determined from the value of the conventional emery replaced by solar emery over a 20—year period, at an annual interest rate of 10 percent and a fuel increase of 5 per- cent per year. Equations (3.14) and (3.15) were used to determine the relationship between the allowable investment and the present value of the conventional emery saved, P o' A simplified relationship of equation (3.14) for the above criteria is shown in equation (4.2): P = (PO)(16.6) (4.2) The value of P0 was determined by multiplying the annual energy supplied by the solar emery system, by the emery cost in dollars per million kilo—Joules, for each convertional fuel; electricity, oil, or gas. The allowable investment , P , was then determined for each emery source for comparison with the investments required by the solar water heating system . 5. SIMULATION RESULTS AND DISCUSSION 5.1 Insolation Models — Group A The simulation results for the 6 insolation models for each season are given in Tables 5.1, 5.2, 5.3 and 5.4. Each table shows the total insolation striking the collector (RAUIOI‘AL), the total emery collected (QCDL), the total energy delivered to the load by the tank (QI‘ANK), and the total fluid flow through the collector (MCOL). From this information, the overall collector efficiency (CDLEF), percentage of the demand supplied by collectors (SOLAR), and the average temperature of the water supplied by the tank were determined for each model. The seasonal behavior of each model generally resulted in a decrease in the solar emery supplied to the load. Table 5.5 summarizes the per— cent SOLAR for each run and compares the annual percentage for each model. The annual percentages were calculated by summing the emery delivered by the tank for each season and dividing it by the total design load. The percent deviation of each test model from the control model and the control model with constant temperature is illus— trated. The seasonal performance variations of each model is further illus— trated in Figure 5.1. This figure shows the total insolation (RADIUI‘AL) striking the collector for each test model. The daily ASHRAE model (M) shows a higher insolation in the summer period and a lower insolation 107 108 N.—e o.mm o.Ne P.0e m.mm m._e Auv.azme.o>< o.mm w.me o.em o.Fm o.om m.mm “NV m_xmm3v «xsweev AH ucepmcoov As XFrzozv mamas smwppwzz m< a.ms o.ss m.ms w.as m.ws N.Pw Ase madam N.AN m.mm P.wm e.sN e.am N.om Ase dance meo.e _ee.m smm.m e_e.m woo.m . mwe.e haemo_xv nous smF.N wm_.m _mN.N eeN.N OMN.N wom.m Aezeo_xv azaso Nma.e eem.e oeo.s __m.s Nem.s Aes.s leeee_xv Loco emm.m Noe.m eom.~ mNA.N Nam.m Nem.m lexao_xv n< o.se A.NA m.es ..Ns m.~a e.es Ase “snow e.sm s.wm _.am e.wm A.wm N.om Ase assoc e.m.m mem.s .mw.e _wm.s mem.e mee.e lesmo_xv has: moa._ meo.m mmN.N emo.m _ee.m ea..~ lexeo_xv xzaso aNP.e som.e _om.s wme.e smm.e esm.e Aexeeixv some NNN.N sem.m mom.m eeN.N eAN.N esN.N lexso_xc assesses Axhxwmzv axflwmev AH pceumcoov AH Apazozv mcese amw__w;3 m< e.sm N.wm s.ae e.se e.om e.om Ase macaw o.mm o.e~ m.eN m.sm m.em m.eN Ase assoc Pam._ me_.N swm.m mNP.N _mm.m eme.m lesmova nee: saw.o mwc._ Nee._ Nem._ mme._ _ms._ lexeoaxv azaso _wm.N NNA.N wem.e eme.m mea.m mam.m Aegeo_xv Loco m_a.o mm_._ aee._ Nwm._ Nem._ som._ Aexso_xv nwo o.m_- m.a- N.e o.e- w.m e Ase 4 sets eosoea>oe o.sm e.mm e.me m.me o.me m.me Ase asaro>< _esee< e._m N.mm s.se a.se s.om e.om Ase amaze: e.ae A.NA m.ss _.Ns m.ms e.ea lschnau e.ma o.as m.ms w.aa m.wa N._w lsvawzzsm e.mm m.ae s.sm e._m 8.0m a.mm Ase ezcaam aanom AAF4ww2v Ax_wmev A» ucepmcocv AH x—asosv meets Laa__.;3 maazm< sesame Poconos _oaseec H_oeoz a o z z 4 x ceaseseaa enommna .o .z .z .4 .x ”H3 . ace peace cow mopsmom coves—seem preseasom Lo assessm 1 Np.m m_nee a -‘-"I ' ' "' ' a _— - .H._. .- .s... .~ ”‘3'. 137 Table 5.18 - Summary of Yearly Simulation Results for Fruit and Vegetable Plants (Percentage of demand supplied by solar energy). Demand Period: A B C 0 Small Plant Collector area 1340 East Lansing, 1974 57.8 75.9 32.1 134.2 (35.2)* Columbia, 1952 86.8 118.8 50.7 184.1 (52.6)* Medium Plant Collector area 1050 East Lansing, 1974 38.6 31.0 Columbia, 1952 58.1 49.1 Large Plant Collector area 8030 East Lansing, 1974 32.7 (35.9)* Columbia, 1952 51.9 (54.4)* *September results 138 off season demand and its contribution to the overall economic feasibility. Table 5.19 gives the annual performance projections for the small fruit and vegetable plant for two years: 1974 at East Lansing and 1952 at Columbia. For each location the performance for each demand period is given. For demand period C, scale b at both locations the value is the average for two simulations, indicated by the bar over the number, occurring at different times during the plant demand. Several periods indicate over 100 percent supply to the load. A low and high annual percentage is given for both locations. The difference between these two percentages represents the amount of oversupply of the solar system for certain periods of the year. For the large scale, the amount of oversupply for each location is over 14 percent. Since only the low annual percentage can be justified for the normal plant demand, it is used to determine the average annual percentage for each location. The annual performance results for the medium fruit and vegetable plant are presented in Table 5.20. The medium plant exhibits a demand 12 months per year. For a period in August the demand is greater corresponding to demand period B. To determine the annual performance for this plant the annual percentage of solar supply taken for the September demand was calculated and is shown as demand period A (annual) in the table. An allowance for high demand period B was made to determine the actual annual system performance. The annual performance for the East Lansing location ranges from121.6 to 58.9 percent for scales a through e and 32.9 to 75.5 for scales a through d at the Columbia location. These values are used to predict the average annual perfor- mance for fruit and vegetable plants at all three test locations. 139 Table 5.19 - Annual Projected Performance of Solar Water Heating System for Small Fruit and Vegetable Plants (Percentage of demand supplied by solar energy). Scale: a b c d e Collector area 670 1340 2340 3340 5365 East Lansing, 1974 Demand Period A 37.7 57.8 77.3 90.5 107.4 8 49.2 75.9* 101.6 118.8 141.0 C 21.8 45.1 52.8 62.6 D 86.9 134.2 179.2 210.0 249.3 Annual High 30.0 38.2 62.1 73.0 86.3 Low 30.0 35.3 55.7 63.5 72.3 Columbia, 1952 Demand Period . A ‘ 56.6 86.8 115.5 134.3 8 77.5 118.8* 158.1 184.0 C 33.6 5 68.7 79.9 D 120.0 184.1 245.0 285.0 Annual High 45.0 69.2 92.0 107.0 Low 43.5 62.0 76.8 85.1 *actual simulation results 140 Table 5.20 - Annual Projected Performance of Solar Water Heating System for Medium Fruit and Vegetable Plant (Percentage of demand supplied by solar energy). Scale: a b c d e Collector area 525 1050 1850 2650 4200 East Lansing, 1974 Demand Period A 25.3 38.6 51.1 59.2 69.2 A (annual) 22.0 33.5 44.4 51.4 60.1 B 20.3 31.0 41.0 47.5 55.6 Annual 21.6 32.9 43.5 50.4 58.9 Columbia, 1952 Demand Period A 38.4 58.1 76.4 88.1 A (annual) 33.4 50.5 66.4 76.6 B 32.5 49.1 64.6 74.5 Annual 32.9 49.8 65.5 75.5 141 Table 5.21 gives the annual projections for the large plant. Since this plant has only one demand period, determining the annual solar contribution can be simplified. Runs for August and Septerber periods were made for this plant at the two locations shown. These runs were assumed to give reasonable indicat ion of the performance of the system for the total 8-week demand period. The September and August percentages were averaged to give the annual performance . These annual results for each system scale are used to predict the average long-term performance for the large plant at all three test locations. The final results of all three fruit and vegetable plants are shown in Table 5.22. The results were obtained using the ASHRAE weekly average model similar to the dairy and meat plants except that the simulated annual performance for each plant was used instead of the September simulation results in the other plants. The Indianapolis results were interpolated from the projections at East Lansing and Columbia based on the average year simulations for the medium dairy plant . These results are combined with similar results for the dairy and meat plants in the next chapter for a discussion of the economic feasibility. The overall simulation results for the processing plants are presented as the percentage of the annual demand which can be supplied by the solar collectors. This long-term annual percentage, used for economic considerations, does not indicate the actual seasonal or year to year performance of these system. It must be understood that day to day performance may fluctuate from 0 to 100 percent. Actual design of a solar system needs to be based on other factors besides the long-term performance . 142 Table 5.21 - Annual Projected Performance of Solar Water Heating System for Large Fruit and Vegetable Plant (Percentage of demand supplied by solar energy). Scale: a b c d e Collector area 4015 8030 14050 20070 32100 East Lansing, 1974 Demand Period August 32.7* September 22.7 35.9* 49.0 58.0 69.8 Average 21.7 34.3 46.8 55.4 66.7 Columbia, 1952 Demand Period August 51.9* September 34.7 54.5* 73.7 86.6 Average 33.9 53.2 71.9 84.5 *actual simulation results 143 Table 5.22 Long Term Annual Performance of Solar Water Heating System for Fruit and Vegetable Plants at all Test Location (Percentage of demand supplied by Solar energy). Scale: a b c d e Small plant Collector area 670 1340 2340 3340 5365 East Lansing 32.8 38.6 60.9 69.5 79.1 Indianapolis 35.0 46.2 63.2 71.0 Columbia 36.9 52.6 65.2 72.3 Medium Plant Collector area 525 1050 1850 2650 4200 East Lansing 23.6 36.0 47.6 55.1 64.4 Indianapolis 25.9 39.4 52.0 60.0 Columbia 32.9 49.8 65.5 75.5 Large Plant Collector area 4015 8030 14050 20070 32100 East Lansing 23.7 37.5 51.2 60.6 73.0 Indianapolis 26.5 41.7 56.6 66.6 Columbia 28.8 45.2 61.1 71.7 6. FEASIBILITY RESULTS AND DISCUSSICN This chapter discusses the economics of solar water heating for each size dairy, meat, and fruit and vegetable plant. Each plant type; dairy, meat, and fruit and vegetable is discussed separately. The economic analysis presented shows only the general feasibility trends. The costs of conventional emery, life time of the system, and interest rates assumed for this discussion represent present day economic condit ions . It should be realized that changes in or deviations from these assumptions can significantly affect the economic results. In future years changes in these assumptions will have a positive effect upon the feasibility of solar water heating for food processing plants. 6.1 Dairy Plant Feasibility Dairy plant solar water heating annual performance curves, shown in Figures 6.1a, b, c, were constructed from simulation results given in Table 5.13. The abscissa shows the collector areas (system size), the left ordinate shows the predicted annual percentage of total emery demand supplied by the solar water heater, and the right ordinate gives the corresponding value of this emery. Each geographic test location is shown by a single curve. Identification is made using the method described in Table 4.4. For example: E:S:2 represents the small dairy plant at the Indianapolis test location. Table 3.2, showing the warm 144 J1 SMALL / ‘. , 100 .1 [323:2 ‘- 90 7 551 _ °“ 80 —- r r- 50 70 - r 60 7‘ _ :10 menu 0:: armor DIE-1W!) 50-4 ” sum'llhu. smwmuo _ ,0 (100101 By 5013411 . 4 m ”‘1 / . 3° F / 1— 20 20 -1 " — 10 10 - _ 0 1 1 F 1 1 1 1 1 1 F 1 1 1 1 1 , U 20 40 50 30 100 120 140 100 b) MEDIUM ) 1001 murmur AREA (111‘ 1 '1 904 .1" wt- ~~3o 70A —1 60‘ r PIERCLNI‘ or r— 1--2.0 0131.010 50‘ a sm-vtugu _ - - _ ulnar arsenal)... suvguto ,. 110 1C) ’07.. “_10 20— — P h— 10—1 -* 0.0 U l 1 1 1 l l l T l l l f I T T 7 7 " " C) 1.414135 0 50 100 150 -00 -50 300 350 400 1001 COLLL'CI‘OR AREA (5.3) - _ ._ 2.0 90") —< 30'“ E L23 -— LZ:L:2 70-4— / E Z L; l LiNL-RCY 60 T / - 5013514120 PL—RCPNI‘ 0F (10 L1) 019.me 50 _ suwuru — ,_ 1,0 BY 501.4840 _) / (9.) 30" //////// '- . // 1 21H 4 104 — 0 1 1 T r T 1 1 1 r . 1 1 1 I r r " ”-0 0 300 600 900 1200 1500 1300 2100 3400 common AREA (ml) Figure 6.1a,b,c Dairy Plant Solar Water Heater Performance Curves. 146 water usage, and Figure 3.5, showing the demand schedules for each plant size are useful. for interpreting these results. All performance curves in Figure 6.1 exhibit a decreasing rate of increase of the percent of energy supplied by the solar energy system as the collector area increases. This is expected since at the higher system operating temperatures, corresponding to large collector areas, energy losses increase and collector efficiencies decrease because higher collector operating temperatures cause a decrease in the daily period of energy collection. Each size plant; small, medium, and large, exhibits a similar performance trend. Differences in performance due to the energy demand loads for each plant are not evident in these curves. The effect of geographic location is clear. The East Lansing test location shows the worst performance while the Columbia location the best . The Indianapolis location appears approximately midway between the Columbia and East Lansing locations. The values for percent of demand supplied by solar (is shown in Figure 6.1) are used in the following analysis as an indication of system size. These curves serve to relate a given percentage to the appropriate collector size for determining the cost of the solar energy system. 'lhe prefient day1 energy costs, for each size plant, of solar energy, electricity, fuel oil, and natural gas are shown in Figure 6.2. The abscissa indicates the percentage of demand supplied by the solar energy system. The ordinate indicates the energy costs for each source for one million kilo-Joules, on an annual basis. 1These costs include boiler inefficiencies, operating costs, etc. , as recommended by Fryling (1966). .1) SMALL 147 10.. E:S:l 30— ENERGY 11m U/MKJ) 20—1 13115011110 10~ 011. s 0.15 0 1 ‘1 1 1 1 1 1 1 1 1 1 1 1 1 1 r 1 1 1 1 o 10 20 10 10 50 '0 -0 30 )0 110 1.) 111201114 ° ° ’ ‘ 110111210111 01‘- 013111110 SUPPLIED 01' 501.111 11.) / “‘31 1"" ' . /IS;M:7 20a 1311:1ch (1151‘ LS/MKJ) IS 1 // / ELLCI'RIC _‘d", 10~ 01L 5 GAS 0*11111111111TTj11117 0 10 20 30 40 50 00 70 so 00 100 c) LARGE 111110011111 OF 01.11010 Slfl’l"L1L-'D 01' 501.111 (1.) Bil £sz: E:L:3 20 J 15 - LNLRCY // 0051' L‘LLTCI‘IZIC (S/MKJ) i. 4 10 ‘ y 011. s 0.15 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 7 o 10 20 3o 40 so 60 70 30 00 100 mend-N11 01? 0121111110 511111114150 011 501.1111 11.1 Figure 6.2a,b,c Dairy Plant Solar'Water Heater Energy Cost Camparison. 148 The costs of conventional sources (electricity, fuel oil, and natural gas) are constant. Since each of these is a high grade (meaning capable of producing high.temperatures) energy source, the cost is the same for producing one million kilo-Joules at any temperature. In actual practice, energy losses, due to low efficiencies, also increase for these energy sources at high.temperatures. Also, large volume consumers of petroleum.fuels and electricity often receive rate cuts for using more energy. However, these factors of conversion efficiency and unit costs are assumed constant for this analysis. Each plot of solar energy costs in Figure 6.2 shows a gradual and then rapid increase in the cost of energy as the percent of demand supplied by the collectors increases. The East Lansing curves for each plant size exhibit the largest cost and the Columbia curves the lowest. The small dairy plant shows a.mmch higher energy cost than either the medium or large plant. Costs for the small plant nearly level off below the 40 percent supply level at the East lensing location at a cost of 23 $/MKJ and'beIOW1the 50 percent supply level for the Cblumbia location at a cost of 18 $/MKJ. At higher percentages the costs increase rapidly. The medium and large plants show costs below 15 and 13 $/MKJ , respectively, at the same percentages. One reason for the high costs of the small plant is evident from Figure 3.5. The small plant uses energy only 3 days per week at a temperature lower than the other plants (Table 3.2). Although the lower temperature tends to increase the percentage of solar delivered to the load, the off days and ‘weekends appear to decrease the economic usability of the systenn The medium size dairy plant curve for Cblumbia.frcm1Figure 6.2, shows a comparable present day cost to that of electricity at the 20 to 25 percent 149 supply level. The cost of solar energy at Indianapolis and East Lansing approaches the cost of electricity at a much lower percentage. Costs for oil and gas remain far below the costs of solar energy at the lowest percentages. The large dairy plant shows the most favorable result. At 33 to 45 percent of the energy demand, solar energy compares favorably with electricity for all locations. As the percentage supplied approaches zero, the cost of solar energy nears the cost of oil and gas. The lower costs of the large plant compared to the medium plant appear to be related to the demand schedule. Figure 3. 5 shows a longer and mm constant demand for the large plant compared to the intermittent 5—day demand of the medium plant. From Table 3.2 the large plant also requires a higher temperature than either of the smaller plants. Thus, the overall use capacity appears to be higher for the large plant. Based on this cost comparison, the benefit of using a solar water heater appears negative for the small dairy and limited to less than 30 percent for the large and medium plants when the current energy source is electricity. For oil and gas, there is still a large cost differential. Since most dairy plants use petroleum fuel to generate warm water, the benefit of using solar energy for this purpose appears unacceptable at this time. As oil and gas prices increase the cost differential will decrease. However, the present day energy cost comparison presented in this sect ion does not consider future energy price increases. The long—term feasibility of solar water heating can be evaluated by comparing the total cost of supplying hot water using different types of 150 energy over a 20-year t ime period . A break even point is reached when the capital investment cost of a solar energy system becomes equal to the allowable investment. The allowable capital investment, for oil and electricity, is equal to the present value of the future cost of energy which can be supplied by the solar system over a period of 20 years at an interest rate of 10 percent and a fuel price increase of 5 percent per year. From this comparison the size of solar water heater which is justified can be determined. This comparison was made for the solar energy system, electricity, oil, and gas in order to observe the possible benefits of using solar energy in the long term. Figure 6.3 shows the result of the capital investment analysis for each size dairy plant. For each plant a plot of capital investment is drawn as a function of percent of demand supplied by solar. The capital investment 1 (from equation 4.1) required to construct solar water heaters at each test location and the allowable capital investments for electricity and oil are shown . Curves for natural gas are not included because of the present day small price difference between oil and natural. gas. From Figure 6.3a the small dairy results indicate a capital invest- ment break even point with electricity for all test locations below the 50 percent range. This result appears marginal and offers no real incen- tive to invest in a solar energy system. The medium and large plants shown in Figure 3.6b, c, show a significant advantage compared to electricity below the 70 percent supply range. The break even point of the solar energy system compared to oil is nearly reached by the large and medium plant at the 20 percent supply level. Compared to the present energy costs in Figure 6.2, these results 1The capital investment for the solar energy system does not include annual operating costs for maintenance and electricity. These factors must be considered along with similar considerations for conventional energy installations. 30 '1 52522 E:' 3 15 .1 CAPITAL I NVF. S'l I1 LLNI' (100031 L-‘LL'CTRI c 10 -1 S - d 0 1 1 1 1 1 1 1 1 1 1 r 1 1 1 10 20 30 40 50 60 100 b) MLUlUM l’ERCl-Nl' 0F DUINID SUPPLIED Ul' SOLAR (”’o) GOJ ELECTRIC 50 -1 40 -—1 / UJ‘ITAL / 1 1:1-msnu::.11' ' (101.03) / 30 41 ' f”/ 01'. U1111171111111111111“1 U 10 20 30 40 SO 60 71') 80 90 lUU 1.1 LARGE L11 E:L:7 E L ’ {‘ERL'LLN'I' 0F D"-W~IU SUl’l’LlL' BY 50 ' ‘3 Li D LAN ( ) ELLCTIUC 30 -1 20.. C-Wl'l'AJ. [ M115 [21 [1 N1 (10,000” r IL 10 —4 / O 1 I 1 I l I 1 I m T 1 | 1 ' 1 1 1 l 1 j 0 10 20 30 1H) ‘30 60 70 30 ')0 100 l’lillCLNI' 0F 011-le SUPPLIED BY SOLAR (‘8) Figure 6.3a,b,c Dairy Plant Solar Water Heater Capital Investment Comparison. 152 indicate a significant advantage in the long run for using solar energy to replace 30 to 60 percent of the electric energy demand for the large and medium plants. A small contribution can be made to replace the petroleum demand. The overall feasibility in the dairy industry appears to be limited to a solar energy contribution of less than 20 percent of the total demand in View of the present day primary industry dependence on oil and gas. The use factor has an important effect upon the overall feasibility. The small dairy may be able to justify solar energy use by expanding its processing periods or finding other uses for the solar heated warm water . 6.2 Meat Plant Feasibility Meat plant solar water heater performance curves are shown in Figure 6.4a, b, c. These curves are similar to those described for the dairy plants. The decreasing rate of supply with increasing collector area and the increase in system performance for Columbia over East lensing is easily observed. Figure 6.5 gives the energy cost comparisons for the meat processing plants. All three plants show a favorable comparison of solar energy with electricity at the 30 to 50 percent supply range, with the medium and large plants showing an advantage over the small plant. Both of these larger plants also show a slightly greater decreasing rate of energy cost compared to the small plant. This trend indicates a favorable comparison of these plants with oil and gas at the less than 10 percent supply range . 1f5£3 J) SIALL 100 _ 7‘ 1- 90—1 _. 80"4 _ 70— -—-1 L — (30-I _. PMUNT 0? 0110001 .2 _ SUPPLIED ”I 501.11140_ _ It) 1 30" - 20—1 .. 10—1 .. U I I I I I l I I I fifI l T 1) 20 40 60 80 100 120 140 108 b) 1L010M , 100 fi G)LLI£CIUR AREA (01") — _ F:M:3 goJ _ I- sz-I: 2 '— 80-1 _ Ferl *- II- 70- -— 61) “‘1— 'L. 111113¢r 0F ”/,,//”' UD-IAND 50— / _1 SUPPLIED BY ammm,0_f _I 1%) m—_ -_ 30" -1 10 _ _I 0 f 1 1 1 1 1 1 1 1 1 _1 1 50 100 ISO 200 250 30‘.) 330 400 1;) LARGE mo— (curmu1mmn1mh 7 90 -1 _ FII. 3 30% / F:L:2 - ~ 70 _.1 I: 2 LI] _‘ 60—" fi— PLRLZLEN'I' 0F DBWJD 50 .... -. SUPPLIED - .. BY SOLAR , —4 (I1) 40 -‘ 30—— // g_ 20- - I. 10- -J 0 1 7 1 1 1 1 m 1 1 1 1““‘1—"" 0 0 11 13 24 30 36 1; 4S comwcmm AREA (100 1.131 Figure 6.4a,b,c Meat.Plant Solar water Heater Performance Curves. 1.0 ENERGY SUP'LIED (10 KJ) 5.0 2.0 ENLRCY SUPPLIED (108 KJ) 1.0 2.0 Ii‘ILRUY SUPPLIED (108 KJ) 0.0 'JI SMALL :0- 15~ 1911311011 1.1151 (5,111“) Euzcmuc f/ 10“ 01L 5.. 0.15 0‘ 1 1 1 1 1 1 1 0 70 so 90 100 0 MEDIUM _ ,. . . ) .0 fl mum 0F 1111.11.10 SUPPLIED 01' mum 1°.) r 114-I ”‘13 ”‘3 15J LNLRGY 0051 1111.1 W ) _ 1214111111: / .10 "" /// 01L 5 (1:15 0 - 1 1 [ fl 0 7D HL- 911 [DU c) [NILE I’LRLZI-Nl' 01? 011111110 SUPPLIH) 111' 501.1111 1“») ““1 ~:L:2 20-1 F:L:3 15—1 [111.1101 0051' (MM) L1u1C1'111C 1... / 011. 0.6 0 w r . 1 1 1 —| 0 PIZRL'JL'I'I' 0F DINNID SUPPLIED b'Y SOLAR 7D 30 90 IUD Figure 6.5a,b,c Meat Plant Solar water Heater Energy Cost Comparison. 155 A look at the demand schedules for the meat processing plants, Figure 3.6, illustrates that the small and medium plants have a time varying schedule, while the large plant schedule is constant during the total 6-day period. Since the medium and large plants show an energy cost curve similarity, the effect of demand schedule appears insignificant. The medium and large plants, however, both require the same warm water temperature, as shown in Table 3.3. Thus, effect of increasing temperature above that of the small plant is responsible for the increase in performance and lower energy costs of the medium and large plants below the 50 percent supply level. Above this level, the rate of increase in energy cost for the two larger plants is greater than for the small plant. This illustrates the limiting effect of the higher collector operating temperature as the system supplies more of the energy demand load. The capital investment analysis results for the meat plants are given in Figure 6.6. All three plants show a cost advantage over electricity below the 70 percent supply level. The small plant shows a justified.percent supply level greater than theimediumland large plants of 20 to 30 percent. This illustrates the effect of the lower warm water supply temperature delivered by the solar water heater for the small plant relative to the medium and large plants. Near the 30 percent supply level all three plants approach the break even point for oil. The benefits of solar water heating for the meat processing industry appear to be real and independent of plant size. The effect of warm water temperatures appears to be minor with respect to the overall performance. The feasibility of using solar energy shows real promise for replacing a) SBV\LI. 156 30‘“ F“ 11:22 Buscrmc F2323 20— CAPITAL INVESTMENT (10003) -1 OIL '1 // 0111111'1'1VT111F11'1 20 30 40 SO 60 70 80 90 101) b) {\LDlLN 0 10 PliRCliNl‘ OF DEMAND SUPPLIED BY SOLAR (’3) 00-4 _ [‘:M:l F:M:2 F:M:3 ELECTRIC SO— 40“ LL'\PIT.~\1. INVESI'AUS‘N (100%) 30 -—1 20" 10-1 mums 60“ SO—J 40 —1 CAPITAL INVESTMENT (lo , 000$) 30 -‘ 20" 10—1 —1— f ‘1' j 30 40 50 60 7 80 90 100 [‘Likkll‘l'l' OF DU'W‘ID SUPPLIED BY SOLAR ("‘o) PERIENT OF DEMNID SUPPLIED BY SOLAR -(%) Figure 6.6a,b,c Meat Plant Solar Water Heater Capital Investment Comparison. 157 electricity. The replacement of petroleum fuel is real for only small demand percentage applications, while a future potential exists for a significant contribution with an increase in oil and gas prices. 6.3 Fruit and Vegetable Plant Feasibility Performance curves for the fruit and vegetable plants are presented in Figure 6.7a,b,c. These curves follow similar trends established by the dairy and meat plants even though the fruit and vegetable plants are generally seasonal in their operation. The medium plant has an annual demand schedule according to Table 3.4 compared to the seasonal demand schedules of the small and large plants. This factor does not noticably affect the performance curves. For the East lansing test location the curves are extended beyond the Indianapolis and Columbia curves. This is a result of the extra simulation performed at East Lansing. Energy cost comparisons for the fruit and vegetable plants are shown in Figure 6.8. The small and large plants show a greater solar energy cost compared to the medium plant. This is primarily a result of the seasonal usage patterns characteristic of many canning plants. The large plant has a greater solar energy cost than the small plant because of the extremely short processing period, as shown in Table 3.4. The small plant indicates a favorable cost comparison with electricity near the less than 10 percent supply level. The effect of daily demand schedules cannot be observed because of the extreme variations due to the seasonal use patterns. The medium plant exhibits an energy cost comparable to electricity near the 40 percent supply level and approaches a break even point with oil in the less than 10 percent range. u) :111LL 158 10111 ‘ —— 1.5 911 q 80" — 70— .. — - 1.0 pumun' 0F 111-11.11111 50- _ 5111111le / 13¢ch 111' zoum sup ‘LIL‘D (a) “0‘ // - (10 1:1) _ . — 0.5 11— / _ zo—— — 10 _ r n v 1 1 1 1 1 1 1 1 r 1 1 1 1 1 1 0-0 o 8 16 24 32 40 111 So 61 b) MiDIle 7 1110— LULLLCI'CII 1112.131 (100 111-1 _ 130— .4 — — 4.0 so— — 70_ G:M:3 _ 13:11:: . — G:M:l — )9) A 11511121211 OF [5315:1310 111—11,an J _. .’ 5.11vawa (109 1.1) BY SOLAR r— 1— 2.0 (’o) 40‘ / _. 30—' —‘ 20_—- xv 1.0 10-— _- ” I 1 1 1 1 F 1 1 1 1 1 1 1 1 U o c) IARGE 0 6 12 18 24 EU 36 42 I8 100- , __ a common mum 11011 111-1 90- _ l _ 7 80‘ G:L:3 " 70;. “i/_ o Gszl 60—— / _—5 . mum PERCENT OF SUPPLIED Dam-1D 50—1— —-»—4 (109 K1) suppnmn BY SOLAR 40__ 1%) “—3 3o - l _2 20_I / _ 10- _1- l 0 o I I I I I I I I I I I I I l o 4 a 12 16 20 24 28 32 Figure 6.7a,b,c Fruit and Vegetable Plant couscxoa AREA (1000 m2) Performance Curves . Solar Water Heater EIECI'RIC 10— OIL n I I I I I I I I I I I I I l' ' l ' l 10 20 30 40 50 60 70 80 90 100 b) MEDILM :le G:M:Z G'M'3 20“ Pm OF DD‘IAND SUPPLIED BY SOLAR '(3) ~ ' 15“ ENERGY CCBT ($/I‘IKJ) EIECI‘RIC 10" OIL 5 GAS 0 f I I I I I I I I II I I I I I I I T 0 10 20 30 40 50 60 70 80 90 100 C) IARGB PERCENT OF EEMND SUPPLIED BY SOLAR -(%) G'L13 60.4 .._._ 50‘— 40.... ENERGY ("DST 13mm 30— 20.1 ELECTRIC 10- OIL t1 1 I 1 I 1 1 1 1 1 1 1 1 1 I r I 0 10 20 30 4O 50 60 70 80 90 100 FEW OF DEMAND SUPPLIED BY SOLAR -(\) Figure 6.8a,b,c Fruit and Vegetable Plant Solar Water Heater Energy Cost Comparison. f 160 Since the medium plant has year around operation, the comparison of present day costs appears quite favorable to a solar energy application. The other plants do not show this result. If a different energy demand can be justified for these plants in order to increase their use factor, the cost comparison is likely to be favorable. The long range results for the fruit and vegetable plants are shown in Figure 6.9. It is notable that even with the very high costs of solar water heating systems in these plants, the capital investment approaches a break even point with electricity near the 30 percent level for the small plant and 10 percent for the large plant. This indicates that a potential does exist for these plants also because an off season energy demand could significantly improve the solar energy feasibility. The medium plant shows favorable results (see Figure 6.9) for longetermlreplacement of petroleum fuel at the 30 percent supply level. The feasibility associated with this plant appears very favorable, since the energy demand remains constant during the winter months and increases during the summer period when the availability of solar energy also increases. The results of this analysis indicate a definite benefit for solar energy applications for those fruit and vegetable processing plants (in this case a freezing plant) which exhibit a uniformlannual demand schedule. A possible application exists for the seasonal canning plants if an appropriate use of the water heating capactiy can be found to increase the overall use factor. a) SMALL 8“ 7-—1 6— CAPITAL (100,000514— }— 2- l— “ . 1 1 10 100 b) MEDILH 'I enzyme 7—1 6— 5— CAPITAL INVESTMENT 1100,000514- 3.. 2.. a 0 I I I I I I 1 I 1 I I I 1 I 1 I 1 I 1 I c, 111mm" 10 20 30 40 so 60 70 110 90 100 S-I PEREN'X‘ OF DEMAND SUPPLIED BY SOLAR -(%) G:L:l 5—4 G:L:2 C:L:3 4 _1 CAPITAL (1,000,0003) 3.. 2... WC 1.. OIL e l I I I I I 1' I If I7 I I r I I I 17 r I I 0 10 20 30 40 50 60 70 30 90 100 PERCBII‘ 0F IILMFND SUPPLIED BY SOLAR -I%) Figure 6.9a,b,c Fruit and Vegetable Plant Solar Water Heater Capital Investment Carparison. 1 Five insolation models were tested for their accuracy in predicting hourly insolation data for simulating solar water heaters. The ASHRAE weekly model shown in Appendix A, which uses weekly averages of daily total measured insolation (Appendix F) and average air temperature (Appendix E) data to calculate hourly data, was chosen for simulating input insolation data for the “average" year. This model was shown to give annual simulation results within 0.2 percent of a control model which used actual measured hourly values of insolation and air temperatures for the same period. The ASHRAE daily and the Whillier models also gave results within 10 percent of the control model. Tests using 1, 3, and 6—hour average insolat ion data showed an unacceptable decrease in per— formance, when using averages greater than one hour. Parametric tests were performed to determine the best storage tank volume and collector fluid flow rate for a solar water heater of 100 m2 and a small dairy demand load. Results from these tests produced ratios, of storage volume and collector fluid flow rate to collector surface area, which were used to simulate the food processing plants. Values of the storage tank volume ratio of 0.03875 m3/m2 of collector and the collector fluid flow rate ratio of 34.11 kg/hr/m2 of collector were obtained. A solar water heating model developed with TRNSYS, was used to simulate a solar water heater using water usage surveys for three sizes 162 163 of dairy, meat, and fruit and vegetable processing plants chosen as representative of the Midwest region. Each plant was simulated for a period of two weeks in September using data for two years at two locations: 1965 and 1974 at East Lansing, Michigan, and 1949 and 1952 at Columbia, Missouri. The medium dairy plant with a collector area of 140 m2 was simulated for three actual years: 1974 at East Lansing, Michigan and 1949 and 1952 at Columbia, Missouri, and for the average year using the ASHRAE weekly insolation model for all three test locations: East Lansing, Michigan, Indianapolis, Indiana, and Columbia, Missouri. Projected results of these simulations are presented in Tables 5.3, 5.16, and 5.22, and Figures 6.1, 6.4, 6.7. The long—term annual percentage of solar energy whid1 can be delivered to the load as a func— tion of collector area, for each plant and geographic location is given. From the resulting annual performance projections, an economic comparison of present day energy costs and long-term capital investments was made between solar energy, electricity, fuel oil, and natural gas. A summary of these results is given in Table 7.1. The present day cost of solar energy for most plants compares favorably with electricity when replacing 20 to 45 percent of the demand. The small dairy and the small and large fruit and vegetable plants indicated an overall higher cost of solar energy compared to electricity, oil, and gas. Only the large dairy, medium and large meat plants and the medium fruit and vegetable plant indicated a favorable cost comparison with oil and gas at a supply level below 10 percent. The long—term capital investment analysis showed a favorable compar- ison of solar energy with electricity for all plants, ranging from 100 fi—i 7;? 164 Table 7.l - Summary of Economic Results of Solar Water Heating and Conventional Energy Sources (Location of the break even point of percentage of demand supplied by solar). Solar Energy Unit Cost Comparison Capital Investment Plant Electric Oil Gas Electric Oil % % % % % Small Dairy high high high below 50-70 near 0 Medium Dairy below 20 high high below 65-80 near 0-5 Large Dairy 30-40 O-lO O—lO below 60—75 below 20 Small Meat 0—40 high high below 80-lOO near 20 Medium below Meat 35-45 O—lO O-lO below 70-85 near 20 Large below Meat 35-50 0-5 0-5 below 65—80 near 25 Small F & V near 0 high high below 30-35 near 20 Medium below F & V 30-45 D-lO D-lD below 60—70 below 25—35 Large F & V high high high near 20 high 165 percent supply for the small meat plant to 20 percent supply for the large fruit and vegetable plant. The long—term results of solar emery and oil showed an advantage of solar emery, below 35 percent for all plants. The small dairy and the large fruit and vegetable plants indicated an overall higher cost of solar emery compared to oil . Generally the application of solar water heating to food processing plants is economically feasible for supplying a major percentage of the energy demand when replacing electricity and a significant percentage when replacing fuel oil and natural gas over the long run. The differences between the type and size of processing plant generally reflected the plant solar water heater use factor. A constant annual demand schedule and a lower warm water supply temperature were shown to improve the overall use factor and feasibility. These economic results showed the future contribution solar energy can make in supplying energy for food processing plants. Future changes in emery prices and economic trends will realize a greater potential for solar water heating applications. 8 . OCNCLUSICNS The following conclusions of this study are: l.’ The simulation of hourly insolation and temperature data using the ASHRAE weekly model is acceptable for simulating a solar emery system for long-term periods and for locations where hourly insolation data is unavailable. An engineering feasibility exists for solar water heating for food processing plants for supplying up to 90 to 100 percent of the annual emery demand. Over 100 percent of the demand may be supplied during the summer periods. Auxiliary emery is st ill necessary for winter time operation and periods of low insolation . Geographic location has a definite effect upon solar water heating feasibility for food processing plants. The Columbia test location showed a significant advantage over East Lansing for solar water heating. The current economic feasibility of solar water heating for food processing plants in the midwestern United States is limited to saving 20 to 50 percent of the conventional emery sources . The realization of this savings is dependent upon the type of plant, use factor, annual demand schedule, warm water supply temperature, type of conventional emery currently employed in the plant, the cost of this emery, and the required 166 167 payback period of the capital investment . Larger savings may be realized by the replacement of electricity than of fuel oil or natural gas. All processing plants studied (dairy, meat, and fruit and vegetable) show some degree of emery savings potential . Fruit and vegetable processing plants show a lower use potential because of tlreir seasonal demands. In order to realize the fossil fuel savings, a capital investment is required with a payback period of up to 20 years. 9. SUGGESTIONS FOR FUTURE RESEARCH The economic analysis performed on solar water heaters for food processing plants generally illustrates the current overall feasibility. Further simulation work should be conducted. In particular: 1. Test other solar water heating models using TRNSYS, to determine the potential of using heat pumps, and of other types of collectors. 2. Study the effects of daily processing plant demand schedules and water temperatures on solar water heater performance. 3. Test the validity of the ASHRAE weekly data simulation model for other geographic locations. 168 REFERENCES 10. REFERENCES ASHRAE ApplicatiOns Handbook, 1974. American Society of Heating, Refrigeration and Air Conditioning Engineers, New York, NY. Baker, D. G. and D. A. Haines, 1969. Solar Radiation and Stmshine Duration Relationships , North Central Regional Research Publication 195, University of Minnesota, Agricultural Experiment Station Technical Bulletin 262, 372p. Baker, D. G. and J. C. Klink, 1975. Solar Radiation Reception, Probabilit ies, and Areal Distribution in the North Central Region , North Central Regional Research Publication 225 , University of Minnesota Agricultural Experiment Station Technical Bulletin 300, 54p. Daniels, F. and J. A. Duffie, ed., 1955. Solar Emery Research, _: The University of Wisconsin Press, Madison, WI, 290p. Dansbury, K. P. , 1977. A study of the warm water usage in food process- ing plants. Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, Personal communication. Duffie, J. A. and W. A. Beckman, 1974. Solar Energy Thermal Processes. John Wiley and Sons, New York, NY, 386p. Edenburn, M. W. , 1973. Systems Analysis Computer; Program for Solar Community Total Emery Concept. NTIS, SLA—73—O950, October, 1973. Edenburn, M. W. , 1975 . Sandia laboratories Energy System Simulation Computer Pr0gram. Solar Systems Division, Sandia Laboratories, Albuquerque, NM, NTIS—75-O712. Edenburn, M. W. and Grandjean, N. R., 1975. ' Energy System Simulation ‘ COmputer PrOgramz' ‘ SESYS . Sandia laboratories, Albuquerque, NM, 22p. Fritz, S. , 1957 . ”Solar Energy on Clear and Cloudy Days", The Scientific Monthly, 84:55—65. Fryling, G. R. ed. , 1966. ' Combustion Engineering, Combustion Engineering Co._, New York, NY. Furnival, G., Wyler, E., Reifsnyder, W., and Siccama, T. G., 1969. SOLAR, ”a Fortran computer program. Yale School of Forestry , New Haven, CT. 170 171 Graven, Robert M. , 1974. ‘A Comparis0n Of Computer Programs "Used for Modeling Solar Heating and Air Conditioning Systans for Buildings. Lawrence Berkley Laboratory, University of California, Berkley, CA, lBL—3066. Gutierrez, G., F. Hincapie, J. A. Duffie, and W. A. Beckman, 1974. "Simulation of Forced Circulation Water Heaters; Effects of Auxiliary Energy Supply, Load Type, and Storage capacity", Solar Energy, 15: (4) :287-298. Holman, J. P., 1972. Heat Transfer, McGraw Hill, Inc., New York, NY, 462p. Kays, W. M. , and A. L. London, 1958. Compact Heat Exchangers, McGraw Hill, Inc., New York, NY. Klein, S. A. , 1974. "Calculation of flat-plate collector loss coeffi- cients", Solar Energy, vol. l7:(ll):79-80. Klein, S. A., P. I. Cooper, W. A. Beckman, and J. A. Duffie, 1974. TRNSYS - A Transient Simulation Program, Madison, University of Wisconsin Engineering Experiment Station Report #38, Madison, WI. Klein, 8. A., P. I. Cooper, T. L. Freeman, D. M. Beekman, W. A. Beckman, and J. A. Duffie, 1975. "A Method of Simulation of Solar Processes and its Application", Solar Energy, vol. 17 :(l):29-37 . Kreider, Jan F. , and Frank Kreith, 1975. Solar Heating and "Cooling: Engineering ., Practical Design, and Economics. McGraw Hill, Inc. , _— New York, NY, 341p. Kreith, Frank, 1973. Principles 'of Heat Transfer, Intext Educational Publishers, New York, NY, 656p. Linvill, Dale, 1977. Professor, Department of Agricultural Engineering, Michigan State University, East lansing, MI, Personal communication. local Climatological Data, National Oceanic and Atmospheric Administration, March 1974 to February 1975 at Capitol City Airport, Lansing, MI, Washington, DC. lbf, G. O. G. , 1977. Director, Solar Energy Applications Laboratory, Colorado State University, Ft . Collins, CO. Presentation at the Management Briefing Seminar: Solar Energy—Opportunities and Applications, Dearborn, MI, May 24, 1977. Liu, Benjamin Y. H., and Richard C. Jordan, 1960. "The Interrelation—- ship and Characteristic Distribution of Direct , Diffuse and Total Solar Radiation", Solar Energy, 4(3):l—19. Michigan Department of Agriculture, 1974. Climate of Michigan, Michigan Weather Service /NOAA/US Department of Commerce, Washington, DC. l72 Moon , P. , 1940 . "Proposed Standard Solar Radiation Curves for Engineering Use", Journal of Franklin Institute, 230:583. Oonk, R. L., W. A. Beckman, and J. A. Duffie, 1975. "Modeling of the CSU Heating/Cooling System", Solar Energy, l7:(l)21-28. Omens-Illinois, 1977. The Owens-Illinois SUNPAK Solar Collector. Solar Research, 1977. The Flat Plate Solar Collector, Solar Research, Division of Refrigeration Research, Inc. , Brighton, MI . Ramsey, J. W. , 1975. Development of Flat-Plate Solar Collectors for Heating and Cooling of Buildings, NASA—CR—134804, 209p. Reding, J. T., and B. P. Shepard, 1975. Energy Consumption: Paper, Stone/Clay/Glass/Concrete/and Food Industries. Report No. EPA-650/ 2—75—032—C. Reynolds, William C. , and Henry C. Perkins, 1970. Engineering ‘ Thermodynamics, McGraw Hill, Inc., New York, NY, 585p. Sadler, G. W. , 1975. "Direct and Diffuse Insolation Using Approxima- tion Methods Applied to Horizontal Surface Insolation", Solar Energy, 17 :39—46. Siegel, Robert, and John R. Howell, 1972. ‘ Thermal Radiation Heat ‘ '_'I:r_an__s_§§_r_, McGraw Hill, Inc. , New York, NY, 814p. Solar Radiation Considerations in Building Planning and Design, 1976. Proceedings of a Working Conference, National Academy of Sciences , Washington, DC. Thomas, S. M., 1977. Michigan Solar Insolation and Its Weather Dependence . Unpublished Technical Problem Report , Department of Agricultural Engineering, Michigan State University, East Lansing, MI. Threlkeld, J. L., and R. C. Jordan, 1957. "Direct Solar Radiation Available on Clear Days". ' Heating, Piping, and Air Conditioning, 29: (12) :135-145. Whillier, A. , 1956. "The Determination of Hourly Values of Tbtal Solar Radiation from Daily Smmations", Arch, Met. Geoph, Biolk, B. Bd. 7. H.2z-97-244. Whillier, A. , 1967. "Design Factors Influencing Solar Collector Performance" . low Temperature Engineering Applications of Solar Energy, ASHRAE, New York, NY, pp 27-39. Williams, W. A., R. S. loomis, and M. B. Carter, 1974. ”Computing Hourly Values of Diffuse and Direct Sunlight", CrOp Science, 14:492-493. 173 U. S. Department of Commerce, 1964. Climatic Smuary of the United State; — Supplement for 1951-1960, Indiana. Climatography of the United States No. 86-10, Decennial census of U. 8. Climate, Washington, DC. U. S. Department of Commerce, 1968. Climates of the Untied States- Missouri, Washington, DC. APPENDICES 175 APPENDIX.A Table A. ASHRAE Insolation Model Ratios (Hourly horizontal insolation per daily total horizontal insolation)? Solar Time1 Periodsz 11 30/ 10 30/ 9:30/ 8:30/ 7 30/ 6:30/ 5 30/ 4 30/ l2z30 ll 30 l0:30 9:30 8:30 7:30 6:30 5:30 3/1-3/143 .146 .139 .123 .093 .057 .016 3/l5-3/28 .139 .133 .118 .093 .062 .025 3/29-4/11 .132 .127 .114 .080 .050 .015 4/12-5/2 .126 .122 .110 .067 .038 .009 5/3-5/15 .122 .118 .108 .078 .053 .018 .090 5/17-5/30 .118 .115 .105 .089 059 .045 .019 5/31-6/133 .116 .114 .104 .089 .069 .046 .021 6/14-6/27 .115 .112 .103 .088 .069 .047 .023 .002 5/28-7/11 .113 .113 .104 .088 .069 .046 .021 7/l2—8/l .118 .114 .105 .089 .069 .045 .020 8/2-8/l5 .122 .ll8 .108 .090 .069 .042 .015 8/16-8/29 .126 .122 .110 .091 .067 .039 1009 8/30—9/123 .132 .128 .114 .092 063 .042 .005 9/13-10/3 .139 .134 .118 .093 .061 .039 10/4-10/17 .146 .141 .122 .093 .056 .031 l0/l8-l0/3l .145 .148 .126 .094 .050 .024 11/1-11/14 .164 .155 .130 .090 .040 .014 11/15—11/28 .173 .162 .134 .087 .030 .005 11/29-12/123 .178 .167 .136 .085 .024 .003 13/13-12/25 .183 .171 .137 .083 .0l8 l2/27-l/l6 .178 .166 .135 .086 .024 1/17-1/30 .173 .162 .134 .088 .030 1/31-2/13 .163 .154 .130 .090 .041 .0036 2/14-2/28 .153 .146 .125 .093 .052 .007 \)...J (A) ll ’From ASHRAE 1974 Ratios used for the insolation model Ratios are the same for morning and afternoon tests Based on climatological weekly periods beginning March l Daylength Table for East Lansing (Calculated using program SOLAR) . Table B . JULY L‘zUG S'PT an! 48: 301 JUN" F53 Jfa N DATE (A {_NJWO' Hv-JHHH O o 0 0 a O‘C‘O‘O‘C‘ Th. 30001 Hr-iv—iHCJ O O o o o QJHCDCD‘J Hv-‘c-iq—is—i Lame-1x4 J rMN‘M . 0 O O O .4.—1.4.4.4 HHHHH Hafimf‘dlg «22¢;th O O O O O MPOM-‘OM Hv—JHMW C‘L“Ii ”jg—4 MNOJKJC‘J . . . . O J: .J J J HHHHi—‘l N'DUWJVI HHHf‘ir-i o o o o o l(\u\i:\l(\L‘1 v-ir-‘lw-tt-iw-d NG‘O‘CJv-i CJCDOHH o 0 O O o LDLGU‘LOU. Hardy-4T4 112.130an OLD—£11414 O O o 0 Q .3 'TJJJ r—h—iq-h-h—q M-UC‘NL1. 3.74 m'n . O ' . . N0-.NC‘-N awn-1.4.4 i’iwcfi‘aw wit-4.4mm O O O . . .4.-1.an HHHH—i 6:“??me LDC’OOCD o o o o o O‘CJCDCJCD rifle-4‘4 uktDC-cn DCDCDCZ‘H 0 o o o o “_‘l‘rii‘C‘C‘r‘h «10qu u\ ran-«r11. HC'C'C‘L“ 0 o o o O CJO‘C‘?‘ N~I'C\JT!\ OOC‘J‘U1 . . . . . DDDaj‘C H'1H H'DLJC‘JC‘ MAJNOJH O I O c Q .4.—4.4.4.4 Hv-‘v-iH-r‘ NJHQIA U1...\.l\J-J O O O O O (‘JNNNN P‘Hr1g—lf—d GNU-3 WU“ HHHV-JL'! 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MMMJ .f HHHHH 9— ‘J‘C‘J u\{\ NNMMM' O O O O O NNNNN «.4.—1.4.... .."I\C'.‘QD DOHOCD . . . . . fiHH’DO HHv-i .1?\C‘HM _J‘ J JUHD '. o O n o gTi‘ij‘Tm 13.11. 3.9 0.00 10. 0.00 15.05 0.03 12.90 91': 51 177 APPENDIX C moo. mmo. mmo. who. Fo_. m_F. w~_. om.m_ coo. _No. mac. mac. _o_. m__. .mi. mN.mF moo. m_o. «so. mac. No_. _N_. Nm_. oo.m_ moo. m_o. “so. who. mo_. m~_. mm_. m~.N_ moo. m_o. mac. ago. ¢o_. ¢N_. mm_. om.N_ o «_o. mes. ago. eo_. o~_. oe_. mN.N_ __o. Pee. who. mop. amp. N82. oo.N, o_o. . mmo. mac. mo_. om_. me_. mg.__ moo. Rmo. «no. eo_. mm.. Ne_. om.P_ . Boo. emo. mac. No_. em_. 85.. mm._n moo. Nmo. Nee. moi. amp. Nm_. oo._l soc. wNo. _No. mOP. mm_. mm_. mg.o_ moo. gmo. sec. 50.. oe_. mm_. om.o_ moo. mmo. Koo. o__. me_. ~62. mm.o_ o omo. moo. o__. me_. mo_. oo.o_ 6_o. moo. o__. Ne_. mop. mm.m _Po. mmo. o_P. om_. NN_. om.m moo. omo. mo.. mmp. may. mm.m o Nmo. mo_. mmi. om.. oo.m N\_ e N\_ m N\_ 8 N\~ m N\~ N N\_ _ N\_ Amaze cpmcm_ mac .. A:o_umpomcw _muco~_noz Page“ xpwmu on cowpm_6mcw _8a=o~_.o; _6360 »_.=o; no o_namv mowuam _6eoz coi08_oaei l6____;3 .8 6_580 178 So. mpo. 2o. «8. opo. moo. woo. Koo. mmo. «mo. mmo. Fmo. omo. omo. mmo. mmo. omo. smo. omo. mmo. vmo. mmo. mmo. mmo. oso. omo. oso. wuo. omo. omo. omo. omo. moo. moo. ooo. moo. moo. moo. moo. oo— . mop. mop. mop. o__.. :7 m:. 3P. o:. 90.2 3:553 59$ _ m:. m:. NZ. oZ. oNP. NNF. emp. RF. . Aegfiflhoov omgm; 8.2 8.3 m5: om.3 3.: 8.: m5? . U magma. - — .M"'._._." .‘C..'T'__". in..- ..‘- "- 179 , APPENDIX D Table D. Atmospheric Transmissivity Insolation Model Ratios (Average weekly ratio of hourly total horizontal insolation to extraterrestrial hourly horizontal radiation)‘. Period ' Atmospheric Transmissivity 3/l-3/7 .370 3/8-3/l4 .42l 6/l-6/7 .494 6/8-6/l4 .446 9/l-9/7 .470 9/8-9/l4 .4l7 ll/30—l2/6 .395 l2/7-l2/l3 .292 1From Thomas T977 180 APPENDIX E Table E. Average Ambient Air Temperature Used In Average Year ASHRAE Weekly Insolation Model. 1974 East Lansing1 Indianapolis2 Columbia3 Month (MI) (IN) (M0) January 22.2 30.3 29.l February 22.2 33.8 3l.l March 32.3 4l.9 38.9 April 44.8 54.6 50.8 May 56.5 64.4 6l.4 June 66.2 74.0 7l.l July 70.7 78.7 75.2 August 68.9 77.2 73.7 September 6l.9 69.3 66.5 October 50.8 58.7 55 4 November 38.0 43.3 40.9 T December 26.6 33.8 3l. 1Michigan Department of Agriculture l974 2U. S. Department of Commerce 1964 U. S. 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Runs - G:S:l:74:SP:K:864:b G:S:3:52:SP:K:864:b Location: 1 (East Lansing, 74). 3 (Columbia, 52) Collector area (m2) 1340 1340 RADTOTAL (109KJ) 0.767 1.146 QCOL (108KJ) 1.940 2.902 QTANK (l08KJ) 1.379 2.073 QAUX (108KJ) 1.008 0.374 QTOTAL (l08KJ) 2.487 2.447 QENV (107K0) 1.990 3.166 QHX (108K0) 1.818 2.738 MCOL (107Kg) 1 087 1.188 COLEF (%) 25.3 25.3 SOLAR (%) 57.8 86.8 SOLAR-—Total (%) 55.4 84.7 SYSOP (hrs/day 6.6 7.2 TANK LOSS (2) 10.3 10.9 Average Temperature (°C) 54.4 75.3 234 Table l<fiH15mall Fruit and Vegetable Plant Simulation Results for Period B. Runs - G:S:l:74:SU:K:360:b G:S:3:52:SU:K:360:b Location: 1 (East Lansing, 74) 3 (Columbia, 52) Collector area (m2) l340 1340 RADTOTAL (l08KJ) 2.892 4.823 QCOL (107KJ) 6.221 9.699 QTANK (107KJ) 2.283 3.570 QAUX (lO7KJ) 0.735 0.129 QTOTAL (107KJ) 3.020 3.699 QENV (107KJ) 1.169 l.758 QHX (107KJ) 5.499 8.640 MCOL (l06Kg) 3.602 4.566 COLEF (%) 21.5 20.1 SOLAR (%) 75.9 118.8 SOLAR-4Total (%) 75.6 96.5 SYSOP (hrs/day) 5.2 6.7 TANK LOSS (%) 18.8 18.1 Average Temperature (°C) 67.5 98.3 235 Table K.l5 Small Fruit and Vegetable Plant Simulation Results for Period C. Runs - G:S:l:74:SU:K:432:b G:S:3:52:SU:K:432:b Location: 1 (East Lansing, 74) 3 (Columbia, 52) Collector area (m2) 1340 1340 RADTOTAL (l08KJ) 3.274 5.171 QCOL (108KO) 1.531 2.261 QTANK (l08KJ) 1.119 1.766 QAUX (108KJ) 2.363 1.716 QTOTAL (108KJ) 3.482 3.482 QENV (106KJ) 5.839 8.728 QHX (108KJ) 1.388 2.096 MCOL (106Kg) 6.166 7.775 COLEF (%) 46.8 43.7 SOLAR (%) 32.1 50.7 SOLAR--Total (%) 32.1 50.7 SYSOP (hrs/day) 7.5 9.5 TANK LOSS (%) 8 .9 Average Temperature (°C) 35.9 49.3 236 Table K.l6 Small Fruit and Vegetable Plant Simulation Results for Period D. Runs - G:S:l:74:FA:K:l440:b G:S:3:52:FA:K:l440:b Location: 1 (East Lansing, 74) 3 (Columbia, 52) Collector area (m2) l340 l340 RAOTOTAL (109KJ) 1.451 1.931 QCOL (108KJ) 3.110 4.362 QTANK (108KJ) 1.613 2.213 OAux (l08KJ) 0.021 0.023 QTOTAL (l08KJ) 1.634 2.236 QENV (107KO) 7.784 10.79 QHX (108KJ) 2.937 4.123 MCOL (l06Kg) 8.612 8.694 COLEF (%) 21.4 22.6 SOLAR (2) 134.2 184.1 SOLAR--Total (%) 98.7 99.0 SYSOP (hrs/day) 3.1 3.2 TANK LOSS (2) 25.0 24.7 Average Temperature (°C) l09.4 l45.3 237 Table K.l7 Medium Fruit and Vegetable Plant Simulation Results for Period 5 Runs - G:M:l:74:SU:K:432:b G:M:3:52:SU:K:432:b Location: l (East Lansing, 74) 3 (Columbia, 52) Collector area (m2) 1340 1340 RADTOTAL (108KJ) 2.565 4.052 QCOL (l08KJ) 1.234 1.836 QTANK (108KJ) 0.905 1.432 OAux (l08KJ) 2.012 1.485 QTOTAL (108KJ) 2.917 2 917 QENV (l06KJ) 4.470 6.658 QHX (108KJ) 1.118 1.698 MCOL (106K9) 4.944 6.233 COLEF (%) 48.1 45.3 SOLAR (%) 31.0 49.1 SOLAR--Total (%) 3l.0 49.l SYSOP (hrs/day) 7.7 9.7 TANK LOSS (%) 6 .6 Average Temperature (°C) 35.0 47.9 238 Table K.l8 Large Fruit and Vegetable Plant Simulation Results for Summer Period. Runs - G:L:l:74:SU:K:432:b G:L:3:52:SU:K:432:b Location: 1 (East Lansing, 74) 3 (Columbia, 52) Collector area (m2) 8030 8030 RAOTOTAL (109K3) 1.962 3.099 QCOL (109KJ) O 959 1.433 QTANK (l09KJ) 0.721 1.146 QAUX (109K3) 1 485 1.060 QTOTAL (109KJ) 2.206 2 206 QENV (107KO) 2.341 3.463 QHX (108KJ) 8.717 1.330 MCOL (107Kg) 3.806 4.847 COLEF (%) 48.9 46.2 SOLAR (%) 32.7 51.9 SOLAR--Total (%) 32.7 5l.9 SYSOP (hrs/day) 7.7 9.8 TANK LOSS (%) 4 2.4 Average Temperature (°C) 34.0 46.5 9 2 1 3 M111 U Emil... Tlll A“ T“ S'[|.T Nulllll. Am m“. H .‘TW '1