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I _._. an: LIBRARY Michigan State University This is to certify that the dissertation entitled The Simulation of Combine Harvester Performance as Affected by Bulk Crop Properties presented by Wilbur T. Mahoney III has been accepted towards fulfillment of the requirements for _I_’hID_.___degreein Agric. Engr. Tech. Date 2/2/88 MS U it an Affirmative Action/Equal Opportunity Institution 0-12771 MSU RETURNING MATERIALS: Place in book drop to 1,139,512,155 remove this checkout from —c—. your record. FINES will be charged if book is returned after the date stamped below. THE SIMULATICN OF CGBINE HARVESTER PERFORMANCE AS AFFECTED BY BULK CROP PROPERTIES By Wilbur 'Ihomas Mahoney, III A DI SSER'I‘ATIG‘J Submitted to Michigan State university in partial fulfillment of the requirements for the degree of MR OF PHILOSOPHY Department of Agricultural Engineering 1988 Copyright by WILBUR THOMAS MAHONEY, 1988 III ABSTRACT THE SIMILATIW OF CCMBINE HARVESTER PERFORMANCE AS AFFECTED BY BULK CROP PROPERTIES By Wilbur Thomas Mahoney, III Physical changes in crop properties have been reported to affect the operational characteristics of carbine harvesters. Performance, measured as mass other than grain 04.0.6.) feed rates at fixed grain loss levels, varies as a result of crop property changes (Stephens and Babe, 1977). Combine performance may vary substantially during the course of a single day making comparisons of performance between combines in field tests difficult. I A research project was undertaken to determine the effect of bulk crop properties on combine performance. This thesis describes the collection and measurement of bulk crop properties, the correlation between property changes and combine performance, and the development of .a combine simulation model based on bulk crop properties. Fourteen bulk crop properties were collected for wheat and barley from 1980 through 1984. In addition, the performance of the cleaning component and the straw walker was also measured on a conventional type John Deere 6620 combine. Crop properties were measured on grain, chaff, and straw components of wheat and barley. The performance criteria was chaff feed rate at 0.5 percent cleaner grain loss and total M.O.G. (mass other than grain) feed rate at 1.0 percent walker grain loss. Grain density, grain angle of repose, chaff density, chaff coefficient of friction, chaff canpressibility nodulus, grain:M.O.G. ratio, straw density, straw canpressibility modulus, and straw coefficient of friction were shown to affect oanbine performance. In general, cleaner performance appeared to be three time more sensitive to crop changes than strawwwalker performance. Cleaner performance and straw'walker performance prediction equations were developed which explained 92.0 percent of the variation in cleaning performance and 30.0 percent of the variation of the performance in straw'walker performance. A.computer simulation model was developed using crop property and combine performance data. The model predicted cleaner, walker, and overall processing performance as functions of ground speed, width of cut, crop yield, and a set of crop properties which vary in a stochastic manner. Implemented as an interactive program, the user specifies initial crop properties and variability. Each property is then simulated over a range of selected moisture conditions. The model can be used by students and test engineers to study the effects of crop properties on combine performance. 9%4/25/5322223‘572 2- 2/22 Ajity .Srivastava ,_ . //f>/ (_ Q:;¢é;hrzéggggié4?§;% (22;g¢¢g§:29.,42/§2//2/57/ Donald:Ms‘Edwards I wish to thank the following: The Department of Agricultural Engineering and its head, Dr. Donald Edwards, for accepting me in the Agricultural Engineering Technology program. Dr. Ajit K. Srivastava, my major professor, for his guidance, instruction, and assistance during my tenure as a graduate research assistante. Drs. Thomas Burkhardt, Ivan Mao, and Alan Rotz for their guidance and assistance. Deere and Company Harvester Works of Moline, Illinois for their financial support and assistance. My wife for her enduring support. My fellow graduate students, especially Steve Richey, my office partner and co-worker, for his help and advice. iv TABLE OFCONI‘ENI‘S Page LIST OF TABLES . ....... . ................... 8 LIST OF FIGURES ...... . .................... CHAPTER I: INTRODUCTION.AND PROBLEM STATEMENT ............ 1 1.1 Background ........................ 1 1.2 Problem Statement . . . . . . . . . ..... . . . . . . 2 1.3 Objectives ........................ 2 CHAPTER II: REVIEW OF LITERATURE ................... 3 2.1 Introduction ....................... 3 2.2 Harvest Methods and Principles .............. 3 2.3 Cbmbine Performance ................... 6 2.3.1 Performance Measurement ..... . . . . . . . . 6 2.3.2 Factors Affecting Canbine Performance ...... 7 2.4 Threshing cylinder Models ................ 9 2.5 Straw walker ....................... 11 2.6 Cleaner Models ...................... 13 2.7 System Research ................... . . 14 2.7.1 Definition . . . . . . . ............. 14 2.7.2 System Models ................... 14 2.7.3 Testing and Implementation ............ 14 2.8 Crop Properties Research . . . . . . . . . ........ 15 CHAPTER III: C(NBINE PERFOMANCE AND BULK CIDP PHDPERTY MEASUREMENT . ...................... 17 3.1 Introduction .......... _ ............. 17 3.2 Cbmbine Performance ................... 18 3.3 Meas suring Bulk Crop Properties ............. 21 3.3.1 Material Collection and Crop Properties Measured . 21 3.3.2 Crop Component Moisture . . . . . . . . . . . . . . 21 3.3.3 Crop Bulk Density . . . .............. 22 3.3.4 Canpressibility Modulus .............. 27 3.3.5 crop Friction . . . . ............... 27 3.3.6 Particle Distribution . . . . . ........ . . 28 3.3.7 M.O.G. Ratios ................... 31 3.3.8 Instrumentation . . . . .............. 31 CHAPTER IV: CDRRELATION OF CIDP PmPERTIES T‘O CCI‘IBINE PEWORMANCE AND‘Ii-IEEFFEXZTOFMOISI'UREONCKDPPmPERTIES . . . . . . 36 4.1 Introduction ...................... . 36 4.2 Effect of Crop Properties on Cleaner Perfonmance . . . . . 46 4.2.1 Grain Angle of Repose . ....... . ...... 46 4.2.2 Grain Density ................... 48 V § 4.3 mmasaxmmmmmm O HWQQO‘U‘IthUND-J \14 O HO \l . N 0 Conclusions I: . . . mdmmbw mfifibbnbh C O wwuwgnmnwww 0 cf “05“ . . . . o . WIFNH H usions Q C 3 Introduction bbbbbbhfibhb O mmmmmmmmmmm O HH‘DmQO‘UIfiUNH PREDICTION HEELS Intrwmtim O O O O O I O O O O O Cleaner Prediction Equations . . . Straw Walker Predictions Equations Conclusions CROP PROPERTY BASED MINE Introduction . . . . . . Objectives . . . . . . . Model Concept Crop Property Simulation Feeding and Cutting Cleaning and Walker Loss 'Ibtalloss....... Randan Variable Generation Simulation Results . . . . WANDCONCUJSICNS sum 0 O O O O O O O 0 Conclusions Chaff Coefficient of Friction ChaffDensity........ Chaff Ccmpressibility Modulus Grain and Chaff Moisture ChaffMeanLength. . . . . . M.O.G.Ratios.......... of Crop Properties on Straw Walker Per Grain Angle of Response . . . . . GrainDensity.......... Straw Coefficient of Friction . Ranaining Straw Properties ect of Moisture on Crop Properties 0 O O O O O O O O O O O O GrainDensity........ Grain Angle of Repose . . . . ChaffDensity. . . . . . . . Chaff Caupressibility Modulus Chaff Coefficient of Friction ChaffMeanLength . . . . . . StrawDensity. . . . . . . . Straw Canpressibility Modulus 0 Straw Coefficient of Friction lconclusions . . . . . . . . . l-h...... SIMJLATION MODEL 7.1.1 Affect of Crop Properties 7 . l. 2 Canbine Simulation vi 0 O O O I O O 0 O O O O O O O C O O O O O 0 O ormance on Combine Performance mcamendations For Further Research . . . . . . . . . . 48 49 50 50 51 51 52 52 53 54 54 54 54 54 56 $6 56 56 57 57 57 58 58 60 65 69 74 74 74 74 76 79 80 84 84 95 96 96 96 97 98 98 APPENDIX A: Crop Bulk Properties and Machine Performance masurmts mm Set 0 O O O O O O O O O O O O 0 O O O 100 APPENDIX B: Scatter Plots of Canbine Performance Versus Crop Prmrties O O O O O O O O O O O O O O O O O O O O O O 112 APPENDIX C: Scatter Plots of Crop Properties Versus Crop miSture O O O O O O O I O O O O O O O O O O O O O O O 129 APPENDIX D: Canbine Simulation Dbdel Source Code . . . . . . . . . 140 APPENDIX E: Combine Simulation Interactive Session . . . . . . . . 154 BIBLIW O O O O O O O O O O O O O O O O O O O O O O O O O O O O 162 vii LIST'OF TABLES TABLE PAGE 10. ll. 12. 13. Bulk crop property collection information listed by location, year, crop, test envirorment and machine masmamnts O O O O O O O O O O O O O O O O O O O ....... 23 Means and standard of property data and machine parameters listed by location and crop type ...... . . . ....... 37 Measurement errors for each bulk crop property . . ....... 38 Correlation of cleaner performance to crop properties listed bylocationandcroptype.................... 40 Correlation of straw walker performance to crop properties listedbylocationandcroptype . . . . . . . . . . . . . . . . 41 Slopes of single variable regression equations which describe cleaner and straw walker performance as function crop promrties O O O O O O O O O I O O O O O O O O O O O O 0 O O 42 Property changes associated with a measurable change in cleaner performance and the probability of such a change ........... . . . ............... 44 Property changes associated with a measurable change in straw walker performance and the probability of such a change ............................ 45 Unit change in cleaner properties to crop moisture listed per unit change in a crop property ............ . . . 55 Correlation of crop properties to crop moisture listed by lxatim O O O O O O O O O O O O O O OOOOOOOOOOOOO 55 Correlation prediction equation coefficients, adjusted R squares, and partial F ratios as determined by stepwise regression. Properties are listed as they entered the model . . 61 Cleaner prediction equation coefficients, adjusted R squares, and partial F ratios as determined by stepwise regression. Properties are listed as they entered the model . . 63 Effect of location on cleaner performance. The location effect was tested with covariate models using properties previously selected by stepwise regression ........... 66 viii 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. Straw walker prediction equation coefficients, adjusted R squares, and partial F ratios as determined by stepwise regression. Properties are listed as they entered the model . Straw walker prediction equation coefficients, adjusted R squares, and partial F ratios as determined by stepwise regression. Properties are listed as they entered the model Effect of location on cleaner performance. The location effect was tested with covariate models using properties previously selected by stepwise regression . . . . . ...... Equation coefficients used to predict properties as functions of moisture. The data set used to develop the regression equations and statistics are also listed . . . . . Equations coefficients used to predict cleaner loss in simulation model. Partial F ratios are listed as generated by stepwise regression ..................... Equation coefficients used to predict walker loss in simulation model. Partial 1“ ratios are listed as generated by stepwise regression ...... . . . . . ..... . . . Crop means and standard deviations used in the sensitivity arBlYSj-S O O 0 O O O O O O I I O O O O O 0 O O 0 O O O O O 0 Sensitivity analysis of simulated cleaner performance ...... Sensitivity analysis of simulated walker performance ...... Crop properties , crop yield, and machine parameters used simulation of cleaner and walker performance. The data is and actual barley data set . . . ........ . . ..... ix 67 68 72 82 83 83 87 89 91 LISPOPPIQJRES mom 1. Schematicofacombineharvester........... 2. Typical wheat cleaner performance curve . . . . . . . 3. Typical wheat straw walker performance curve . . . . . 4. Test stand used to measure density and compressibility modulusofchaffandstraw. .. . . . . . . . . . . . 5. Typical graph of data from test stand used to measure density and compressibility modulus of chaff and straw 6. Test stand used to measure chaff and straw emffiCients Of friCtim O O O O O O O O O O O O O O O 7. Test stand used to measure grain angle of repose . . . 8. Test stand used to sieve chaff into size components . 9. Typical chaff size distribution curve . . . . . . . . 10. Electronic instrumentation used for crop property dam mllxtim I O O O O O O O O O O O O O O I O O O 11. Predicted cleaner performance versus actual cleaner performance based on the entire data set . . . . . . . 12. Predicted cleaner performance versus actual cleaner performance based on data gathered after 1981 . . . . 13. Predicted straw walker performance versus actual straw walker performance based on entire data set . . . . . l4. Predicted straw walker performance versus actual straw walker performance based on data gathered after 1981 . 15. Flow diagram of combine simulation model . . . . . . . 16. Simulated cleaner performance versus actual cleaner ”r fomm O C O O O O O O O O O O O O O O O O O O O PACE 19 20 24 26 29 30 32 33 34' 62 64 70 71 75 85 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. Simulated straw walker performance versus actual strawwalkerperformance............... Simulated cleaner performance curve and actual cleaner performance curve for North Dakota barley . . . . . . Simulated straw walker performance curve and actual straw walker performance curve for North Dakota barley Simulated chaff feedrate at 0.5 percent grain loss and total M.O.G. feedrate at 1.0 percent grain loss versus grain moisture. Chaff properties variation was 5.0 percent and straw walker properties variation was 10.0percent..................... Scatter plot of chaff feedrate (t/h) at 0.5 percent grain loss versus grain density (kg/m3) . . . . . . . Scatter plot of chaff feedrate (t/h) at 0.5 percent grain loss versus grain angle of repose (degrees) . . Scatter plot of chaff feedrate (t/h) at 0.5 percent grain loss versus chaff to M.O.G. ratio . . . . . . . Scatter plot of chaff feedrate (t/h) at 0.5 percent grain loss versus grain to M.O.G. ratio . . . . . . . Scatter plot of chaff feedrate (t/h) at 0.5 percent grain loss versus chaff moisture (percent dry basis) . Scatter plot of chaff feedrate (t/h) at O. 5 percent grain loss versus chaff density (kg/m3) . . . . . . . Scatter plot of chaff feedrate (t/h) at 0.5 percent grain loss versus chaff compressibility modulus (kPa) Scatter plot of chaff feedrate (t/h) at 0.5 percent grain loss versus chaff coefficient of friction . . . Scatter plot of chaff feedrate (t/h) at 0.5 percent grain loss versus chaff mean length (mm) . . . . . . . Scatter plot of M.O.G. feedrate (t/h) at 1.0 percent grain loss versus grain moisture (percent dry basis) . Scatter plot of M. O. G. feedrate (t/h) 3at 1.0 percent grain loss versus grain density (kg/m3) . . . . . . . Scatter plot of M.O.G. feedrate (t/h) at 1.0 percent grain loss versus grain angle of repose (degrees) . xi 86 92 93 94 112 113 114 115 116 117 118 119 120 121 122 123 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. Scatter plot of M.O.G. feedrate (t/h) at 1.0 percent grain loss versus straw moisture (percent dry basis) . Scatter plot of M.O.G. feedrate (t/h) at 1.0 percent grain loss versus grain to 24.0.8. ratio . . . . . . . Scatter plot of M.O.G. feedrate (t/h) at 1.0 percent grain loss versus straw density (kg/m3) . . . . . . . Scatter plot of M.O.G. feedrate (t/h) at 1.0 percent grain loss versus straw compressibility modulus (kPa) Scatter plot of M.O.G. feedrate (t/h) at 1.0 percent grain loss versus straw coefficient of friction . . . Scatter plot of Grain density (Kg/m3) versus grain misture (mrcent) O O O O O O O O O O O O O O O O O O Scatter plot of grain angle of repose (degrees) versus grain moisture (percent) . . . . . . . . . . . Scatter plot of Grain to M.O.G. radio versus grainmoisture (percent) . . . . . . . . . . . . . . . Scatter plot of chaff density (kg/m3) versus chaff moisture (percent dry basis) . . . . . . . . . . Scatter plot of chaff compressibility modulus (kPa) versus chaff moisture (percent dry basis) . . . . . . Scatter plot of chaff coefficient of friction versus chaff moisture (percent dry basis) . . . . . . . Scatter plot of chaff mean length (mm) versus chaff moisture (percent dry basis) . . . . . . . . . . Scatter plot of chaff to M.O.G. ration versus chaff moisture (percent dry basis) . . . . . . . . . . Straw density (kg/m3) versus straw moisture (percentdrybasis) Straw compressibility modulus (kPa) versus straw moisture (percentdrybasis) . . . . . . . . . . . . . Straw coefficient of friction versus straw moisture (percentdrybasis) . . . . . . . . . . . . . Example of cleaner loss curve displayed on screen bypressing theF3 functionkey . . . . . . . . . . . xii 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 158 50. Example of Walker loss curve displayed on screen by pressingtheF3functionkey. . . . . . . . . . . . . . . . 159 51. Scatter plot displayed on screen by pressing F4 function key the option prototes the user for two crop proper ties or Whine mrmters O O O O O I I O O O O O O O O O O O O 160 52. Example of simulation statistics displayed by pressingtheFSfunctimkey................ 161 xiii CHAPTERI Immowcnov AND mm STAT'D‘ENT 1.1 Background combine harvester performance varies throughout the harvesting period. Performance shifts often occur within the same harvest day. Stephens and Babe (1977) reported performance decreased 30 percent in less than one week while conducting combine performance tests. During that week, grain moisture remained constant while straw'moisture fell from 35 percent to 10 percent. Although the performance shift was not quantitatively explained much of the variation in performance was attributed to the change in straw moisture contrary to the popular belief that performance improves as straw'moisture decreases. Research has been conducted to evaluate design changes or to compare machines in performance tests but little has been done to determine the effects of the crop parameters on combine performance. Combine manufacturers conduct field and laboratory tests with prototype harvesters to determine if design changes significantly alter combine performance. Suspected crop property shifts often make comparisons between prototypes and production machines difficult. Presently, it is necessary to generate a performance curve for a production machine each time a prototype machine is operated to eliminate variability caused by the crop. Stephens and Rabe (1977) estimated 30—50 percent of testing time is spent generating the performance curve for the production machine. Chmponents are often tested with stored plant material in a laboratory setting. Performance of combines in field tests is not often 2 duplicated using stored plant material in a laboratory setting. The failure to duplicate field tests has been attributed to changes in properties of the crap as a result of storage. 1.2 Problem Statement The goal of field and laboratory testing is to produce valid and repeatable results. Changes in crop properties are thought to cause combine performance shifts. A.method is needed to measure crop bulk properties and to explain the variation in combine performance as a function of crop properties. Ideally, a model of a combine harvester could be constructed such that production machine performance could be predicted thus minimizing the need for labor and cost intensive field performance curves. 1.3 Ob ectives The purpose of this dissertation is to relate crop property changes to variations in combine harvester performance. The specific objectives are: 1) to identify and measure bulk crop property changes, 2) to correlate bulk crop property changes to combine harvester performance, 3) to predict losses as a function of crop properties, and 4) to develop a computer simulation of a combine harvester based on bulk crop property changes. CHAPTERII REVIEW OF LITERATURE 2.1 Introduction Almost all seed and grain crops in the united States are harvested by combine harvesters. Conventional combines, those with a threshing component, straw walker, and cleaning component are the focus of this review; An overview of combine harvest methods, terminology, operating principles, and the factors reported to affect combine performance are presented. 2.2 Harvest Methods and Principles Two primary harvest methods are used for harvesting small grain crops. Direct cutting, the most common harvest method, involves cutting the standing crop and processing in a continuous operation. Processing in this case refers to threshing, separation, and cleaning. ‘Windrowing, another harvest method, is also used in some parts of the united States. The practice is common in the Northern United States and Western Canada. (Although windrowing requires an extra operation, the practice facilitates curing in areas where drying conditions are variable. Generally, conventional combines are of two types: self-propelled and pull-type. Self-propelled combines can be further categorized by three machine types: level land, hillside, and sidehill machines. Level-land combines are intended for use on level or nearly level land. Hillside machines, as the name implies, are designed to allow combining on hillsides. Hillside machines are equipped with automatic leveling devices that allow the machine to remain horizontal while the cutting mechanism follows the contour of the ground. Hillside combines are 4 designed to operate on maximum slopes of 30-45 percent. Sidehill combines are essentially level land combines with slightly altered components which do not allow the material distribution to overload a component. For example the cleaning unit is equipped with baffles to assure even material distribution. Pull-type combines are usually PTO driven versions of level land machines. Their widest application is in those areas that windrow small grains. They are less expensive and less mneuverable than self-propelled machines. Also, pull-type combines do not provide the continuous operator adjustment available on self-propelled combines. Combines perform four basic operations during the harvesting process: 1. Cutting standing plants or picking up the windrow, 2. Threshing, 3. Separation, and 4. Cleaning. A conventional combine is equipped with several components to accomplish the basic operations of harvesting (Figure 1). When direct cutting standing plants a feeding and cutting mechanism called a header is employed. The mechanism's principle components are a reel, a sickle bar cutter, and a conveying system. As the combine moves into a standing crop, the reel momentarily holds the plant in place for cutting and then directs the crop rearward for conveying into the feeder house. The threshing component of the cylinder-concave is comprised of a drum or cylinder partially enclosed by a concave or grate mounted perpendicular to the crop flow. The concave is slotted such that grain 265m; 3 ohm Emma: 9:98 a no oEaEUm a 953. 6 may fall through it at separation. .As the cylinder spins, crop is fed between the cylinder and concave. Rasping, squeezing, and impact between the revolving cylinder and stationary concave detach the grain from the plant. The straw'walker or rack is typically an oscillating bed which separates grain from straw as it agitates the crop material rearward. Grain and chaff fall through openings in the rack onto grain return pans which lead to the cleaning component. The remaining material, predominantly straw, is carried out of the machine by the straw walkers. The cleaning component, usually comprised of a chaffer sieve, a cleaning sieve, and a fan , is the final step in separation. .Air directed on the mixture of grain, chaff, and broken straw carries the lighter debris out of the machine while the grain which is more dense falls through the sieves. The cleaned grain is then augured into the combine holding tank. 2.3 Combine Performance 2.3.1 Performance Measurement Cbmbine performance of the cleaner and the straw walker is commonly evaluated by measured processing grain loss. Loss can be described as percentage of grain lost, rate of loss, or amount of loss per unit of land area (Hailander et al, 1983). The most common performance criterion found in the literature is loss expressed as a percentage of grain available on a component for a specified time interval and is the criterion adopted by the Society of Agricultural Engineers (Agricultural Engineers Yearbook, 1983). Header loss (loss at the feeding and cutting stage) is considered ‘more difficult to measure than walker or cleaner loss. When measured, header loss is usually expressed in one of the three forms previously 7 mentioned. Header loss is thought to account for most of the loss during combine harvesting and may vary from 0:5 to 2.0 percent (Ridenour et a1,l968). The cylinder-concave component can be evaluated in a number of ways. Percentage of grain lost is sometimes used. Perhaps the most common standard of cylinder-concave performance is efficiency. Cylinder-concave efficiency is defined as the percentage of grain separated through the concave. Grain damage is also a measure of performance but varies in importance. Grain damage is very important in crops that are harvested for seed because germination is affected. In grain harvested for consumption, damage is not as important because there is no incentive for high quality grain beyond.minimum requirements (Mailander et al, 1983). 2.3.2 Factors That Affect Combine Performance Cbmbine performance is affected by many factors such as machine adjustments, crop conditions, and ground speed. ASAE standard 8396T assumes that increased feedrate causes increased total processing loss as well as increased walker loss and cleaning loss. Grain processing loss has been described as an exponential function of feedrate (Kirk et al, 1977; Kumar and Cass, 1978; Friesen, 1966) while other researchers have used a linear function of feedrate raised to a power (Wrubleski, 1977; Reed et al, 1968; Audsley, 1979). .Although the relationship between loss and feedrate are documented, values for the coefficients describing the relationship between feedrate and grain loss vary substantially. The discrepancy between coefficients of various equations based on similar machines suggests that factors other than feedrate affect grain loss. Researchers have examined the effects of machine design, machine adjustments, and to a lesser extent crap properties. Cylinder speed and concave clearance have been shown to affect cylinder-concave performance when M.O.G. (mass other than grain) feedrate is constant (Vas and Harrison, 1964; Ridenour, 1968; Cooper, 1971; Rainer, Kepner, and Barger, 1980). Generally, faster cylinder speeds and more narrow concave settings are associated with higher threshing efficiency. Excessive cylinder speeds cause straw break up in some crops such that the cleaning colponent is loaded to a point that performance decreases. Goss et al (1958) and Vas and Harrison (1964) concluded that cylinder speed has a greater effect upon threshing efficiency and threshing loss than concave clearance. In addition to machine adjustments, orientation of the material entering the cylinder-concave has also been determined to affect separation efficiency (Arnold, 1964) . The straw walkers are also similarly affected by factors other than feedrate. Machine parameters are known to significantly affect walker performance. Walker crank speed and crank throw were investigated by Reed, Zoerb, and Bigsby (1974) as was straw walker length. In general, walker performance is optimum when the material is aggressively tossed upward and moved rearward. Goss (1958) and Reed, Zoerb, and Bigsby (1970) found grain to straw ratio to have a negative affect upon walker performance (higher grain loss). Straw length was found to be of little importance by Reed, Zoerb, and Bigsby (1970) in contrast to Huisman (1977). Huisman also found relative humidity and bulk density of straw to correlate with walker performance. He also reported that straw coefficient of friction, straw moisture, grain moisture, and modulus of elasticity to be poorly correlated with walker performance. Conversely, Nath (1982) found grain moisture to be an important factor relative to walker performance. Nyborg, McColly, and Hinkle (1969) found reducing the grain to chaff ratio decreased cleaning shoe loss in some instances. Laboratory experiments have shown that shaker frequency, air flow characteristics and material entrance conditions affect cleaning performance (MacAulay and Lee, 1969; Rumble and Lee, 1970). Cylinder speed and concave clearance were reported to affect cleaning performance (Nath, Johnson, and.Milliken, 1982). Other parameters such as chaffer opening, and cleaner slope also affect performance. In addition to feedrate and machine parameters, crap factors also affect cleaning performance. Nath, Johnson, and Milliken (1982) reported loss increased with increased grain moisture. Huynh (1982) reported increased moisture was responsible for increases in chaff coefficient of friction. Higher coefficients of friction resulted in faster conveying times over the component such that grain was not allowed to pass through the crop mat. 2.4 Threshing Cylinder Models Nyborg, McColly, and Hinkle (1969) developed the following equation to describe cylinder loss in small grains: n. - 4.76E—4 (m)1°5 c;/s"1'69 .................................. [11 where TL - threshing loss (percent) FR - M.O.G. feedrate (pounds per minute) G/S - ratio of grain to M.O.G. feedrate. The correlation coefficient for the equation was reported as 0.50. Fairbanks, Johnson, Schrock, and Nath (1979) described threshing loss in grain sorghum by the following equations: TL - 10.35 - 4.76(CS) + 0.27 1(CC) ............................ [2] TL - 3.46 + 0.217(M) - 0.261(CS) + 0.208(CC) ................... [3] 10 where TL - threshing loss (percent) CS - cylinder speed (m/s) CC - concave clearance (mm) M - grain moisture (percent) Correlation coefficients were reported as 0.49 and 0.71, respectively. Nath, Johnson, and.Milliken (1982) developed the following equation to predict threshing loss in grain sorghum: TL - 9.105 + 0.144(M) + 0.150(8) + (0.111)(C)(2613(F2) + 350.0 (Fs)(1o'4)) + (2573.0 (m2)(cs) + 16.0(MSZ)(C2)10'4 ........ [41 where - grain moisture (percent) cylinder speed (NV!) concave clearance (mm) M S C F feedrate (kg/S) The correlation coefficient for the model was 0.50. Huisman (1983) proposed what he termed a "simplified model": TL - TLF(FGT) ................................................. [5] TL - threshing loss (kg/S) TCF - threshing loss fraction FGT - grain feedrate (kg/S) Threshing loss fraction (TLF) is expressed as follows: TLF - (l - TSE)(0.025) ......................................... [6] where TSE - threshing separation efficiency Huynh, Powell, and Siddall (1982) developed a stochastic model to describe the threshing and separation process in cereal grains. The 11 time required for a kernel of grain to be threshed after entering the cylinder concave, the time required for a kernel to pass through the straw'mat, and the time required for a kernel to pass through the concave grate were treated as random variables with characteristic distributions. They'were able to determine the probability that a kernel would be threshed and separated before being carried out with the straw mat . 2.5 Straw Walker Models Nyborg, McColly, and Hinkle (1969) proposed the following equation: m. - 0.102 (“0'82 (c/S)'1°73 ................................. [71 where ‘WL - rack or walker loss (percent) FR - feedrate (pounds per minute) G/S - grain to straw ratio The correlation coefficient for the model was 0.74. Reed, Zoerb, and Bigsby (1970) used a slightly different approach than Nyborg, McColly, and Hinkle (1969) to model walker performance. Reed, Zoerb, and Bigsby (1970) concluded that wheat separation can be described by a decaying exponential function and developed an equation to predict walker length for specified separation efficiency. They proposed evaluating walker performance by the walker length required for a given efficiency. The equation: L - ln(100 — eff)/b ........................................... [8] eff - exp(-b * L) ............................................. [9] where L - length (m) of the walker 12 b - an empirically derived value dependent upon the grain feedrate, the M.O.G. feedrate, grain to M.O.G. ratio, crop factors, and walker design Huisman, Heining, van Loo, and Bergman (1974) determined that a model incorporating M.O.G. feedrate, grain feedrate, and relative humidity best described walker loss. Other factors such as grain moisture, straw moisture, and stubble length were considered to be less significant. The equations: WL - -11.96 + 1.40(FRS) + l.64(ln(RH) + 0.017(MCS) +ln(MCG) + 3.2E—4(SL) + 0.021(FRG) ................................. [10] where WL - walker loss (kg/s) FRS - straw feedrate (kg/s) FRG - grain feedrate (kg/s) RH - relative humidity (percent) MCS - straw moisture (percent) MCG - grain moisture (percent) The model had a correlation coefficient of 0.91 and 0.77. Nath, Johnson, and Milliken (1982) determined that walker loss in grain sorghum was a function of grain moisture, cylinder speed, cylinder-concave clearance, and feed rate. The correlation coefficient of the model was 0.51. The equation: WL - 32.78 - 3.57(M) + 0.97(M2) - 0.091(C) - 7.8 (F)(0.00047(SC2)) + 0.87 - (227.04(m>2(r) + 0.4 (mzusm - 0.805(MSC)2)(10-4) ....................................... [11] where M - grain moisture (percent) S - cylinder speed (m/s) 13 C - concave clearance (mm) F - M.O.G. feed rate (kg/S) 2.6 Cleaning Performance Models Nyborg, McColly, and Hinkle (1969) proposed a model to describe cleaning loss in Canadian wheat. The equation: CL - 0.116(FR)°°37(c/S)'1’35 ................................. [12] where CL - cleaning loss (pounds) FR - feed rate (poundsfininute) G/S - grain to straw ratio Fairbanks, Johnson, Schrock, and Nath (1979) developed two models to predict cleaning losses in grain sorghum. The equations: CL - 9.953 - 0.3382(MG) + 0.00069(MGZ) + 7.0(10—6)(CC3) ........ [13] CL - 7.507 + 0.358(CS) + 0.00547(MGCS) ........................ [14] where CL - cleaning loss (percent) MG - grain moisture (percent) CC - concave clearance (mm) CS - cylidner speed (mVS) Huynh and Powell (1978) developed a probalistic model based upon two events: the migration of kernels through the chaffer openings, and crop dwell time on the cleaning component. The equation: R _ e -t/T ........ ............................................ [15] where R - the fraction of grain lost t - the reciprocal of the mean time required for the grain to pass through the material mat r - the crop dwell time in the chaffer 14 2.7 Systems Research 2.7.1 Definition Systems research is an analytical study of a system and its sub-systems. The method is a means to rationally quantify the parameters of a system and the inter-relationships of the parameters. Systems research activities can be categorized as system analysis and system synthesis. System analysis involves the separation of a system into fundamental components, while system synthesis utilizes the information gained from the analysis to observe or modify the existing system (Manetsch and Park, 1982). 2.7.2 System Models Models are quantitative representations of a process (system). They are used to gain knowledge and convey information. Models are typically used for any or all of the following reasons: 1) economic considerations, 2) availability, 3) information. There are two broad categories of models: deterministic and probabilistic. A deterministic model produces a repeatable set of outcomes while a probabilistic model introduces an element of uncertainty. The output from a probabilistic model varies if repeatedly provided with the same set of inputs while a deterministic model will yield the same values for a repeated set of inputs. 2.7.3 Testim and Implementation After constructing a systems model, it is necessary to prove that the model is an adequate representation of the real process depicted. First the model must be verified. Verification is the process of 15 checking the mathematical correctness of the expressions in the model. Second, the model is validated to compare the output to reality. In some cases, it is not possible to validate the model because: 1) the real world process may not exist, or 2) there may be to little information about the the model as he operated the combine through the simulated field of corn which was projected on the screen. The simulation allowed engineers to gather data in a laboratory setting where there was more control of the experiment and less cost. Systems research is a technique to examine a complete system. Agricultural engineers and other researchers have successfully employed this methodology to study existing or future systems. 2.8 Crop Properties Research The bulk of crop properties research has been related to material handling of fruits and vegetables and for processing of commercial food products. Mohsenin (1965) stated," certain physical characteristics and engineering properties of material (food and agricultural products) should constitute important engineering data. Despite ever increasing applications of machinery, little is known about the physical properties of materials which influence the efficiency of the machine and the quality of the product. Early research was conducted by Zink (1935) to determine the specific gravity of seeds and by Oxley (1944) who reported bulk densities for various grains. Most early research was conducted to aid the development of seed sorting and cleaning. Research has been 16 conducted to measure various aspects of many types of seed (Zoerb,l960; Harmond 1965; Kazarian and Hall, 1965; Garrett and Brooker, 1965; Brubaker and Pos, 1965; Chung and Converse, 1965; Zoerb, 1972;and Kusterman, 1984). Most testing of seed involved testing individual seeds and not bulk quantities. Huisman (1977) investigated the effects of straw moisture content, bulk density of straw, straw modulus of elasticity, kinetic coefficient of straw on straw, and straw length distribution. In doing so, he developed several methods to measure the properties. Straw bulk density was determined by placing a known volume of straw in a circular tub and loading the material to 120 Pa. The container was then shaken for one minute with a frequency of 33.1 cycles per second and an amplitude of 2.5 centimeters. The volume occupied by the straw was then used as the bulk density. Modulus of elasticity was determined with a specially constructed instrumented test stand. Bundles of straw were subjected to a 3-point simple bending test from which the modulus of elasticity was derived. Coefficient of friction between straws was also determined by a specially constructed instrumented test stand. A straw stem was attached such that it was pulled across another similarly attached stem. The normal force was known and the frictional force was read directly from a force transducer. CHAPTER III MINE PERFORMANCE AND BULK CROP PROPERTY MEASUREMENT 3.1 Introduction Measured combine performance is known to change significantly during repeated performance tests. Much of the variation in combine performance can be attributed to changes in crop conditions such that slight changes in crop conditions can cause significant changes in combine performance. Stephens and Rabe (1977) reported that cloud cover or overnight frost caused changes in the crop which resulted in significant performance changes. .Also, laboratory tests conducted with stored crops often are not duplicated in field tests and this led researchers to believe that the properties of the stored crop had changed. A.research project was initiated to determine the effect of crop properties on combine performance. The results of the study have particular relevance for combine performance testing procedures. Prototype combine performance is evaluated by comparison to the performance of a production model combine. Typically, the performance of a production machine for a test day in the field is established first. Subsequent performance prototype tests are compared with this standard. The decision to establish a new standard of performance is not based on quantitative information but upon intuition and the time available. The information from this study will enable test engineers to determine when crop conditions have shifted such that performance is affected. .Also, laboratory test results can be extrapolated for field conditions. 17 18 3.2 combine Performance The performance of two combine components was measured during the study: the cleaner and the straw walker as defined by ASAE Standard 5343.1. Performance measurement of each component was determined from loss curves which were generated with the bag catch method as described in .ASAE Standard 8396. In this method, the combine was operated at a predetermined ground speed and the material which exited the cleaner and the straw walker was caught in two separate bags and the time required to make the catch noted. The bags were then sieved to remove any grain. The amount of grain in each sample was recorded as percentage loss of the total grain processed during the the time the bags were open. Since the time required to collect the material was noted, the feedrate of the material on each component could be calculated. Material feedrates for the cleaner and the straw walker were recorded as the (MOS) feedrates expressed in metric tons per hour. The combine was operated at different ground speeds and the procedure repeated to include a range of feedrates. The performance for the walker or the cleaner was determined by plotting grain loss percentage versus the total MOG feedrate or chaff MOG feedrate (Figures 2 and 3). The relationship between grain loss and feedrate was found to best fit an exponential equation. Simple regressions of the natural logarithm of grain loss on feedrate were performed for each series of bag catches. One equation was calculated for the cleaner and one equation was calculated for the straw walker. The general form.of the loss equation after the simple regression was: 1085' - a' + b * f ........................................... [16] where CLEANER LOSS Z CLEANER-BER I ES #82518 T I CHAFF FEEDRATE (T/H) FIGURE 2 TYPICAL WHEAT CLEANER PERFORMANCE CURVE --- max. Y'a'mtp (h'X) a- l. mass-m b- l. 5298+” RSanee' .829 .52 LOSS IS AT 4.8 I!" 11 L055 15 AT 4.5 II" Z SEPARATOR LOSS SEPARA U U MOO FEEDRATE (T/H) FIGURE 3 TYPICAL WHEAT STRAW WALKER PERFORMANCE CURVE TOR-SERIES #82518 ---uxc.1.' VicfinqfllfiX) aeitnflflihlla be 4.517E-Bfll IlSqmro- .877 '1: was Is.” 12.. m ZIIJEE HEAT laJSTYH 21 loss'- natural log of grain loss (percent) a' - natural log of regression coefficient . b - regression coefficient f - chaff or M.O.G. feedrate (tons/hour) The equation was further manipulated by taking the exponential of each term in Equation 16 to yield the following general form.for cleaning loss or walker loss: loss - a * exp(b * f) ...... [17] where loss - grain loss (percent) a,b - regression coefficients f - chaff or M.O.G. feedrate (tons/hour) Cleaner performance was expressed as the chaff M.O.G. feedrate at 0.5 percent grain loss and straw walker performance was expressed as the total MOG feedrate at 1.0 percent grain loss as calculated from each respective equation. This method was used to detemmine the performance of components both in the field and in the laboratory. During laboratory testing, the crop material was placed on a conveyer belt and the feedrate was varied by altering the conveyer speed. Performance data was gathered on two types of John Deere conventional harvesters, a 6620 combine, and a 8820 combine. .All performance information was expressed in terms of a 6620 combine which is know to have two—thirds the capacity of an 8820 machine. 3.3 Measuring Bulk Crop Properties 3.3.1 Material Collection 29g Crop Properties Measured Bulk samples of each crop component were collected from a production model combine harvester during generation of a loss curve. .A sample of chaff MOG*was collected from the a bag catch collected during 22 the determination of each cleaner curve and a sample of walker 1006 was collected during the determination of each walker curve. Grain was collected directly from the storage bin of the combine. Approximately 20 kilogram of grain was collected from the grain auger outlet in the storage bin during the generation of a loss curve and used in the determination of grain properties. Twelve property measurements were performed upon the collected crop material without sorting or grading. They were grain moisture, grain density, grain angle of repose, chaff moisture, chaff density, chaff coefficient of friction, chaff mean length, chaff compressibility modulus, straw moisture, straw density, straw coefficient of friction, and straw compressibility modulus. In addition to loss curves, grain to DOG ratios and chaff to mom ratios were measured (Table 1). 3.3.2 Crop Component Moisture Grain moisture was determined with a John Deere portable moisture meter. The moisture contents of three to five grain sub-samples were determined and the mean moisture cmtent calculated. Chaff and straw moistures (dry basis) were determined by oven drying samples using guidelines established by ASAE Standard 8358.1. 3.3.3 Crop Bulk Density Grain bulk density (kg/m3) was determined by weighing a l—litre sub-sample of grain which was collected from the grain tank. Three to five sub-sample densities were measured and averaged to obtain the final value for entry into the data set. Chaff bulk density (kg/m3) and straw bulk density (kg/m3) were both determined using an automated test stand (Figure 4). Initially, chaff density was determined using a pexiglass cylinder loaded with 400.0 BULK CROP PROPERTY COLLECTION INFORMATION LISTED BY LOCATION, YEAR, CROP, TEST ENVIRONMENT AND MACHINE MEASUREMENTS. 23 TABLE 1 Cleaner Walker Location Year Crop Test Curves Curves Idaho 1980 Wheat, Field 22 21 Barley Corchran, CA 1982 Wheat Field 4 6 Fargo, ND 1982 Wheat Field 3 4 Coal Valley, IL 1982 Wheat Lab 0 10 Coal Valley, IL 1983 Wheat Lab 3 0 Grand Forks, ND 1983 Wheat, Field 9 9 Barley Coal Valley, IL 1984 Wheat Lab 0 0 Coal Valley, IL 1984 Wheat Field, Lab 0 6 24 859m DE hut—LU ho man—D002 whnqumummflmgo 02¢ panama ”mama! Oh. numb Oztam 9mg 0 gnu 25 grams of chaff. The cylinder was calibrated such that density was read directly from the side of the cylinder. A surface pressure of 280.0 (kPa) was used to empress the material for density determination because the pressure was thought to approximate the loading commonly found in a working combine. The stand was used to collect data during the 1980 and 1981 growing season. An automated test stand, constructed prior to the 1982 harvest season, consisted of a metal cylinder and a flat circular plate which was driven into the bore of the cylinder to compress either chaff or straw. The cylinder was mounted on three cantilevered strain gauged beams to sense loading and a potentiometer used to determine the distance of the plunger from the bottom of the cylinder. The crop was compressed as the circular plate was driven downward by a screw type drive attached to an electric motor. Figure 5 contains a typical graph of the output measurements produced by the test stand. Chaff density and straw density were determined at the volume which the material was subjected to 280.0 (kPa). Three kilograms of chaff and one kilogram of straw were used for each respective test of crop material. A simple regression of force over the range, 80.0 to 200.0 newtons and height (Figure 5) was performed for each sample. The range was chosen because 80 (N) corresponded to 280.0 (kPa) used in the previous stand. The equation: F - a + b * h ................................................ [18] where F - force exerted by the plunger a,b - estimated regression coefficients h - height (11:) of the plunger from the bottom of the tub was used to predict force for a given height and used in the calculation 26 Itflfim 02¢ hh‘ZU ho mDASDOt abndunnmwmdmtoo 02¢ NPHmZNO uzamdu: OB Gum: 02¢Pm Ema? Scam <94: ho 3&410 Atonmrfi n nssous .I. hxmnu: + .oom + .oon + .oov + .oon soon .0334 a a _+ .oos (N) 39801 27 of bulk density and compressibility modulus of chaff and straw. The calculation of density for chaff or straw'was performed using the known mass of material, the volume occupied at 280 (kPa), and the height at 280 (kPa) as calculated from.Equation 18. 3.3.4 compressibility Modulus Compressibility modulus (kPa) is defined as: a-mfig—r. ....... ....... [19] where o Ap - the change in applied pressure to the bulk sample AV - the corresponding volumetric change and V6 - the initial sample volume .As the circular plate descended, the volume of the crop material in the tub decreased while the area of the cylinder remained constant. The following calculating form.of compressibility modulus was used: AF B-m ................................................. [20] o where AF - the change in force(N) A.- the area(m) of the cylinder AL - the corresponding change in height(m) Lo - the initial height(m) of the sample The change in force (AF) was predicted using Equation 18. .As previously mentioned, the minimum value corresponds to the loading found in a combine. Two hundred newtons was chosen to standardize the fit and to provide the maximum number of linear points. 3.3.5 Crop Friction Two types of friction measurements were conducted. The coefficient of friction between stainless steel and chaff or straw'was determined as 28 was the internal friction of grain on grain as related by angle of repose. Chaff and straw coefficients of friction were measured with an automated stand (Figure 6). A.sled was loaded with crop material and placed on a stainless steel surface which revolved when driven by an electric motor. The stand described by Hall and Husman (1981) was supplied by Deere and Company. An additional l-kilogram weight was added to the sled. The sled was adjusted such that only crop material was in contact with the stainless steel surface. The sled was attached by a length of wire to a strain-gauged cantilevered beam. .As the steel surface revolved, the frictional force was sensed by the can cantilevered beam. Since the normal force was known, the coefficient of friction was easily determined. The sampling procedure was conducted such that one revolution of the table was the duration of the test. Thirtybthree coefficients of friction were averaged for a single value. Grain angle of repose as defined by Hall and Huisman (1981) was measured by placing a l-litre sample of grain in a hollow cylinder (Figure 7). The cylinder was then slowly raised and the resulting cone height measured. The angle of repose was then determined knowing the volume and the height of the cone formed by the grain. 3.3.6 Particle Distribution Chaff mean length was the only particle size measurement used. Measuring straw mean length was attempted by hand-counting a large straw sample. The measurement did not prove to be repeatable and required an excessive amount of time consequently, the determination of straw'mean length was discontinued. Chaff mean length is the mean particle size of a bulk chaff sample. 29 ZOHPUHmm MO mBZHHUHMMNOU Sam at NEED and”: On. DNMD avian amp“. 0 gum Mmgflm ho $021 Zugu Ema On. ONmD madam 9mg h gum 31 .A.test stand constructed by Deere and Company was used to sort chaff into various sizes (Figure 8). Four sieves (19.03, 12.70, 6.35, 3.175 mm) were cmtained in a metal shaker box. A l-kilogram sample was placed in the top of the box. The entire box was then driven by a crank mechanism for two minutes. The amount of material in each sieve was weighed as was the contents in the bottom pan and each weight expressed as a percentage of the total catch. .A cumulative curve of percent catch versus sieve size (Figure 9) shows a typical sample distribution. Least squares linear regression was performed on the cumulative distribution data after taking the natural logarithm of each cumulative sieve contents. The resulting equation, where the independent variable was the sieve size of each tray and the dependent variable was the cumulative percentage of material in each tray, was used to calculate the particle size corresponding to 50.0 percent probability. 3.3.7 M.O.G. Ratios .Although not true crop properties, grain to M.OuG. ratio and chaff to M.O.G. ratio were measured by Deere and Company. Both ratios were calculated with the average feedrates of chaff, grain, and straw as determined by the bag catches for loss curve determination. Grain to M.O.G. ratio was the average grain feedrate (t/h) divided by the average total M.O.G. feedrate (t/h) while chaff to M.O.G. ratio was the average chaff feedrate (t/h) divided by the average total M.O.G. feedrate (t/h). 3.3.8 Instrumentation .A Hewelett Packard 85 computer, Hewelett Packard 3497 data acquisition unit, and two specially constructed John Deere signal conditioners were used to collect data from instrumented test stands (Figure 10). A.K—tron electronic scale with digital read-out was used 32 FIGURE 8 - TEST STAND USED TO SIEVE CHAPF INTO SIZE COMPONENTS 33 3550 ZOHBDmHmBmHG ”sum hhgu Atom—awn. a ”flaunt .ZH. mNHm m>mHm no.0 0n.0 nm.0 00.0 p F _ b _ .0 .0« .0m .00 .01 .00 .00 .05 .00 .00 .00u BOVINEOUId 3A l IV'IDHNDO 34 .8 .... g . ~¥3§t$i 7-(3 ; «137.3! 3c» in: i4? . $3“! :30 MGM 10 ELECTRONIC INSTRUMENTATION USED FOR CROP PROPERTY DATA COLLECTION 35 to weigh crop material. The accuracy of the instrument was rated at plus or minus two grams. CHAPTERIV CDRRELATIG‘T OF CROP PROPERTIES TO CG‘IBINE PERFORMANCE AND THE EFFECT OF MIST‘URE (N CROP PROPERTIES 4.1 Introduction This chapter is presented in two major sections. The first portion of the chapter addresses the effects of crop properties on cleaner and straw walker performance. The second portion of chapter discusses the influence of moisture on each crop property. Several methods were employed to determine the effect of a crop property on cleaner or straw walker perfonmance. .An important distinction must be made in this chapter, the effect of each property on performance was analyzed singly. No attempt was made to control the influence of other properties on performance during the analysis of a single property. The objective was to provide a field engineer with a set of information which will enable him to determine when the performance of a combine component has measurably changed by monitoring a single property. To accomplish the objective it was necessary to determine the changes in each property associated with a measurable shift in performance. The complete data set is located in Appendix A. Means and standard deviations of all the crop property and machine performance measurements are recorded by test location in Table 2 while measurement errors for each property were estimated and recorded in Table 3. The measurement errors are an estimation of the ability to measure each property. For example, grain angle of repose can be measured with an accuracy of 0.40 degrees. .A property measurement was the average of several sub—sample measurements. In the case of grain angle of repose, several 36 ecodueaaoo ouoocoum A 0 ca one: a . .nac.o. As~o.c. Asao.o. . .dmo.c. .aoo.c. 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ANO.N. Human} acouuum 0.u 9‘ no.0 00.0 no.0 I ~N.h h0.v~ hm.0 ~0.m 0~.v hn.h Cumu000h .D.O.t dGDOF I I ANN.0. on.00 I .mu.0v Ao~.0. A0m.0v .ov.0. .00.0. .000 nacaflau accouum I 0N.N 0A.~ n0.N I n~.n 00.v n0.~ o~.~ vfi.d no.0 u. CumuQOOh accaodo I .00.0. A00.0_ .00.00 I .50.00 A00.0. I I aon.00 I vn.0 vn.0 0~.0 I 0n.0 no.0 I I mn.0 Oqunfl .0.0.: On uuoso I AON.~. .n~.00 .NN.00 I An~.00 A00.00 I I .vo.0. I ~0.~ hm.a 00.0 I 00.0 0m.a I I nh.~ Ody.“ .U.O.x 0» ouluo Avmo.0v I -00.0. I .HN0.0. .v0.00 .0u0.0. AG~0.0. .0N0.00 .000.0v coduuuuh 00n.0 ahu.0 ~0n.0 I n0n.0 0~n.0 «no.0 mmn.0 vmn.0 ann.0 «0 u:0«o«uuooo Buuuu .uconlv «H.028. ~fi0~uunv .u60530 AUQOSIV Auumzlv “00083. Aubuucn. .uowslv Ivan 30:3,: to» 303.0 Co... to... 30x00 3:3»38 053 one“: a: so~0a> Iguana» sumo: -ssoaao unnamed eusoz .s-ssosoo coda code #000 u. voau .IxuOh mood Noon Cohen «and O~.«h a vcauo ”and away mood assess voou alflauuabo N Idllh 38 TABLE 3 MEASUREMENT ERRORS FOR EACH BULK CROP PROPERTY. Property Measurement error Grain Moisture (%) 0.18 Chaff Moisture (%) 0.40 Straw Moisture (%) 1.20 Grain Density (kg/m3) 9.5 Chaff Density (kg/m3) 1.6 Straw Density (kg/m3) 1.0 Chaff Compressibility Modulus (kPa) 0.42 Straw Compressibility Modulus (kPa) 0.38 Grain Angle of Repose (degrees) 0.40 Chaff Coefficient of Friction 0.015 Straw Coefficient of Friction 0.025 Chaff Mean Length (mm) 0.5 39 sub-samples of grain were drawn and the grain angle of repose of each sub-sample determined. The mean and standard deviation of the sub—sample measurements was then calculated and recorded as the grain angle of repose of the sample. The error of measurement was estimated by calculating the mean of all the standard deviations for each property sample. The correlation of crop properties to the performance of the cleaner and the walker was performed (Tables 4 and 5) but the correlations did not explain the property change required before a measurable performance shift occurred. Scatter plots of cleaner and walker performance versus each crop property are located in Appendix B. Single variable equations derived from simple regressions were generated to express performance as a function of properties for both the cleaner and the straw walker. The slope of each equation (Table 6) was evaluated to determine the property change associated with a l—ton/hour feedrate performance increase. For example, the slope of the equation which described cleaning performance as a function of grain angle of repose was -0.34 (ton/hour)/(degree). The reciprocal of the slope was 2.95 (degree)/(ton/hour). In other words, based on the relationship between chaff feedrate at 0.5 percent grain loss and grain angle of repose, a 2.95 degree property change produced a 1—ton/hour change in chaff feedrate. .A 10.0 percent performance shift was thought to be the minimum detectable difference in combine performance as opposed to a l-ton/hour change in feedrate which was equivalent to a 42.0 percent change in cleaner performance and a 13.5 percent change in walker performance. In fact, a 20.0 percent shift may be a more realistic figure. It was desired to determine what property changes were required to d0>3 000000 «invaw .ea .soaumauoaoo cc acouuduuooo souueaouuou o ~n~.o -u.a mo~.o nn~.o I I ma~.o a n n . 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Reference points were required to establish a basis for determining the chaff feedrate and total M.O.G. feedrate equivalent to a 20.0 percent shift in cleaner and walker performance. The overall performance means for cleaner and walker performance were used. The mean performance for the cleaner was 1.77 (tons/hour) chaff feedrate at 0.5 percent grain loss and 7.37 (tons/hour) total M.O.G. feedrate at 1.0 percent grain loss for the walker (Table 2). .A 20.0 percent performance shift was equal to 0.35 (tons/hour) for the cleaner and 1.47(tons/hour) for the walker. Property changes required to cause a 20.0 percent shift in performance from the overall mean were calculated by multiplying the 20.0 percent. feedrate by the inverse slope from each equation. For example, using data from Table 6, the property change for grain angle of repose was 1.04 degrees, or 2.95 (degrees)/(tons/hour) multiplied by 0.35 (tons/hour). Probabilities were calculated to express the likelihood of observing a property change which would result in a measurable performance shift. The results of the these calculations for cleaner performance and walker performance are shown in Tables 7 and 8. A.large sample of properties data was assumed such that the standard normal tables were used to determine the associated probabilities. The required change in each property which caused a measurable change in performance was normalized using each respective overall data set standard deviation (Table 2) and the probability determined from statistical tables. For example, ”2" values for the standard normal curve were calculated for grain angle of repose by dividing 1.04 degrees (Table 6) times 2.0, the property change required for a measurable shift in cleaner performance, by 2.7 degrees (Table 2), 44 TABLE 7 PROPERTY CHANGES ASSOCIATED WITH A MEASURABLE CHANGE IN CLEANER PERFORMANCE AND THE PROBABILITY OF SUCH A CHANGE. Change in Property Required for 20 Percent Performance Shift Probability of Property Change Associated with 20 Percent Performance Shift Grain Grain Chaff Chaff Chaff Grain Chaff Chaff Grain Chaff Density 47.0 (kg/m3) Angle of Repose 1.04 (degrees) Coefficient of Friction 0.025 Density 5.6 (kg/m3) Compressibility Modulus 0.9 (kPa) Moisture 3.2 (%) Mean Length 2.3 (mm) Moisture 4.0 (%) to M.O.G. Ratio 0.7 to M.O.G. Ratio 0.05 21.0 44.0 30.0 13.0 37.0 18.0 5.0 5.0 5.0 3.0 45 TABLE 8 PROPERTY CHANGE ASSOCIATED WITH A MEASURABLE CHANGE IN STRAW WALKER PERFORMANCE AND THE PROBABILITY OF SUCH A CHANGE. Change in Property Required for 20 Percent Performance Shift Probability of Property Change Associated with 20 Percent Performance Shift Grain Grain Straw Straw Grain Straw Straw Grain Density 113.0 (kg/m3) Angle of Repose 2.7 (degrees) Coefficient of Friction 0.040 Modulus 0.5 (kPa) Moisture 32.0 (%) Moisture 77.0 (%) Density 150.0 (kg/m3) to M.O.G. Ratio 0.46 5.0 11.0 15.0 46 the standard deviation of grain angle of repose for the entire data set. The resulting "Z” value, 0.77, describes a standardized distance from.the mean of grain angle of repose which lies at 0.0. The "2" value corresponds to an area under the curve for values of grain angle of repose which lie outside plus or minus 1.04 degrees from the mean of grain angle of repose. The area under the curve which is approximately 44.0 percent is the percentage of grain angle of repose values in the sample which were plus or minus 1.04 degrees from the mean value. ‘This percentage is also the sample probability that a grain angle of repose is plus or minus 1.04 degrees from the mean. Table 9 lists the performance to property ratios for the cleaner and the straw walker. The ratio of change in performance to change in each a property is an indicator of the relative importance of each crop property. The ratios were calculated by dividing 20.0 percent (measurable machine shift) by the change in property (Tables 6 and 7) expressed as percentage of the property mean. For example, the percentage change in grain angle of repose from the mean value of grain angle of repose is 1.04 (Table 2) divided by 22.4 (Table 2) multiplied by 100 percent which equals 4.6 percent. The ratio (change in performance/Change in property) is 4.3 (Table 9) or 20.0 percent divided by 4.6 percent. The ratio indicates how responsive performance was to corresponding property changes. 4.2 Effect 9; Crop Properties 99 Cleaner Performance 4.2.1 Grain.Angle‘gf Repgse Grain angle of repose was negatively correlated with cleaning performance. .As a correlation of -0.75 (Table 4) indicates, performance tended to improve as grain angle of repose decreased. It appears that grain was less likely to pass through the chaff mat as the angle of 47 TABLE 9 UNIT CHANGE IN CLEANER PERFORMANCE AND WALKER PERFORMANCE FOR A UNIT CHANGE IN A CROP PROPERTY. Ratio of Cleaner Ratio of Straw Walker Performance to Performance to PrOperty Change Property Change Grain Density 3.3 1.4 Grain Angle of Repose 4.3 1.7 Chaff Coefficient of Friction 2.8 - Straw Coefficient of Friction - 1.4 Chaff Density 1.4 - Straw Density - 0.0 Chaff Mean Length 0.8 - Grain Moisture 0.9 0.1 Chaff Moisture 0.6 - Straw Moisture - 0.0 Grain to M.O.G. Ratio 0.5 0.7 Chaff to M.O.G. Ratio 1.3 - 48 repose increased. Cleaner performance appeared to be most sensitive to changes in grain angle of repose based upon a calculated performance to property ratio of 4.3 (Table 9). .A 1.04 degree change was required to observe a measurable change in cleaning performance (Table 7) while the likelihood of observing such a property change was 44.0 percent. The ability to measure a 1.04 degree change was well within the accuracy of measurement (Table 3) as the mean sample variation was 0.44 degrees. 4.2.2 Grain Density Grain density (kg/m3) was positively correlated with cleaning performance based on a correlation coefficient of 0.69 (Table 4). Cleaner performance increased 3.3 times for every corresponding increase in grain density (Table 9). A 43.0 (kg/m3) change in grain density was required to detect a measurable shift in cleaner performance. The probability of such a property change was 21.0 percent. The required property change was well within the sampling variation of 9.5 (kg/m3) (Table 3). 4.2.3 Chaff Coefficient 9f Friction Chaff friction was inversely related to cleaning performance. .A correlation of -0.68 indicates a strong relationship to cleaning performance (Table 4). Higher levels of chaff friction tended to impede movement of the chaff mat across the cleaner and probably impeded the flow of grain through the chaff mat. Cleaning performance had a performance to property ratio of 2.8 ‘with chaff friction (Table 9) which indicates the cleaner is relatively sensitive to changes in chaff coefficient of friction. .A friction change of 0.025 was required to observe a measurable performance shift 49 while the probability of a measurable property shift was 30.0 percent (Table 7). The property change was readily detected based upon a sampling variation of 0.015 (Table 3). 4.2.4 Chaff Density Chaff density (kgAm3) was positively related to cleaner performance based on a correlation coefficient of 0.36 (Table 4). This was not the expected result. It was believed that higher chaff densities, having less pore space, would be more difficult to clean. The decrease in voids was thought to inhibit grain movement through the chaff mat. The cleaner was sensitive to changes in chaff density as evidenced by a performance to property ratio of 1.40 (Table 9). .A 5.62 (kgAm3) change in chaff density was required to observe a measurable performance change (Table 7). The probability of observing a significant property shift was 11.0 percent (Table 7). Based on accuracy of measurement data, it was possible to detect shifts in chaff density. 4.2.5 Chaff Compressibility Modulus Chaff compressibility modulus (kPa) was positively correlated with cleaner performance. The correlation coefficient was 0.85 (Table 4). The relationship seems intuitively correct because chaff with larger modulus values indicates resistance to volumetric change. Chaff which was resistive to volumetric change would likely have more pore space while being cleaned. .A.performance to property ratio of 1.0 indicates that a 20.0 percent performance shift required a 20.0 percent property shift (Table 9). An 0.90 (kPa) change in compressibility modulus was required to observe a measurable performance shift (Table 7) while the probability of observing a property shift was 37.0 percent. The accuracy of measurement for chaff compressibility modulus was 0.42 (kPa) 50 which was well within the change required (Table 3) to detect a measurable property change. 4.2.6 Grain Egg Chaff Moisture Grain moisture appeared to have a negative effect upon cleaner performance based on a correlation coefficient of -0.31 while chaff moisture had little if any effect on cleaning performance based on a correlation of -0.03 (Table 4). The performance to property ratios for grain moisture and chaff moisture were 0.90 and 0.60, respectively (Table 9). .A 3.2 percent shift in grain moisture and 4.0 percent shift in chaff moisture were required to observe a measurable performance shift (Table 7). The probability of observing a significant grain moisture shift was 18.0 percent and 5.0 percent for chaff moisture (Table 7). Both property changes are measurable based upon sampling accuracy. The mean variation for grain moisture was 0.24 percent and 0.40 percent for chaff moisture (Table 3). 4.2.7 Chaff Mean Length Chaff mean length (mm)‘was inversely related with performance. The correlation between cleaner performance and chaff mean length is -0.37 (Table 4). A.higher mean length was a indication of more loading on the cleaning component and decreased performance. The performance to property ratio for chaff length was 0.80 (Table 9). IA change of 2.33 (and was required to observe a measurable property shift (Table 7). The probability of observing a significant property shift was 5.0 percent (Table 7) while the mean sample variation for chaff mean length was 0.52 (mm) (Table 3). 51 4.2.8 M.O.G. Ratios Grain to M.O.G. ratio had a.mdnimal effect upon cleaner performance as indicated by the low correlation of -0.19 (Table 4). Based on the poor correlation, the effect of grain to M.O.G. ratio on cleaner performance appears inconclusive. Chaff to M.O.Gu was more highly correlated to cleaner performance than was grain to M.O.G. ratio based on a correlation of 0.58. Cleaner performance tended to increase as chaff to M.O.G. ratio increased. .A change of 0.05 in the chaff to M.O.G. ratio resulted in a 20.0 percent change in cleaner performance. The likelihood of observing a measurable change in cleaner performance as a result of a shift in chaff to M.O.G. ratio was 32.0 percent. 4.3 Effect 9f Crop Properties 99 Straw“walker Performance 4.3.1 Grain Angle 9f Repgse Grain angle of repose (degrees) affected walker performance in much the same manner as cleaner performance. The correlation between grain angle of repose and walker performance was —0.44 (Table 5). IAs grain angle of repose increased, performance tended to decrease. Grain with larger values of angle of repose appeared to be less likely to pass through the straw mat. The performance to property ratio for grain angle of repose and walker performance was 1.7 (Table 9). .A 2.77 degree change in angle of repose was required to observe a measurable shift in walker performance. The probability of observing a property change was 5.0 percent (Table 8). The sample accuracy for angle of repose was 0.44 degrees (Table 3). 52 4 . 3 .2 Grain Density Grain density (kg/m3) was positively correlated with walker performance as evidenced by a correlation coefficient of 0.38 (Table 5). Larger grain densities were associated with better grain movement through the straw mat. A performance to property ratio of 1.4 indicated the influence of grain density on walker performance relative to the other properties (Table 9). A 113.0 (kg/n3) change in grain density was required to observe a measurable performance shift (Table 8). The probability of observing such a property shift was less than 1.0 percent (Table 8). The sample accuracy was 9.5 (kg/m3) (Table 3). 4.3.3 Straw Coefficient of Friction Straw coefficient of friction was inversely related to walker performance. The correlation coefficient between straw friction and walker performance was -0.41 (Table 5). .As the coefficient of friction increased, the movement of straw across the walker was reduced as was capacity. The performance to property ratio for straw friction was 1.4 (Table 9). A.0.04 change in straw friction was required to observe a measurable change in walker performance (Table 7). The probability of observing a property change which corresponded to a measurable machine shift was 11.0 percent. Since the sample accuracy was 0.25, a property shift was measurable (Table 3). 4.3.4. Straw Compressibility Modulus Straw compressibility modulus (kPa) was positively related to walker performance. The correlation coefficient was 0.43 (Table 5). Straw with higher modulus values tended to resist compaction and 53 maintained its porosity. The performance to property ratio for compressibility modulus was 0.80 (Table 9). .A 0.51 (kPa) change in compressibility was required to detect a measurable performance shift (Table 8). The probability of observing a property change which corresponded to a measurable performance shift was 2.0 percent. The sampling accuracy was 0.38 (kPa) (Table 3). 4.3.5 Remaining Straw Properties The remainder of the walker performance related properties appeared to have little affect on performance. Grain moisture, straw moisture, straw density, and grain to M.O.G. all appeared to have a negligible affect on performance. IAll were poorly correlated and required extreme property changes to alter performance. 4.4 Conclusions Cleaning performance was more sensitive to crop changes than walker performance and more properties were directly related to cleaning performance. In addition, property changes associated with measurable performance shifts were less for cleaning performance than for walker performance. For example, a 1.0 degree change (Table 7) in grain angle of repose was required for a measurable shift in cleaning performance while a 2.7 degree change (Table 8) in grain angle of repose was required for a measurable change in walker performance. Overall, the cleaner was approximately three times more sensitive to changes in crop properties than the straw walker based on the ratio of percentage performance change to percentage property change required for a measurable performance shift (Table 6). In general, the ratios were approximately three times greater for the cleaner than the straw walker. 54 4.5 Effect of Moisture 99 Crop Properties 4.5.1 Introduction Moisture affects crop properties in a predictable fashion. Many combine operators base field adjustments on crop moisture. Grain moisture is the most common criterion due to ease of measurement and near instantaneous determination using an electronic moisture tester. Table 10 shows the correlation between each crop property and moisture. Specifically, grain properties are correlated to grain moisture, chaff properties to chaff moisture, and straw properties to straw moisture. Scatter plots of each crop plotted as a function of its component moisture are located in.Appendix C. 4.5.2 Grain Density Grain density tended to decrease as grain moisture increased. The overall data set correlation was -0.33. The correlation between grain density and grain moisture for wheat using data from the Coal valley, Illinois data set was -0.57 (Table 10). This data set was thought to be most representative of field conditions due to the number of observations, wide moisture range, and maturity level of the crop. 4.5.3 Grain Angle of Repose Grain angle of repose decreased with associated increases in grain moisture as evidenced by a correlation 0.42 for the entire data set (Table 10). Once again, the Coal valley, Illinois data set illustrates the relationship for a single crop. The correlation for Coal valley was 0.80. 4.5.4 Chaff Density Chaff density did not correlate well with chaff moisture. The correlation coefficient was 0.0, however the relationship between chaff ~o>oq sandbauucoum can OCO«uI>HUODO cc vocabuuuoou coquoaouuou a ncc.a I ec~.c I nec.a nea.c asa.a I I naa.e an I a I as a so I I as nasano: Hm.aI I ~M.aI I ea.aI ma.cI aa.cI I I an.cI assessed-ana-oo sauna acc.a I ann.a I as~.a ana.c sam.e eaa.c eea.a sac.c an I a I as a as «a as as ma.c I ee.oI I H~.aI nc.a c~.aI «m.c an.aI ea.c aaeacac sauna scene I ann.e aac.c I sac.c ea~.e nam.a mne.a asa.a an I a a I a as as as an ma.a I e~.eI na.c I ce.aI a~.c c~.aI ac.e an.c season can: «peso I Hae.a ee~.c a~.e I ena.c em~.c mec.c ~ac.e acc.c I an as a I a as no on ma coaauann I ae.a .m.a oa.a I He.c am.aI ae.e aa.c aa.a no acoaueauooo ended I ec~.c «ma.c cuc.a I enc.c acs.a I I Hca.c I an as a I s as I I as ceased: I -.aI on.e ea.c I ~e.a a..aI I I ee.aI sassanaaaoua-oo noose I aac.c nne.c can.c I eec.c as..e aae.a nec.a nmc.a I as as a I a as as as an I -.a ne.a em.c I ca.e ne.c ac.c am.a -.e sad-cad moose eca.e I mm~.c anc.c ~ce.a e-.c aca.c ae~.c Hca.a sea.a an I as a as e as no as an ae.a I a~.c aa.e ea.c ~n.c as.aI a~.cI ~a.e na.c access no cause cacao ecc.c I ana.c eea.c ecc.c nae.c can.e can.c nac.c sac.a .a. an I as a as s so as as an .. en.cI I ma.aI na.aI ea.cI am.c an.e no.aI ca.cI nn.eI . sun-sac cacao .ueoaa. uses: aces: aas~a> aces: aces: aces: scenes seas: sued aces: ao-a> aaoxad dado soaaa> aaoaaa ascuouasso asses cases one saeea> dado sand: «can dado capo: «and cane cane ease ease macs name naaa an ldfldfi IZO~B¢UOJ an cabana MKDPmHOt mOKU OF mflnhflflmomm @020 MO ZOMBo-o> anon: anon: anon: xoauum anon: anon anon: ao-o> coexdo deco >0-o> coaxed oucDOUquo enema onooa ~H¢ >o-o> deco sumo: «cad deco :uuoz ~ca~ coma coon coma vaaa «cad ~oou «mod concmuaoo on laugh 56 density and chaff moisture is generally positive for location sub—sets (Table 10). Chaff density appeared to increase with corresponding increases in chaff moisture. For example, increasing moisture causes crop material to loose its resiliency and compress more easily. 4.5.5 Chaff compressibility Modulus Chaff compressibility modulus tended to decrease as moisture increased as indicated by a correlation of -0.46 for the entire data set (Table 10). IAs chaff became less moist, more pressure was required to change a volume of chaff. 4.5.6 Chaff Coefficient of Friction Chaff coefficient of friction tended to increase as chaff moisture increased. This effect was evidenced by the overall data set correlation coefficient of 0.49 and the various location sub-set correlations (Table 10). 4.5.7 Chaff Mean Length Chaff mean length tended to increase as chaff moisture increased based on the correlation of the overall data set (Table 10). The correlations of the location sub-sets do not support the theory that chaff length increases as moisture increases. It was theorized that wet crop material was less likely to break than dry material. 4.5.8 Straw Density Straw density appeared to increase as straw moisture increased based on an overall data set correlation of 0.44 (Table 10). The correlation between straw density and straw moisture was strongest for the Coal valley, Illinois data set. The sub-set data was most indicative of the true relationship because Coal valley was the only 57 test location where the crop was harvested at less than optimal conditions. 4.5.9 Straw Compressibility Modulus Straw compressibility modulus tended to decrease as straw'moisture increased. An overall correlation coefficient of -0.34 supported the relationship. Examination of the relationships for the various sub—sets (Table 10) also tended to support an inverse relationship. Like chaff, as the material became drier, it became more resistive to changes in volume. 4.5.10 Straw Coefficient of Friction Straw coefficient of friction tended to increase with increased moisture. The overall correlation coefficient of 0.56 indicated a positive relationship between friction and moisture (Table 10). 4.5.11 Conclusions .Although the relationships between properties were subject to considerable variability, there appeared to be discernible trends in most cases. Data from.selected test sites appeared to be more representative of the true relationships than the relationships derived from the entire data set. The test program was conducted such that the machines were tested in a narrow range of conditions at a given location. ‘While the moisture range may have been similar at different locations, the properties and the performance of the machines was vastly different. For example, the grain moisture at two sites is 12.0 percent but the crop properties are not the same nor is the performance of the machine. The overall data were useful but it should be noted that the variation in moisture was controlled by the nature of the testing program. CHAPTERV PREDICTICN PDDELS 5.0 Introduction Stepwise linear regression analysis was performed on the property data sets to develop predictive equations for the cleaner and the straw walker. The Statistical Package for the Social Sciences (SPSS) was used on the the Michigan State university Control Data Cyber 750 mainframe computer. Crop properties were chosen for the analysis such that cleaner performance was expressed as a function of grain and chaff properties. Only grain and straw properties were used to describe straw walker performance. It was assumed that the cleaner was affected by grain and chaff properties while the walker was affected by grain and straw properties and not chaff properties. During the course of the experiment, several types of data transformations were performed on the data set before stepwise regression analysis was used. The transformations included logarithmic transformations properties, properties raised to powers, and properties expressed as mulitplicative combinations of one another. Models of the following general form explained the most variation in cleaning and walker performance: ‘ § Yi - be + blxli .. bkxik ei ................................. [21] where y1 - M.O.G. feedrate (t/h) at a fixed grain loss estimated regression coefficients xi - crop properties I! I error not accounted for by the model i - 1,2, ..., n observations 58 59 j - 1,2, ..., k independent variables Equation 21 can be manipulated in to a power equation by expressing each term as an exponential: yi - bO * xub1 ... xnibn .................. [22] where yi - M.O.G. feedrate (t/h) at a fixed grain loss bj - estimated regression coefficients xi - crop properties i - 1,2, ..., n observations j - 1,2, ..., k independent variables Covariate models of cleaner and straw walker performance were analysed after stepwise regression was used to develop predictive equations for the cleaner and the straw walker. This was done to determine if location effects contributed to the explaination of combine performance after the effects due to crop properties was removed. Equation (21) was revised to add the classification variable, location. The general covariate model was: xij - Li + blxij ... + bnxi j ................................ [23] yij - M.O.G. feedrate (t/h) at a fixed grain loss Li - fixed effects (test sites) bj - estimated regression coefficients xij - crop properties eij - error not accounted for by the model i - 1,2, ... n treatments (test sites) j - 1,2, ... k observations 60 5.1 Cleaner Prediction Equations The entire set of chaff and grain prOperties were used for possible inclusion using the stepwise regression process. The most possible observations was insured by including all the cleaning data. The criteria for inclusion or exclusion from the resulting equation was chosen such that a 0.10 level of significance was maintained. Table 11 contains coefficients, constants, adjusted R square values, and number of observations used to develop the prediction equation. Figure 11, a scatter plot of predicted cleaner capacity versus observed cleaner capacity was constructed to graphically depict the overall correlation of the equation. Grain angle of repose, chaff coefficient of friction, chaff density, and chaff mean length were selected in the that order to describe 72.0 percent of the variation in cleaning performance. Each variable entered the equation at the 10.0 percent level of significance. In the previously described analysis, the maximum number of observations was made available for stepwise regression. Chaff compressibility modulus was not collected with the instrumented test stand until the 1982 growing season. Including chaff compressibility modulus in the analysis would not have allowed the maximum number of observations for the analysis because missing value option used by the statistical package would discard any data record with missing observations. The more recently collected properties were included in the analysis at the expense of twentybtwo observations. Table 12 lists the prediction equation coefficients, number of observations used in the stepwise regression, F ratios for each variable as they entered the model, and adjusted R squares as each variable entered the model. Figure 12 shows observed cleaner performance plotted 61 TABLEll CLEANER PREDICTION EQUATION CDEFFICIEN'I‘S, ADJUSTED R-SQUARES, AND PARTIAL F-RATIOS AS DERIVED BY S'I'EPWISE REERESSION. PROPERTIES ARE LISTED AS THEY ENTERED 'IHE I‘DDEL. Constant Adjusted Partial Property Coefficient ReSquare F-Value Grain Angle of Repose -1.901 0.55 26.5 *Chaff Coefficient of 0.897 0.69 6.7 Friction Grain Density 1.603 0.71 7.4 Chaff Mean length -0.241 0.73 3.4 0.010 m: The model was observations. developed using the entire data set consisting of 41 62 (810 fidufizm NIB tomb om>~¢mo mmuszOubtdflz 20 ommtm ”02¢:¢Ob¢mm KEZCHAU A‘DEU‘ mam¢m> QUZ¢Z¢Ob¢mm ¢u2¢m40 DHFUuOflzm dd ”Isaak _I\h. mh(¢0mmk uu~¢mn mmHImZOHFCAflK 20 oumcm flUz¢2¢Oh¢um zmz¢m40 A¢=FU< mammm> NUZCEZOhmmm muz¢mdu omFU~oflzm Nu u¢=0~h .I\h. wh F Location 5.0 0.02 0.003 Based on Data Collected After 1981 Source of Partial Variation F-Ratio R—Square PR > F Location 16.6 0.03 0.0001 67 TABLEIM ST‘RAW WALKER PREDICTION EDUATIQ‘I, ADJUSTED R-SQUARE‘S AND PARTIAL F-RATIOS AS DERIVED BY STEPWISE REERESSION. PROPERTIES ARE LISTED AS THEY ENTERED THE MODEL. Adjusted Partial Property Coefficient R—Square F-Ratio Straw Coefficient of -l.081 0.21 13.8 Friction Grain Angle of Repose -1.216 0.30 6.87 Constant 90. 378 mm: The model was observa tions . developed using the entire data set consisting of 54 68 TABLEIS STRAW WIKER PREDICTION EDUATICN, ADJUSTED R-SQUARES, PARTIAL F-RATIOS AS DERIVED BY STEPWISE REBRESSION. PROPERTIES ARE LISTED AS THEY ENTERED THE MODEL. Adjusted Partial Property Coefficient ReSquare F-Ratio Grain Angle of Repose -0.629 0.18 3.28 Straw Density —0.629 0.21 5.77 Grain Density 0.824 0.30 4.52 Constant mm: The model was based on data gathered after 1981 consisting of 33 observations. 69 equation. This resulted in an equation based on 54 observations. Straw coefficient of friction and grain angle of repose entered in the equation at the 10.0 percent level of significance. The equation explained 30.0 percent of the variation in straw'walker performance. Another regression analysis was performed on a properties data set which included straw compressibility modulus. The addition of straw compressibility modulus reduced the number of observations in data set because the property was not measured prior to 1982. The resulting equation based on 33 observations explained 30.0 percent of the variation in straw'walker performance. Grain angle of repose, straw density, and grain density were selected using the stepwise procedure. Scatter plots of predicted straw walker performance versus observed straw walker performance are presented in Figures 13 and 14. Location effects explained an additonal 27.0 percent in straw walker performance in a model which included straw friction and grain angle of repose (Table 16). ,Location effects were also significant in a model which contained straw compressibility modulus, straw density, and grain density. An additional 28.0 percent variation in straw walker was accounted for by the addition of location effects to the model (Table 16). 5.3 Conclusions Cleaner performance can be predicted by grain angle of repose, chaff friction, chaff compressibility modulus, chaff mean length, and grain density. Straw walker performance can best be predicted by straw coefficient of friction, straw compressibility modulus, straw density, and grain angle of repose. Ninetybtwo percent of variation in cleaning 70 8mm (Bio Ndnfilfl ”I? 02mm: DN>HKNO mmuzmzcub ”Ulttzchmflm zmzdtz I‘dhm aflFU~DN¢m nu also—h —I\h— mh460mmm .o.o.! J¢Dh0< nu an en Mu mu «« cu m o h m n . _ . . . _ . a _ _ . . 'D'O'H GEJOIOEUd (H/l) BJVNOBEJ 71 @« dead muhmt afldmzbtu dfito Dzuma om>u¢mo mm~=mz°~9¢aufl 20 Oflmdn HUZ‘ZflOhmmm KNXJ<3 Itflfim AdDPU< mammu> NUZ F Location 16.6 0.27 0.0001 Based on Data Collected After 1981 Source of Partial Variation F-Ratio R—Square PR > F Location 20.4 0.28 0.0001 73 performance was explained while only 30.0 percent of variation in walker performance was explained by the properties data. Location effects explained an additional 3.0 percent variation in cleaner performance and an additional 30.0 percent variation in walker performance. CHAPTER'VI CROP PROPERTY BASED COMBINE SIMULATION MODEL 6.1 Introduction A.computer simulation model of a John Deere 6620 combine harvester is presented. The model, which was implemented in the Basic programming language on an IBM compatible micro computer will predict grain loss as a function of ground speed, yield, width of cut, and crop parameters. The program listing is found in Appendix D while examples of the program output and instructions for use are located in.Appendix E. 6.2 Objectives The objectives of the model were to: 1. Predict grain loss on the major components of the combine as a function of ground speed, yield, width of cut, and crop bulk property 2. Graphically show the relationship between crop changes and combine performance. 6.3 Model Concept Figure 15 is a flow diagram of the combine simulation model. The flow of material can be traced from component to component. The user inputs to model are: grain moisture, chaff moisture, straw moisture, grain density, grain angle of repose, chaff mean length, chaff coefficient of friction, straw density, straw compressibility modulus, crop yield, grain to M.O.G. ratio, and chaff to M.O.G. ratio. Model outputs are: cleaning loss, walker loss, and total loss. 74 75 COMBINE SIMULATION GRAIN YIELD GROUND CNAFF:M.O.G. SPEED GRAIN:M.O.G. FEEDING AND CUTTING CHAFF FEEDRATE CLEANER WALKER INITIAL GRAIN PROPERTY MOISTURE VALUES GENERATION! otsmrn 31'3“” L088 (II) "4““ 1033 (III) m TOTAL LOSS (SI FIGJRE 15 mow DIAGRAM OF COMBINE SIMULATICN MODEL 76 6.4 Crop Propterty Simulation The user can describe the properties used to model the crop or select a set of parameters for a give geographic location. The user may also specify various moisture levels for a give crop and/or select crop property data from actual test locations (California wheat, Nerth Dakota wheat, and Nbrth Dakota barley) types for simulation. Crop properties were assumed to be functions of crop type, and environment (weather and soil fertility). It was also assumed that the bulk properties for a given location were functions of moisture at crop maturity. Equations were derived to express all crop properties in the model as functions of moisture. Specifically, chaff coefficient of friction was expressed as a function of chaff moisture while straw density was expressed as a function of straw'moisture. It was further assumed that crop component moistures can be expressed as functions of one another. Since grain moisture is the most common measurement performed by farmers and test personnel, it was decided to express chaff and straw moisture as functions of grain moisture. Relationships were derived by regression to predict properties as functions of moisture. The relationships were assumed to be of forms raised to powers. Coefficients for each equation, R squares, F values, and the data used to develop each equation are listed in Table 17. Crop property data was analyzed by location, by crop type and as a complete data set. numerous equations to predict a property as a function of moisture were generated by using subsets. The criteria used to select an equation was: best R square and‘widest range of data. Simply using the entire data set to develop predictive equations was not 77 nououmqoaco n homuncum ”couumsvn a couuuaum Anallom0 aoqao> uo ucoao 0m 0m.0 ~.0~ deco 000a Hv~.0 mn~.0 ououmfio: smuum quumou suuum Canaan. sodas, 0n on.0 0.0a deco vcoa 0N~.0 a-.o ousumuo: acuum amamcoo monum 00 m~.0 0.0~ dado -< 000.0 050.0 muouuqo: cuouu ououmuo: aouum 335: 59.3 0a m~.0 0.0 Osman 000A mm~.0 0m0.n unsunuo: uuozo com: mumso noduOAuh .msqumm. aoH~m> 00 90000 on Hm.0 H.0v deco 000a asm.0 -~.0 ououmao: uuozo quuoou uumzo euoxao s nn.0 0.v sumo: mama pv~.0 0mm.- endanger uucnu muuecoo muono so n~.0 m.h~ moon an: Noa.~ 005.0 musumgo: cacao ououmao: uumnu Ans-Bow. aoaqo> 0n 0m.0 s.m~ deco vama 0000.0I 0.m00 endgame: cameo audacoa cacao Guiana. >032, coon—om uo 0n 00.0 m.>m Hoou woaa 0mv.0 00h.m unaunuoz cacao enact cacao acouuo>uoono unmovam omuszh umm sumo n o manouuo> manouuo> oouoaooHao acoauauuooo acouuuuuooo acoosoaoocn acoucoaoo comuoovm scuuoovm .OHand Omfl‘ Md‘ avafimnfidam 624 mZOHB¢DOM ZOHmmNmUmm NIB moam>flo O? can: Bflm (8‘0 NIB .HKDPQ~OZ ho mZOuBUZDM m4 mmnbflflmozm FUHOMflm 09 can: mbzmnonhhflou 20~H¢=Ofl pd Minsk 78 used because the effects of moisture were controlled by the machine testing'method. Single variable equations used to predict crop changes as functions of moisture were assumed to be representative for any small grain type. However, a typical grain density at 12.0 percent moisture in California wheat is 900 [Kg/m3 while a typical grain density for North Dakota barley is 700 kg/m3 at the same moisture content. The equation derived from Illinois wheat data is not an adequate predictor of California wheat or North Dakota barley unless the equation is adjusted. The following example illustrates the method used to adjust an equation. The equation which describes grain density as a function of grain moisture is based upon data gathered at the Coal valley, Illinois, test site during the summer of 1984. If it is desired to predict the change in grain density for a simulation of Nbrth Dakota barley, the equation must be adjusted to predict the Nbrth Dakota condition. Grain density at 12 percent grain moisture as predicted by the equation is 868.0 (kgAmB). Based on prior personal experience, grain density for barley averaged 645.0 (kgAm3). These values are used to illustrate the adjustment. The equation which describes grain density as a function of grain moisture, found in Table 17, must be altered as follows: 1. Linearize the equation, 2. Substitute the value of the property, 3. Substitute the value for moisture, 4. Solve for coefficient "a", 5. Convert equation to power form. The equation in its power form.as shown in Table 17 is: Gden - 965.0 exp"°°°°8°‘G“‘°5t” .............................. [24] 79 where Gden - grain density (kgAmB) 965.0 - estimated regression coefficient . -0.0088 - estimated regression coefficient Gmost - grain.moisture (percent) The linearized equation, obtained by taking the natural logarithm of each side of the equation, is: Ln(Gden) - Ln(965.0) + (-0.0088(Gmost)) ............ . ......... [25] A.new coefficient can be solved for after substituting the new'values of grain angle of repose and grain moisture. The resulting equation is: Gden - 717.0 exPI-0.0088(GIDSI;)) .............................. [25] Typically, the values used to adjust the equation are selected as those that correspond to the optimum.performance of the machine for a particular crop. 6.5 Feeding and cutting The feeding and cutting component of the simulation model was developed using assumptions that feedrate is: 1. a function of width of cut, 2. a function of ground speed, 3. a function of yield. The equation used to simulate feedrate is: m - SPIm)WD(1/Gm)(0.00329) ............................... [26] where FR - M.O.G. feedrate (t/h) SP - ground speed (miles/hour) Wd - cutting width (feet) GMOG - grain to M.O.G. ratio 0.00329 - unit factor 80 The chaff feedrate was determined by the following equation: CF - CMOG(FR) ............................. . .................. [27] where CF - chaff feedrate (t/h) CMOG - chaff to M.OuG ratio FR - M.O.G feedrate (t/h) 6.6 Cleaning and walker Loss Inputs to the cleaning component and straw'walker component in the model were crop properties, chaff feedrate, and total M.O.G. feedrate. In order to predict losses on each component, it was necessary to develop relationships which described cleaning and.walker loss as a function of feedrates and crop properties. The performance curves (Figures 2 and 3) which describe percentage of grain lost versus feedrate were developed using logarithmically transformed grain loss in a linear regression on feedrate. The form.of the relationship was: Loss - a * exp((b)(FR)) ....................................... [28] where Loss - percentage of grain lost a - estimated regression coefficient b - estimated regression coefficient FR - M.O.G feedrate (t/h) Relationships were developed using multiple regression to describe cleaning and straw walker performance as functions of the performance curve regression coefficients. For example, a regression analysis using the "a" coefficients of all cleaning performance curves as the dependent variable and grain and chaff properties as the independent variables was performed. The resulting equation described the intercept as a function 81 of properties. Likewise, a similar regression was performed on the "b" coefficients of all cleaner curves. Both the intercept and the slope of a cleaner curve can be predicted as functions of crop properties. Loss for any feedrate can then be predicted. Equations of the following form were produced: b b aij - bbxil 1x12 2... xikbk ................................. [29] bij - boxilblxizbz... xikbk ................................. [30] where bij - estimated regression coefficients xi - crop properties i - 1,2, ..., n observations j - 1,2, ..., k independent variables Straw'walker equations of the same form were also produced. The coefficients and statistics for the cleaner and walker loss equations used in the model are found in Tables 18 and 19. The equations enabled the prediction of grain loss as a function of crop properties and feedrate. For a unique set of crop properties, a unique performance curve was described based on the predicted cofficients. Cleaner loss was calculated in the model using the chaff feedrate (FR) from.the feeding and cutting component (Eq. 27) the following fonm: ((b (CPI) c Straw walker loss was calculated using the total M.O.G. feedrate (FR) cleaning loss - ac exp ............................... [31] from the feeding and cutting component (Eq. 26) in an equation of the following fonm: ((b (FR)) walker loss - as exp s ................................. [32] 82 TABLE18 EQUATION mEFFICIENTS USED TO PREDICT CLEANER LOSS IN SIMULATION IDDEL. PARTIAL F-RATIOS ARE LISTED AS GENERATED BY STEPWISE REGRESSION. 'a' Coefficient Statistics Equation Partial Property Coefficients F-Ratio Grain Angle of Repose 3.76 18.1 Chaff Mean Length 3.31 3.7 Chaff Coefficient of 9.0 9.26 Friction Constant 3.34 E+6 NOTE: IModel based on 23 observations. Adjusted R—square is 0.64. 'b‘ Coefficient Statistics Equation Partial Property coefficients F-Ratio Grain Density -5.03 7.01 Chaff Coefficient of —3.41 6.09 Friction Constant 9.18 E-12 mm: deel based on 23 observations. Adjusted R-square is 0.32. *Grain Loss = a*exp(b*chaff feedrate) 83 'BSBLEl9 EQUATION COEFFICIENTS USED TO PREDICT WALKER 1058 IN SIMULATION DDDEL. PARTIAL F-RATIOS ARE LISTED AS GENERATED BY STEPWISE REERESSION. 'a ' Term Statistics quation Partial F-Ratio Property Coefficients as Variable Entered Model Grain Density 4.44 15.1 Constant 1.80 E45 101E: Model based on 34 observations. Adjusted R-square is 0.34. ' b' Term Statistics Equation Partial F-Ratio Property Coefficients as Variable Entered Model Grain Density -2.24 16.1 Straw Modulus -0.67 2.1 Straw Density 0.61 2.6 Constant 0.46 E+3 m'I'E: Model based on 34 observations. Adjusted R-square is 0.29. * .1055 = a*exp(b total M.O.G. feedrate) 84 6.7 Total Loss Total losses can be calculated by adding the loss from the walker and the cleaner for each simulated groundspeed., 6.8 Random variable Generation AIrandom.variable generator was implemented to introduce variation into the model. Specifically, crop yield and the crop properties were treated as random variables in the simulation. The inverse transformation method with piecewise piecewise approximation was used to code the Gaussian generator as described by (Manetsch and Park, 1985). Inputs to the generator to provide a normal distribution were the value of each property and variation expressed as a percent of the property. 6.9 Simulation Results The relationships in the model have been checked for mathematical correctness. The model can be validated by plotting predicted feedrates versus actual feedrates used to develop the initial equations. Figure 16 shows the relationship of simulated chaff feedrates at 0.5 percent cleaner loss versus actual chaff feedrates at 0.5 percent cleaner loss. An R square of 0.62 was calculated. Figure 17 shows the relationship of simulated total M.O.G. feedrate at 1.0 percent walker loss to total M.OuG. feedrate at 1.0 percent walker loss. The simulated values explained 70.0 percent of the variation in walker performance. .A sensitivity analysis using the data in Table 20 was performed by holding all but one of the properties at its mean level while varying one property from a value equal to plus one standard deviation to minus one standard deviation from its mean value. 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VN. 6 :0« .N« :V« emu 1: '9'0 auuaaud 0°; an uzaapoud 3391 APPENDIX C Scatter Plots of Crop Properties Versus Crop Moisture 129 .szmommm. mmaemHo: zeeeo mammm> .ms\mx. wenmzmo zHemo mo poem emeaeom mm MMDUHm auCOULOQ- ULZUUAO! CHDLD mw om .m. o. m — — p — \L .oco * * * e e" * * ... fl * * ...... * 1...... .2: * * * t * us * * * i * *srw * * a * * a * .weiw & e e .u s. ..Ir foam * s at * * * u .. _. * * *9 Jr .2 ’ room * * .ooo. Kignuoa UIDJQ (c w/bn) 130 fay—mama: ".559ng anu mag—g Ammaomn: among mo mauzc 2:30 MO San mEB .ms\mx. weemzmo mmezo mo poem mmsseom aw mmDUHh .ucoocon. ornament ueozu mm om m. o« r p b — * «*1, on * e h“ ..e * * * * * * 9* * fl 1 * .._ 3.... * n.:.n..~...._ .....n flu ... e .3 * ** * .* * ea 1w * o * i is. * * t i * * f ** * e; * * * .om *.* * s * e * .cm * Ton .oo (: w/Bn) ‘zzcu-o :rouo 133 mm Amamdm Mme Bzmummmv mMDBmHOZ whflzu mammm> “max. mDQDOOZ NEHAHmHmmmumzoo mmdxu m0 Boga “uhbflom we MMDUHm .ucouuon. uLaunaox ueocu ON mm 10 1m wow w«« #m« 1m« Inlnpon Kaunas183'adwoo 65°HO (0d!) 134 mm “mamdm wan Ezmummmv wdDBmHOZ mmdmo mammm> ZOHBUHMm ho EZMHUHthOO hhfimo ho 904m “HBECUm new 5:55p .ucoucun. unaumqoz eeoco om mu . _ ** 1m~d rnmd 3m... Ln... .nn.o fund Tom... 3:. 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P L — — _ m * Tau * * * * £1 * i *m * *1. ....— ..”Mie.“ a“ * * tn“ * ** ;z** A. * 133' e.* * * * .w ** i i * s * * e * wow «i * e * .nm 1on (c w/On) Kai-Uta noazs 138 Amnmdm wan Ezmummmv NMDBmHOZ 3¢m9m mammm> Ammxv mDQDDOZ wfimanmmmmmxmzou Sdmbm no mmDuHh .ucouron. monument rotum om 0v on ON Du bl]. _ _ _ * *.w i * *_w * .1 * * ** * i it! * * ~* 1" ... ..u ...." i * i * * i i i * * i i # rn.« to.N rm.~ r°.n 1m.n IfilhpOl' ‘3|'||q1llIDJ¢hH°3 nuaazs (°dfl) 139 Amnmdm Nan Ezmummmv NKDEmHOZ ZCMBW mam¢m> ZOHBUHmh m0 Ezmnonhmmou 3-9 THEN SS-COEFNNCGNLZ) 930' PRED(N)-Y5 940' W+l 950’ NEXT N 958' calculate ”a" and "b” terms for cleaner loss and walker loss 959' 960 CLAr-CROHMJMWASS),2)“3.76*CROP(NUM(PASS),6)“3.31* CRDP(NUM(PASS),5)‘8.03*3.34E-06 970 CLE-CROP(NUM(PASS),3)‘-5.03*CROP(NUM(PASS),5)‘—3.4l*9.18E+12 980 SLAPCROP(NUM(RASS),3)‘4.438*1.79GE-15 990 SLB-CROP(NUM(PASS),3)‘—2.24*CROP(NUM(PASS),8)“-.674* 9 CROP(NUM(PASS),9)‘.614*460268! 91' 995' calculate losses 996' 1000 CLOSS(CQNI,LOOPS)-CIA*ED{P(CFEED(C(XJNT,LOOPS)*CLB) 1001 IF CIDSS(CCIJNT,LOOPS)>-100 'I'HEN CIOSS(COUNT,LOOPS)-100 1005 WLOSS(C(1NT,ImPS)-SLA*ED{P(MFEED(COUNT,WPS)*SLB) 1006 IF WIOSS(CGJNT,LOOPS)>-100 THEN WLOSS(COUNT,LOOPS)-100 1020 HDSS(CGM,LmPS)-WIOSS(C(IJNT,LOOPS)+CIOSS(CCXM,LmPS) 1021 IF TLDSS(CC1NI‘,I£DPS)>-100 THEN TLOSS(OQJNT,LOOPS)-100 1330 1340 1350 1360 1361' 1370 1371' 1380 1390 1400 1410 1420 1430 1440 143 IF CLOSS(C(1N1',LOOPS)<0! m CLOSS(COtNr,IOOPS)-lE—33 IF‘WLOSS(COUNT,LOOPS)<0! THEN WDOSS(OOUNT,LOOPS)-1E-33 IF TLOSS(COUNT,DOOPS)<0! THEN TLOSS(OOUNT,LOOPS)-lE-33 GCOUNTHCOUNT IF MINT )1 THEN 60508 3100 GMOSTAvO-CROP(NUM(PASS),1)+GMOSTAVG: .MFEEDAVG-MFEED(COUNT,LOOPS)+MFEEDAVG GANRAVG—CROP(NUM(PASS),2)+GANRAVG: CFEEDAVG-CFEED(OOUNT,LOOPS)+CFEEDAVG GDENAVG-CROP(NUM(RASS),3)+GDENAVG CMOSTAVG-CROP(NUM(EASS),4)+CMOSTAVG CFRICTAVG-CROP(NUM(PASS),5)+CFRICTAVG CMLNTGAVG-CROP(NUM(PASS),6)+CMLNTGAVG SMOSTAVG-CROP(NUM(PASS),7)+SMOSTAVG SMODAVG-CROP(NUM(PASS),8)+SMODAVG SDENAVG-CROP(NUM(PASS),9)+SDENAVG GMDGAVG-GMOG+GMOGAVG¢CMOGAVG-CMOG+CMOGAVG:COUNTbCOUNT+1 NEXT SPEED OOUNThOOUNTbl GOOUNTbCOUNT 'pass count to plot INFO(LOOPS,10)-GMOGAVG/OOUNT:INFO(LOOPS,11)-CMOGAVG/OOUNT INEO(DOOPS,1)-GMOSTANG/COUNT:INFO(DOOPS,2)-GANRAVG/OOUNT INPO(LOOPS,3)-GDENAVG/COUNT INFO(LOOPS,4)-CMOSTAvG/COUNT INFO(DOOPS,5)-CFRICTAVG/OOUNT INEO(DOOPS,6)-CMLNTGAVG/OOUNT INEO(LOOPS,7)-SMOSTAVG/COUNT INFO(LOOPS,8)-SMODAVO/COUNT INFO(LOOPS,9)-SDENAVG/OOUNT MFEEDAVG-MFEEDAVG/COUNT CFEEDAVG-CFEEDAVG/OOUNT INFO(LOOPS,12)-(-.69-BOC)/81C INEO(LOOPS,13)-Bow731w INFO(DOOPS,14)-(.69—80T)/BlT INFO(LOOPS,15)-R2C:INFO(DOOPS,16)-R2wzINEO(LOOPS,17)-R2T INFO(DOOPS,18)-BOC:INFO(LOOPS,19)-BOW}INFO(LOOPS,20)-BOT INFO( LOOPS, 21 )-BlC: INFO( LOOPS, 22 )-BlW: INFO( LOOPS, 23 )-Bl'1' INEO(DOOPS,24)-NUM(PASS) NEXT MOISTPASS,PASS CLS RETURN 'subroutine normal distribution FOR.J - 1 TO 100 x1-RND(1) FOR I - 1 TO 21 IF x1 < XX(I) THEN 1430 NEXT I YZ-(Xl-XX(I-l))*(YY(I)-YY(I-1))/KXX(I)-XX(I-1))+YY(I-1) Y3-Y5 + SS*Y2 1450 NEXT J 1460 1465' 1470 1475' 1480 RETURN 'subroutine least squares 82-0:B3-0:B4-0:B6-0:87-0:RB-0:BO-O:Bl-0 144 1490 FOR I-l '1!) COM 1500 B3-B3+RATE( I ) :B4-B4+LOSS( I ) 1510 86-86+RA'I'E( I ) ‘2 :BZ-BZ+RA'I‘E( I )*LOSS( I) 1520 R3-R3+LOSS(I)“2 1530 NEXT I 1540 Sl-B6-OOlNr*(B3/OaNr)‘2 1550 SZ-RB-CQNI‘HM/CGNTVZ 1560 87-83? 1570 88-82-B3*B4/C(1N1' 1580 W9-B6-B7/CCKN1‘ 1590 81-88/W9 1600 BO-B4/CCXM-Bl*(B3/C(XJNT) 1610 R4-Bz-s3*s4/ootm 1620 R5-(86—B7/CQM)*(R3-B4‘2/CCINI‘) :RSNm-( .0001/R4 ) “2:IF R5<-R5NEW THEN R2-.000001:GO'IO 1640 1625 RZ-R4/R5‘.5 1630 S3-SZ-Bl‘2*Sl 1640 'S4-SQR( (S3/(CGJNT-2) )) 1650 RETURN 1660 'parameter change subroutine 1661 CLS - 1662 IF 2$<>"m" THEN LOCATE 10,20:PRIN'I"'You have not selected any crop information”:LOCATE 11,20:FOR 1-1 '10 2000:NEXT I:CLS:RE'IURN 1690 RS-"OFF":VARIATIm$-"m" 'turn on random generator 1700 GOSUB 7500'go to et crop variation 1740 IF TEMP-13 m -"1 % WALKER LOSS" 1830 CLS 1840 RETURN 1845' 1850 'plotting routine 1860 ' 1870 XRANGE-300/(XMAX—XMIN) 1880 YRANGE-lZO/(YMAX-YMIN) 1890 PSET (330,130) 1900 DRAW "u120 r300 d120 1300" 1910 FOR I-l 'IO GCCXJNT 1920 X(I )-330+ABS( (ME*()U‘IIN-RATE( I) ) )) 1930 Y(I)-130-ABS((YRAmE*(YMIN-LOSS(I)))) 1940 NEXT I 1950 FOR I-GRAPH '10 GCGJNT 1960 IF X(I)>638 6010 2030 1970 IF Y(I)<20 GOTO 2030 1980 IF Y(I)<0 6010 2030 1990 IF X(I)<0 0010 2030 2000 PSEI' (X(1),Y(I)) 2010 IF G$-"off" THEN GOSUB 3930 'get symbol to plot 2020 IF (SS-"on” 'mEN 60808 4000 2030 NEXT I 2040 Y-10:x-330 2041 XLABEL-40 2045 FOR LABEL-MIN '10 W STEP (XMAX-XMIN)/5 2046 LOCATE 18,}{LABELzPRINT USING"###.##";LABEL 2047 HABEIPXIABEL-fl 2048 NEXT LABEL 2049 YIABEL-Z 2050 FOR LABEL IIYMAX '10 YMIN STEP - (W-YMIN)/S 2060 IOCATE YIABEL,34:PRINT USIINBWN'JV'HABEL 2070 2000 2090 2100 2110 2120 2130 2140 2150 2160 2170 2100 2190 2200 2210 2220 2350 2360 2370 2380 2390 2400 2410 2420 2430 2440 2450 2460 2470 2480 2490 145 YIABEL-YIABWB NEXT LABEL PSET(330,130) FOR I-1 'IO 5:x-(300/S)+X:PSET(K,130):DRAW"08":NEXT I PSET(330,10) FOR 1-1 '10 5 Yh(120/5)+Y:PSET(330,Y):DRNW”R15":NEKT I RETURN 'subroutine to toggle between curves GCGNr-O:GRAPH-1:NEWLOOPS-LOOPS:G$-"off" CLS LOCATE 10,20:PRINT'"1he first five or less curves are plotted LOCATE 11,20:PRINT"by default. To select specific curves LOCATE 12,20:PRINr"strike the space bar. To select the LOCATE 13,20:PRINT"default condition strike any key Its-11mm IF mum-0 (3010 2220 IF K$-” ' THEN 60808 5030 CLS IF 'IOG>3 'I'HEN TOG-1 IF TOG-1 THEN LS-"CLEANING LOSS CURVE" IF TOG-2 THEN L$-"WALKER LOSS CURVE” IF TOG-3 mm 1.5-”m1. Loss CURVE" GCQJNT-CCXJNT IF NEWLOOPS>5 THEN WPS-S IF 'IOG><1 THEN com 2440 FOR 00-1 TO NEWLOOPS NN-CURVE(OO) FOR J-l ‘10 CGNI‘:RATE(J)-CFEED(J,NN):LOSS(J)-(CLOSS(J,IW)):NEXT J XMIN-O :XMAX-S :YMIN-O :YMAX-S 60508 1850 NEXT 00 60508 2930 CS-"CHAFF FEEDRATE (TVH)" 60608 5200 'label x-axis Cs-"CLEANER LOSS %" 60808 5230 'label y—axis IF 'IOG><2 THEN GOTO 2560 FOR 00-1 '10 WPS NN-CURVE(OO) FOR J-l 'IO CGNT:RATE(J)-MFEED(J,IW):IOSS(J)-(WLOSS(J,M)):NEXT J MAX-15:YMAX-5:MN-0:YMIN-0 60508 1850 2500 NEXT CD 2510 2520 2530 2540 2550 2560 2570 2580 2590 2595 2600 2610 60808 2930 C$-"I'DG FEEDRATE (T/H)" 60508 5200 '1abel x-axis C$-"WALKER LOSS %" GOSUB 5230 'label y-axis IF 'IOG <3 THEN GOTO 2680 FOR 00-1 '10 WPS m-CLJRVE(OO) FOR J-1 '10 CONT:LDSS(J)-CIDSS(J,I‘N)+WLDSS(J,NN):NEXT J FOR J-l '10 C(1JNT:RATE(J)-SPEED(J,M\I):NEDCT J XMAX-S : YMAX-S:JO41N-0 : YMIN-O (30508 1850 2620 NEXT CD 146 2630 (13508 2930 2640 CS-"ground speed (In/h)” 2650 00503 5200 'label x-axis 2660 cs-"m LOSS a" 2670 00503 5230 'label y-axis 2680 LOCATE 1,50:PRINT L$ 2690 TOG-m1 2700 LOCATE 22,1:PRINT"strike the space bar to exit plot" 2710 LOCATE 23,1:PRINT”strike any other key to continue” 2720 KS-INKEY$ 2730 IF mum-o com 2720 2740 IF K$<>" " 6010 2250 2750 CLS 2760 RETURN 2765 ' 2770 'graph all curves on same screen 2771' 2780 IF GS-"CN" THEN 6010 2850 2790 PSET(20,10):GOSUB 2890 2800 PSET(230,10):GOSUB 2890 2810 PSET(440,10):GOSUB 2890 2820 LOCATE 1,10:PRINT"CLEANER LOSS CURVE" 2830 LOCATE 1,35:PRINT"WALKER LOSS CURVE" 2840 LOCATE 1,60:PRIN1‘"'IOI‘AL LOSS CURVE" 2850 PSEI'(X,Y) 2860 DRAW”E2 G4 82 F2 H4” 2870 GS-"ON" 2880 RETURN 2890 DRAW"D100 R190 0100 L190" 2900 DRAWMDZO R190 D20 L190 D20 R190 D20 L190 080" 2910 DRAW”R38 D100 R38 U100 R38 0100 R38 0100" 2920 RETURN 2930 ' print info about each curve at plot edge 2940 PSET(180,3) 2950 YPOS-O 2960 LOCATE 1,1 2970 FOR INN-l '10 LOOPS 2980 60508 3930 'get symbol and plot 2990 GOSUB 4980 'GET CROP LABEL 3000 PRINT HS 3010 PRINT "GRAIN MISTURE - "3INEO(NN, 1) 3020 11" 106-1 THEN PRINT”CAPACITY :- "3INFO(NI\J,12)3"(T/H)" 3030 IF TOG-2 mm PRINT ”CAPACITY - "3INF'O(M\I,13)3"(T/H)" 3040 IF TOG-3 'I'HEN PRINT"SP-m - "3INFO(LOOPS,14)3"(PVH)" 3050 YPOS-YPOS+32 3060 PSET(180,YPOS+3) 3095' 3100 ' subroutine calculates statistics for each loss curve 3101 ' ”a", "b" and r-square 3102' 3110 FOR Jul '10 CGJNT: RATE(J)-CFEED(J,LOOPS):LOSS(J)-LOG(CLOSS(J,LOOPS)): NEXT J 3120 GOSUB 1470:80C-80:81C-81 :R2C-R2 147 3130 FOR J-l ‘10 CQNI': RATE(J)-MFEED(J,LOOPS):LOSS(J)-LOG(WLOSS(J,LOOPS)): NEXT J 3140 GOSUB 1470:80w-BO:81W-81:RZW-R2 3150 FOR 3.1 '10 CLINT: LOSS(J)-LOG(WLOSS(J,LmPS)+CLOSS(J,L(X)PS)): NEXT J 3155 FOR J-l '10 comm RATE(J)-SPEED(J,LmPS): NEXT J 3160 CXJSUB 1470:80‘Ii-BO:BlTbBl:R2T-R2 3170 RETURN 3175' 3180 'subroutine to plot properties and loss information 3185 ' 3190 CLS:G$-"on" 3200 LOCATE 1,1:PRINT"1 - grain moisture" 3210 PRINT”2 - grain angle of repose" 3220 PRINT"3 - grain density" 3230 PRINT” - chaff moisture" 3240 PRINT”5 - chaff friction” 3250 PRINT“6 - chaff mean length" 3260 PRINT” - straw moisture" 3270 PRINT"8 - straw modulus" 3280 PRINT”9 - straw density” 3290 PRINT"10 - grain to mog ratio" 3300 PRINT"11 - chaff to mog ratio" 3310 PRINT”12 - chaff feedrate at 1/2 % loss" 3320 PRINT”13 - mog feedrate at 1 % loss" 3330 PRINT"14 - mog feedrate at 2 % total loss" 3340 LOCATE 1,40:PRIN'1"'select the x—axis variable" 3350 LOCATE 2,40:INPUT XVAR 3360 LOCATE 4,40:PRIN'r"select the y-axis variable" 3370 LOCATE 6,40:INPUT YVAR 3380 XMAx-9999I:YMAx-—99991 3390 MN-99999!:YMIN-99999! 3400 FOR I-l TO LOOPS 3410 IF INFO(I,XVAR)>XMAX THEN MAX-INFO(I,XVAR) 3420 IF INFO(I,XVAR)YMAX THEN YMAX-INFO(I,YVAR) 3440 IF INEO(I,YVAR)LmPS THEN RAT-O CLEANII‘B LOSS WALKER LOSS 7,21:PRINT USIm "3.##”3IN1‘0(CYCLE,15) 7,41:PRINT USIhK; "3.4%"3INFO(CYCLE,16) LOCATE 7,66:PRINT USINC§"#.§#"3INFO(CYCLE,17) 9,1:PRINT "INTERCEPT 'a'” 9. 9. 9. 11 20:93:13 USING "##.“““‘"3INFO(CYCLE,18) 40:PRINT USING”##.H““":INFO(CYCLE,19) 65:PRINT USING"##.##“‘“‘"3INFO(CYCLE,2O) ,1:PRINT"SLOPE 'b'" 11,20:PRINT USING"##.##““";INF0(CYCLE,21) 11,40:PRINT usxm"##.##““":1NF0(CYCLE,22) 11,65:PRINT USING"##.##““";INF0(CYCLE,23) 13,1:PRINT "FEEDRATE (t/h)” 14,1 :PRINT” ( see below) " 13,20:PRINT USING"##.*#";INFO(CYCLE,12) 13,41:PRINT USING "##.##";INFO(CYCLE,13) 13,65:PRINT USING"##.##"3INFO(CYCLE,14) 16,1:PRINT"GRAIN ADELE OF REPOSE -" PRINT"GRAIN DENSITY -" PRINT "CHAFF MOISTURE -" PRINT"CHAFF FRICTION] -" PRINT "CHAFF LENGTH II" 16,40:PRINT "STRAW MIST‘URE -" 17,40:PRINT"STRAW MOUJLUS II" 18,40:PRINT "STRAW DENSITY -” 19,40:PRINT "AVG. GRAINflm -" 20,40:PRINT "AVG. CHAFFdW II" 16 , 28 :PRINT USING 17 , 28 : PRINT USING 18 , 28 : PRINT USIm 19,28:PRINT USING 20,28:PRINT USING 16,65:PRINT USING 17,65:PRINT USING 18 , 55 : PRINT 081m 19 ,65:PRINT USING 20,65:PRINT USING "###. ”### ° "###. "###. "###. "###. "###. "###. "###. ”###. ###";INFO(CYCLE.2) ###";INFO(CYCLE,3) ###";INFO(CYCLE,4) ###":INFO(CYCLE,S) ###";INFO(CYCLE,6) ###":INFO(CYCLE,7) ###”3INF0(CYCLE,8) ###"31NFO(CYCLE,9) ###";INFO(CYCLE,10) ###"3INFO(CYCLE,11) 22,1:PRINT"cleaner - chaff feedrate at 1/2 % cleaner loss - mog feedrate" 23,1:PRINT"at 1 % walker loss total - mog feedrate at 2 % total loss" CYCLE-CYCLE+1 IF CYCLE>LmPS THEN CYCLE-1 KS-INKEY$ IF W(K$)-0 THEN 4530 IF KS-"P" THEN GOSUB 4600 I WEND 4580 SCREEN 2 4590 RETURN 4591' 4600 4610 150 'subroutine to dump screen contents to printer I 4620 WIDTH "LPT1:" ,80 4630 4640 4650 4660 4670 4680 4690 4700 4701 4702 4701 4702 4703 4705 4710 4711 4712 4720 4721 4722 4730 4731 4732 4740 4741 4742 4750 4751 4752 4760 4761 4762 4770 4771 4772 4776 4777 4778 4780 4800' 4801' 4802' 4890 4900 4910 4920 4930 4940 4950 4951 4970 4975' 4976' FOR m I 1 T0 24 FOR COL - 1 TO 80 CHAR-SCREEI‘J(RGV,COL) IF CHAR-0 THEN CHAR-32 LPRINT CHR$(CHAR)3 NEXT COL,“ W I ' subroutine to calculate properties as functions of moisture I IF RS-"OFF" THEN YS-CROP(NUM(PASS),l):SS-CROP(NUM(RASS),1)*GMOSTVAR IF RS-"OFF' THEN’MOISTEMP-Y5:DEVTEMP-SS IF RS-"ON" THEN Y5-MOISTEMP:SS-DEVTEMP IF VARIATIONS-"ON" THEN GOSUB 1370:CROP(NUM(RASS),1)-Y3 CROP(NUM(PASS),4)-CROP(NUM(PASS),1)‘OOEFF(3,2)*OOEFF(3,1) Y5-CROP(NUM(RASS),4):SS-CROP(NUM(RASS),4)*CMOSTVAR IF VARIATICNS-"m" THEN GOSUB 1370:CROP(NUM(PASS),4)-Y3 CROP(NUM(PASS),7)-CROP(NUM(PASS),1)‘OOEFF(5,2)*OOEFF(5,1) Y5-CROP(NUM(RASS),7):S5-CROP(NUM(RASS),7)*SMOSTVAR IF‘VARIATIONS-"ONM THEN GOSUB 1370:CROP(NUM(RASS),7)-Y3 CROP(NUM(PASS),2)—CROP(NUM(PASS),1)‘OOEFF(1,2)*OOEFF(1,1) Y5-CROP(NUM(PASS),2):SS-CROP(NUM(PASS),2)*GANGVAR IF VARIATICNS-"CN" THEN GOSUB 1370:CROP(NUM(PASS),2)-Y3 CROP(NUM(PASS),3)-CROP(NUM(PASS),1)‘OOEFF(2,2)*OOEFF(2,1) Y5-CROP(NUM(RASS),3):SS-CROP(NUM(RASS),3)*GDENVAR IF VARIATIai$-”CN" THEN GOSUB l370:CROP(NUM(PASS).3)-Y3 CROP(NUM(RASS),5)-CROP(NUM(PASS),4)‘OOEFF(4,2)*OOEFF(4,1) Y5-CROP(NUM(RASS),5):SS-CROP(NUM(RASS),5)*CFRICTVAR IF VARIATIONS-"ON" THEN GOSUB 1370:CROP(NUM(RASS),5)-Y3 CROP(NUM(PASS),8)-CROP(NUM(PASS),7)‘OOEFF(7,2)*OOEFF(7,1) Y5-CROP(NUM(RASS),8):SS-CROP(NUM(PASS),8)*SMODVAR IF VARIATICN$-"(N" THEN GOSUB 1370:CROP(NUM(PASS),8)-Y3 CROP(NUM(PASS),9)-CROP(NUM(RASS),7)“COEFF(6,2)*OOEFF(6,1) Y5-CROP(NUM(PASS),9):SS-CROP(NUM(PASS),9)*SDENVAR IF‘VARIATION$-"ON" THEN GOSUB 1370:CROP(NUM(RASS),9)-Y3 CROP(NUM(RASS),6)-CROP(NUM(PASS),4)‘OOEFF(8,2)*OOEFF(8,1) Y5-CROP(NUM(RASS),6):SS-CROP(NUM(RASS),6)*CMLNTGMAR IF VARIATIONS-"0N“ THEN GOSUB 1370:CROP(NUM(RASS),6)-Y3 RETURN adjust each property curve for moisture reference COEFF(1,1)-CROP(NUM(RASS),2)/GMOST“COEFF(1,2) COEFF(2,1)-CROP(NUM(RASS),3)/GNOST‘COEFF(2,2) OOEFF(3,1)-CROP(NUM(PASS),4)/GMOST“COEFF(3,2) COEFF(4,1)-CROP(NUM(RASS),5)/CROP(NUM(RASS),4)“OOEFF(4,2) OOEFF(S,1)-CROP(NUM(RASS),7)/GMOST“OOEFF(5,2) COEFF(6,1)-CROP(NUM(RASS),9)/CROP(NUM(PASS),7)‘OOEFF(6,2) COEFF(7,1)-CROP(NUM(RASS),8)/CROP(NUM(RASS),7)“OOEFF(7,2)‘ OOEFF(8,1)-CROP(NUM(RASS),6)/CROP(NUM(RASS),4)‘OOEFF(8,2) RETURN subroutine to assign labels used to plot loss curves 151 4977' 4980 It Mum-1 mm HS-"CALIMNIA mm" 4990 IF NUMMU-Z THEN H$-"PDRTH DAKOTA WHEAT” 5000 IF NUM(NN)-3 THEN H$-"M)RTH DAKOTA BARLEY" 5010 IF whom-4 THEN [is-"Special Blend" 5020 RETURN 5021' 5022' subroutine to select loss curves to plot 5023' 5030 CLS:LOCATE 5,20:PRINT LOOPS3" are available to choose from” 5040 LOCATE lO,20:PRIN'1"'You may plot (1-5) curves on the same plot" 5050 LOCATE ll,21:INPUI"'Enter the number of curves you wish to , plot"3NEWIOOPS 5060 IF NFWLOOPS <- S 6010 5110 5070 BEEP:CLS 5080 LOCATE 10,20:PRINT"you choose more than 5 curves 5090 LOCATE 11,20:PRIN1"'try again (1-5) 5100 6010 5030 5110 CLS 5120 FOR NN-l '10 LOOPS 5130 (30508 4980 ' go get crop identification label 5140 PRINT "Curve (”3NN3") "33$,” at"3INFO(NN,1) 5150 NEXT NN 5160 FOR I-l '10 WPS 5170 LOCATE 10,40:INPU'1"'mter curve number",CURVE(I) 5180 NEXT I 5190 RETURN 5191' 5200 '1abel x—axis subroutine 5205 ' 5210 room's 20,42+(37-LEN(C$))/2:PRINT cs 5220 RETURN 5225' 5230 'label y-axis subroutine 5235 ' 5240 As-"":L-Lm(C$):L1-(18-L)/2 5250 FOR x-1 '10 L1:A$-A$+"":NEXT x 5260 A$nA$+C$ 5270 FOR x-mes) 'IO 10:A$-A$+"":chr x 5280 FOR X-3 '10 20 5290 LOCATE X,33:PRINT MID$(A$.x-2,1) 5300 NEXT X 5310 RETURN 5315 ' 233g 'crop and grain moisture selection routine 3 F 5321 Z$-"(N":CLS 5340 LOCATE 10,15:PRINT”You may select a maximum of 4 crops per simulation" 5350 LOCATE 11,15:PRIN'1"‘and a maximum of 5 moisture levels per crop." 5360 LOCATE 18,25:PRIN'1' "strike any key to continue" 5370 K$-INKEY$ 5380 IF LEN(K$)-0 6010 5370 5381 cls 5400 LOCATE 7,20:INPUT"H(M MANY CROPS DO Y0] WANT T0 USE"3CROPS 5410 FOR 1-1 '10 CROPS 5420 00508 5560 'display crop codes for the user 152 5430 LOCATE 9,20:INPUT"FN1‘ER THE CROP"3MJM(I) 5440 IF MJM(I)>4 THEN BEEP:CLS:LOCATE 9,20:PRINT"there are 4 crops and no more":LOCATE 10,20:INPUT"ENTER THE CROP AGAIN"3MJM(I) 5450 LOCATE 11,20:INPUT"ENTER THE CROP YIELD"3YIELD(I) 5460 IF NLM(I)-4 THEN GOSUB 5780 'enter crop properties 5470 CLS:LOCATE 7,20:mwr"aow MANY MISTURE LEVELS "3 FDISTURES 5480 FOR J-l T0 MISTURES 5490 LOCATE 9+J,20:INPUT"ENTER THE CEAIN [DISTURE LEVEL DESIRED" mors'm I ,J ) 5500 NEXT J 5510 LOCATE 11+J,20:INPUT"ENTER THE REFERENCE FDISTURE FOR CURVE ADJUSTMENT" :noxs'rum 5520 NEXT I 5530 a. 5540 CLS 5550 RETURN 5551' 5552' subroutine to display available crop data 5553' 5560 CLS 5570 LOCATE 2,30:PRINT "(1)-california wheat” 5580 LOCATE 3,30:PRINT "(2)-north dakota barley" 5590 LOCATE 4,30:PRINT "(3)-north dakota wheat" 5600 LOCATE 5,30:PRINT "(4)-you describe crop" 5610 RETURN 5611' 5612' property labels 5613' 5620 IF TEMP-1 THEN CS-"GRAIN MISTURE" 5630 IF TEMP-2 THEN C$-"AI‘BLE OF REPOSE" 5640 IF TEMP-3 THEN C$-"GRAIN DWSITY" 5650 IF TEMP-4 THEN C$-"CHAFF PDISTURE” 5660 IF TEMP-5 THEN C$-"CHAFF FRICTION!” 5670 IF TEMP-6 THEN C$-"CHAFF M" 5680 IF TEMP-7 TED! CS-"ST'RAW POISTURE" 5690 IF TEMP-8 THEN CS-"STRAW MUS" 5700 IF TEMP-9 THEN CS-"STRAW DENSITY" 5710 IF TEMP-10 THEN C$-"GRAIN:MOG RATIO" 5720 IF TEMP-11 THEN C$-"CHAFF:MOG RATIO" 5730 IF TEMP-12 THEN CS-"l/Z % CLEANER LOSS" 5740 IF TEMP-13 THEN C$-"1 % WKER LOSS" 5750 IF map-14 THEN Cs-"z % m LOSS" 5770 RETURN 5771' |3772' subroutine to enter crop properties 773' 5780 CLS:LOCATE 2,20:PRINT"CROP PROPERTIES RCMTINE" 5790 FOR TEMP-1 '10 11 5800 60808 5620 5810 LOCATE TmP+5,15:PRINT Cs 5820 NEXT TEMP 5830 LOCATE 6,40:INPUT "* ",CROP(NUM(I),1) 5840 LOCATE 7,40:INPUT "* ",CROP(NUM(I),2) 5850 LOCATE 8,40:INHJT ”* ",CROP(NUM(I),3) 5860 LOCATE 9,40:INPUT "* ",CROP(NUM(I),4) 5870 LOCATE 10,40:INPUT "* ",CROP(NUM(I),5) 5880 LOCATE 11,40:INPU'1‘ "* ",CROP(MJM(I),6) 153 5890 LOCATE 12,40:INPUT "* ",CROP(NUM(I),7) 5900 LOCATE 13,40:INPU‘1"'* ”,CROP(NUM(I),8) 5910 LOCATE 14,40:INPUT"* ”,CROP(NUM(I),9) 5920 LOCATE 15,40:INPUT"* ”,CROP(MJM(I),10) 2323 mm 16,40:INPU'1"'* ”,CROP(NUM(I),12) 5941' 3322' subroutine to input variation for each crop property 3' 7500 CLS:LOCATE 2,20:PRINT”CROP VARIATION! RCXJTINE" 7510 FOR TEMP-1 '10 9 7520 GOSUB 5620 8000 LOCATE TEMP-+5,15:PRINT CS 8010 NEXT TEMP 8020 LOCATE 6,40:INPUT "* ",G‘OSTVAR 8030 LOCATE 7,40:INPUT "* ”,GANGVAR 8040 LOCATE 8,40:INPUT "* ”,GDFNVAR 8050 LOCATE 9,40:INPUT "* ”,CMOSTVAR 8060 LOCATE 10,40:INPUT "* ”,CFRICTVAR 8070 LOCATE 11,40:INPUT "* ”,CMLN'IOVAR 8080 LOCATE 12,40:INPUT "* ”,SMOSTVAR 8090 LOCATE 13,40:INPUT"* ”,SMODVAR 8095 LOCATE 14,40:INPU'1"'* ”,SDENVAR 8400 RETURN 9000 CLOSE:END 9001 RETURN APPENDIX E Combine Simulation Interactive Session SIMULATION INSTRUCTIONS AND SAMPLE MAM QJTPUTS The simulation program was written in Microsoft Basic and implemented on an IBM-compatible micro—computer which utilized an Intel 8086 central processing unit. The simulation of a single loss curve required approximately five minutes to complete when the program was executed as interpreted code. The execution time was reduced to approximately one minute per curve simulation by compiling and linking the source code into a single executable module. The documentation of the program is contained within the source code. The major variables are explained in a block of comment lines at the beginning of the program. Function keys (F1 - F7) are used to select program.options from a menu display. The inputs to the program.are a series of crop properties and the random variation of each property expressed as a percentage, grain to M.O.G. and chaff to M.O.G. ratios, ground speed (mph), header width (feet), and crop yield (bushels). The simulation provides options to display cleaner, strawaalker, and total loss curves and a means to construct scatter plots of each property or machine parameter expressed as a function of another property or machine parameter. The following text and figures describe the execution of the combine simulation program, Throughout the instruction, input from the user‘will be highlighted. Some inputs must be terminated by pressing the RETURN key which is denoted as (RET). The instructions assume that you are already familiar with the MS-DOS operating system.and are able to boot the computer and begin the execution of a program. .A typical interactive session begins as follows: 154 155 1. Random Number Seed (-32768 to 32767) utter a Mr (RED 2. Wait for the following screen display: F1. select crop location F2. run simulation F3. plot loss curves F4. plot scatter plots of properties F5. set parameters for stochastic process F6. display historical data from simulation run F7. exit program 3. Press ftmction key F1. At this point it is necessary to select the number of machine performance curves to simulate and the crop properties values which describe a crop. This example will select one set of crop properties and three moisture conditions to simulate. The following message is displayed: You may select a maximum of 4 crops per simulation and a maximum of 5 moisture levels per crop. Strike any key to continue. Press any key. How many crops do you want to use? Enter 1 (REF) Select the crops you wish to use. Enter 4 (RED Note: You may choose crop properties which are representative of a California wheat crop, a North Dakota wheat crop, or a North Dakota barley crop. You may also elect to describe the crop by its properties. If you are unfamiliar with the range of crop property values reference Table 2 on page of Chapter IV. 156 Crop Properties Routine . Grain Moisture 12.00 Angle of Repose 20.00 Grain Density 775.00 Chaff Density 35.00 Chaff Friction 0.27 Chaff Length 6.50 Straw Moisture 12.00 Straw Modulus 2.20 Straw Density 19.00 GrainzMOG Ratio 1.39 Chaff:MOG Ratio 0.43 How'many moisture levels? Enter 3 Enter the grain moisture level desired? 12 Enter the grain moisture level desired? 13 Enter the grain moisture level desired? 14 Enter the reference moisture level? 12 4. It is now necessary to select the amount of variation for each crop as a percentage. Press F5. Crop variation Routine Grain Moisture 1.0 Angle of Repose 1.0 Grain Density 1.0 Chaff Moisture 1.0 Chaff Friction 1.0 Chaff Length 1.0 Straw Moisture 1.0 Straw Modulus 1.0 l 0 Straw Density Note: The simulation of cleaner performance appears most realistic when chaff and grain properties vary by approximately 5.0 percent. Likewise, walker performance appears most realistic when the properties in the walker loss equation vary by 10.0 percent. 157 5. Press F2 to simulate the performance of a combine harvester. 6. Press F3 to produce loss curves for the cleaner, the walker, or the total loss curve. Ybu will be prompted for the curves to display from a menu or select the first five performance curves. For this example press , default to display the performance curves for one crop at three moisture levels. Ybu may toggle between each type of curve by pressing the space bar. The display begins with cleaner curves. Press the space bar to display the walker curves. Each time you press the spacebar another set of curves is displayed. 7. Press F4 to create a scatter plot of one property versus another property or to plot the machine performance versus a property. For example, to plot grain density versus grain moisture, select the appropriate property from the display by entering the number of the property at the prompt(Figure 21). Enter a property. 1 Enter a property. 3 8. Press F6 to display a summary of the crop property mean values, the coefficients of the predicted loss curves, and the predicted feed rates. Figure 22 is an example of the statistics provided for each simulation run. For example, there are three such summaries created by this example set of program inputs. Press the spacebar to display the next summary. 9. Press F8 to exit the program.and return to DOS. 158 we. ZOHBUZDM nu ma. Dszmmmm wm zany—Um 20 DadammHO N>~SU was mmzfimdu mo mamzcxm ~==_L=°o :1 a“; aoswe ace .31amm e was: :2 :13 3 e3 3... m a: 9:...“ =51 ease Ea . e.” a: .2 s.“ a: a“... _ filuji m“ a a: 11 1 .3 x .... S.~ l 8.” 1_~ . I A; Us—J‘flczmm o—IOMM SE 3836 u Eggs . 8.“ 583.2 N 5:.on :25 was was 225.5 * 225 2.2238 159 N8— ZOHBUZDh mm NIB OZmemmm Mm zmmdum 20 ogmqmmmo ”EEOC mmoq ”EU—AC3 mo flam2¢xm on .5593 2:: mas—Em 8: 8.2 3.2 a: 2: a.” a... _ _ WK; 1 I was as mass. 84 8;.“ 8.” 86 so.“ ~==_a=°o a” my; gas“: aga ~x_g.m E.— :.a 3 as .3 a 2: 3:: X zcaxmm AOMM at: 3336 n 2888 38.2 ... “.2222. :35 x .22.: 25238 160 aEN—Lgm ”22.-0‘2 mo mausfimomm homo 039 “Oh mum: NIH. mBOZCmm ZOHBQO mama. vb uzummam >m zmauw 20 ogsmmmn 904m mgr—Rum we. ZOHEUZDM Hm mmDUHh among: mo HAQZG .-.m~ mq.m~ a¢.- ~a.od ¢~.a~ ¢m.¢d # em.” we.” ca.” -.¢ an.q aa.e uamczmm acme-a x ~—I\N ~==_a=°o a“ am; agm ~:_gam K h¢u== ¢_z=om_g¢o 161 ouuuooou 00! u noxami omv.o can.— Noa.aH o-.~ voc.a cp.~ oo+fldm.d oo+flcr.na ma.o mas.- . .(POF am! ZOHBUZDM @h are UZHmmumm an Dada—mm:— momfimHBflfim ZOHE§DZHm ho Bantam— Nm mmDUHm amen «duo» v N an ovuuooou 00! I gave» ones nexus) a A an I OOtuhh<=U .0>¢ I chu2u4m0 .0>‘ I uaumzmo 3¢MBm I MDJDOOZ 3¢mhm I HMDPMuO! B‘mfim NM.¢ HOIHNH.C oo+flv¢.nl oc.d mmcd maxe" mood uncounu a «\H as cumuooou wanna n nonmoHu ovv.o I ZBOZHJ bhflzu OFN.O I ZO~PUHGN hh¢20 'co.m I UNDPWHOZ hh‘zu HOG-mOQ I fiFHmZMO ZN‘GU OOF.ON I "momflz b0 ”£021 Zn‘flO ~3°H3 ”flaw 90.. A£\Hv Hh‘fiaflflh OG+MON.H .n. ”moan oo+flwm.ml .l. hhflOflflbZH co.fi MN‘fiOm M umOd OZuZ‘flQO flmDBmHOt z~¢m0 BZHUMEQ Nbvco.NH h‘ B¢fl=3 dulflOhnd‘U BIBLICXBRAPHY BIBLICXEAPHY Agricultural Engineers Yearbook. 1983. American Society of Agricultural Engineers. 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