As a state with rich forest resources and a good transportation system, Michigan is in a position to promote the use of woody biomass for bioenergy production. To achieve sustainable development in Michigan’s woody biomass supply chain, the goals this research were to: 1) to develop a cost prediction model in Excel using Visual Basic for Application (VBA) programming language; 2) to perform cradle-to-grave Life Cycle Assessment (LCA) to account for the GHG emissions, energy return on investment, and nutrient removal; and 3) to design an eco-efficient (Define) woody biomass supply chain with minimal logistic cost and GHG emissions in Michigan. Five woody biomass production systems were monitored to develop predictive regression equations for different harvesting machines and to predict the total production cost of woody biomass in Michigan. Based on the predictive machine productivity equations and machine hourly cost obtained from each studied system, a spreadsheet model was developed in Excel 2016 using VBA programming language. In order to better understand field storage of woody biomass, 5 studies were conducted to monitor the biomass quality (biomass Higher Heating Value (HHV) and biomass moisture content) change under different storage forms (wood logging residues piles and wood chips piles). The results indicated that storing woody biomass in logging residue pile could effectively reduce the biomass moisture content and maintain the HHV at a stable level. On the contrary, increases in moisture content were observed in all wood chips piles. Based on the above findings, an improved operations system structured with linear programming was developed for minimizing the total cost of woody biomass preprocessing, storage, and transportation. The operation details suggested by the improved operations system can be used as a guideline of real operations to achieve the lowest possible operations cost. To evaluate the total GHG emissions, energy return on investment and nutrient removal in each studied biomass production system, five cradle-to-grave LCAs were performed. Results suggested that over 90% of GHG emissions were from the combustion stage, which can be effectively reduced by increasing biomass HHV and decreasing biomass moisture content. Including soil carbon sequestration in LCA can largely offset the total global warming effect caused by woody biomass production and utilization. However, a better approach is needed to estimate soil carbon sequestration to avoid uncertainties caused by vegetation types, considered soil depth, and time duration. A multi-criteria optimization framework was developed to design a woody biomass supply chain with minimal GHG emissions and production cost in Michigan. The trade-off between rising cost and reducing GHG emissions was that by increasing the cost by 1.46 ¢/kWh, the total GHG emissions could be reduced by 0.66 kg CO2-eq/kWh. The sensitivity analysis indicated that biomass HHV and biomass moisture content had a larger impact on the optimized solutions and the trade-offs, as compared to the transportation distance. This again, confirmed that in order to improve the efficiency and sustainability of the woody biomass supply chain, future research efforts should be spent on improving the HHV and decreasing the moisture content of woody biomass.
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