Economic Optimization of Forest Biomass Processing and Transport.

详细信息   

• 作者：Zamora Cristales ; Rene Arturo.
• 学历：Ph.D.
• 年：2013
• 导师：Sessions, John,eadvisorBoston, Kevinecommittee memberMurphy, Glenecommittee memberGraham, Jamesecommittee memberMorrell, Jeffecommittee member
• 毕业院校：Oregon State University
• Department：Forest Engineering, Resources, and Management
• ISBN：9781303492631
• CBH：3574346
• Country：USA
• 语种：English
• FileSize：6722780
• Pages：210

文摘

An economic analysis and optimization of forest biomass processing and transportation at the operational level is presented. Renewable sources of energy have captured the interest of public and private institutions to develop cost-effective supply chains to reduce dependence on fossil fuels. The production of energy from forest harvest residues constitutes an opportunity to develop a supply chain for producing heat, electricity and liquid fuels from renewable materials. Special interest has been directed to the production of aviation fuel given the characteristics of the commercial aircraft technology that cannot use other renewable sources such as electricity, nuclear power or wind turbines. In economic terms, the production of energy from forest harvest residues at actual market prices requires efficient cost management and planning in order to compete with traditional fossil fuel supply chains. Efficient cost management requires an understanding of the operational stages in order to propose alternatives to improve the planning process, reduce costs, and increase the chance of success of this emerging supply chain. The main goal of this study is to improve cost-efficiency of an emerging energy supply chain from forest harvest residues. A general objective is the economic optimization of forest biomass processing and transportation at the operational level. We developed a model and frame-work to analyze the economics of forest biomass processing and transportation using mixed integer programming MIP), simulation, Geographic Information Systems GIS) and forest operation analysis. We developed an economic costing model that accounts for the cost of machinery and truck waiting time. The study is primarily focused on difficult access steep-land regions although it can also be applied to areas with less restricted road access. A stochastic discrete-event simulation model was developed to estimate cost management strategies to improve economics of mobile chipping operations and analyze the effect of uncertainty in this type of operation. The model was successful in predicting productivity of actual forest biomass recovery operations. The model also allowed analyzing the economic effect of truck-machine interactions when using mobile equipment to process the forest residues. With stationary processing equipment, the economic effect of truck-machine interactions on closely coupled operations was analyzed through a simulation model. It was demonstrated that truck-machine interactions affect machine utilization rates and, thus, the economics of the operation. Truck-machine interaction must be accounted for when analyzing forest recovery operations to avoid inaccurate cost estimation. Finally a mathematical solution procedure based on mixed integer programming, GIS and simulation was developed to support planning decisions in forest biomass recovery operations, including economic modeling of the effect of waiting times. The solution procedure was incorporated in the decision support system, Residue Evaluation and Network Optimization RENO) developed in JAVA platform. The decision support system was demonstrated to be an accurate and effective tool to estimate the most cost effective processing machinery and transport configuration given road access, material physical properties, spatial location of the residue piles and accounting for truck-machine interactions. Additionally, an Ant Colony heuristic is included in the model to bring support to the MIP branch and bound solution method by providing an initial solution for objective function. The model is also flexible to user changes to allow the analyst to analyze the sensitivity of the results to main production variables.