A Modeling, Optimization, and Analysis Framework for Designing Multi-Product Lignocellulosic Biorefineries
Doctor of Philosophy (PhD)
The objective of this research is to propose a methodology to develop modular decision analysis frameworks to design value chains for enterprises in the renewable fuels and chemicals sector. The decision support framework focuses on providing strategic decision support to startup and new product ventures. The tasks that are embedded in the framework include process and systems design, technology and product selection, forecasting cost and market variables, designing network capacities, and analysis of risks. The Decision support system (DSS) proposed is based on optimization modeling; systems design are carried out using integer programming with multiple sets of process and network configurations utilized as inputs. Uncertainty is incorporated using real options, which are utilized to design network processing capacity for the conversion of biomass resources. Risk analysis is carried out using Monte Carlo methods. The DSS framework is exemplified using a lignocellulosic biorefinery case study that is assumed to be located in Louisiana. The biorefinery utilizes energy crops as feedstocks and processes them into cellulosic biofuels and biobased chemicals. Optimization modeling is utilized to select an optimal network, a fractionation technology, a fermentation configuration, and optimal product recovery and purification unit operations. A decision tree is then used to design incremental capacity under uncertain market parameters. The valuation methodology proposed stresses flexibility in decision making in the face of market uncertainties as is the case with renewable fuels and chemicals. The value of flexibility, termed as “Option Value” is shown to significantly improve the net present value of the proposed biorefinery. Monte Carlo simulations are utilized to develop risk curves for alternate capacity design plans. Risk curves show a favorable risk reward ratio for the case of incremental capacity design with embedded decision options. The framework proposed here can be used by enterprises, government entities and decision makers in general to test, validate, and design technological superstructures and network processing capacities, conduct scenario analyses, and quantify the financial impacts and risks of their representative designs. We plan to further add functionality to the DSS framework and make available the tools developed to wide audience through an “open-source” software distribution model.
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Sharma, Paritosh Kumar, "A Modeling, Optimization, and Analysis Framework for Designing Multi-Product Lignocellulosic Biorefineries" (2012). LSU Doctoral Dissertations. 2375.