(580g) Effect of Market and Technical Parameter Uncertainties on the Optimal Design of Integrated Biorefineries

As research is exploring possibilities for developing renewable bio-based fuels and chemicals, there arises a need for development of strategies which can design sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits. Recent ventures in biofuel production have been fraught with corporate failures. A driving reason for these unsuccessful ventures, in part is governed by the lack of proper planning in designing plants and supply networks. Modeling strategic and operational level decision processes of an enterprise which produces renewable-based fuels and chemicals, using engineering and financial tools, can provide valuable insight into the inter-play of technology and process interactions for different product streams.

In this study we present the development of a comprehensive decision support tool, within which the business value of a multiproduct biorefinery is optimized by considering all types of uncertainties including uncertainties at strategic and operational level. A stochastic linear model is first developed to optimize production capacity of the plant for the desired planning horizon and then process simulation coupled with stochastic optimization algorithm is employed to optimize the operating condition of the plant under uncertainty. The proposed methodology is based on an iterative framework that utilizes systems-based optimization in conjunction with detailed mechanistic modelling and simulation of the process. Market uncertainties are taken into account at the strategic planning level, and uncertainties related to parameters characterizing the processing technologies are addressed in operational level optimization. Monte-Carlo based simulation and global sensitivity analysis are utilized to identify the most critical parameters and optimize the operating conditions of the plant accordingly. Additionaly, risk management strategies are introduced to the framework for explicit treatment of strategic and operational risks. The other key feature of our approach is the development of dynamic data exchange strategy embedded as part of the process simulation to incorporate the complex kinetics of bio-reactions in the simulation model. The final results of the proposed framework include strategic capacity plan, optimal expected NPV, and optimal operating conditions of the process.