In recent years there has been a marked surge in the search for alternative sources of energy that wean the world off of dependence on fossil fuels and reduce the carbon foot-print. As the world has recognized the importance of diversifying its energy resource portfolio away from fossil resources and more towards renewables such as biomass, there arises a need for developing strategies which can design renewable sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits from energy utilization. After a boom in U.S. corn-based ethanol in the beginning of the 21st century, the interest has gradually shifted towards more viable sources of biofuels and biochemicals. Second generation biofuels are examples of such fuels that are extremely attractive owing to the fact that the raw materials can be composed completely of “left-over” wastes of food crops and forest harvests that don't interfere with the human food chain and the natural ecosystem. It also can provide new income and employment opportunities in rural areas.

In this study we present the development and implementation of a multi-layered decision support tool that can be utilized by energy entrepreneurs, resource and technology investors, and value chain actors in the renewable energy industry to carefully design and optimize the business value of their energy endeavors. We apply a distributed, systematic approach which is composed of different layers including strategic, tactical, and operational tasks. To demonstrate the effectiveness of the proposed methodology, a hypothetical case study of a multiproduct lignocellulosic biorefinery based on sugar conversion platform is utilized.

Linear programming (LP) models are suggested for the purpose of strategic planning. To overcome the mismatch between nonlinear process mechanisms and LP-based strategic optimization, a decomposition strategy is proposed that combines net present value (NPV) optimization for long term planning with rigorous non-linear process simulation and process-level optimization. Different scenarios are developed based on stochastic forecasts for uncertain market parameters including price and demand of biofuels and value-added chemicals. The process is formulated as a mixed integer linear programming (MILP) model which incorporates stepwise capacity expansion and minimization of the financial risk. The output of the model includes optimal design of production capacity of the plant for the planning horizon by maximizing the net present value (NPV). Then this capacity is sent to the lower level of the optimization algorithm. The second stage, which optimizes the operating conditions of the plant, consists of three main steps including simulation of the process in the simulation software (nonlinear modeling), identification of critical sources of uncertainties through global sensitivity analysis, and employing stochastic optimization methodologies to optimize the operating condition of the plant under uncertainty.