(98a) A Multi-Layered Optimization-Based Framework for Design and Analysis of Renewable Energy Processes

A Multi-layered Optimization-Based Framework for Design and Analysis of Renewable Energy Processes

Owing to finite nature of fossil fuels, rapid increase in their prices and concerns about their environmental impact, efforts around the world to develop renewable transportation fuels and biobased chemicals have intensified. As the world has recognized the importance of diversifying its energy resource portfolio away from fossil resources and more towards renewable resources, the focus has shifted from recognizing the importance of the renewable resources sector towards designing sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits from renewable energy utilization. In order to design sustainable renewable resource value chains, one needs to not only study their environmental impacts, but also design conversion and logistical systems that provide a net financial benefit to stakeholders in the industry. 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. Often insufficient levers in plant and supply chain design for risk mitigation have led to companies failing to maintain solvency when lab- and bench-scale innovations are commercialized for the production of bio-products. An essential part of the planning process is garnering sufficient decision support to guide long and short actions.

In this study we present the development and implementation of a novel multi-layered decision support tool for the optimal design of biorefinery systems which can integrate 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. The proposed methodology is based on an iterative framework that utilizes systems-based strategic planning and optimization in conjunction with detailed mechanistic modelling, simulation and optimization of non-linear systems. While process conversion mechanisms are inherently non-linear in nature, owing to complex kinetic and thermodynamic relationships, non-linear strategic optimization models can quickly become complex to solve with solution performance suffering as more nonlinearities are added to a model. Consequently, 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. Further, to better estimate the complex nonlinear reaction dynamics of biological reactions, experimentally-derived kinetic models are utilized to simulate the reactions. The proposed strategy has the advantage of not only being able to integrate long term planning based on financial optimization with nonlinear process mechanisms, but also optimize process operating conditions, using metaheuristic algorithms for non-linear optimization.

The final results of the proposed framework include feedstock selection, product portfolio design, technological superstructure design, strategic capacity plan, optimal NPV, and optimal operating conditions of the process. The framework shows a deviation in process yields, and a deviation in the production capacities and operating conditions, from initial literature estimates. This is attributed to the framework’s use of nonlinear modeling and optimization strategies, which served to impart a greater degree of realism to the representation of the actual biorefining process.