Multi-Objective Optimization of Chemical and Biochemical Processes

近年来，解决工程问题的多目标优化技术的发展受到了极大的关注。优化与决策有关。传统的优化技术是将多个目标组合成单个目标函数，由单个目标的加权和组成，或者只关注一个目标，而将其他目标转化为约束。多目标优化是指同时优化多个相互冲突的目标，从而产生一组备选解，称为帕累托域。这些解决方案是最优的，因为当与所有目标相比时，没有解决方案比领域中的任何其他解决方案更好。然后根据决策者的偏好对帕累托最优解决方案进行排名，使用排名算法将决策者的偏好纳入其中。优化的成功很大程度上依赖于准确和有代表性的流程模型的可用性，而这些模型又依赖于丰富的流程数据的可用性。因此，实验、建模和优化必须协同使用，以进行最合适的实验，开发最准确的过程模型，并确定过程必须运行的最佳条件。该研究计划的长期目标是改进和优化丁醇生产过程，同时将执行增强优化算法的方法。特别是，本研究计划的应用目标是优化生物丁醇生产过程，其中渗透蒸发膜系统与丙酮-丁醇-乙醇（ABE）连续发酵系统集成。渗透蒸发膜单元用于连续去除部分ABE溶剂，以部分减轻丁醇抑制作用。丁醇产率、总丁醇浓度和底物转化率显著提高。这是迈向经济上可行的丁醇生产工艺的必要的第一步。本研究计划的主要重点是生物丁醇生产工艺策略的开发和优化。生物丁醇具有能量密度接近汽油、吸湿性低、挥发性低等诸多优点，作为生物燃料具有巨大的潜力。本研究计划通过多目标优化、过程建模和实验室实验的协同作用，解决生物丁醇生产过程中最终浓度低和分离成本高的主要挑战。这个研究项目将培养大量的学生在生物燃料生产，过程建模和多目标优化。

In recent years, the development of multi-objective optimization techniques to solve engineering problems has received significant interest. Optimization is concerned with decision making. Traditional optimization techniques have dealt with multiple objectives by combining them into a single objective function composed of the weighted sum of the individual objectives, or by focusing on one objective while transforming the others into constraints. Multi-objective optimization refers to the simultaneous optimization of multiple conflicting objectives, which produces a set of alternative solutions called the Pareto domain. These solutions are optimal in the sense that no solution is better than any other in the domain when compared with all objectives. Pareto-optimal solutions are then ranked based on the preferences of a decision-maker using ranking algorithms to incorporate the decision-maker's preferences.The success of optimization strongly relies on the availability of accurate and representative process models, which in turn rely on the availability of rich process data. As a result, experimentation, modelling and optimization must be used synergistically to perform the most appropriate experiments, develop the most accurate process models, and to determine the optimal conditions under which processes must operate. The long-term objective of the research program is to improve and optimize the butanol production process, while the methodology to enhance optimization algorithms will be performed simultaneously. In particular, the application targeted for this research proposal is the optimization of the biobutanol production process where a pervaporation membrane system is integrated with the acetone-butanol-ethanol (ABE) continuous fermentation system. The pervaporation membrane unit is used to continuously remove a fraction of the ABE solvents produced to partly alleviating butanol inhibition. As a result, the butanol productivity, the overall butanol concentration and the substrate percentage conversion increase significantly. This is a first step necessary in moving towards an economically viable butanol production process.The main emphasis of this research proposal is the development and optimization of process strategies for the production of biobutanol. Biobutanol has tremendous potential as a biofuel with its numerous advantages: energy density closer to gasoline, low hygroscopicity and low volatility. This research program addresses the major challenges of biobutanol production process, namely low final concentration and high cost of separation, through the synergy of multi-objective optimization, process modelling and laboratory experimentation. This research program will train numerous students on biofuel production, process modelling and multi-objective optimization.