Multi-Objective Optimization of Chemical and Biochemical Processes

化学和生化过程的多目标优化

• 批准号: RGPIN-2018-04433
• 负责人: Thibault, Jules
• 金额: $ 4.08万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746803
• 关键词: Multi; Objective; Optimization; Chemical; Biochemical

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.

近年来，发展多目标优化技术来解决工程问题引起了人们的极大兴趣。优化与决策有关。传统的优化技术通过将多个目标组合成由单个目标的加权和组成的单个目标函数来处理多个目标，或者通过关注一个目标而将其他目标转换为约束来处理。多目标优化是指同时对多个相互冲突的目标进行优化，从而产生一组称为帕累托域的备选方案。这些解决方案是最优的，因为与所有目标相比，在该领域没有任何解决方案比其他任何解决方案更好。然后，基于决策者的偏好使用排序算法对帕累托最优解进行排序，以结合决策者的偏好。优化的成功在很大程度上依赖于准确且具有代表性的流程模型的可用性，而这又依赖于丰富的流程数据的可用性。因此，必须协同使用实验、建模和优化来执行最适当的实验，开发最准确的过程模型，并确定过程必须在其下运行的最佳条件。该研究计划的长期目标是改进和优化丁醇生产工艺，同时将实施增强优化算法的方法学。特别是，本研究方案的应用目标是优化生物丁醇生产工艺，其中渗透汽化膜系统与丙酮-丁醇-乙醇(ABE)连续发酵系统集成在一起。渗透汽化膜单元用于连续去除为部分缓解丁醇抑制而产生的部分ABE溶剂。结果表明，丁醇产率、丁醇总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。这项研究提案的主要重点是开发和优化生产生物丁醇的工艺战略。生物丁醇具有能量密度接近汽油、低吸湿性和低挥发性等优点，作为一种生物燃料具有巨大的潜力。这项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，解决了生物丁醇生产过程中的主要挑战，即低最终浓度和高分离成本。这项研究计划将培训许多学生在生物燃料生产、过程建模和多目标优化方面。