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: $ 2.04万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682605
• 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，多目标优化技术的发展，以解决工程问题已收到显着的兴趣。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各个目标的加权和组成的单个目标函数，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个目标。多目标优化是指同时优化多个相互冲突的目标，产生一组称为帕累托域的替代解决方案。这些解决方案是最优的，因为与所有目标相比，没有任何解决方案比域中的任何其他解决方案更好。然后，根据决策者的偏好，使用排名算法对帕累托最优解决方案进行排名，以纳入决策者的偏好。优化的成功在很大程度上依赖于准确和有代表性的过程模型的可用性，而过程模型又依赖于丰富的过程数据的可用性。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工艺，同时将执行增强优化算法的方法。特别是，针对本研究建议的应用是生物丁醇生产过程的优化，其中渗透蒸发膜系统集成与丙酮-丁醇-乙醇（ABE）连续发酵系统。渗透蒸发膜单元用于连续地去除所产生的ABE溶剂的一部分以部分地减轻丁醇抑制。结果，丁醇生产率、总丁醇浓度和底物转化率百分比显著增加。这是迈向经济上可行的丁醇生产工艺所必需的第一步。**本研究建议的主要重点是开发和优化生产生物丁醇的工艺策略。生物丁醇具有能量密度接近汽油、低吸湿性和低挥发性等诸多优点，作为生物燃料具有巨大的潜力。该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的主要挑战，即最终浓度低和分离成本高。该研究计划将培训众多学生生物燃料生产，过程建模和多目标优化。