Novel Optimization Models and Methods for Process Systems Engineering Under Uncertainty

不确定性下过程系统工程的新型优化模型和方法

• 批准号: 418411-2013
• 负责人: Li, Xiang
• 金额: $ 1.97万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=657512
• 关键词: Novel; Optimization; Models; Methods; Process

In today's highly competitive world, companies must achieve efficient and effective design and operation of their systems while satisfying stringent constraints incurred by the concerns about sustainability of business and the natural environment. These challenges require novel computational methods to handle complex large-scale systems that are subject to uncertainties and nonlinear models. Optimization models and algorithms play a central role in the development of these methods. The proposed research program targets the development of systematic and efficient approaches to optimization techniques to solve complex process systems engineering problems subject to uncertainty, which cannot be satisfactorily solved with the current technologies. The major goals are to develop tractable problem formulations to address a huge (sometimes infinite) number of possible uncertainty realizations and to develop optimization methods that can guarantee global optimality for large-scale complex problems. ** The proposed research includes four topics that cover key process systems engineering elements, including process design, operation and control. Specifically, an efficient method with guaranteed optimality will be developed for systematic process design under uncertainty. A novel stochastic model predictive control method will be developed to reduce the conservativeness in robust process control. A novel stochastic programming formulation and solution approach will be developed for efficient and systematic decision-making for supply chain operation under uncertainty. The novel methodologies will be applied to process and supply chain optimization for biorefineries. Stochastic programming and global optimization techniques will be tailored, enhanced, and integrated to create novel methodologies for each topic. The results of this research program will contribute to the literature of process systems engineering and mathematical programming, and also help Canada's energy and manufacturing industries to improve the efficiency of their operation and the profitability of their business while reducing adverse impacts on the environment.****

在当今竞争激烈的世界中，公司必须实现其系统的高效设计和操作，同时满足对业务和自然环境的可持续性的担忧所产生的严格限制。这些挑战需要新颖的计算方法来处理不确定性和非线性模型的复杂大规模系统。优化模型和算法在这些方法的开发中起着核心作用。拟议的研究计划针对的是开发系统有效的优化技术方法，以解决符合不确定性的复杂过程系统的工程问题，而这些问题无法通过当前技术来令人满意地解决。主要目标是开发可探讨的问题公式，以解决大量（有时是无限）可能的不确定性实现的数量，并开发出可以保证大规模复杂问题的全球最优性的优化方法。 **拟议的研究包括四个涵盖关键过程系统工程元素的主题，包括过程设计，操作和控制。具体而言，将在不确定性下为系统过程设计开发具有保证最优性的有效方法。将开发一种新型的随机模型预测控制方法，以降低稳健过程控制中的保守性。将开发一种新型的随机编程公式和解决方案方法，以实现不确定性下的供应链操作的高效和系统决策。新型方法将应用于生物精制的过程和供应链优化。随机编程和全球优化技术将被量身定制，增强和集成，以创建每个主题的新方法。该研究计划的结果将有助于流程系统工程和数学编程的文献，还可以帮助加拿大能源和制造业提高其运营效率和业务盈利能力，同时减少对环境的不利影响。**************