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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637826
• 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和有效的设计和操作，同时满足对商业和自然环境可持续性的关注所带来的严格限制。这些挑战需要新的计算方法来处理复杂的大规模系统，受到不确定性和非线性模型。优化模型和算法在这些方法的发展中起着核心作用。拟议的研究计划的目标是开发系统和有效的方法来优化技术，以解决复杂的过程系统工程问题的不确定性，这不能令人满意地解决与目前的技术。主要目标是开发易于处理的问题公式，以解决大量（有时是无限的）可能的不确定性实现，并开发优化方法，可以保证大规模复杂问题的全局最优性。拟议的研究包括四个主题，涵盖关键的过程系统工程要素，包括过程设计，操作和控制。具体而言，一个有效的方法，保证最优性将开发系统的过程设计下的不确定性。为了降低鲁棒过程控制中的保守性，提出了一种新的随机模型预测控制方法。本文提出了一种新的随机规划方法和求解方法，为不确定条件下供应链运作的有效和系统决策提供了理论依据。新的方法将被应用于生物炼制的过程和供应链优化。随机编程和全局优化技术将被定制，增强和集成，为每个主题创建新的方法。该研究计划的结果将有助于过程系统工程和数学规划的文献，并帮助加拿大的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。