Robust Optimization of Nonlinear Processes under Uncertainty

不确定性下非线性过程的鲁棒优化

• 批准号: EP/I014640/1
• 负责人: E Pistikopoulos
• 金额: $ 97.79万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI014640%2F1
• 关键词: Robust; Optimization; Nonlinear; Processes; under

Many important questions and challenges in process systems design, operation and control can be typically posed as nonlinear optimization problems. To date, most optimal decision making tools for such problems are mainly based on deterministic mathematical models, where all parameter values in the model are assumed to be known precisely. In practice, however, mathematical models are merely approximate descriptions of the real system, and parameters such as future demands, prices, equipment wearout and process conditions are subject to significant uncertainty. It has been frequently shown that disregarding such uncertainty can lead to severe performance losses, increased costs, and energy/environmental penalties. We propose to develop robust, local and global optimization methods for the efficient solution of such nonlinear optimization problems in the presence of uncertainties. Depending on their nature, uncertainties can be accounted for in a static/proactive or reactive way. Two important industrial applications will be investigated and the developed methods will be applied for the integrated design, optimization and control of process systems under uncertainty.

过程系统设计、运行和控制中的许多重要问题和挑战都可以典型地归结为非线性优化问题。到目前为止，大多数最佳决策工具，这类问题主要是基于确定性的数学模型，在模型中的所有参数值被假定为已知的精确。然而，在实践中，数学模型仅仅是对真实的系统的近似描述，并且诸如未来需求、价格、设备磨损和工艺条件等参数受到很大的不确定性的影响。它已被频繁地表明，忽视这种不确定性会导致严重的性能损失，增加成本，和能源/环境处罚。我们建议开发强大的，局部和全局优化方法的有效解决方案，这样的非线性优化问题的存在下的不确定性。根据其性质，不确定性可以静态/主动或被动方式加以说明。两个重要的工业应用将被调查和开发的方法将应用于集成设计，优化和控制过程系统的不确定性。

项目成果

Interdiction Games on Markovian PERT Networks

马尔可夫 PERT 网络上的拦截博弈

• DOI: 10.1287/mnsc.2014.1973
• 发表时间: 2015
• 期刊: Management Science
• 影响因子: 5.4
• 作者: Gutin E

Robust multi-parametric model predictive control for discrete-time LPV systems

• DOI: 10.1109/acc.2013.6579875
• 发表时间: 2013-06
• 期刊: 2013 American Control Conference
• 作者: Hyeygjeon Chang;E. Pistikopoulos;A. Astolfi

An algorithm for robust explicit/multi-parametric model predictive control

• DOI: 10.1016/j.automatica.2012.11.035
• 发表时间: 2013-02
• 期刊: Autom.
• 作者: K. Kouramas;Christos Panos;Nuno P. Faísca;E. Pistikopoulos

Distributionally robust multi-item newsvendor problems with multimodal demand distributions

• DOI: 10.1007/s10107-014-0776-y
• 发表时间: 2014-04
• 期刊: Mathematical Programming
• 影响因子: 2.7
• 作者: G. A. Hanasusanto;D. Kuhn;S. Wallace;Steve Zymler

A distributionally robust perspective on uncertainty quantification and chance constrained programming

• DOI: 10.1007/s10107-015-0896-z
• 发表时间: 2015-03
• 期刊: Mathematical Programming
• 影响因子: 2.7
• 作者: G. A. Hanasusanto;Vladimir Roitch;D. Kuhn;W. Wiesemann