Process Optimization Without an Algebraic Model

无需代数模型的流程优化

• 批准号: 1033661
• 负责人: Nikolaos Sahinidis
• 金额: $ 36.41万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1033661&HistoricalAwards=false
• 关键词: Process; Optimization; Without; Algebraic; Model

1033661SahinidisThe area of optimization is a strong focus of the process systems engineering community. As a result, a plethora of models, algorithms, and software have been developed for algebraic nonlinear programs (NLPs) and mixed-integer nonlinear programs (MINLPs). These techniques have had and will continue to have an impact in process synthesis, design, and operations. Yet, the algebraic NLP/MINLP paradigm:o often requires modelers to make restrictive assumptions in order to make possible the solution of their models with current optimization software;o is inefficient when expensive simulations must be carried out for modeling complex systems via proprietary software; ando is not in line with engineering practice, where technological developments are almost always based on experimental measurements rather than algebraic models.Experiments, in particular, provide measurements of the objective function to be optimized but no direct information on derivatives or any other information required by algebraic NLP/MINLP optimization. This project aims to develop optimization algorithms and software capable of optimizing without an explicit algebraic model. Towards this goal, the PS plans to:o complete a critical comparison of existing methods for this problem, especially in regard to their ability to find global solutions and improve starting points;o develop novel local and global optimization algorithms for optimizing systems described by any combination of algebraic, simulation, and experimental components;o use previously developed algorithms to optimize systems involving multiple scales, hidden constraints, and noisy objective functions;o develop and make available innovative software that relies on modern cyberinfrastruc-ture and implements the algorithms developed in this research.Intellectual Merit The project will lay the foundations of a new generation of optimization algorithms and software capable of solving complex problems for which algebraic NLPs and MINLPs are not available. Such problems abound in all scientific fields that rely on simulation or experiments for design and optimization. The task of optimizing algebraic NLPs and MINLPs is, in general, a very challenging one. Optimizing without explicit algebraic models can be even more challenging. This research addresses that challenge by capitalizing on recent progress in global optimization of algebraic NLPs and MINLPs to develop new, more efficient algorithms for algebraic-model-free optimization.Broader Impacts The project involves graduate student mentoring, integration of research results in course work, targeted minority student recruitment, and broad dissemination of the results through innovative cyber-enabled software implementing the results of the research. In addition, the research will have an immediate and wide impact on industrial practice as it specifically provides algorithms for experiment-based optimization and design.

1033661 Sahinidis优化领域是过程系统工程界的重点。因此，大量的模型，算法和软件已经开发的代数非线性规划（NLP）和混合整数非线性规划（MINLP）。这些技术已经并将继续对工艺合成、设计和操作产生影响。然而，代数NLP/MINLP范式：o经常要求建模者做出限制性假设，以便能够用当前的优化软件求解他们的模型;o当必须通过专有软件进行昂贵的模拟来对复杂系统进行建模时，效率低下;安藤的理论与工程实践不符，因为工程实践中的技术发展几乎总是基于实验测量而不是代数模型。尤其是实验，提供要优化的目标函数的测量，但不提供关于导数的直接信息或代数NLP/MINLP优化所需的任何其他信息。该项目旨在开发优化算法和软件，能够在没有显式代数模型的情况下进行优化。为实现这一目标，方案支助计划：o完成对解决这一问题的现有方法的批判性比较，特别是在它们找到全局解决方案和改进起点的能力方面;o开发新的局部和全局优化算法，用于优化由代数、模拟和实验组成部分的任何组合描述的系统;o使用以前开发的算法来优化涉及多尺度、隐藏约束和噪声目标函数的系统; o开发和提供创新的软件，该软件依赖于现代网络基础设施，并实现本研究中开发的算法。智力价值该项目将为新一代优化算法和软件奠定基础，这些算法和软件能够解决代数NLP和MINLP无法解决的复杂问题。这些问题在所有依赖于模拟或实验进行设计和优化的科学领域中比比皆是。优化代数NLP和MINLP的任务，在一般情况下，是一个非常具有挑战性的。在没有显式代数模型的情况下进行优化可能更具挑战性。这项研究通过利用代数NLP和MINLP全局优化的最新进展来解决这一挑战，为无代数模型优化开发新的、更有效的算法。更广泛的影响该项目涉及研究生指导、将研究成果整合到课程工作中、有针对性的少数族裔学生招聘、并通过创新的网络软件广泛传播研究结果。此外，该研究将对工业实践产生直接和广泛的影响，因为它专门为基于实验的优化和设计提供算法。