RUI: Global Optimization of Chance-Constrained Programming for Reliable Process Design

RUI：机会约束编程的全局优化，实现可靠的流程设计

• 批准号: 2151497
• 负责人: Yu Yang
• 金额: $ 9.62万
• 依托单位: California State University-Long Beach Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151497&HistoricalAwards=false
• 关键词: RUI; Global; Optimization; Chance; Constrained

Incomplete knowledge of the accuracy of mathematical models used for the optimization-based design of chemical processes can lead to degraded quality of fuels, vaccines, manufactured foods, and other chemical products, potentially giving rise to further economic, safety, health, and environmental effects. Current computer-aided process optimization methods are deficient in handling uncertainties due to the high computational cost of rigorously evaluating the designs of complex, highly interconnected chemical plants, inevitably resulting in conservative, sub-optimal design solutions. Motivated by this challenge to advancing U.S. chemical manufacturing technology, this project will establish entirely new, deterministic global optimization techniques combined with flexible data-driven modeling methods that will make it possible to design high-performance chemical processes under uncertainties without sacrificing safety. The resulting chemical products and processes will meet quality and operational constraints with predictable probability while minimizing the plant and operational costs. The fundamental research to be carried out in this project will build a deeper understanding of how data-driven uncertainty models can be used in optimization to simplify the process of identifying the true optimal solution. This proposal would support the integration of research and educational activities through the addition of new content to process design and chemical engineering laboratory courses, the mentoring of undergraduate researchers, and organizing workshops for K-12 students. This work will educate a new generation of students from traditionally underrepresented groups to solve process design problems using data analytics and global optimization strategies.The objective of this project is to build and test a global optimization framework to solve chance-constrained programs (CCPs) formulated for chemical process design under uncertainties. The proposed research plan will focus on advancing the theory behind single- and two-stage CCP subject to large-scale joint chance constraints affinely dependent on general uncertainties. In the single-stage CCP, Gaussian Mixture Models (GMMs) will be investigated for their effectiveness in describing generic uncertainties. To achieve this objective, the CCP-GMM framework will be reformulated into a bi-convex structure. In the two-stage CCP, a piecewise linear decision rule will be integrated with GMM to facilitate more flexible policy representations. The resulting bi-convex problem then can be solved to the global optimum through a combination of second-order cone relaxations, branch-and-bound methods, reformulation-linearization techniques, and optimality-based interval reductions. A vegetable oil blending experiment will be constructed to validate the proposed optimization algorithms, with an objective of edible oil cost minimization subject to the viscosity, energy, and total fat constraints as uncertainties. The proposed research program has transformative potential in that it seeks to improve the use of available data in the optimization process by embedding the identified GMM into the optimization process. This innovative strategy can bypass the difficulty of integral over chance constraints and enable much improved computational efficiency of global optimization. If proven effective, the proposed global optimization algorithms for CCP can be widely applied to complex design problems in the chemical, oil, fuel, and pharmaceutical industries, to reduce cost, enhance safety, and mitigate environmental impact.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

不完整的了解用于基于化学过程的基于优化设计的数学模型的准确性可能会导致燃料，疫苗，制造食品和其他化学产品的质量降解，从而有可能带来进一步的经济，安全，健康和环境影响。由于严格评估复杂的，高度相互联系的化学植物的设计的高计算成本，当前的计算机辅助过程优化方法处理不确定性不足，因此不可避免地导致了保守的，最佳的设计解决方案。由于这一挑战促进美国化学制造技术的挑战，该项目将建立全新的，确定性的全球优化技术，再加上灵活的数据驱动建模方法，这将使在不牺牲安全性的情况下设计高性能化学过程是可能的。由此产生的化学产品和工艺将满足质量和运营限制，并以可预测的概率降至最低限制工厂和运营成本。该项目中要进行的基本研究将对如何使用数据驱动的不确定性模型进行更深入的了解，以简化识别真正最佳解决方案的过程。该建议将通过在过程设计和化学工程实验室课程，本科研究人员的指导以及为K-12学生组织研讨会上，来支持研究和教育活动的整合。这项工作将教育来自传统代表性不足的小组的新一代学生，以使用数据分析和全球优化策略来解决过程设计问题。该项目的目标是建立和测试一个全球优化框架，以解决在不认识下为化学过程制定的机会限制的计划（CCPS）。拟议的研究计划将着重于促进单阶段和两阶段CCP背后的理论，但受到大规模关节机会约束，依赖于一般不确定性。在单阶段的CCP中，将研究高斯混合模型（GMM）在描述通用不确定性方面的有效性。为了实现这一目标，CCP-GMM框架将被重新构成双凸结构。在两阶段的CCP中，分段线性决策规则将与GMM集成，以促进更灵活的策略表示。然后，可以通过二阶锥度放松，分支和结合方法，重新构造线性化技术和基于最佳的间隔降低的结合来解决所得的双凸问题。将构建一个植物油混合实验，以验证所提出的优化算法，目标是可食用的油成本最小化，但要以粘度，能量和总脂肪约束为不确定性。提出的研究计划具有变革性的潜力，因为它试图通过将确定的GMM嵌入到优化过程中，以改善优化过程中可用数据的使用。这种创新的策略可以绕过不可或缺的机会限制的困难，并使全球优化的计算效率得到了极大的提高。如果证明有效，则可以广泛地将CCP的全球优化算法应用于化学，石油，燃料和制药行业中的复杂设计问题，以降低成本，提高安全性并减轻环境影响。这奖反映了NSF的立法使命，并认为通过基金会的智力效果和广泛的评估，可以通过评估来进行评估。

项目成果

Optimal blending under general uncertainties: A chance-constrained programming approach

• DOI: 10.1016/j.compchemeng.2023.108170
• 发表时间: 2023-02
• 期刊: Comput. Chem. Eng.
• 作者: Y. Yang