Globally convergent optimization for data-dependent systems enabled through a novel data-driven branch-and-bound framework

通过新颖的数据驱动分支定界框架实现数据依赖系统的全局收敛优化

• 批准号: 1805724
• 负责人: Fani Boukouvala
• 金额: $ 30.03万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1805724&HistoricalAwards=false
• 关键词: Globally; convergent; optimization; data; dependent

Decision-making for complex engineering systems depends on the development of algorithms for data-driven optimization based on data generated either by high fidelity simulations and/or experiments. Despite the high potential of data-driven optimization, there is currently a lack of efficient and scalable methods that can provide high quality solutions for a general class of data-dependent problems. The proposed work is motivated by the increasing number of applications that can benefit from data-driven optimization and control, including chemical process synthesis, enhanced oil recovery, carbon dioxide sequestration, energy efficiency of buildings, and many more.The proposed research is focused on the integration of traditional process systems engineering with machine learning and uncertainty quantification concepts to overcome key challenges of data-dependent optimization which currently hinder their efficiency and scalability in applications with a high number of dimensions and constraints. The objectives of the proposed research are (a) the identification of efficient space and variable decomposition strategies for creating tractable optimization sub-problems, (b) the formulation of theoretically overestimating and underestimating approximating functions for data-dependent correlations by leveraging data and model uncertainty, and (c) the study of convergence rates and optimality bounds of data-driven branch-and bound optimization for a large set of challenging benchmark problems, as well as challenging case studies for oil-field operations, enhanced oil recovery and building design and efficiency. The central idea of this work is the formulation of novel under/over-estimating approximations, which will be incorporated within a novel customized branch & bound search to systematically identify optimal solutions with a tractable number of samples and improved convergence rates. Scalable data-driven optimization tools and a benchmarking library will be created and made publicly available with examples drawn from prominent fields, such as mechanical and structural design, chemical flowsheet design, oilfield control, parameter estimation, and protein folding. There is also a plan to incorporate data-science concepts into the chemical engineering education in the form of teaching modules that will be made available to the academic community at large. A Vertically Integrated Projects (VIP) program is also proposed aimed at attracting undergraduate students from science and engineering disciplines to work as members of interdisciplinary teams towards solving challenging data-driven optimization problems.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.

复杂工程系统的决策取决于基于高保真仿真和/或实验生成的数据的数据驱动优化算法的开发。尽管数据驱动优化具有很高的潜力，但目前缺乏有效且可扩展的方法，可以为一般类型的数据依赖问题提供高质量的解决方案。拟议的工作是由越来越多的应用程序，可以受益于数据驱动的优化和控制，包括化学过程合成，提高石油采收率，二氧化碳封存，建筑物的能源效率，以及更多。拟议的研究重点是将传统的过程系统工程与机器学习和不确定性量化概念相结合，以克服数据的关键挑战，依赖优化，这目前阻碍了它们在具有大量维度和约束的应用中的效率和可伸缩性。所提出的研究的目标是（a）识别有效的空间和变量分解策略，以创建易于处理的优化子问题，（B）通过利用数据和模型的不确定性，为数据相关性制定理论上高估和低估的近似函数，以及（c）对于大量具有挑战性的基准问题的数据驱动的分支和界限优化的收敛速率和最优性界限的研究，以及油田作业、提高石油采收率和建筑物设计与效率方面具有挑战性的案例研究。 这项工作的中心思想是制定新的下/高估的近似，这将被纳入一个新的定制分支界搜索系统地确定最佳解决方案，一个易于处理的样本数和改进的收敛速度。 将创建可扩展的数据驱动优化工具和基准库，并公开提供来自突出领域的示例，如机械和结构设计，化学流程设计，油田控制，参数估计和蛋白质折叠。还有一项计划是将数据科学概念以教学模块的形式纳入化学工程教育，这些模块将提供给整个学术界。此外，还提出了一个垂直整合项目（VIP）计划，旨在吸引来自科学和工程学科的本科生作为跨学科团队的成员，解决具有挑战性的数据驱动优化问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Data-driven Branch-and-bound Algorithms for Constrained Simulation-based Optimization

用于基于约束仿真的优化的数据驱动分支定界算法

• 发表时间: 2021
• 期刊: 31st European Symposium on Computer Aided Process Engineering
• 作者: Zhai, Jianyuan;Shirpurkar, Sachin;Boukouvala, Fani.
• 通讯作者: Boukouvala, Fani.

Managing uncertainty in data-driven simulation-based optimization

• DOI: 10.1016/j.compchemeng.2019.106519
• 发表时间: 2020-05
• 期刊: Comput. Chem. Eng.
• 作者: Gordon Hüllen;Jianyuan Zhai;Sun Hye Kim;Anshuman Sinha;M. Realff;Fani Boukouvala

DATA-DRIVEN SPATIAL BRANCH-AND-BOUND ALGORITHMS BLACK-BOX OPTIMIZATION

数据驱动的空间分支定界算法黑盒优化

• 发表时间: 2019
• 期刊: Foundations of Computer Aided Process Design
• 作者: Zhai, Jianyuan;Boukouvala, Fani
• 通讯作者: Boukouvala, Fani

Data-driven Spatial Branch-and-bound Algorithm for Box-constrained Simulation-based Optimization

用于基于框约束仿真的优化的数据驱动空间分支定界算法

• DOI: 10.1007/s10898-021-01045-8
• 发表时间: 2021
• 期刊: Journal of global optimization
• 影响因子: 1.8
• 作者: Zhai, J.;Boukouvala, F.
• 通讯作者: Boukouvala, F.

Surrogate-based optimization for mixed-integer nonlinear problems

• DOI: 10.1016/j.compchemeng.2020.106847
• 发表时间: 2020-09
• 期刊: Comput. Chem. Eng.
• 作者: Sun Hye Kim;Fani Boukouvala