文献类型：期刊文献

英文题名：Distributionally robust joint chance-constrained programming for multi-objective optimization of utility systems

作者：Zhao, Liang[1];Rong, Jiyun[1];Li, Hanxiu[1];Long, Jian[1];Liang, Chen[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 20237, Peoples R China

年份：2025

卷号：220

起止页码：623

外文期刊名：CHEMICAL ENGINEERING RESEARCH & DESIGN

收录：;EI(收录号：20253118887107);WOS:【SCI-EXPANDED(收录号:WOS:001543428400002)】；

基金：The work was supported by National Natural Science Foundation of China (22178103, 62373154) .

语种：英文

外文关键词：Industrial utility system; Wasserstein ambiguity set; Distributionally robust joint chance; constrained programming; Life cycle assessment; Multi-objective optimization

摘要：Increasing utility energy efficiency has received renewed attention as carbon neutrality has become a more prominent issue. Nevertheless, modeling and optimizing utility systems is still challenging due to the uncertainties of the energy demand of production processes. To minimize the operating costs and environmental impacts simultaneously, a multi-objective optimization framework was proposed. Life cycle assessments determine the environmental impacts. Energy consumption determines the operating costs of the utility system. In order to cope with steam demand uncertainty, an approach is proposed for DRJCC, which is a data-driven distributionally robust joint chance-constrained approach. As a part of the DRJCC framework, the data-driven ambiguity set is generated using Wasserstein distance, where Wasserstein distance comprises the empirical distribution. A deterministic convex reformulation of the problem can be derived using a dual representation of the worst-case probability. Case studies from industrial ethylene plants serve to verify the proposed method. With increasing confidence coefficients, the cost of the project and its environmental impact are reduced. We can also select optimal solutions flexibly with the Pareto frontier while minimizing the operational costs.