文献类型：期刊文献

英文题名：Multi-objective robust optimization design framework for low-pollution emission burners

作者：Hu, Guihua[1];Tao, Qingfeng[1];Ying, Rui[1];Long, Jian[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：210

起止页码：180

外文期刊名：CHEMICAL ENGINEERING RESEARCH & DESIGN

收录：;EI(收录号：20243617002512);WOS:【SCI-EXPANDED(收录号:WOS:001308221900001)】；

基金：The work was supported by the National Natural Science Foundation of China (Basic Science Center Program: 61988101) , the National Natural Science Foundation of China (62273149, 62373155, 62303186) , the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Low-pollution emission burners; Uncertain variable; Polynomial chaos expansion; Legendre polynomial; Continuous robust optimization

摘要：The optimal design of low-pollution emission burners plays an important role in controlling pollutant emissions of industrial equipment, and is crucial for the sustainable development of the national economy and environmental protection. However, many uncertain factors challenge the optimal design of low-pollution emission burners. The Latin hypercube sampling (LHS) method was used to obtain sampling data representing the distribution of the uncertain variable. The training dataset was obtained using the turbulent combustion coupling model. A high-precision sparse polynomial chaos expansion (PCE) model was constructed by the degree-adaptive scheme and least angle regression (LAR) algorithm. Furthermore, the Legendre polynomial is introduced to establish a continuous robust optimization model. The model is carried out by the non-dominated sorting genetic algorithm II (NSGA-II). The results show that the excess air coefficient of 1.227 is optimal. Compared with the excess air coefficient of 1.20 under the discrete robust optimization, the optimal coefficient can further reduce pollutant emissions and bring strong robustness to the ethylene cracking furnace. It has also been proven that the continuous robust optimization scheme improves the optimization granularity. Compared with discrete robust optimization, this method reduces the number of samples by 66.7 %.