文献类型：期刊文献

英文题名：Adaptive Weighted Hybrid Modeling of Hydrocracking Process and Its Operational Optimization

作者：Song, Wenjiang[1];Du, Wei[1,2];Fan, Chen[1];Yang, Minglei[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200092, Peoples R China

年份：2021

卷号：60

期号：9

起止页码：3617

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20211410179517);WOS:【SCI-EXPANDED(收录号:WOS:000629059100014)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101), International (Regional) Cooperation and Exchange Project (61720106008), National Natural Science Fund for Distinguished Young Scholars (61725301), Shanghai Sailing Program (20YF1411000), and National Natural Science Foundation of China (61873093).

语种：英文

外文关键词：Refining - Structural optimization

摘要：Hybrid modeling, aiming to integrate the advantages of both first-principles models and data-driven models, is an important technology for refinery process simulation and optimization. The commonly used hybrid models include series models, parallel models, and series-parallel models. Many studies have reported the operational optimization results based on these models. However, it is unknown whether the results obtained based on these models are consistent with the actual plant optimal operation. Moreover, the hybrid models that have been used in operational optimization are of traditional structures and cannot ensure competent performance. To fill these gaps, we investigate the modeling and operational optimization of a typical refinery unit-hydrocracking unit. First, we establish a first-principles model, a data-driven model, and three hybrid models and analyze the strengths and weaknesses of these models. Next, we propose the concepts of "mechanism-dominated models" and "data-dominated models" to classify the existing models according to the prediction abilities instead of the structural features. Furthermore, a novel type of hybrid model termed adaptive weighted hybrid model (AWHM) is proposed to gain a better balance between extrapolation and interpolation capabilities. Then, the performance of the operational optimization based on these models is compared under four optimization scenarios. Experimental results demonstrate that the two variants of the proposed AWHM have the best optimization performance and their optimization results are the most consistent with the actual process.