文献类型：期刊文献

英文题名：Operation optimization of hydrocracking process based on Kriging surrogate model

作者：Zhong, Weimin[1];Qiao, Cheng[1];Peng, Xin[1];Li, Zhi[1];Fan, Chen[1];Qian, Feng[1]

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

年份：2019

卷号：85

起止页码：34

外文期刊名：CONTROL ENGINEERING PRACTICE

收录：;EI(收录号：20190306385257);WOS:【SCI-EXPANDED(收录号:WOS:000463312400004)】；

基金：This study is supported by the National Natural Science Foundation of China (Major Program: 61890933; General Program: 61873093), International Cooperation and Exchange Project, China (61720106008), and Programme of Introducing Talents of Discipline to Universities, China (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Hydrocracking process; Kriging surrogate model; GLSS algorithm; Optimization of operating condition

摘要：Hydrocracking is one of the key technologies in oil refining. It has become a critical secondary processing unit in the refinery for improving the quality of product oil and increasing the light oil volume of production. As such, operation optimization for this process is significant. The basis of operation optimization is the model, and several mechanisms for hydrocracking models have been proposed and studied. However, these models usually require time consuming and exhibit low efficiency especially when applied to optimize operating conditions. In this study, a Kriging surrogate model of hydrocracking is developed based on the mechanism and industrial data. An optimization algorithm is then proposed to optimize operating conditions. The proposed algorithm integrates adaptive step-size global and local search strategy (GLSS) for minimizing the predictor. Simulation results indicate that this optimization strategy integrating GLSS and Kriging surrogate model obtains better revenue of the process production than conventional algorithms such as EGO, DDS, and CAND.