Deep Learning Aided Multi-Objective Optimization and Multi-Criteria Decision Making in Thermal Cracking Process for Olefines Production

被引：11

作者：

Nabavi, Seyed Reza ^{[1
]}

Jafari, Mohammad Javad ^{[1
]}

Wang, Zhiyuan ^{[2
,3
]}

机构：

[1] Univ Mazandaran, Fac Chem, Dept Appl Chem, Babolsar, Iran

[2] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore

[3] DigiPen Inst Technol Singapore, AI Res & Computat Optimizat AIRCO Lab, Singapore 139660, Singapore

来源：

JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS | 2023年 / 152卷

关键词：

liquefied petroleum gas (LPG); thermal cracking; machine learning (ML); deep learning (DL); multi-criteria decision making (MCDM); multi-objective particle swarm optimization; (MOPSO); PARTICLE SWARM OPTIMIZATION; STEAM CRACKING; EVOLUTION; OPERATION; SYSTEM; FUEL;

D O I：

10.1016/j.jtice.2023.105179

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Background: Multilayer perceptron (MLP) aided multi-objective particle swarm optimization algorithm (MOPSO) is employed in the present article to optimize the liquefied petroleum gas (LPG) thermal cracking process. This new approach significantly accelerated the multi-objective optimization (MOO), which can now be completed within one minute compared to the average of two days required by the conventional approach.Methods: MOO generates a set of equally good Pareto-optimal solutions, which are then ranked using a combi-nation of a weighting method and five multi-criteria decision making (MCDM) methods. The final selection of a single solution for implementation is based on majority voting and the similarity of the recommended solutions from the MCDM methods.Significant Findings: The deep learning (DL) aided MOO and MCDM approach provides valuable insights into the trade-offs between conflicting objectives and a more comprehensive understanding of the relationships between them. Furthermore, this approach also allows for a deeper understanding of the impact of decision variables on the objectives, enabling practitioners to make more informed, data-driven decisions in the thermal cracking process.

引用

页数：13