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

被引:8
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
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
收藏
页数:13
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