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

TL;DR

This paper introduces a deep learning-assisted multi-objective optimization framework using MLP and MOPSO for LPG thermal cracking, significantly reducing optimization time from days to minutes and improving decision-making insights.

Contribution

It presents a novel integration of deep learning with multi-objective optimization and decision-making methods, enhancing efficiency and understanding in thermal cracking process optimization.

Findings

Optimization time reduced from two days to one minute.

Provides comprehensive insights into trade-offs between conflicting objectives.

Enables data-driven decision-making in thermal cracking processes.

Abstract

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 combination 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.