Artificial intelligence-based optimization for ring-opening metathesis polymerization of proton exchange membrane

Feng, Zhiming, Jin, Shan, Xiang, Hang, Li, Da, Sun, Shuai, Zhang, Huagui and Chen, Yi (2023) Artificial intelligence-based optimization for ring-opening metathesis polymerization of proton exchange membrane. Journal of Polymer Research, 30 (418). ISSN 1572-8935

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Official URL: https://link.springer.com/article/10.1007/s10965-0...

Abstract

The proton exchange membrane (PEM) is one of the significant components in PEM fuel cells. However, conventional synthesis experiments for proton exchange membrane (PEM) require considerable workload and time due to complicated conditions and various influencing factors. Here we initially utilized artificial intelligence (AI) techniques based on the artificial wolf pack algorithm (AWPA) to optimize the synthesis reaction conditions of the ring-opening metathesis polymerization (ROMP) reaction of norbornene derivatives for PEM preparation. An empirical model was established based on four variables, including temperature, reaction time, catalyst amount and ratio of reactants, with two fitness functions, including molecular weight (MW) and molecular weight distribution (MWD). Four trend indices were used, including the mean average precision (mAP), the mean, standard deviation (mSTD), the moving mean of the average precision (mmAP) and the moving mean of standard derivation (mmSD). The theoretical optimum values of operating conditions were obtained successfully, including reactant ratio (0.71), temperature (41.23 °C), catalyst content (0.20) and reaction time (47.94 min). The method in this paper helps optimize PEM preparation conditions and guides a database for AI-aid ROMP reactions.

Item Type:	Article
Keywords:	Catalyst Synthesis, Chemical Process Engineering, Chemical Engineering, Metathesis Reaction, Polymer Synthesis, Artificial Intelligence
Divisions:	Land and Property Management
Depositing User:	Dr Andy Jin
Date Deposited:	05 Feb 2026 19:26
Last Modified:	05 Feb 2026 19:26
URI:	https://rau.repository.guildhe.ac.uk/id/eprint/17041

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