Analysis of PEM and AEM electrolysis by neural network pattern recognition, association rule mining and LIME

作     者：M.Erdem Gunay N.Alper Tapan 

作者机构：Department of Energy Systems EngineeringIstanbul Bilgi UniversityEyupsultanIstanbul 34060Turkey Department of Chemical EngineeringGazi UniversityMaltepeAnkara 06570Turkey 

出 版 物：《Energy and AI》 (能源与人工智能（英文）)

年 卷 期：2023年第13卷第3期

页      面：169-181页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Machine learning Data mining Hydrogen production Current density 

摘      要：In this work,as an extension of previous machine learning studies,three novel techniques,namely local inter-pretable model-agnostic explanations(LIME),neural network pattern recognition and association rule mining(ARM)were utilized for proton exchange membrane(PEM)and anion exchange membrane(AEM)electrolyzer database for hydrogen *** main goal of LIME was to determine the positive or negative effects of a variety of descriptor variables on current density,power density and *** this technique,it was possible to uncover rules or paths that lead to high current density,low power density and low *** provided the dominant rules leading to high current density such as using ELAT as the cathode gas diffusion layer,using pure Pt on the cathode surface and using pure carbon as the cathode *** addition,LIME and neural network pattern recognition successfully uncovered the importance of catalytic materials such as cath-ode/anode support/surface elements,operational variables like K_(2)CO_(3) or KOH concentration in the electrolyte,certain membrane types,gas diffusion layers,and applied potential on current *** was then concluded that machine learning can help determine the ideal conditions for developing a PEM and AEM electrolyzer to maximize hydrogen generation,which can also guide future research.