A comprehensive review of alkaline water electrolysis mathematical modeling

范围(计算机科学) 过程(计算) 计算机科学 建模与仿真 过程建模 电解 领域(数学) 系统工程 工艺工程 管理科学 生化工程 模拟 工程类 工艺优化 化学 物理化学 操作系统 环境工程 程序设计语言 电解质 纯数学 数学 电极
作者
Song Hu,Bin Guo,Shun-Liang Ding,Fuyuan Yang,Jian Dang,Biao Liu,Junjie Gu,Jugang Ma,Minggao Ouyang
出处
期刊:Applied Energy [Elsevier BV]
卷期号:327: 120099-120099 被引量:199
标识
DOI:10.1016/j.apenergy.2022.120099
摘要

Alkaline water electrolysis (AWE) is a relatively mature water electrolysis technology that plays an important role in large-scale green hydrogen production and electrical energy storage. Modeling is a powerful tool for the phenomenon understanding, control analysis, and optimization management of AWE. AWE has various modeling forms, but reviews summarizing the current situation and problems of modeling development are lacking. This review provides a detailed and comprehensive investigation of existing modeling efforts on thermodynamic, electrochemical, thermal, and gas purity models. In the process of investigating these models in the published reference, a concise modeling guideline was created to show the relationship between different sub-models. This review also summarized and compared the different modeling approaches for the same processes or mechanisms. On this basis, the effects of characteristic parameters and operating conditions on AWE performance were summarized in detail. Meanwhile, the strengths, weaknesses, and lacks in this research field were pointed out. Electrochemical modeling studies are comprehensive, but the accuracy of each sub-model during model calibration requires specialized experimental validation. Gas purity modeling research is rare, and the model prediction accuracy can reach a satisfactory level. The control strategy and optimization method of gas purity based on the model need to be developed urgently. Thermal modeling-related studies are rare, and the prediction accuracy still needs to be further improved. The application scope and thermal management strategy based on thermal model need to be explored in depth. This work can provide guidelines for beginners and a future direction for further improvement of AWE modeling.
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