The solid oxide electrolysis cell(SOEC)holds great promise to efficiently convert renewable energy into ***,traditional modeling methods are limited to a specific or reported SOEC ***,four machine learning models are ...
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The solid oxide electrolysis cell(SOEC)holds great promise to efficiently convert renewable energy into ***,traditional modeling methods are limited to a specific or reported SOEC ***,four machine learning models are developed to predict the performance of SOEC processes of various types,operating parameters,and feed *** impact of these features on the SOEC's outputs is explained by the Shapley additive explanations and partial dependency plot *** preferredmodel is integratedwith a genetic algorithmto determine the optimal values of each input *** show the improved extreme gradient enhanced regression(XGBoost)algorithm is the core of the machine learning model of the process since it has the highest R^(2)(>0.95)in the three *** electrolytic cell descriptors have a greater impact on the system performance,contributing up to 54.5%.The effective area,voltage,and temperature are the three most influential factors in the SOEC system,contributing 21.6%,16.6%,and 13.0%to its *** temperature,high pressure,and low effective area are the most favorable conditions for H_(2)production *** conducting multi-objective optimization,the optimal current intensity and hydrogen production rate were determined to be 1.61 A/cm^(2)and 1.174 L/(h⋅cm^(2)).
作者：
Raymond, DavidMankar, DivyaniGudadhe, Amit
Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India
Faculty of Engineering And Technology Department of Basic Science And Humanities Maharashtra Wardha India
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