首页|Machine learning-driven optimization of plasma-catalytic dry reforming of methane
Machine learning-driven optimization of plasma-catalytic dry reforming of methane
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
This study investigates the dry reformation of methane(DRM)over Ni/Al2O3 catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data.To address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML model.The ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM process.The model was sub-sequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance.We found that the optimal discharge power(20 W),CO2/CH4 molar ratio(1.5),and Ni load-ing(7.8 wt％)resulted in the maximum energy yield at a total flow rate of～51 mL/min.Furthermore,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process.The results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35％for each output,while the Ni loading had the least impact on the overall reaction performance.This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.
Plasma catalysisMachine learningProcess optimizationDry reforming of methaneSyngas production
Yuxiang Cai、Danhua Mei、Yanzhen Chen、Annemie Bogaerts、Xin Tu
展开 >
Department of Electrical Engineering and Electronics,University of Liverpool,Liverpool L69 3GJ,UK
Research Group PLASMANT,Department of Chemistry,University of Antwerp,Universiteitsplein 1,BE-2610 Wilrijk-Antwerp,Belgium
College of Electrical Engineering and Control Science,Nanjing Tech University,Nanjing 211816,Jiangsu,China
European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-CurieNational Natural Science Foundation of China
NETL
NSTL
万方数据
