Modeling the performance of cold plasma in CO2 splitting using artificial neural networks

Abstract

Using dielectric barrier discharge reactor (DBD) to convert CO2 has attracted considerable attention, recently. The primary challenge with its industrial use, however, is the eligibility and effectiveness of this technology in CO2 conversion, as well as its cost of energy. In this research use has been made of Artificial Neural Network to investigate the effective factors on a DBD reactor, a unique modern instrument for CO2 conversion. A multilayer perceptron approach of feed-forward back-propagation (BP) has been utilized to increase both the energy and CO2 conversion efficiency (outputs) by modeling the effective factors, such as chamber size, gas flow rate, and plasma-generator power (inputs). The findings revealed that an artificial neural network can be used to explain the eligibility and efficiency. Despite the network’s complexity in terms of input and output parameters, the predicted and actual results were found to be in good agreement. The results showed that multilayer perceptron with structure 3-6-2 was the most suitable (MSE = 0.62 and R2 > 0.99). As a result, the artificial neural network can be utilized as a practical and effective tool in predicting the efficiency of energy and carbon dioxide conversion in a DBD reactor.