ScholarWorks@경상국립대학교

초록

An artificial neural network (ANN) model is developed in this study to predict and analyze the specific capacitance of activated-carbon-based supercapacitors by utilizing a 12-dimensional data set related to physicochemical and operational parameters from the literature. A total of 61 ANN model architectures are constructed using a backpropagation algorithm to estimate the specific capacity. The 12-11-11-11-1 ANN architecture achieves good accuracy, with an average error of 5.8 and adjusted R-2 of 0.99. A standalone ANN software is developed for modeling specific capacity for infinite combinations of physicochemical and operational parameters. The findings illustrate the significance of structural characteristics and pyrolytic and oxidized N groups in determining the supercapacitor performance. Furthermore, the suggested approach can potentially guide future experiments to choose high-performance activated-carbon-based supercapacitor electrodes based on desired specific capacity.

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