Comparative study of machine learning based prediction of supercapacitance performance of activated carbon prepared from Bio-based Materials

Abstract

The performance of electrochemical double-layer capacitors (EDLCs) is evaluated by the capacitance of activated carbon (AC) electrodes. The capacitance of AC electrodes is influenced by many factors such as precursor type, activation method, pore structure, surface chemistry and electrolytic properties. In this paper, we present a comparative study of machine learning based prediction of surface area, mesopore volume and total pore volume of activated carbon for energy storage applications. The ML models were trained on a dataset of synthetic data that were generated from the limited number of experimental data and which included the activation temperature, methylene blue number and iodine number of the activated carbon (AC). The best performing ML model was random forest model and XG boost model. The results of this study can be used to optimize the production of activated carbon and improve its performance in energy storage applications.