Bulletin of ANPA

Abstract submitted to ANPA Conference July 14–16, 2023

Volume 5, Number 1

Data Science, Quantum Computing, Artificial Intelligence

Abstract ID: ANPA2023-N00076

Abstract:

ANPA2023-N00076: Comparative study of machine learning based prediction of capacitance of activated carbon prepared from bio based materials

Authors:

• Kirti Bir Rajguru; Department of Applied Sciences And Chemical Engineering IOE, Pulchowk campus

• Sujan Bhandari; Central Department of Physics TU

• Chhabi Lal Gnawali; IOE Pulchowk campus

• Parshuram Dahal; AI research scientist, USA

• Bhadra Prasad Pokharel ; IOE Pulchowk campus

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 capacitance of AC electrodes prepared from different precursors. In this study, different machine learning (ML) models were used to predict the specific capacitance, surface area, mesopore volume, and total pore volume of activated carbon from different biomass in efficient manner. The ML models were trained on a dataset of experimental and synthetic data that included the activation temperature, methylene blue number and iodine number of the activated carbon (AC). The best performing ML model was random forest model which had an R2 score of 0.968 for specific capacitance. The analysis revealed temperature was the most significant factor in predicting capacitance. The results of this study can be used to optimize the production of activated carbon and improve its performance in energy storage applications. Keywords: Machine Learning, Activated Carbon, Energy storage

To cite this abstract, use the following reference: https://anpaglobal.org/conference/2023/ANPA2023-N00076