首页|Reports on Machine Learning Findings from Tribhuvan University Provide New Insig hts (Comparative study of machine learning based prediction of supercapacitance performance of activated carbon prepared from Bio-based Materials)
Reports on Machine Learning Findings from Tribhuvan University Provide New Insig hts (Comparative study of machine learning based prediction of supercapacitance performance of activated carbon prepared from Bio-based Materials)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting out of Lalitpur, Nepal, by N ewsRx editors, research stated, “The performance of electrochemical double-layer capacitors (EDLCs) is evaluated by the capacitance of activated carbon (AC) ele ctrodes.” Our news reporters obtained a quote from the research from Tribhuvan University: “The capacitance of AC electrodes is influenced by many factors such as precurs or type, activation method, pore structure, surface chemistry and electrolytic p roperties. In this paper, we present a comparative study of machine learning bas ed prediction of surface area, mesopore volume and total pore volume of activate d carbon for energy storage applications. The ML models were trained on a datase t of synthetic data that were generated from the limited number of experimental data and which included the activation temperature, methylene blue number and io dine number of the activated carbon (AC). The best performing ML model was rando m forest model and XG boost model.”
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