Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial

Abstract

Due to the excessive use of paracetamol (PCM), a significant amount of its metabolite has been released into the surroundings, and its removal from the surroundings must happen quickly and sustainably. Multicomponent adsorption modelling is difficult because it is challenging to anticipate the relationships among the adsorbates in this artificial intelligence-based modelling, a choice among different algorithms. Utilizing various algorithms, many studies assessed the single and binary adsorption of paracetamol on activated carbon. The present study implements that the effectiveness of PCM adsorption on a carbon-activated nanomaterial was predicted using an artificial neural network, a machine learning technology. As a factor of adsorbent particle size, adsorbent dosage, training time, and starting concentrations, the adsorption capacity for each medicinal ingredient was examined. SEM was used to analyze a nanomaterial that had been chemically altered with orthophosphoric acid (FTIR). To determine the residual proportion of PCM in solvent, batch adsorption of PCM was then carried out at various operation conditions, including contact time, temperatures, and initial dosage. The adsorption effectiveness of paracetamol on carbon-activated nanoparticle was calculated using experimental results. Thus, by using machine learning framework, the adsorption efficiency of paracetamol on a carbon-activated nanomaterial was predicted.