Machine learning approach for prediction of paracetamol adsorption efficiency on chemically modified orange peel

Abstract

High consumption of paracetamol (PCM) has led to the discharge of a large quantity of its metabolite into the environment and there is an urgent need to remove this harmful contaminant in a sustainable manner. In this work, Artificial Neural Network (ANN) was used as a Machine Learning tool for prediction of PCM adsorption efficiency on chemically modified orange peel (CMOP). Orange peel was chemically modified with orthophosphoric acid and then characterized using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). Thereafter, batch adsorption of PCM on CMOP were conducted at different operating conditions namely: contact time (0-330 min), temperature (30-50 °C) and initial drug concentration (10 mg/L-50 mg/L) to obtain the residual concentration of PCM in solution. Experimental data was used to compute the adsorption efficiency of PCM on CMOP. To predict the adsorption efficiency, different ANN architectures were examined. A neural network structure with Levenberg Marquardt (LM) training algorithm, 17 hidden neurons, and tangent sigmoid transfer function at both the input and output layers gave the best level of prediction. Comparing with experimental data, the optimal model yielded Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation coefficient (R²) of 5.8985 × 10^-04, 0.0243 and 0.9958 respectively. The results obtained showed that ANN is efficient in predicting the adsorption efficiency of PCM on CMOP.

Keywords: Adsorption; Artificial Neural Network; Paracetamol; Prediction.