Acid-activated Hibiscus sabdariffa seed pods biochar for the adsorption of Chloroquine phosphate: Prediction of adsorption efficiency via machine learning approach.

Abstract

ABSTRACT: A constant increase in the release of pollutants into the environment poses a threat as this practice continually degrades water quality. One of the proffered solutions is the adsorption of pollutants onto adsorbents. The present study emphasized the performance of Hibiscus sabdariffa seed pods (HSP1) for removing Chloroquine phosphate (CQ) from aqueous media via adsorption. A machine learning tool (Artificial Neural Network, ANN) was used to predict the efficiency of CQ removal onto the prepared biochar. Different operational parameters were investigated for their influence and impact on CQ adsorption. HSP1 was also characterized using different physicochemical and spectrophotometric techniques. HSP1 remarkably displayed an outstanding performance for the elimination of CQ with a qmax of 161.29 mg/g and percentage removal of 96.01%. The experimental data are well predicted by Pseudo Second Order (PSO) and Freundlich isotherm, indicating multilayer adsorption. The practicability and spontaneity of the adsorption process were confirmed through thermodynamic studies. A three-layer feedforward backpropagation network for the adsorption data was established using the Levenberg Marquardt training algorithm. The ANN model with structure 5-14-1, with Mean Square Error (8.01) and R2 value (0.9823) was able to envisage good removal efficiency. It was concluded that the ANN model could predict the adsorption behavior of CQ on HSP1.

Keywords

• Characterization
• Artificial Neural Network
• Adsorption
• Pharmaceuticals