Adsorption of copper(II) on chemically modified biochar: a single-stage batch adsorber design and predictive modeling through artificial neural network

Abstract

Waste materials generated from agriculture and fruit-processing industries can provide cost-effective and cost-efficient materials for the removal of contaminants from aqueous medium. This study deals with adsorption of copper ions from aqueous phase by employing orthophosphoric acid–modified biochar derived from coconut (Cocos nucifera) husk. Biochar characteristics demonstrated increased surface area (24 times) and a porous structure with functional groups such as –OH, –N-H, –CH2, C=O, and –C-N. These were responsible for active adsorption sites. Key process parameters were optimized and maximum removal efficiency was obtained at dose (0.4\xa0g/L), time (60\xa0min), pH (6), and initial concentration (10\xa0mg/L). To predict the adsorptive removal, artificial neural network (ANN) modeling was performed. The mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2) provided by ANN model at optimized conditions were found to be 2.63, 4.60, and 0.91, respectively. The behavior of Cu(II) adsorption was closely predicted based on a feed-forward ANN (back propagation) learning algorithm with 4–2–1 topological arrangement. The equilibrium data suggested Langmuir isotherm with a maximum monolayer adsorption capacity of 175.44\xa0mg/g and R2\u2009=\u20090.990 to be the best-suited model. Coconut husk, being an easily available agro-waste, can therefore be used efficiently as a low-cost precursor for the development of an economical adsorbent for inorganic and organic pollutants.