Process optimization and adsorption modeling using activated carbon derived from palm oil kernel shell for Zn (II) disposal from the aqueous environment using differential evolution embedded neural network

Presence of toxic heavy metals like Zn (II) in an aqueous medium is growing environmental problem. Researchers are continuously thriving to develop efficient techniques to remove pollutants from wastewater and industrial effluents. Adsorption is found to be an effective treatment technique, but its applications in process industries limit itself due to the high cost of adsorbent. In this regard, a low-cost adsorbent produced from palm oil kernel shell based agricultural waste were examined for its efficiency to remove Zn (II) from the aqueous effluent. The performance of adsorption technique depends on the independent process variables. Therefore, the influence of parameters like initial solution concentration, pH, residence time, activated carbon dosage and process temperature on the removal of Zn (II) from the batch adsorption process were studied systematically. The optimal values of independent process variables to achieve maximum removal efficiency were studied using conventional response surface methodology (RSM) and data-driven modeling technique like artificial neural network (ANN). The meta-heuristic differential evolution optimization is embedded on to the ANN architecture to optimize the search space of neural network. The optimized trained neural network depicts the testing data and validation data with R2 equal to 0.995 for both cases. The multiple outcomes of this study indicate the superiority of ANN-DE based model predictions over the traditional quadratic model predictions provided by RSM.