Contents lists available at ScienceDirect Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman Research article Biosorptive removal of Zn(II) ions by Pongamia oil cake (Pongamia pinnata) in batch and fixed-bed column studies using response surface methodology and artificial neural network Muthusamy Shanmugaprakash a , Sivakumar Venkatachalam b , Karthik Rajendran c , Arivalagan Pugazhendhi d,* a Downstream Processing Laboratory, Department of Biotechnology, Kumaraguru College of Technology, Coimbatore, India b Department of Chemical Engineering, A.C. Tech, Anna University, Chennai, 600 025, India c Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR, United States d Innovative Green Product Synthesis and Renewable Environment Development Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam ARTICLE INFO Keywords: Artificial neural networks Biosorption Zn(II) ions Pongamia oil cake Response surface methodology Isotherm Kinetics ABSTRACT Design of experiment and artificial neural networks (ANN) have been effectively employed to predict the rate of uptake of Zn(II) ions onto defatted pongamia oil cake. Four independent variables such as, pH (2.0–7.0), initial concentration of Zn(II) ions (50–500 mg/L), temperature (30ºC-50 °C), and dosage of biosorbent (1.0–5.0 g/L) were used for the batch mode while the three independent variables viz. flowrate, initial concentration of Zn(II) ions and bed height were employed for the continuous mode. Second-order polynomial equations were then derived to predict the Zn(II) ion uptake rate. The optimum conditions for batch studies was found to be pH: 4.45, metal ion concentration: 462.48 mg/L, dosage: 2.88 g/L, temperature: 303 K and on the other hand the column studies flow rate: 5.59 mL/min, metal ion concentration: 499.3 mg/L and bed height: 14.82 cm. Under these optimal condition, the adsorption capacity was 80.66 mg/g and 66.29 mg/g for batch and column studies, re- spectively. The same data was fed to train a feed-forward multilayered perceptron, using MATLAB to develop the ANN based model. The predictive capabilities of the two methodologies were compared, by means of the ab- solute average deviation (AAD) (4.57%), model predictive error (MPE) (4.15%), root mean square error (RMSE) (3.19), standard error of prediction (SEP) (4.23) and correlation coefficient (R) (0.99) for ANN and for RSM AAD (16.27%), MPE (21,25%), RMSE (13.15%), SEP and R (0.96) by validation data. The findings suggested that compared to the prediction ability of RSM model, the properly trained ANN model has better prediction ability. In batch studies, equilibrium data was used to determine the isotherm constants and first and second order rate constants. In column, bed depth service time (BDST) and Thomas model was used to fit the obtained column data. 1. Introduction Due to the extensive utilization of machines and rapid in- dustrialization, the disposal of heavy metals into the environment persists as a major threat to the ecosystem. The presence of heavy metals though in trivial amounts can significantly pollute the aqueous streams and lands, which poses a potential risk of pollution. Hence, it becomes mandatory to reduce the heavy metal content of the waste- water to the permissible limits (Jacob et al., 2018; Paduraru et al., 2015; Pugazhendhi et al., 2018). Zinc is considered to be a toxic heavy metal that can be observed in water streams, and in effluents of several industries, such as electroplating, metal finishing, foundry and mining (Ahmad et al., 2013; Viswanadham et al., 2000). On an average, the human body holds about 2 g of zinc, which is vital for DNA poly- merization activity and protein synthesis. Nevertheless, the inhalation of zinc fumes may result in “Zinc Fever”, characterized by fevers and chills (Gupta and Sharma, 2003). The presence of zinc ions beyond the permissible range causes serious health issues, such as stomach cramps, vomiting, skin irritations, nausea, accumulative poisoning, anemia, brain damage, cancer, etc (Hall, 2002). Hence, the effective and safe removal of zinc ions from wastewater is a difficult task, partially due to lack of cost effective treatments (Weng and Huang, 1994). https://doi.org/10.1016/j.jenvman.2018.08.088 Received 23 January 2018; Received in revised form 11 August 2018; Accepted 23 August 2018 * Corresponding author. E-mail address: arivalagan.pugazhendhi@tdt.edu.vn (A. Pugazhendhi). Journal of Environmental Management 227 (2018) 216–228 0301-4797/ © 2018 Elsevier Ltd. All rights reserved. T