Pollutants removals and energy consumption in electrochemical cell for pulping processes wastewater treatment: Artificial neural network, response surface methodology and kinetic studies.

Response surface methodology (RSM) and artificial neural network (ANN) were used for modelling the electrocoagulation removal of pollutants from wastewater from pulping processes. The Design of Experiment based on central composite design was used to investigate the combine effects of pH (5.4-9.0), time (10-45 min) and current density (j) (9-39 mA/m), on the removal efficiency of the Chemical Oxygen Demand (COD), Total Dissolve Solids (TDS) as well as Turbidity while Energy consumption (EC) was estimated per kg [COD] removed. The kinetics of the process was modelled with pseudo first and second order models. The removability of the COD, TDS and Turbidity were found to be 76.4, 57.0 and 97.13% with Energy consumption of 2.72 kWh/kg at optimal pH 6.83, current density of 22.06 mA/m, and reaction time of 45 min. The ANN model gave a better fitting of the electrocoagulation process than the RSM, considering the R of 0.999 and MSE of 0.00753 obtained for the former. The pseudo first order model gave a better analysis of the kinetic data. The characterization of the sludge produced showed the potential of its use as adsorbent for organic or mineral contaminants and recovery of aluminium and other metals. Thus, electrocoagulation with monopolar aluminium electrodes displayed effective and a viable alternative for the pollutants removal from pulp processing wastewater.