Modelling of lead adsorption from industrial sludge leachate on red mud by using RSM and ANN

In this study, response surface methodology (RSM) and artificial neural network (ANN) were employed to develop prediction models for lead removal from industrial sludge leachate using red mud. The leaching characteristics of industrial sludge were observed by Toxicity Characteristics Leaching Procedure (TCLP). Dosage, time and pH were considered as independent experimental factors. Box–Behnken design (BBD) was chosen for the response surface design setup and was also used as Neural Network Training Set for comparison purposes. To evaluate the accuracy of results, several experiments were then conducted. The results of ANN were found to be more reliable than RSM since better statistical parameters were obtained.

► Lead is a toxic metal known to be harmful to the nervous systems of humankind.
► Adsorption is an effective process for removing lead from wastewater.
► Red mud has been recently considered as a cheap adsorbent for Pb(II) removal.
► We have used RSM and ANN to investigate the capability of red mud for lead removal.