Modelling of lead removal from battery industrial wastewater treatment sludge leachate on cement kiln dust by using Elman's RNN

Abstract

In this study, an Elman type recurrent neural network (RNN) was employed to develop a prediction model for lead removal from industrial sludge leachate using cement kiln dust. The leaching characteristics of industrial sludge were observed through the toxicity characteristics leaching procedure (TCLP). Dosage, contact time, and temperature were considered as independent experimental factors. A comparison between the model results and experimental data showed that the Elman's RNN model is able to predict lead removal from industrial sludge leachate. The outcomes of suggested Elman's RNN modelling were then compared to batch experimental studies. The results show that industrial sludge leachate using cement kiln dust is an efficient sorbent, and Elman's RNN is dynamic in nature and is able to model the batch experimental system.