Optimization and prediction of lead removal from aqueous solution using FO–MD hybrid process: Statistical and artificial intelligence analysis

Abstract

Heavy metals (HMs) has become one of the most serious pollutants that are harmful to the environment and ecology. This paper focused on the removal of lead contaminant from wastewater by forward osmosis–membrane distillation (FO–MD) hybrid process using seawater as draw solution. Modeling, optimization, and prediction of FO performance are developed using complementary approach based on response surface methodology (RSM) and an artificial neural network (ANN). FO process optimization using RSM revealed that under initial lead concentration of 60 mg/L, feed velocity of 11.57 cm/s and draw velocity of 7.66 cm/s, FO process achieved highest water flux of 6.75 LMH, lowest reverse salt flux of 2.78 gMH and highest lead removal efficiency of 87.07%. Fitness of all models was evaluated based on determination coefficient (R2) and mean square error (MSE). Results showed highest R2 value up to 0.9906 and lowest RMSE value up to 0.0102. ANN modeling generates the highest prediction accuracy for water flux and reverse salt flux, while RSM produces the highest prediction accuracy for lead removal efficiency. Subsequently, FO optimal conditions are applied on FO–MD hybrid process using seawater as draw solution and evaluate their performance to simultaneously remove lead contaminant and desalination of seawater. Results displays that FO–MD process shows a highly efficient solution to produce fresh water with almost free heavy metals and very low conductivity.