Assessment of effective parameters in landfill leachate treatment and optimization of the process using neural network, genetic algorithm and response surface methodology

Abstract

Abstract In this paper, the Fenton process was found to be an effective technique to treat leachates when the drawbacks of this process were minimized by choosing proper catalyst, considering effective parameters and introducing optimum conditions. Analytic hierarchy process (AHP) was used to select the favorable catalyst between FeSO4 and FeCl2. Meanwhile, central composite design was used for test design of the experiments along with response surface methodology (RSM) and artificial neural network (ANN) for modeling. The effective variables included pH, [ H 2 O 2 ] / [ Fe 2+ ] , Fe 2+ dosage and initial COD concentration while removal COD, sludge to iron ration (SIR) and organic removal to sludge ratio (ORSR) were considered as targets. For all three targets, the effective factors were considered using sensitivity analysis. Finally, response surface methodology and genetic algorithm (GA) were used for optimization of the process. According to AHP sensitivity analysis results, priority percentage for FeCl2 and FeSO4 were 64% and 36%, respectively. Comparing RSM and ANN, it was found that ANN provided higher prediction ability while both models were statistically adequate (with R 2 > 0 .90 ) . The effective factors for COD removal, SIR and ORSR were respectively as follows: Fe 2+ dosage, pH and [ H 2 O 2 ] / [ Fe 2+ ] . The main focus of optimization was to achieve high removal efficiencies for COD when generated sludge is still low. Hence, results from RSM and GA indicated that both methods proposed approximately similar operational conditions. In the end, a practical formula was proposed for BOD5/COD ratio enhancement for each leachate.