Textile wastewater heavy metal removal using Luffa cylindrica activated carbon: an ANN and ANFIS predictive model evaluation

Abstract

This study investigated the application of soft computing models [Artificial neural network (ANN) and Adaptive neuro-fuzzy inference system (ANFIS)] in removing heavy metals [chromium (VI), vanadium (V) and iron (II)] from textile wastewater using Luffacylindrica activated carbon (LAC). The effect of pH, contact time and adsorbent dosage on the adsorptive potential of the prepared LAC were determined using a batch mode experiment. Fourier Transform Infrared Spectroscopy and scanning electron micrograph assessed the potential of the adsorbent in this study. ANN and ANFIS were evaluated using the coefficient of determination (R2) and mean square error (MSE). The result showed that the models demonstrated significant predictive behavior with R2 (9.9999E−1), MSE (5.985E−14) for chromium(VI) removal, R2 (9.9999E−1), MSE (2.33856E−13) for iron(II) removal and R2 (9.9999E−1), MSE (7.22197E−12) for vanadium(V) removal for ANN, while ANFIS predicted R2 (0.76305), MSE (0.037105) for chromium(VI) removal, R2 (0.67652), MSE (0.846) for iron(II) removal, R2 (0.22673), MSE (0.65925) for vanadium(V) removal. Sensitivity analysis carried out with ANFIS (exhaustive search) indicated that the parameters (time, pH and adsorbent dosage) significantly impact the heavy metal removal. Thus, this study shows that ANN and ANFIS are reliable tools for modelling heavy metal removal using LAC. The parameter results obtained are relevant in process design and control.

Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

“…Compared to the others, developed ANFIS model in this study performed better than the studies [ 13 , 15 , 16 ] in terms of RMSE and/or R 2 , indicating better predictive accuracy. In addition, our study performed well in terms of R 2 and is comparable with the studies [ 14 , 17 , 18 ].…”

“…It was concluded that MF numbers for each inputs can affect the predictions in different ranges, and depending on the variety and data type optimum MF number could be different. In the literature, the researchers tend to use the same MF numbers for each input [ [6] , [7] , [8] , [9] , [10] , [11] , [13] , [14] , [15] , [16] ]. As we observed that in order to make good predictions these MF numbers should be optimized for each input and for each different dataset.…”

“… 3-3-3-3 0.007–0.997 [ 14 ] Cr(VI) removal from textile wastewater using activated carbon/ANFIS Time pH Adsorbent (g) Cr(VI) adsorption percentage (%) Psigmf Linear 3-3-3 n.a. - 0.3916 [ 15 ] Cr(VI) adsorption onto activated eucalyptus biochar/ANFIS Cr(VI) conc. (mg/L) Sorbent (g/L) pH Cr(VI) removal percentage (%) Gbellmf Constant 3-3-3 5.5090–0.920 [ 16 ] Cr(VI) removal by NiO nanoparticles/ANFIS-PSO (Particle swarm optimization) Contact time Cr(VI) conc.…”

“…They indicated that the number of MFs for each input and the type of MF for each input are the structural parameters for grid partitioning ANFIS, and they tested all MFs but only with limited MF numbers (2 and 3) for each inputs [ 12 ]. There are some studies [ [13] , [14] , [15] , [16] , [17] ] related to the prediction of chromium adsorption via ANFIS, however, none of them focused on optimization of ANFIS parameters. Zafar et al [ 13 ] employed Gaussian MF for each inputs (pH, adsorbent dosage, initial concentration) on Cr(VI) adsorption data.…”

“…Banza et al [ 14 ] indicated that compared to ANN and RSM, ANFIS is the best prediction model for Cr(VI) adsorption onto cellulose nanocrystals–sodium alginate and, however, there is no information about the chosen MF type for inputs (time, dosage, pH and concentration) and output (adsorption capacity). Cr(VI) removal in textile wastewater using activated carbon was predicted with ANFIS in another study [ 15 ], as typical, without any systematic approach all MF types with one and two input numbers were examined. Yusuff et al [ 16 ] predicted Cr(VI) adsorption onto activated eucalyptus biochar by applying ANFIS with generalized bell-shaped MF and 3 of MF numbers for each inputs (pH, concentration and time).…”