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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  Luffa  cylindrica  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 ( R  2 ) and mean square error (MSE). The result showed that the models demonstrated significant predictive behavior with  R  2  (9.9999E−1), MSE (5.985E−14) for chromium(VI) removal,  R  2  (9.9999E−1), MSE (2.33856E−13) for iron(II) removal and  R  2  (9.9999E−1), MSE (7.22197E−12) for vanadium(V) removal for ANN, while ANFIS predicted  R  2  (0.76305), MSE (0.037105) for chromium(VI) removal,  R  2  (0.67652), MSE (0.846) for iron(II) removal,  R  2  (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.

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