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.
Highlights
Over the years, the increase in industrialization has adversely affected the environment
The intense peak at 879.7 cm−1 is attributed to the C−O stretching of alcohol, the band at 674.6 cm−1 indicates the C–I aromatic ring formation, the FT-IR spectrum suggests that the surface functional groups containing O2, which include the carboxyl groups and hydroxyl groups, influences the adsorption characteristics of Luffa cylindrica activated carbon (LAC); the FT-IR spectra of the adsorbent after adsorption are presented in Fig. 4, the adsorption of the heavy metal ions caused the intensity of the broad bands at 2985 cm−1 for CH bond vibration stretching, 1379.1 cm−1 for C=C stretching and 1006.4 cm−1 for C−O stretching of alcohol to increase, this proves that the heavy metal ions bonded with oxygen-containing functionalities on the adsorbent surface of LAC (Eletta et al.2019; Ullah et al 2020)
A similar result was reported for adsorption of lead(II) from aqueous solution using Africa elemi seed, mucuna shell and oyster shell as adsorbents (Okolo et al 2020) and Applied Water Science (2022) 12:52 Fig. 5 SEM image of LAC before adsorption
Summary
The increase in industrialization has adversely affected the environment. Excessive heavy metal levels get discharged into the atmosphere by releasing effluents from industries, leading to underground and open water bodies (Nwosu-obieogu et al 2021, 2020a). Toxic metals such as vanadium (V), chromium (Cr) depict as extremely dangerous even at trace levels on human. Several researchers have reported adsorption using LAC to be effective in heavy metals removal from industrial effluents; the removal of P b2+ in water, removal of divalent metals, methylene blue dye, cyanide ions, cadmium ions, common laboratory dye and adsorption of brilliant green from aqueous solutions have been reported by Adewuyi and Pereira (2017), Oboh et al (2011), Demir et al (2008), Arana et al (2017), Lindino et al (2014), Calciedo et al. . Several researchers have reported adsorption using LAC to be effective in heavy metals removal from industrial effluents; the removal of P b2+ in water, removal of divalent metals, methylene blue dye, cyanide ions, cadmium ions, common laboratory dye and adsorption of brilliant green from aqueous solutions have been reported by Adewuyi and Pereira (2017), Oboh et al (2011), Demir et al (2008), Arana et al (2017), Lindino et al (2014), Calciedo et al. Vol.:(0123456789)
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