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In this paper, nonlinear system identification of the activated sludge process in an industrial wastewater treatment plant was completed using adaptive neuro-fuzzy inference system (ANFIS) and generalized linear model (GLM) regression. Predictive models of the effluent chemical and 5-day biochemical oxygen demands were developed from measured past inputs and outputs. From a set of candidates, least absolute shrinkage and selection operator (LASSO), and a fuzzy brute-force search were utilized in selecting the best combination of regressors for the GLMs and ANFIS models respectively. Root mean square error (RMSE) and Pearson's correlation coefficient (R-value) served as metrics in assessing the predicting performance of the models. Contrasted with the GLM predictions, the obtained modeling results show that the ANFIS models provide better predictions of the studied effluent variables. The results of the empirical search for the dominant regressors indicate the models have an enormous potential in the estimation of the time lag before a desired effluent quality can be realized, and preempting process disturbances. Hence, the models can be used in developing a software tool that will facilitate the effective management of the treatment operation.

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This study investigated the viability of using waste groundnut shells (GSs) to produce efficient biosorbent capable of removing nitrophenol from aqueous solution. Waste GSs were washed thoroughly, dried, reduced to 2 mm particle size, and activated with ZnCl 2 . The surface and physical properties (moisture and ash content) of the activated GSs (AGSB) were characterized using Fourier transform infrared spectrometer (FTIR), and standard methods. Biosorption of nitrophenol was optimized using Box-Behnken design (BBD). Analysis of the optimization processes was carried out using statistical tools embedded in the Design Expert software (10.0.1). Batch biosorption was conducted to generate data to determine suitable adsorption isotherm models, adsorption kinetics models, and adsorption thermodynamics. The FTIR spectra of AGSB indicate a major shift in the functional groups after the activation process. The moisture and ash content of AGSB were 9.0% and 3.1%, respectively. The optimum adsorption capacity (AC) and removal efficiency (RE) obtained at 20 mg/L, 80 min, and 1.6 g were 7.888 mg/g and 90.62%, respectively. The AC and RE were well suited to quadratic models with correlation coefficient (R 2 ) of 0.9853 and 0.9914, respectively. Langmuir isotherm and pseudo-second-order kinetic models with R 2 of 0.999 and 0.9843 were most suitable. The enthalpy, entropy, and Gibb's free energy were in the ranges m − 20.46 to − 4.36, 20.14 to 86.17, and − 21.49 to − 5.02 kJ/mol, respectively. This study demonstrated the effectiveness of AGSB as biosorbent for wastewater treatment, thus facilitating the conversion of solid wastes for the amendment of nitrophenol concentration in the wastewater stream.

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