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Humic substances (HSs) are a group of complex macromolecular polymeric compounds originating from the decomposition of plant residuals and other organic matter. Within the presence of micro-pollutants and heavy metals, HSs negatively act upon potable water quality by contributing to aesthetic problems such as yellowish or brownish color and annoying taste and odor. They are also responsible for re-growth of pathogenic microorganisms and fouling of membranes in water distribution systems. More importantly, these high-molecular-weight polymers have been noted to be the major contributor to the formation of disinfection by-products (DBPs) such as trihalomethanes (THMs) and haloacetic acids (HAAs). Considering these harmful effects, removal of HSs is one of the significant tasks in drinking water treatment. For this purpose, this study aimed to explore the effects of various operating parameters (initial concentration, initial pH, electrical conductivity, pulse time, pulse number, and process time) on the humic acid (HA) removal efficiency and energy consumption. In this study, a new current supply method called alternating pulse current electrocoagulation-flotation (APC-ECF) process was proposed, and a detailed comparative optimization of four different ECF processes (direct current (DC)-simple electrode, DC-perforated electrode, pulse current-simple electrode, and pulse current-perforated electrode) was conducted within the framework of Taguchi-based experimental design methodology. According to scanning electron microscopy (SEM), the morphology of electrode surfaces with APC and perforated electrode showed less disordered (irregular) pores and a regular structure of aluminum compared to the DC, which confirmed the difference in the corrosion rates. Moreover, the proposed APC-ECF method led to the production of less dewatered and dense sludge. The results of the performance analysis revealed that the APC with a perforated electrode provided 3.2-fold lower energy consumption and 2.5-fold lower aluminum consumption compared to the DC with a simple electrode. Considering the expenses associated with power consumption and sludge disposal costs for the electrocoagulation unit, the experimental findings corroborated that the proposed APC-ECF process could be used as a promising and cost-effective technology in water treatment for the removal of HSs. [ABSTRACT FROM AUTHOR]

A novel application of artificial neural networks (ANN) combined with Taguchi orthogonal experimental design methodology (27 runs, 3 levels, 6 factors) was introduced for modeling and optimization of a new alternating pulse current electrocoagulation-flotation (APC-ECF) process for the removal of humic acid (HA) from aqueous media. Two different ANN architectures, such as multilayer perceptron (MLP NN) and generalized feed forward (GFF NN), were proposed and trained to describe the nonlinear behavior of a laboratory-scale batch APC-ECF reactor. Various operating parameters, such as initial HA concentration (C0), initial pH (pH0), electrical conductivity (EC0), current density (CD), and number of pulses (Npls), were used as inputs for the proposed networks, and the HA removal was selected as the output. According to the goodness-of-fit criteria, the computational results showed that the single hidden-layered GFF NN (5:6:1), where a sigmoid axon transfer function was used at its hidden layer and its output layer was trained by the Levenberg–Marquardt algorithm, showed the best performance (R2 = 0.999, MSE = 0.00006). For the optimal conditions of C0 = 42 mg/L, pH0 = 6.63, CD = 24.3 A/m2, EC0 = 856 μS/cm, and Npls = 3, the maximum HA removal was obtained based on the predicted outputs of the best ANN model (GFF NN). The results of the computational analysis clearly corroborated that ANN integrated design of experiments (DOE)-based modeling was rapidly and effectively used for predicting the optimum performance of a complex electrochemical process in removal of HA from water using aluminum electrodes in monopolar arrangement. [ABSTRACT FROM AUTHOR]

• A new alternating pulse current electrocoagulation-flotation process is introduced. • APC-ECF system is first modeled for removal of humic acid from water using ANN/DOE. • About 100% of HA is removed from water with an energy consumption of 1.08 kWh/kg HA. • ANN/DOE-based approach can describe behavior of a complex electrochemical process. A novel application of artificial neural networks (ANN) combined with Taguchi orthogonal experimental design methodology (27 runs, 3 levels, 6 factors) was introduced for modeling and optimization of a new alternating pulse current electrocoagulation-flotation (APC-ECF) process for the removal of humic acid (HA) from aqueous media. Two different ANN architectures, such as multilayer perceptron (MLP NN) and generalized feed forward (GFF NN), were proposed and trained to describe the nonlinear behavior of a laboratory-scale batch APC-ECF reactor. Various operating parameters, such as initial HA concentration (C 0), initial pH (pH 0), electrical conductivity (EC 0), current density (CD), and number of pulses (N pls), were used as inputs for the proposed networks, and the HA removal was selected as the output. According to the goodness-of-fit criteria, the computational results showed that the single hidden-layered GFF NN (5:6:1), where a sigmoid axon transfer function was used at its hidden layer and its output layer was trained by the Levenberg–Marquardt algorithm, showed the best performance (R 2 = 0.999, MSE = 0.00006). For the optimal conditions of C 0 = 42 mg/L, pH 0 = 6.63, CD = 24.3 A/m2, EC 0 = 856 μS/cm, and N pls = 3, the maximum HA removal was obtained based on the predicted outputs of the best ANN model (GFF NN). The results of the computational analysis clearly corroborated that ANN integrated design of experiments (DOE)-based modeling was rapidly and effectively used for predicting the optimum performance of a complex electrochemical process in removal of HA from water using aluminum electrodes in monopolar arrangement. [ABSTRACT FROM AUTHOR]