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 (EC0), current density (CD), and number of pulses (No,), 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/m(2), EC0 = 856 mu S/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. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.