Multivariate optimization of the electrochemical degradation for COD and TN removal from wastewater: An inverse computation machine learning approach

[Display omitted] •Stability and generalization ability of FFNN trained by small dataset is enhanced.•A neuro-genetic hybrid ML framework is developed to perform inverse computation.•6.1% and 9.9% improvement in COD and TN removal are achieved by GANN optimization. An inverse computation machine learning (ML) framework that couples genetic algorithm (GA) to feedforward neural network was developed to optimize the operating parameters for maximizing electrochemical removal of chemical oxygen demand (COD) and total nitrogen (TN) from wastewater. Conventional neural networks (NN) are generally unable to implement inverse computation from a desired abatement to feasible input conditions, and NN trained from small-scale datasets tends to be unreliable for multivariate simulation. To address this issue, we employed GA for tuning the weights and biases of the network to enhance the stability and generalization performance of NN, the optimization of multiple operating conditions was performed by the GA strategy of iterative evolution and global search. In this work, we investigated and analyzed the performance of multivariate optimization approaches such as orthogonal design, response surface methodology (RSM), traditional NN, and the developed neuro-genetic model (GANN). Significance and error analysis demonstrate that GANN exhibits superior stability and generalization ability with the highest R2 of 0.946 (COD), 0.874 (TN), and the lowest RMSE of 0.022 (COD), 0.028 (TN). The introduction of GA is significant for improving the prediction accuracy of NN derived from small-scale datasets. The average error of the GANN trained by 25 data points is lower than that of the RSM model derived from 54 data points. According to the validation outcomes, the scheme suggested by GANN achieves the best COD (93.6%) and TN (62.8%) degradation efficiencies, which are 6.1% and 9.9% higher than the optimal values in the original dataset, respectively. The proposed multivariate global optimization strategy can be extended to other cases, and the computational framework of GANN also contributes to the progress of more reliable ML models.