Comparative analysis of feed-forward neural network and second-order polynomial regression in textile wastewater treatment efficiency

This study refines a single-layer Feed-Forward Neural Network (FFNN) for the treatment of textile dye wastewater, concentrating on percentage decolorization (%DEC) and percentage chemical oxygen demand (%COD) reduction. The optimized neural network configuration comprises four input and one output neuron, fine-tuned based on the mean squared error (MSE). The training phase demonstrates a consistent MSE decline, reaching its lowest at epoch 209 for %DEC and epoch 34 for %COD, with corresponding MSEs of $1.799\times {10}^{-5}$ and $1.4\times {10}^{-3}$, respectively. The maximum absolute errors for %DEC and %COD were found to be $4.0787$ and $2.4486$, while the mean absolute errors were $0.4821$ and $0.7256$, respectively. In contrast to second-degree polynomial regression, the FFNN model exhibits enhanced predictive accuracy, as indicated by higher $R^2$ values of $0.99363$ for %DEC and $0.99716$ for %COD, and reduced error metrics.