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 x 10-5 and 1.4 x 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 R2 values of 0.99363 for %DEC and 0.99716 for %COD, and reduced error metrics.