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

Abstract

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.

Keywords

• feed-forward neural network
• machine learning
• multi-objective optimization
• treatment of textile wastewater
• decolorization
• chemical oxygen demand
• sustainable environment