Insight mechanism of ANN model for denitrification in spouted bed bioreactor

Numerous technologies have been developed to remove nitrate from wastewater due to its significant health and environmental impacts. In the present study, an isolate of Pseudomonas syringae was utilized to investigate the denitrification rate using immobilized granular activated carbon (GAC) in a draft tube spouted bed reactor. Developing a theoretical model for this reactor requires an understanding of multiple interrelated parameters, many of which may vary under different operating conditions. Given the inherent nonlinearities of the system, such modelling can be both complex and resource-intensive. Artificial Neural Networks (ANNs) offer a promising solution for modelling such nonlinear systems due to their flexibility and generalization capabilities. In this work, a feed-forward backpropagation neural network with three layers was constructed, consisting of three input neurons, nine neurons in the hidden layer, and one output neuron. The ANN model effectively predicted the effluent nitrate concentration in the reactor, achieving a correlation coefficient of 98.8%, a root mean square error (RMSE) of 9.25 × 10^-5%, an average absolute error of 0.57%, and a residual sum of squares (RSS) of 30.84. In comparison, the multiple regression analysis (MRA) model produced a lower correlation coefficient of 90%, a higher RMSE of 0.01%, an average absolute error of 1.32%, and an RSS of 166.63. These results demonstrate that the ANN model outperforms the regression model across all evaluated performance metrics, making it a more effective tool for capturing the complex dynamics of the denitrification process in the reactor.