Modeling nitrate concentrations in a moving bed sequencing batch biofilm reactor using an artificial neural network technique

In this study, the performance data of a moving-bed sequencing batch biofilm reactor (MBSBBR) treating synthetic wastewater were simulated using multi-layer perceptron neural-network technique. Multi-linear regression (MLR) technique is also used for a comparison. The performance of MBSBBR was evaluated using these models for a set of experimental results obtained from a model reactor operated with different cycle times and temperatures. The experimental data were retrieved from a previous reported work. Operational time, temperature, ammonium nitrogen, and pH were used as inputs for modeling, whereas nitrate concentration was the output variable. The results of the models were compared using statistical criteria, such as mean square error, mean absolute error, mean absolute relative error, and determination coefficient (R2). The results showed that the multi-layer perceptron neural-network produced more accurate results than those of MLR, although the latter gave reasonable results.