Artificial neural networks (ANNs) are powerful data-driven modeling tools which have the potential to approximate and interpret complex input/output relationships based on given sets of a data matrix. In this paper, a predictive computerized approach is proposed to predict the performance of an immobilized-cell biofilter treating NH 3 vapors in terms of its removal efficiency (RE) and elimination capacity (EC). The input parameters to the ANN model were inlet concentration, loading rate, flow

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... te and pressure drop, and the output parameters were RE and EC. The data set was divided into two parts: a training matrix consisting of 51 data points, and a test matrix with 16 data points representing each parameter considered in this study. Earlier experiments of continuous biofilter operation showed removal efficiencies from 60 to 100% at inlet loading rates (ILRs) varying between 0.5 to 5.5 g NH 3 /m 3 /h. Internal network parameters of the ANN model during simulation were selected using the 2 k factorial design, and the best network topology for the model was thus estimated. Predictions were evaluated based on their d coefficient values (R 2 ). The results showed that a multilayer network (4-4-2) with a back propagation algorithm was able to predict biofilter performance effectively with R 2 values of 0.9825 and 0.9982. The proposed ANN model for biofilter operation could be used as a potential alternative for knowledge-based models through proper training and testing of variables.