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BioMed Research International
Volume 2013 (2013), Article ID 463401, 9 pages
http://dx.doi.org/10.1155/2013/463401
Research Article

Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia

1Core Group Pollution Prevention and Resource Recovery, Department of Environmental Engineering and Water Technology, UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands
2Chemical Engineering Laboratory, Faculty of Sciences, University of La Coruña, Rúa da Fraga 10, 15008 La Coruña, Spain
3Department of Civil and Environmental Engineering, University of Ulsan, P.O. Box 18, Ulsan 680-749, Republic of Korea

Received 7 August 2013; Accepted 9 September 2013

Academic Editor: Kannan Pakshirajan

Copyright © 2013 Eldon R. Rene et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Lab scale studies were conducted to evaluate the performance of two simultaneously operated immobilized cell biofilters (ICBs) for removing hydrogen sulphide (H2S) and ammonia (NH3) from gas phase. The removal efficiencies (REs) of the biofilter treating H2S varied from 50 to 100% at inlet loading rates (ILRs) varying up to 13 g H2S/m3·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m3·h. An application of the back propagation neural network (BPNN) to predict the performance parameter, namely, RE (%) using this experimental data is presented in this paper. The input parameters to the network were unit flow (per min) and inlet concentrations (ppmv), respectively. The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R2) values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently.