Eldon R. Rene, Jung Hoon Kim, Hung Suck Park

  • Department of Civil and Environmental Engineering, University of Ulsan,, P.O. Box – 18, Ulsan 680-749, South Korea

Received: August 31, 2009
Revised: August 31, 2009
Accepted: August 31, 2009
Download Citation: ||https://doi.org/10.4209/aaqr.2008.10.0046  

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Cite this article:
Rene, E.R., Kim, J.H. and Park, H.S. (2009). Immobilized Cell Biofilter: Results of Performance and Neural Modeling Strategies for NH3 Vapor Removal from Waste Gases. Aerosol Air Qual. Res. 9: 379-384. https://doi.org/10.4209/aaqr.2008.10.0046


 

ABSTRACT


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 NH3 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 rate 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 NH3/m3/h. Internal network parameters of the ANN model during simulation were selected using the 2k factorial design, and the best network topology for the model was thus estimated. Predictions were evaluated based on their d coefficient values (R2). The results showed that a multilayer network (4-4-2) with a back propagation algorithm was able to predict biofilter performance effectively with R2 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.


Keywords: Removal efficiency; Neural network; Prediction; Immobilization; Biofilter; Elimination capacity


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