title: Back propagation neural network model for predicting the performance of immobilized cell biofilters handling gas-phase hydrogen sulphide and ammonia.
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/m(3) ·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m(3) ·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 (R (2)) values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently.
authors.affiliation: Core 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.
authors.forename: Eldon R
authors.initials: ER
authors.lastname: Rene
authors.forename: M Estefanía
authors.initials: ME
authors.lastname: López
authors.forename: Jung Hoon
authors.initials: JH
authors.lastname: Kim
authors.forename: Hung Suck
authors.initials: HS
authors.lastname: Park
mesh_terms: D000641:Ammonia / Q000737:chemistry* / Q000633:toxicity; D005740:Gases / Q000737:chemistry*; D006801:Humans; D006862:Hydrogen Sulfide / Q000737:chemistry* / Q000633:toxicity; D016571:Neural Networks, Computer; D044367:Phase Transition
chemical_list: D005740:Gases; D000641:Ammonia; D006862:Hydrogen Sulfide