An intelligent neural network model for evaluating performance of immobilized cell biofilter treating hydrogen sulphide vapors

Abstract

Biofiltration has shown to be a promising technique for handling malodours arising from process industries. The present investigation pertains to the removal of hydrogen sulphide in a lab scale biofilter packed with biomedia, encapsulated by sodium alginate and poly vinyl alcohol. The experimental data obtained under both steady state and shock loaded conditions were modelled using the basic principles of artificial neural networks. Artificial neural networks are powerful data driven modelling tools which has the potential to approximate and interpret complex input/ output relationships based on the given sets of data matrix. A predictive computerised approach has been proposed to predict the performance parameters namely, removal efficiency and elimination capacity using inlet concentration, loading rate, flow rate and pressure drop as the input parameters to the artificial neural network model. Earlier, experiments from continuous operation in the biofilter showed removal efficiencies from 50 to 100 % at inlet loading rates varying up to 13 g H 2 S/m 3 h. The internal network parameter of the artificial neural network model during simulation was selected using the 2 k factorial design and the best network topology for the model was thus estimated. 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 0.9157 and 0.9965 for removal efficiency and elimination capacity in the test data. The proposed artificial neural network model for biofilter operation could be used as a potential alternative for knowledge based models through proper training and testing of the state variables.

Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model

“…In the previous years, the fuzzy logic has been used in the simulation of uncertainties in the water resources and environmental engineering such as river pollution management (Nasiri et al, 2007), centralized return centers location evaluation in a reverse logistics network (Tuzkaya and Gülsün, 2008) environmental performance evaluation of suppliers (Tuzkaya et al, 2009) and integrated water systems modeling (Nguyen et al, 2007). Artificial neural networks have been successfully applied to many tasks in environmental engineering (Bandyopadhyay and Chattopadhyay, 2007;Rene et al, 2008). Neuro-fuzzy modeling is another method that refers to the approach of applying deferent learning algorithms developed in the neural network literature to fuzzy modeling or a fuzzy inference system (FIS) (Brown and Harris, 1994).…”

Applying an ANFIS-based Algorithm in Comparison with Mechanistic Modelling in a Biofilter Treating Hexane

“…Elias et al [11] applied MLP (multi-layer perceptron with topology 2-2-1) neural network to predict removal efficiency (RE) in a biofilteration of polluted air treating hydrogen sulphide, Ibarra-Berastegi et al [12] compared the two MLP and Multiple Linear Regression (MLR) methods in a biofilter that eliminates hydrogen sulphide and it is revealed that MLP (2-2-1) model outperforms the MLR. Rene et al [13] proposed an (ANN) through aback propagation algorithm with 2 layers (topology of 4-4-2). The model is able to effective prediction of RE and elimination capacity (EC) in a hydrogen sulphide biofilteration.…”

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

“…It has been shown quite recently that the performance of biofilters and/-or biotrickling filters can be predicted from prior estimation of easily measurable operational parameters using ANNs [ 27 – 30 ]. In our previous studies, ANN-based predictive approach was proposed to model the performance of individually operated ICBs for H 2 S and NH 3 removal, respectively [ 31 , 32 ]. The outputs of the model were RE and EC, respectively, while the input parameters to the model were inlet concentration, loading rate, flow rate, and filter-bed pressure drop, respectively.…”