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.

: An additive of reactive hot melt adhesive (RHMA), thermoplastic polyurethanes (TPUs) was modified with neat sodium montmorillonite (Na-MMT) or Na-MMT intercalated with poly(ethylene glycol) (PEG), and their effects on the adhesion, rheological, and mechanical properties of RHMA were examined. The neat Na-MMT or Na-MMT intercalated with PEG (Na-MMT/PEG) effectively reduced the set time of RHMA, although the contents of neat Na-MMT or Na-MMT/PEG were less than 0.2 %. The increase of complex viscosity and pseudo-solid like behavior observed at low shear rate indicated that there were intimate interactions between RHMA molecules and neat Na-MMT or Na-MMT/PEG.