title: Predicting pressure drop in venturi scrubbers with artificial neural networks.
abstract: In this study a new approach based on artificial neural networks (ANNs) has been used to predict pressure drop in venturi scrubbers. The main parameters affecting the pressure drop are mainly the gas velocity in the throat of venturi scrubber (V(g)(th)), liquid to gas flow rate ratio (L/G), and axial distance of the venturi scrubber (z). Three sets of experimental data from five different venturi scrubbers have been applied to design three independent ANNs. Comparing the results of these ANNs and the calculated results from available models shows that the results of ANNs have a better agreement with experimental data.
authors.affiliation: Department of Chemical Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
authors.forename: S
authors.initials: S
authors.lastname: Nasseh
authors.forename: A
authors.initials: A
authors.lastname: Mohebbi
authors.forename: Z
authors.initials: Z
authors.lastname: Jeirani
authors.forename: A
authors.initials: A
authors.lastname: Sarrafi
mesh_terms: D000397:Air Pollution / Q000517:prevention & control*; D016571:Neural Networks, Computer; D011312:Pressure