Nasseh - M.Sc. student, Department of Chemical Engineering, Shahid Bahonar University of Kerman
Mohebbi - Ph.D, Corresponding Author Department of Chemical Engineering, Shahid Bahonar University of Kerman
Jeirani - M.Sc. Student, Department of Chemical Engineering , Shahid Bahonar University of Kerman
Sarrafi - Ph.D, Department of Chemical Engineering, Shahid Bahonar University of Kerman

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 the gas velocity in the throat of venturi scrubber (Vgth), liquid to gas flow rate ratio (L/G), and axial distance of the venturi scrubber (z). Five 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.

Venturi Scrubber - Pressure Drop – Back Propagation - ANNs