Prediction of Pressure Drop in Venturi Scrubbers by Multi-Gene Genetic Programming

What is it about?

In this study, two new approaches based on Multi-Gene Genetic Programming (MGGP) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to predict the pressure drop in venturi scrubbers. The main parameters studied were the throat gas velocity of venturi scrubbers (Vgth), the liquid to gas flow rate ratio (L/G), and the axial distance of the venturi scrubbers (z) as the inputs to the network, while the pressure drop was as the output. One set of experimental data, which was gathered from five different venturi scrubbers including a circular and an adjustable prismatic venturi scrubber with a wetted wall irrigation, a rectangular venturi scrubber and two ejector venturi scrubbers with different throat diameters was applied for this study.

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Why is it important?

In this study, two new approaches based on Multi-Gene Genetic Programming (MGGP) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to predict the pressure drop in venturi scrubbers. The main parameters studied were the throat gas velocity of venturi scrubbers (Vgth), the liquid to gas flow rate ratio (L/G), and the axial distance of the venturi scrubbers (z) as the inputs to the network, while the pressure drop was as the output. One set of experimental data, which was gathered from five different venturi scrubbers including a circular and an adjustable prismatic venturi scrubber with a wetted wall irrigation, a rectangular venturi scrubber and two ejector venturi scrubbers with different throat diameters was applied for this study.