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Use of Neural Network–Ultrasonic Technique for Measuring Gas and Solid Hold‐ups in a Slurry Bubble Column Bubble Colum
Author(s) -
Utomo M. B.,
Sakai T.,
Uchida S.
Publication year - 2002
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/1521-4125(200203)25:3<293::aid-ceat293>3.0.co;2-x
Subject(s) - slurry , artificial neural network , attenuation , ultrasonic sensor , nonlinear system , bubble , acoustics , bubble column reactor , process engineering , ultrasound , column (typography) , biological system , engineering , materials science , computer science , mechanical engineering , gas bubble , mechanics , artificial intelligence , physics , environmental engineering , optics , quantum mechanics , connection (principal bundle) , biology
A novel technique for measuring simultaneously the gas and solid hold‐ups in a slurry bubble column using a combination of neural network–ultrasonic method was investigated in this study. A one‐dimensional model using the basic parameters of ultrasound (the energy attenuation and the velocity change in terms of the transmission time difference) for measuring the gas and the solid hold‐ups has been proposed to show the complexity of the system. The three layers feed‐forward neural network (3‐FFNN) structure has been used to try and solve the nonlinear relationship between parameter sensing and measurement purpose. An adequate selection of the neural network structure has been chosen to perform the relationship between the measurement sensing (input of the network) and the measurement purpose (output of the network). Preliminary representation results of the gas and the solid hold‐ups using the proposed method compare relatively well with measured data.