Impurity gas monitoring using ultrasonic sensing and neural networks: forward and inverse problems

Zhuang, Bozhou; Gencturk, Bora; Oberai, Assad; Ramaswamy, Harisankar; Meyer, Ryan

doi:10.1016/j.measurement.2023.113822

Impurity gas monitoring using ultrasonic sensing and neural networks: forward and inverse problems

Journal Article · Tue Nov 07 00:00:00 EST 2023 · Measurement

DOI:https://doi.org/10.1016/j.measurement.2023.113822· OSTI ID:2441115

^[1]; ^[1]; ^[1]; Ramaswamy, Harisankar ^[1]; ^[2]

Univ. of Southern California, Los Angeles, CA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Ultrasonic sensing is a non-invasive technique for monitoring impurity gas composition in various industrial applications where safety and regulatory compliance are crucial. In this study, ultrasonic sensing and neural networks were used to analyze impurity gases (i.e., air and argon) in helium. An experimental platform was established to acquire ultrasonic data. In the forward problem, an artificial neural network (ANN) model was used to forecast the response and time-of-flight (TOF) based on the excitation, and argon and air concentrations. The inverse problem was solved using a convolutional neural network (CNN) to predict the argon and air concentrations given the ultrasonic response and excitation. The results showed that the ANN accurately predicted the ultrasonic response and the change in TOF with concentration. As the air concentration was increased from 0 to 9.8%, the TOF sensitivity to detect argon decreased by 39.8% and 16.1% from ANN and sound speed theory, respectively. The CNN demonstrated high accuracy in predicting concentrations for inputs in the testing dataset. The application of the trained CNN indicated that it over-predicts air concentration while under-predicting the argon concentration. To improve accuracy, the predicted air and argon concentrations should be corrected by -0.992% and 1.027% bias, respectively.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Nuclear Energy (NE), Nuclear Energy University Program (NEUP)

Grant/Contract Number:: AC05-00OR22725; NE0009171

OSTI ID:: 2441115

Alternate ID(s):: OSTI ID: 2368975

Journal Information:: Measurement, Journal Name: Measurement Vol. 223; ISSN 0263-2241

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Impurity gas monitoring using ultrasonic sensing and neural networks: forward and inverse problems

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