A Machine Centric Computing Model for IoT Based Estimation of Bubble Diameter in Gas Liquid Systems
Ramamoorthy M L
Department of Electrical and Electronics Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi, Tamil Nadu, India.
In gas–liquid systems, the precise estimation of bubble diameter plays a critical role in analyzing mass transfer, interfacial area, and flow dynamics. We have suggested a machine-centric IoT-integrated computing paradigm called BubbleGLS, which can estimate bubble diameter in real-time leveraging multimodal sensor data and hybrid machine learning. The overall system connects pressure, acoustic, flow and optical sensors that are located above the cylindrical reactor. These sensors record dynamic parameters which are denoised and normalised by means of wavelet filtering and Z-score normalisation. Bubble area, circularity, rise velocity, and acoustic signatures are used as feature extraction and combined through Dempster Shafer Theory which provides noise resistance. The learning engine consists of Inception network of spatial features based on an image and XGBoost of structured physical parameters. The model is deployed onto fog and edge devices, and it provides real-time lower than 30 milliseconds latency inference. The validation of 10 different flow regimes reveals that the level of the mean absolute error (MAE) output by BubbleGLS does not exceed 0.25 mm, whereas its R2 score is higher than 0.97, thus being superior to CNN-LSTM, MobileMe, and Random Forest. It is also resilient as it can remain steady in the accuracy in different noise levels that are up to 45dB. To be used in the smart industrial space where fast response and low cloud reliance are the key factors, BubbleGLS has been optimized. Its modular design and the aspect of this design being machine-specific allows it to be implemented on an otherwise distributed fluidic system with much little calibration to fit its recalibration. All in all, the system reveals a powerful potential in the future third-generation fluid monitoring system, promising a high performance, low-latency, and intelligent character of bubble diameter estimation in a full-scale gas and liquid scenario.
Keywords
Bubble Diameter Estimation, Gas Liquid Systems, Machine Learning, Edge Computing, IoT Sensors, Dempster Shafer Theory, Fog Analytics, Inception Network.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Ramamoorthy M L, Gayathri C S, Ramya P, Indhuja N and Sathishkumar R;
Methodology: Ramamoorthy M L and Gayathri C S;
Software: Ramya P, Indhuja N and Sathishkumar R;
Data Curation: Ramamoorthy M L, Gayathri C S, Ramya P, Indhuja N and Sathishkumar R;
Writing- Original Draft Preparation: Ramamoorthy M L and Gayathri C S;
Visualization: Ramya P, Indhuja N and Sathishkumar R;
Investigation: Ramamoorthy M L, Gayathri C S, Ramya P, Indhuja N and Sathishkumar R;
Supervision: Ramamoorthy M L and Gayathri C S;
Validation: Ramya P, Indhuja N and Sathishkumar R;
Writing- Reviewing and Editing: Ramamoorthy M L, Gayathri C S, Ramya P, Indhuja N and Sathishkumar R;
All authors reviewed the results and approved the final version of the manuscript.
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Ramamoorthy M L
Department of Electrical and Electronics Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi, Tamil Nadu, India.
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Cite this article
Ramamoorthy M L, Gayathri C S, Ramya P, Indhuja N and Sathishkumar R, “A Machine Centric Computing Model for IoT Based Estimation of Bubble Diameter in Gas Liquid Systems”, Journal of Machine and Computing, vol.5, no.4, pp. 2006-2018, October 2025, doi: 10.53759/7669/jmc202505157.
