AI-Based Smart Prediction of Liquid Flow System Using Machine Learning Approach

AI-Based Smart Prediction of Liquid Flow System Using Machine Learning Approach

Автор: Pijush Dutta, Gour Gopal Jana, Shobhandeb Paul, Souvik Pal, Sumanta Dey, Arindam Sadhu

Журнал: International Journal of Engineering and Manufacturing @ijem

Статья в выпуске: 1 vol.14, 2024 года.

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Predicting the liquid flow rate in the process industry has proved to be a critical problem to solve. To develop a mathematical, in-depth of physics-based prognostics understanding is often required. However, in a complex process control system, sometimes proper knowledge of system behaviour is unavailable, in such cases, the complement model-based prognostics transform into a smart process control system with the help of Artificial Intelligence. In previous research a number of prognostic methods, based on classical intelligence techniques, such as artificial neural networks (ANNs), Fuzzy logic controller, Adaptive Fuzzy inference system (ANFIS) etc., utilized in a liquid flow process model to predict the effectiveness. Due to system complexity, Computational time &over fitting the performance of the AI has been limited. In this work we proposed three machine learning regression model: Random Forest (RF), decision Tree (DT) & linear Regression (LR) to predict the flow rate of a process control system. The effectiveness of the model is evaluated in terms of training time, RMSE, MAE & accuracy. Overall, this study suggested that the Decision Tree outperformed than other two models RF & LR by achieving the maximum accuracy, least RMSE & Computational time is 98.6%, 0.0859 & 0.115 Seconds respectively.

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Liquid flow process, modeling, machine learning, regression analysis

Короткий адрес: https://sciup.org/15018840

IDR: 15018840   |   DOI: 10.5815/ijem.2024.01.05

Список литературы AI-Based Smart Prediction of Liquid Flow System Using Machine Learning Approach

  • A.Kuoni, R.Holzherr, M.Boillat and N.F. de Rooij,"Polyimide membrane with ZnO piezoelectric thin film pressure transducers as a differential pressure liquid flow sensor," Journal of Micromechanics and Microengineering, vol. 13, no. 4, pp. 103-107, 2003.
  • J. Branebjerg, O.S. Jensen, N.G. Laursen, O. Leistiko and H. Soeberg, "A micromachined flow sensor for measuring small liquid flows," TRANSDUCERS’91: 1991 International Conference on Solid-State Sensors and Actuators. Digest of Technical Papers, IEEE, pp. 41–44, 1991.
  • C.Yang and H. Søeberg,"Monolithic flow sensor for measuring millilitre per minute liquid flow," Sensors and Actuators A: Physical, vol. 33, no. 3,pp. 1143–53,1992
  • S. Wu, Q. Lin, Y. Yuen and Y.C Tai,"MEMS flow sensors for nano-fluidic applications," Sensors and Actuators A: Physical, vol. 89, no. 1-2, pp. 152-158, 2001.
  • M. Ashauer, H. Glosch, F. Hedrich, N. Hey, H. Sandmaier et al., "Thermal flow sensor for liquids and gases based on combinations of two principles," Sensors and Actuators A: Physical, vol. 73, no.1-2, pp. 7-13, 1999.
  • S. I. Ohira and K. Toda,"Miniature liquid flow sensor and feedback control of electroosmotic and pneumatic flows for a micro gas analysis system," Analytical Sciences, vol. 22, no. 1, pp. 61-65, 2006.
  • R. Hagihghi, A. Razmjou, Y. Orooji, M. E. Warkiani and M. Asadnia,"A miniaturized piezoresistive flow sensor for real-time monitoring of intravenous infusion," Journal of Biomedical Materials Research Part B: Applied Biomaterials, vol. 108, no.2, pp. 568-76, 2020.
  • J. Haneveld, T.S. Lammerink, M. Dijkstra, H. Droogendijk, M. J. de Boer et al., "Highly sensitive micro coriolis mass flow sensor," 2008 IEEE 21st International Conference on Micro Electro Mechanical Systems, IEEE, pp. 920-923, 2008.
  • J.H. Jerman, R.E. Toth, D.A. Winchell and D.W. Pennington,"Pulsed thermal flow sensor system, ", U.S. Patent 5,533,412, issued July 9, 1996.
  • J. Maul and F. Ohle, "Vortex flow sensor," U.S patent 6351999B1, 2002.
  • W.Y. Lam, "Disposable flow chamber electro-magnetic flow sensor," U.S Patent 7, 415, 892, 2008.
  • Y. Xu, R. Jiang, T. Zhang, C. Yuan, W. Chen et al.," Research on two-phase flow model for wet gas based on Venturi flow sensor," Transducer and Microsystem Technologies, vol. 35, no. 5, pp. 4-8, 2016.
  • S. G. Nnabuife, B. Kuang, J. F. Whidborne and Z. Rana,"Non-Intrusive Classification of Gas-Liquid Flow Regimes in an S-shaped Pipeline Riser Using a Doppler Ultrasonic Sensor and Deep Neural Networks," Chemical Engineering Journal, vol. 403, pp.126401, 2020.
  • G. Suna, Z. Tao, S. Lijun, Y. Zhen and Y. Wenliang, "Blade Shape Optimization of Liquid Turbine Flow Sensor," Transactions of Tianjin University, vol. 22, issue. 2, pp. 144-150, 2016.
  • M. W. Critchley and C.J. Koehler, "Flow sensor with hot film anemometer, " U.S patent 10,598,30, 2020.
  • P. Dutta and A. Kumar,"Design an intelligent flow measurement technique by optimized fuzzy logic controller," Journal Europen Des Systems Automatiss, vol. 51, no. 1-3, pp. 89-107, 2018.
  • S. K. Venkata and B.K Roy, "A practically validated intelligent calibration circuit using optimized ANN for flow measurement by venturi," Journal of The Institution of Engineers (India): Series B, vol. 97, no. 1, pp.31-39, 2016.
  • P. Dutta and A. Kumar, "Modelling of Liquid Flow Control system Using Optimized Genetic Algorithm," Statistics, Optimization & Information Computing, vol.8, no. 2, pp. 565-582, 2020.
  • P. Dutta and A. Kumar, "Design an intelligent calibration technique using optimized GA-ANN for liquid flow control system," Journal Européen Des Systèmes Automatisés, vol.50, no. 4-6, pp.449, 2017.
  • P. Dutta, S. Mandal and A. Kumar,"Application of FPA and ANOVA in the optimization of liquid flow control process," Review of Computer Engineering Studies, vol.5, no. 1, pp. 7-11, 2019.
  • J. W. Dambros, J. O. Trierweiler, M. Farenzena and M. Kloft,"Oscillation detection in process industries by a machine learning-based approach," Industrial & Engineering Chemistry Research, vol.58, no. 31, pp. 14180-14192, 2019.
  • C. Shang and F. You, "Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era," Engineering, vol.5, no. 6, pp.1010-1016, 2019.
  • J. T. McCoy and L. Auret, "Machine learning applications in minerals processing: A review," Minerals Engineering, vol. 132, no.1, pp. 95-109, 2019.
  • T. Rymarczyk, G. K. losowski, E. Kozlowski and P. Tchórzewski, "Comparison of selected machine learning algorithms for industrial electrical tomography,” Sensors, vol. 19, no.7, pp. 1521, 2019.
  • M. Said, K. ben Abdellafou and O. Taouali,"Machine learning technique for data-driven fault detection of nonlinear processes, " Journal of Intelligent Manufacturing, vol. 31, no. 4, pp.856-884, 2020.
  • K. Hansson, S. Yella, M. Dougherty and H. Fleyeh, "Machine learning algorithms in heavy process manufacturing," American Journal of Intelligent Systems, vol. 6, no. 1, pp. 1-13, 2016.
  • D.H. Kim, T. J. Kim, X. Wang, M. Kim, Y. J. Quan et al. "Smart machining process using machine learning: A review and perspective on machining industry," International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 5, no. 4, pp. 555-68, 2018.
  • M. Alizamir, S. Kim, O. Kisi and M.Z. Kermani, "A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions," Energy, vol. 197, pp. 117239, 2020.
  • O. Genç, B. Gonen and M. Ardıçlıoğlu,"A comparative evaluation of shear stress modeling based on machine learning methods in small streams," Journal of Hydroinformatics, vol.17, no. 5, pp. 805-816, 2015.
  • S. Ferreiro and B. Sierra, "Comparison of machine learning algorithms for optimization and improvement of process quality in conventional metallic materials, "The International Journal of Advanced Manufacturing Technology, vol.60, no. 1-4, pp.237-49, 2012.
  • A. Gouarir, G. Martinez-Arellano, G. Terrazas, P. Benardos and S. Ratchev,"In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis," Procedia CIRP, vol. 77, no. 1, pp.501-504, 2018.
  • D. Knittel and M. Nouari, "Milling diagnosis using machine learning approaches," Surveillance, Vishno and AVE conferences, July 8, 2019.
  • B. K. Pappachan and T. Tjahjowidodo, "Parameter Prediction Using Machine Learning in Robot-Assisted Finishing Process," International Journal of Mechanical Engineering and Robotics Research, vol. 9 , no. 3, pp. 435-440, 2020.
  • P. Dutta, S. Paul and A. Kumar, "Comparative analysis of various supervised machine learning techniques for diagnosis of COVID-19," Electronic Devices, Circuits, and Systems for Biomedical Applications, Elsevier, pp. 521–540, 2021.
  • H. Lu and X. Ma, "Hybrid decision tree-based machine learning models for short-term water quality prediction," Chemosphere, vol. 249, pp. 126169, 2020.
  • T. A. Assegie and P.S. Nair, "Handwritten digits recognition with decision tree classification: a machine learning approach," International Journal of Electrical and Computer Engineering, vol.9, no. 5, pp.4446-4451, 2019.
  • T. Murata, T. Yanagisawa, T. Kurihara, M. Kaneko, S. Ota et al.," Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination," Breast Cancer Res Treat, vol. 177, no. 3, pp.591–601,2019.
  • P. Nalajala L. Rajeev, R. Vallabhuni, K. R. Babu, K. Sravani, B. K. Kumar, A. Srikanth, P. Dutta, S. Lakshmi, V. Papineni, N. Biswas, and KVSN S. K. Mohan. "A Novel Method of Effective Sentiment Analysis System by Improved Relevance Vector Machine." Australian Patent AU 2020104414: 31.2020.
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