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

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Pijush Dutta 1,* Gour Gopal Jana 1 Shobhandeb Paul 2 Souvik Pal 3 Sumanta Dey 1 Arindam Sadhu 1

1. Greater Kolkata College of Engineering and Management, Baruipur, West Bengal, 743387, India

2. Gurunanak Institute of Technology, Kolkata, 700114, India

3. Sister Nivedita University, Kolkata, 700156, India

* Corresponding author.


Received: 16 Aug. 2023 / Revised: 8 Sep. 2023 / Accepted: 15 Oct. 2023 / Published: 8 Feb. 2024

Index Terms

Liquid flow process, Modeling, Machine learning, Regression analysis


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.

Cite This Paper

Pijush Dutta, Gour Gopal Jana, Shobhandeb Paul, Souvik Pal, Sumanta Dey, Arindam Sadhu, "AI-Based Smart Prediction of Liquid Flow System Using Machine Learning Approach", International Journal of Engineering and Manufacturing (IJEM), Vol.14, No.1, pp. 53-65, 2024. DOI:10.5815/ijem.2024.01.05


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