Arindam Sadhu

Work place: Greater Kolkata College of Engineering and Management, Baruipur, West Bengal, 743387, India



Research Interests: Quantum Computing Theory, Cryptography, Machine Learning


Dr. Arindam Sadhu is currently working as an Assistant Professor in Department of Electronics & communication Engineering, Greater Kolkata College of Engineering and Management, India. He received his B Tech, M Tech and PhD from MAKAUT (formerly known as West Bengal University of Technology). Till now Dr. Sadhu published more than 25 research article including journal, Conference, patents, books & Book chapter. His research interests are Optimization, Quantum Cryptography, Machine Learning et.

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

By Pijush Dutta Gour Gopal Jana Shobhandeb Paul Souvik Pal Sumanta Dey Arindam Sadhu

DOI:, Pub. Date: 8 Feb. 2024

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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