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Article

Modeling of Surfactant Enhanced Drying of Polymeric Coatings Using Random Forest Regressor

1
IITB-Monash Research Academy, IIT Bombay, Powai, Mumbai 400076, Maharashtra, India
2
Department of Chemical Engineering, Jaypee University of Engineering & Technology, Guna 473226, Madhya Pradesh, India
3
Department of Chemical Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar 144011, Punjab, India
4
Hydrogen Fuel Cell Laboratory, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Republic of Korea
5
Environmental Engineering Program, School of Mechanical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel
6
College of Technology, Sardar Vallabhbhai Patel University of Agriculture & Technology, Meerut 250110, Uttar Pradesh, India
7
Energy Cluster, UPES, Bidholi, Dehradun 248007, Uttarakhand, India
8
Department of Chemistry, Laboratory of Organic, Chemical Technology, Aristotle University of Thessaloniki (Auth), 54124 Thessaloniki, Greece
*
Authors to whom correspondence should be addressed.
Processes 2025, 13(11), 3722; https://doi.org/10.3390/pr13113722
Submission received: 17 September 2025 / Revised: 29 October 2025 / Accepted: 31 October 2025 / Published: 18 November 2025

Abstract

Surfactant-based drying of polymeric coatings is a highly non-linear complex process due to the involvement of simultaneous heat and mass transfer. It is quite challenging to model this type of system mathematically. We resorted to a data-driven technique to model this type of system. A potent machine learning approach called the random forest (RF) regressor was introduced in this work to model the drying of poly(styrene)-p-xylene coatings improved by surfactants. This model was developed using experimental data. A substantial number of samples were collected by conducting experiments at a wide range of operating conditions. Apart from the accuracy of predicting coating weight loss for given values of inputs, model performance was also evaluated for the variation in the number of samples, and feature extraction was also done, which is an additional advantage of the random forest. Our results demonstrate that the random forest model trained with 80% samples is able to predict coating weight loss with less than ±0.5% error, and even a model trained with just 20% samples is able to predict the remaining 80% samples with just ±2%  error. It also outperformed the regression tree and the artificial neural network (ANN) used in previous studies for the same dataset.
Keywords: random forest; machine learning; drying; coatings random forest; machine learning; drying; coatings
Graphical Abstract

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MDPI and ACS Style

Bhargava, C.K.; Shrivastava, R.; Tiwari, B.; Dubey, K.; Pathania, A.; Gupta, K.N.; Rawat, M.; Kumar, R.; Thakur, A.; Verros, G.D.; et al. Modeling of Surfactant Enhanced Drying of Polymeric Coatings Using Random Forest Regressor. Processes 2025, 13, 3722. https://doi.org/10.3390/pr13113722

AMA Style

Bhargava CK, Shrivastava R, Tiwari B, Dubey K, Pathania A, Gupta KN, Rawat M, Kumar R, Thakur A, Verros GD, et al. Modeling of Surfactant Enhanced Drying of Polymeric Coatings Using Random Forest Regressor. Processes. 2025; 13(11):3722. https://doi.org/10.3390/pr13113722

Chicago/Turabian Style

Bhargava, Chitresh Kumar, Rahul Shrivastava, Bhavya Tiwari, Kaushiki Dubey, Aman Pathania, Kaushal Naresh Gupta, Manju Rawat, Rahul Kumar, Amit Thakur, George D. Verros, and et al. 2025. "Modeling of Surfactant Enhanced Drying of Polymeric Coatings Using Random Forest Regressor" Processes 13, no. 11: 3722. https://doi.org/10.3390/pr13113722

APA Style

Bhargava, C. K., Shrivastava, R., Tiwari, B., Dubey, K., Pathania, A., Gupta, K. N., Rawat, M., Kumar, R., Thakur, A., Verros, G. D., & Arya, R. K. (2025). Modeling of Surfactant Enhanced Drying of Polymeric Coatings Using Random Forest Regressor. Processes, 13(11), 3722. https://doi.org/10.3390/pr13113722

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