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

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.