Thermal performance of a novel ultrasonic evaporator based on machine learning algorithms

Abstract

Ultrasound is a promising method to enhance heat transfer in industrial evaporators. However, there are very limited studies on this topic. This paper explores thermal performance of a novel ultrasonic evaporator based on machine learning methods. The results indicate that the overall heat transfer coefficients can be increased by around 15–20% after adding ultrasound due to acoustic cavitation and acoustic streaming. Two machine learning-based global sensitivity analysis (treed Gaussian Process and polynomial chaos expansion) methods are used to identify important variables influencing overall heat transfer coefficients in the ultrasonic evaporator. It is found that the temperature difference between evaporation and heating steam is the dominant factor affecting thermal performance in this case study. Ultrasound has complicated interactions and non-linear effects in the ultrasonic evaporator. Seven machine learning algorithms are created to compare predictive thermal performance of this evaporator, including linear model, Lasso (least absolute shrinkage and selection operator), MARS (multivariate adaptive regression splines), NNet (averaging neural network), CB (Cubist model), GP (Gaussian process), and SVM (support vector machine). The SVM and NNet models among these seven models can provide accurate prediction of overall heat transfer coefficients in the ultrasonic evaporator.