Random Forest and Autoencoder Data-Driven Models for Prediction of Dispersed-Phase Holdup and Drop Size in Rotating Disc Contactors

Linear regression models are traditionally used to capture the relation between the input and output variables. Linear models cannot account for the nonlinear relations in the data. Hence, the prediction models may not be accurate. For this reason, machine learning-based models are being increasingly used. For modeling, design, and scale-up of rotating disc contactors (RDCs), rational estimation of dispersed-phase holdup and drop size is crucial. We have employed random forest (RF) and autoencoder-RF-based models for the prediction of dispersed-phase holdup and drop size in RDCs. Our results show that both these models predict drop size quite well. For holdup, the autoencoder-RF combination predictions are not satisfactory. The standalone RF model predictions generalize very well. RF-based models can be further used for prediction of different variables of interest in RDCs.

Automated summary

Linear regression models are limited in capturing nonlinear relations in data, leading to potential inaccuracies in prediction models. Therefore, machine learning-based models are increasingly preferred. This study utilized RF and autoencoder-RF models for dispersed-phase holdup and drop size prediction in RDCs, with results indicating good drop size prediction but room for improvement in holdup prediction. RF-based models have potential for predicting various variables of interest in RDCs.