Random forest and autoencoder data-driven models for prediction of dispersed-phase holdup and drop size in rotating disc contactors

Accurate prediction of dispersed-phase holdup and drop size is essential for designing and scaling rotating disc contactors (RDCs) used in chemical extraction processes — yet the underlying relationships are highly nonlinear and poorly captured by classical regression. This paper applies Random Forest (RF) and an autoencoder-augmented RF to these prediction tasks. The standalone RF generalizes well across both targets; the autoencoder combination improves drop size prediction but offers limited benefit for holdup. The work demonstrates that data-driven ML models are a viable replacement for physics-based correlations in chemical engineering design.

Recommended citation: Swetha Saraswathi K., Hrushikesh Bhosale, Prasad Ovhal, Naren Parlikkad Rajan, and Jayaraman Krishnamoorthy Valadi Industrial & Engineering Chemistry Research 2021 60 (1), 425-435 DOI: 10.1021/acs.iecr.0c04149 https://pubs.acs.org/doi/abs/10.1021/acs.iecr.0c04149