Collection Efficiency of Cyclone Separators: Comparison between New Machine Learning-Based Models and Semi-Empirical Approaches

Abstract

Cyclones are employed in many waste treatment industries for the dust collection or abatement purposes. The prediction of the dust collection efficiency is crucial for the design and optimization of the cyclone. However, this is a difficult task because of the complex physical phenomena that influence the removal of particles. Aim of the paper is to present two new meta-models for the prediction of the collection efficiency curve of cyclone separators. A Backpropagation Neural Network (BPNN) and Support Vector Regression (SVR) models were developed using Python environment. These were trained with a set of experimental data taken from the literature. The prediction capabilities of the models were first assessed by comparing the estimated collection efficiency for several cyclones against the corresponding experimental data. Second, by comparing the collection efficiency curves predicted by the models and those obtained from classic models available in the literature for the cyclones included in the validation dataset. The BPNN demonstrated better predictive capability than the SVR, with an overall mean squared error of 0.007 compared to 0.015, respectively. Most important, a 40% to 90% accuracy improvement of the literature models predictions was achieved.