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

Edoardo Bregolin (), Piero Danieli and Massimo Masi
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Edoardo Bregolin: Department of Industrial Engineering, University of Padova, Via Venezia 1, 35131 Padova, Italy
Piero Danieli: Department of Management and Engineering, University of Padova, Stradella S. Nicola 3, 36100 Vicenza, Italy
Massimo Masi: Department of Management and Engineering, University of Padova, Stradella S. Nicola 3, 36100 Vicenza, Italy

Waste, 2024, vol. 2, issue 3, 1-18

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.

Keywords: cyclone; efficiency; machine learning; dust treatment (search for similar items in EconPapers)
JEL-codes: Q1 Q16 Q18 Q2 Q20 Q23 Q24 Q25 Q28 Q3 Q31 Q38 Q5 (search for similar items in EconPapers)
Date: 2024
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