Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Design of a Cyclone Separator Critical Diameter Model based on a Machine Learning and CFD

Version 1 : Received: 3 November 2020 / Approved: 4 November 2020 / Online: 4 November 2020 (10:11:58 CET)

A peer-reviewed article of this Preprint also exists.

Park, D.; Go, J.S. Design of Cyclone Separator Critical Diameter Model based on Machine Learning and CFD. Processes 2020, 8, 1521. Park, D.; Go, J.S. Design of Cyclone Separator Critical Diameter Model based on Machine Learning and CFD. Processes 2020, 8, 1521.

Journal reference: Processes 2020, 8, 1521
DOI: 10.3390/pr8111521

Abstract

This paper deals with the characteristics of the cyclone separator from the Lagrangian perspective to design important dependent variables, develops a neural network model for predicting the separation performance parameter, and compares the predictive performance between the traditional surrogate model and the neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based CFD methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, Unsteady-RANS analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the ML model showed the 32.5 % of improvement rate of R2 compared to the traditional MLR considering the nonlinear relationship between the cyclone design variable and the critical diameter. The proposed techniques have proven to be fast and practical tools for cyclone design.

Subject Areas

Cyclone separator; Computational fluid dynamics (CFD); Machine learning; Unsteady RANS; Critical Diameter

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