A decision support system for predicting settling velocity of spherical and non-spherical particles in Newtonian fluids. (4th July 2022)

• Record Type: Journal Article
• Authors: Rushd, Sayeed; Rahman, Moklesur; Arifuzzaman, Mohammad; Aktaruzzaman, Md
• Is Part Of: Particulate science and technology. Volume 40:Number 5(2022)
• Page Start: 609
• Page End: 619
• Publication Date: 2022-07-04
• Subjects: Artificial intelligence -- model -- machine learning -- random forest -- particle shape; Particles -- Periodicals; 620.43
• DOI: 10.1080/02726351.2021.1982092
• Languages: English
• ISSNs: 0272-6351

Abstract

Abstract: An artificial intelligence-based system was developed to efficiently predict settling velocity (SV) using a large dataset comprised of 2726 samples. The ranges of particle size and fluid viscosity were 0.212 − 98.59 mm and 0.02 − 92800 mPa.s, respectively. Properties of particle and fluid were fed to a model as the inputs to obtain SV as the output. Six machine learning algorithms were tested for the prediction. The random forest (RF) performed better than other algorithms with a coefficient of determination of 0.98 and a mean square error of 0.0027. A simple decision support system was developed using the RF model. The current study demonstrates the complete methodology of modeling SV with ML.