Novel Correlation for Critical Speed for Solid Suspension in Stirred Tanks Developed Using Machine Learning Models Trained on Literature Data

• Published in: Industrial & engineering chemistry research 2023-06, Vol.62 (22), p.8954-8971
• Main Authors: Joshi, Sumit S., Dalvi, Vishwanath H., Vitankar, Vivek S., Joshi, Aniruddha J., Joshi, Jyeshtharaj B.
• Format: Article
• Language: English
• Subjects: Thermodynamics, Transport, and Fluid Mechanics
• ISSN: 0888-5885; 1520-5045
• DOI: 10.1021/acs.iecr.3c00488

Critical speed for solid suspension (N js) in stirred tanks is an important design parameter in several chemical processes. There is a need to develop a generalized correlation that applies to a broad range of literature data. Also, the literature lacks a comparative study of different machine learning (ML) models for the prediction of N js. In this paper, 3240 data points have been extracted from 35 papers on solid suspension and N js has been modeled as the dependent variable, initially using ML models. Three ML models (random forest regression (RF), CatBoost regression (CatB), and artificial neural network regression (ANN)) are compared. The CatB model was the most effective, resulting in R 2 = 0.99 for the testing dataset, better than any empirical correlation published. Further, we have successfully used the CatB model to obtain the functional form of the behavior of various parameters. We then developed novel correlations (in closed form and with parametric uncertainty) for HE-3, PTD, and DT impellers with R 2 = 0.89, 0.84, and 0.86, respectively, where the correlation constants were tuned using experimental data published in the literature. Our closed-form correlations significantly outperform earlier correlations published in the literature. Such a methodology has not been reported in the literature we have surveyed, and we believe that it is novel and original.