In recent years, there has been a significant emphasis on predicting the mixing effectiveness of mechanically agitated tanks, particularly those employing coaxial mixers. Coaxial mixers, characterized by central impellers and a close clearance anchor, have shown superiority over conventional systems in achieving homogeneous gas dispersion and enhancing the volumetric mass transfer coefficient (kLa). Evaluating the efficiency of these mixers, especially with high-viscosity non-Newtonian fluids, poses challenges due to their complex design and intricate interactions between operating variables.
Large-scale coaxial mixers, equipped with multiple central impellers, aim to enhance mass transfer at a constant power consumption, but their complex geometry and rheological characteristics further complicate accurate predictions of mixing effectiveness. Traditional correlations for estimating kLa based on experimental data have limitations within specific operating conditions. Enter machine learning methods, specifically Artificial Neural Networks (ANNs), which have gained traction in various industries, including chemical engineering.
ANNs, trained with experimental data, demonstrate remarkable success in predicting kLa within the covered ranges. They excel in examining the impact of design parameters, operating conditions, and fluid properties on multiphase processes. The study at hand focuses on developing an ANN model for predicting kLa in a double coaxial mixing system with yield-pseudoplastic fluids. The system comprises two pitched-blade impellers and an anchor, using different concentrations of xanthan gum solutions. The model considers the central impeller speed, anchor speed, aeration rate, and rheological parameters of the xanthan gum solution. The ANN model, constructed using experimental data obtained through the simplified dynamic pressure method, outperforms empirical correlations proposed in the existing literature, providing more accurate estimations for kLa. Overall, the study showcases the potency of ANNs in predicting mixing effectiveness in gas–liquid mixing tanks, particularly in complex systems with non-Newtonian fluids.
Author Contributions
Conceptualization, F.S., E.B. and F.E.-M.; Methodology, F.S.; Software, F.S.; Validation, F.S.; Formal analysis, F.S.; Investigation, F.S.; Resources, E.B. and F.E.-M.; Writing—original draft, F.S.; Writing—review & editing, E.B. and F.E.-M.; Supervision, E.B. and F.E.-M.; Project administration, E.B. and F.E.-M.; Funding acquisition, E.B. and F.E.-M. All authors have read and agreed to the published version of the manuscript.
Funding
We express profound gratitude for the financial assistance offered by Natural Sciences and Engineering Research Council of Canada (NSERC) under grant number RGPIN-2019-04644.
Institutional Review Board Statement
Not Applicable.
Informed Consent Statement
Not Applicable.
Data Availability Statement
The data will be available upon request.
Conflicts of Interest
The authors declare no conflict of interest.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).