Abstract

Current research seeks to understand and optimize the aerated stirred tanks’ (ASTs) intensified transfer process by systematically investigating the relationship between gas cavities with hydrodynamic characteristics and operating conditions. A novel integrated in-situ/DIP (based on TWS Machine Learning)/BIV/ROIMI technique was demonstrated for effectively monitoring, identifying and quantifying hydrodynamic characteristics in complex multiphase mixing systems (1130 < Re < 17192). The CFD-PBM model with a Multiple-Reference-Frame approach was employed to simulate and verify experimental results. Experimental and simulated results showed that under vortex clinging (VC) cavity flow zone, with the area of cavity (Acavity) increasing, the bubble dispersion is better, Sauter diameter (de32) is smaller, holdup (εg) and interfacial area (a) are larger. When the Acavity is more than 150 mm2, the hydrodynamic characteristics are optimal. The significant factors for the formation of cavities were comprehensively analyzed via 180 sets of full-factorial analysis, namely, impeller types, rotation speed, solutions, rotation speed × impeller types, solutions × impeller types, flow rate × impeller types and rotation speed × flow rate. Furthermore, a model (R2 = 0.95) was proposed and successfully predicted Acavity (relative error less than 3 %). This work may provide valuable insights for optimizing the design and operating of ASTs for enhance multiphase mixing processes.

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