Liquid mixing time and gas distribution in aerated multiple-impeller stirred tanks

Žák, Adrián; Alberini, Federico; Maluta, Francesco; Moucha, T.; Montante, Giuseppina Maria Rosa; Paglianti, Alessandro

doi:10.1016/j.cherd.2022.06.021

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…S2), while with multiple impellers the characteristic time is close to 200 s for both the configurations. pointed out by Zak et al [28], the behavior observed with multiple impellers is due to the zoning effect that limits the mass exchange between adjacent impellers. Moving from singlephase to gas-liquid flow, the negative effect of the compartmentalization of the tank disappears and the mixing time becomes in the order of some tens of seconds, as illustrated in Fig.…”

Section: Liquid Phase Homogenizationmentioning

confidence: 99%

Impact of Curved‐Blade Impellers on Gas Holdup and Liquid Homogenization Dynamics in Stirred Tanks

et al. 2023

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Design and optimization of aerobic fermentation processes in stirred tanks must factor in specific features, such as the formation of gas cavities on the rear of impeller blades and the need for high power input. Here, the ability of a curved‐blade impeller to reduce these drawbacks, which are typical of flat‐blade impellers, is investigated. The analysis is based on gas holdup distributions and liquid homogenization dynamics collected by electrical resistance tomography in a pilot‐scale stirred tank of geometry similar to typical industrial aerated fermenters. A wide range of gas flow rates and impeller speeds in single‐impeller and multiple‐impeller configurations are considered and the differences arising when a Rushton turbine is replaced with a Bakker turbine are discussed.

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Section: Liquid Phase Homogenizationmentioning

confidence: 99%

Impact of Curved‐Blade Impellers on Gas Holdup and Liquid Homogenization Dynamics in Stirred Tanks

et al. 2023

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“…In chemical manufacturing, failure to provide the necessary mixing can lead to serious production problems 11,12 . Mixing‐related issues can be categorized into three overarching domains: blending, multi‐phase, and reaction.…”

Section: Introductionmentioning

confidence: 99%

“…In chemical manufacturing, failure to provide the necessary mixing can lead to serious production problems. 11,12 Mixing-related issues can be categorized into three overarching domains: blending, multiphase, and reaction. Among these, the attainment of homogeneity mixing takes precedence as the initial and most recognizable mixing problem.…”

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confidence: 99%

Multi‐objective optimization of radially stirred tank based on CFD and machine learning

Zhao,

Fan,

Lin

et al. 2023

AIChE Journal

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Structural optimization is essential to improve the performance of mixing equipment. An efficient optimization strategy based on computational fluid dynamics, machine learning, and the multi‐objective genetic algorithm was proposed to predict and optimize the performance of the stirred tank. Single‐factor analysis was performed to study the effects of structural parameters on power consumption and mixing time, which were reduced by 16.0% and 1.4%, respectively, in the optimized stirred vessel. To further optimize the stirred tank geometries and maximize the integrated performance, XGB coupled NSGA‐ІІ were utilized to minimize the power consumption and mixing time. The optimal design parameters from the Pareto front were identified by two well‐known decision‐making methods (LINMAP and TOPSIS), which decreased power consumption and mixing time by 12.3% and 13.4% compared to the stirred tank with the baseline structure. This research further confirmed the accuracy and reliability of the machine learning‐based optimization method.

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A novel 3D uniformity measurement method in mechanical stirring systems

Zhang,

Fan,

et al. 2024

Can J Chem Eng

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Accurate assessment of mixing uniformity is crucial in industrial mixing processes. This study proposes an evaluation method for three‐dimensional (3D) mixing processes that combines dual‐camera positioning and point pattern density fluctuation (PD) based on disordered hyperuniformity. This study employs a positioning method using dual‐camera to achieve precise capture and reconstruction of tracer particles in 3D space. The 3D reconstruction data is then evaluated for mixing performance using the PD method. A relationship model between |k| and time I values with mixing time was established for λ = 1. The results indicate that mixing time decreases with the increase of |k| and decreases with the decrease of I values. To ensure the accuracy of the PD method, feasibility analysis was conducted using conductometry. Additionally, the superiority of the PD method was validated by comparing it with the 3D‐Q method. The impact of bottom height of stirring paddle and motor speed on mixing effect were also investigated. This study establishes a fundamental groundwork and theoretical framework for optimizing parameters of stirring systems and assessing 3D mixing uniformity. It also offers important references and insights for engineering practices and theoretical research in related fields.

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Liquid mixing time and gas distribution in aerated multiple-impeller stirred tanks

Cited by 5 publications

References 27 publications

Impact of Curved‐Blade Impellers on Gas Holdup and Liquid Homogenization Dynamics in Stirred Tanks

Impact of Curved‐Blade Impellers on Gas Holdup and Liquid Homogenization Dynamics in Stirred Tanks

Multi‐objective optimization of radially stirred tank based on CFD and machine learning

A novel 3D uniformity measurement method in mechanical stirring systems

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