Mixing Performance Analysis of Orbitally Shaken Bioreactors

This study investigated the efficacy of a novel correlation of power input, energy dissipation rate and mixing time as a potential route to identify the orbitally shaken bioreactor (OSB) system. The Buckingham’s π-theorem was used to designate and transform dimensionless Newton numbers with five relevant power input variables. These variables were empirically varied to evaluate the correlation among the dimensionless numbers. The Newton number decreases with the increased shaking frequency and filling volume. Previous work has focused on optimizing the mixing process by evaluating different shaking and agitation mixing methods. We establish a new mixing process and assessable measurement of the mixing time in the OSB. An innovative explanation of mixing time for the thermal method is proposed. The optimal mixing time is independent of the temperature of filled liquid. The dimensionless mixing number remained constant in the turbulent regime and increasing with the increased liquid viscosity and filling volume. Our findings revealed that the observed correlation is a practical tool to figure the power consumption and mixing efficiency as cell cultivation in all OSB scales and is fully validated when scaling–up system.

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Correlations for mixing energy in processes using Rushton turbine mixer‡

Chemical Papers ◽

10.1515/chempap-2016-0008 ◽

2016 ◽

Vol 70 (6) ◽

Cited By ~ 3

Author(s):

Grzegorz Story ◽

Marian Kordas ◽

Rafał Rakoczy

Keyword(s):

Energy Consumption ◽

Mixing Time ◽

Power Input ◽

Rotating Magnetic Field ◽

Mixing Process ◽

Total Energy Consumption ◽

Innovative Strategy ◽

Rushton Turbine ◽

Mixing Energy ◽

Dimensionless Energy

AbstractThis study reports the research results on a mixing process using a stirred tank mixer under the action of a rotating magnetic field (RMF). Dimensionless correlations are proposed to predict the power consumption and mixing time for the mixing systems analysed. The results suggest that the mixing behaviour of the experimental set-ups tested may be assessed using the dimensionless mixing energy as the product of the power input and mixing time. In addition, an innovative strategy is proposed on the basis of the synergistic eﬀect of the rotational Rushton turbine and the RMF generator. The values of the dimensionless energy thus obtained were used to compare the mixing process performed by the mixing devices tested. It is shown that the mixing process under the RMF action has significantly higher values of energy consumption than the conventional Rushton turbine. The total energy consumption for the mixing process performed by the RMF mixer may be reduced by concomitant use of a rotational agitator.

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Granular flow in static mixers by coupled DEM/CFD approach

Hemijska industrija ◽

10.2298/hemind151013060p ◽

2016 ◽

Vol 70 (5) ◽

pp. 539-546 ◽

Cited By ~ 1

Author(s):

Lato Pezo ◽

Milada Pezo ◽

Aca Jovanovic ◽

Nenad Kosanic ◽

Aleksandar Petrovic ◽

...

Keyword(s):

Granular Flow ◽

Fluid Dynamic ◽

Mixing Time ◽

Numerical Approach ◽

Mixing Efficiency ◽

Multiphase Model ◽

Static Mixer ◽

Mixing Process ◽

Static Mixers ◽

Relative Standard

The mixing process greatly influence the mixing efficiency, as well as the quality and the price of the intermediate and/or the final product. Static mixer is used for premixing action before the main mixing process, for significant reduction of mixing time and energy consumption. This type of premixing action is not investigated in detail in the open literature. In this article, the novel numerical approach called Discrete Element Method is used for modelling of granular flow in multiple static mixer applications (1 - 3 Komax or Ross mixing elements were utilized), while the Computational Fluid Dynamic method was chosen for fluid flow modelling, using the Eulerian multiphase model. The main aim of this article is to predict the behaviour of granules being gravitationally transported in different mixer configuration and to choose the best configuration of the mixer taking into account the total particle path, the number of mixing elements and the quality of the obtained mixture. The results of the numerical simulations in the static mixers were compared to experimental results, the mixing quality is examined by RSD (relative standard deviation) criterion, and the effects on the mixer type and the number of mixing elements on mixing process were studied. The effects of the mixer type and the number of mixing elements on mixing process were studied using analysis of variance (ANOVA). Mathematical modelling is used for optimization of number of Ross and Komax segments in mixer in order to gain desirable mixing results.

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Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

Applied Sciences ◽

10.3390/app11094251 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4251

Author(s):

Jinsong Zhang ◽

Shuai Zhang ◽

Jianhua Zhang ◽

Zhiliang Wang

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Digital Microfluidics ◽

Flow Patterns ◽

Machine Learning Algorithms ◽

Dimensionless Numbers ◽

Two Phase ◽

The Difference ◽

Input Variables ◽

Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

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Study of Pumping Capacity of Pitched Blade Impellers

Acta Polytechnica ◽

10.14311/380 ◽

2002 ◽

Vol 42 (4) ◽

Author(s):

I. Fořt ◽

T. Jirout ◽

R. Sperling ◽

S. Jambere ◽

F. Rieger

Keyword(s):

Laser Doppler Anemometry ◽

Radial Profile ◽

Axial Flow ◽

Power Input ◽

Vessel Diameter ◽

Energetic Efficiency ◽

Turbulent Regime ◽

Flat Bottom ◽

Mean Velocity ◽

Liquid Suspensions

A study was made of the pumping capacity of pitched blade impellers in a cylindrical pilot plant vessel with four standard radial baffles at the wall under a turbulent regime of flow. The pumping capacity was calculated from the radial profile of the axial flow, under the assumption of axial symmetry of the discharge flow. The mean velocity was measured using laser Doppler anemometry in a transparent vessel of diameter T = 400 mm, provided with a standard dished bottom. Three and six blade pitched blade impellers (the pitch angle varied within the interval a Îá24°; 45°ń) of impeller/vessel diameter ratio D/T = 0.36, as well as a three blade pitched blade impeller with folded blades of the same diameter, were tested. The calculated results were compared with the results of experiments mentioned in the literature, above all in cylindrical vessels with a flat bottom. Both arrangements of the agitated system were described by the impeller energetic efficiency, i.e, a criterion including in dimensionless form both the impeller energy consumption (impeller power input) and the impeller pumping effect (impeller pumping capacity). It follows from the results obtained with various geometrical configurations that the energetic efficiency of pitched blade impellers is significantly lower for configurations suitable for mixing solid-liquid suspensions (low impeller off bottom clearances) than for blending miscible liquids in mixing (higher impeller off bottom clearances).

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Effect of Bottom Blowing Mode on Fluid Flow and Mixing Behavior in Converter

Metals ◽

10.3390/met12010117 ◽

2022 ◽

Vol 12 (1) ◽

pp. 117

Author(s):

Jiankun Sun ◽

Jiangshan Zhang ◽

Wenhui Lin ◽

Xiaoming Feng ◽

Qing Liu

Keyword(s):

Refractory Lining ◽

Mixing Time ◽

Diffusion Path ◽

Tracer Diffusion ◽

Mixing Efficiency ◽

Molten Bath ◽

Local Flow ◽

Average Value ◽

Type Mode ◽

Bottom Blowing

Bottom blowing agitation plays a crucial role in improving the reaction kinetics condition of molten bath during the steelmaking process. Herein, the influence of bottom blowing mode on the flow and mixing characteristics of molten bath and the abrasion characteristics of refractory lining in a 6:1 scaled-down model of a 100 t converter were investigated using physical and numerical simulations together. Eight bottom blowing modes were designed (uniform, three-point linear co-direction, three-point linear unco-direction, two-point linear, circumferential linear, A-type, V-type, and triangle alternating). The results indicated that bottom blowing mode has a significant effect on the local flow field at the inner ring of bottom tuyeres, the velocity interval distribution, and the turbulent kinetic energy, which in turn determines the tracer diffusion path and rate as well the mixing time of molten bath. Reasonable non-uniform bottom blowing modes promote the interaction between the various stirring sub-zones of the molten bath. Among them, the three-point linear co-direction mode and A-type mode have the highest mixing efficiency under the conditions of bottom blowing and combined blowing, respectively, which is superior to the uniform mode. In addition, the bottom blowing mode changed the location and degree of abrasion of the refractory lining, and the total abrasion of the non-uniform mode was reduced. The average value and fluctuation degree of integral wall shear stress for the A-type mode were minimal.

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Hydrodynamics and Mass Transfer Analysis in BioFlow® Bioreactor Systems

Processes ◽

10.3390/pr8101311 ◽

2020 ◽

Vol 8 (10) ◽

pp. 1311 ◽

Cited By ~ 1

Author(s):

Marian Kordas ◽

Maciej Konopacki ◽

Bartłomiej Grygorcewicz ◽

Adrian Augustyniak ◽

Daniel Musik ◽

...

Keyword(s):

Mass Transfer ◽

Mixing Time ◽

Mass Transfer Rate ◽

Mass Transfer Process ◽

Mixing Process ◽

Stirred Tanks ◽

Mixing Energy ◽

Biotechnological Processes ◽

Liquid Transfer ◽

Mathematical Relationships

Biotechnological processes involving the presence of microorganisms are realized by using various types of stirred tanks or laboratory-scale dual-impeller commercial bioreactor. Hydrodynamics and mass transfer rate are crucial parameters describing the functionality and efficiency of bioreactors. Both parameters strictly depend on mixing applied during bioprocesses conducted in bioreactors. Establishing optimum hydrodynamics conditions for the realized process with microorganisms maximizes the yield of desired products. Therefore, our main objective was to analyze and define the main operational hydrodynamic parameters (including flow field, power consumption, mixing time, and mixing energy) and mass transfer process (in this case, gas–liquid transfer) of two different commercial bioreactors (BioFlo® 115 and BioFlo® 415). The obtained results are allowed using mathematical relationships to describe the analyzed processes that can be used to predict the mixing process and mass transfer ratio in BioFlo® bioreactors. The proposed correlations may be applied for the design of a scaled-up or scaled-down bioreactors.

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Effect of Mixing Process on the Fracture Behavior of HA/PLLA Composite Material

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.297-300.2453 ◽

2005 ◽

Vol 297-300 ◽

pp. 2453-2458 ◽

Cited By ~ 6

Author(s):

Sang Dae Park ◽

Mitsugu Todo ◽

Kazuo Arakawa ◽

Yasuharu Takenoshita

Keyword(s):

Fracture Toughness ◽

Fracture Behavior ◽

Mixing Time ◽

Time Effect ◽

Ductile Deformation ◽

Fracture Surface Morphology ◽

Mixing Process ◽

Rotor Speed ◽

Neat Plla ◽

Scanning Electron

Effect of mixing process on the fracture behavior of HA/PLLA Composites were investigated. Fracture toughness values of HA/PLLA composites prepared under different mixing time and rotor speed were measured. The fracture surface morphology was also examined by scanning electron microscopy. It was found that the fracture toughness of HA/PLLA composite decreases due to decrease of ductile deformation of PLLA matrix and debonding of interfaces with increase of the rotor speed and mixing time. Effect of mixing process on neat PLLA was also assessed, and it was found that the fracture toughness of PLLA decreases due to such pocess. Disappearance of multiple craze formation and thermal degradation were found to be the primary mechanisms of the toughness degradation.

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Comments on "Experimental Studies and CFD Modeling on the Effects of a Cut in the Baffle on Power Consumption"

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1394 ◽

2009 ◽

Vol 4 (1) ◽

Author(s):

Ivan Fort

Keyword(s):

Power Consumption ◽

Cfd Simulation ◽

Experimental Studies ◽

Power Input ◽

Cfd Modeling ◽

Turbulent Regime

Critical comments on results of the CFD simulation of the impeller power input in a cylindrical baffled vessel under turbulent regime of flow of agitated liquid.

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Fluid Mixing Control Inside a Y-Shaped Microchannel by Using Electrokinetic Instability

Volume 2: Fora, Parts A and B ◽

10.1115/fedsm2007-37524 ◽

2007 ◽

Cited By ~ 1

Author(s):

Zheyan Jin ◽

Hui Hu

Keyword(s):

Electric Field ◽

Electric Fields ◽

Fluid Mixing ◽

Critical Voltage ◽

Mixing Efficiency ◽

Mixing Process ◽

Static Electric Field ◽

Electrokinetic Instability ◽

Mixing Control ◽

Static Electric Fields

An experimental study was conducted to further our understanding about the fundamental physics of electrokinetic instability (EKI) and to explore the effectiveness to enhance fluid mixing inside a Y-shaped microchannel by manipulating convective EKI waves. The dependence of the critical voltage of applied static electric field to trig EKI to generate convective EKI waves on the conductivity ratio of the two adjacent streams was quantified at first. The effect of the strength of the applied static electric field on the evolution of the convective EKI waves and fluid mixing process were assessed in terms of scalar concentration fields, shedding frequency of the convective EKI waves and scalar mixing efficiency. The effectiveness of manipulating the convective EKI waves by introducing alternative electric perturbations to the applied static electric fields was also explored for the further enhancement of the fluid mixing process inside the Y-shaped microchannel.

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Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

Advances in Polymer Technology ◽

10.1155/2020/6575326 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Huaiping Jin ◽

Jiangang Li ◽

Meng Wang ◽

Bin Qian ◽

Biao Yang ◽

...

Keyword(s):

Gaussian Process Regression ◽

Soft Sensor ◽

Viscosity Measurement ◽

Finite Mixture ◽

Just In Time ◽

Mixing Process ◽

Industrial Rubber ◽

Viscosity Prediction ◽

Mooney Viscosity ◽

Input Variables

The lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation- (MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. This method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. The proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods.

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