Mixing in a co‐current upflow bubble column reactors with and without internals

Bhusare, Vishal; Kalaga, Dinesh V.; Dhiman, M. K.; Joshi, J. B.; Roy, Shantanu

doi:10.1002/cjce.23162

Cited by 21 publications

(15 citation statements)

References 18 publications

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“…However, it is not always the case, increasing the input numbers, the R 2 increases. Sometimes, especially when there are not logical relations between the selected inputs and the (6) www.nature.com/scientificreports/ output, adding more inputs the regression number falls down, and this might impair the calculations. Regarding CO, the highest R 2 (i.e.…”

Section: Resultsmentioning

confidence: 99%

“…These reactors are continuous type and provide higher mass transfer rates, and lower operational and fixed costs [1][2][3][4] . These efficient reactors have been well understood by experimental and theoretical works in which the effect of underlying parameters on the reactor performance have been studied by underpinning work [5][6][7][8][9][10][11][12] . The results turned out that it is of great importance to well comprehend the turbulent behavior of these reactors.…”

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confidence: 99%

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High-performance hybrid modeling chemical reactors using differential evolution based fuzzy inference system

Babanezhad

Behroyan

Nakhjiri

et al. 2020

Sci Rep

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Bubbly flow behavior simulation in two-phase chemical reactors such bubble column type reactors is widely employed for chemical industry purposes. The computational fluid dynamics (CFD) approach has been employed by engineers and researchers for modeling these types of chemical reactors. In spite of the CFD robustness for simulating transport phenomena and chemical reactions in these reactors, this approach has been known as expensive for modeling such turbulent complex flows. Artificial intelligence (AI) algorithm of the adaptive network-based fuzzy inference system (ANFIS) are largely understood and utilized for the CFD approach optimization. In this hybrid approach, the CFD findings are learned by AI algorithms like ANFIS to save computational time and expenses. Once the pattern of the CFD results have been captured by the AI model, this hybrid model can be then used for process simulation and optimization. As such, there is no need for further simulations of new conditions. The objective of this paper is to obviate the need for expensive CFD computations for two-phase flows in chemical reactors via coupling CFD data to an AI algorithm, i.e., differential evolution based fuzzy inference system (DEFIS). To do so, air velocity as the output and the values of the x, and y coordinates, water velocity, and time step as the inputs are inputted the AI model for learning the flow pattern. The effects of cross over as the DE parameter and also the number of inputs on the best intelligence are investigated. Indeed, DEFIS correlates the air velocity to the nodes coordinates, time, and liquid velocity and then after the CFD modeling could be replaced with the simple correlation. For the first time, a comparison is made between the ANFIS and the DEFIS performances in terms of the prediction capability of the gas (air) velocity. The results released that both ANFIS and DEFIS could accurately predict the CFD pattern. The prediction times of both methods were obtained to be equal. However, the learning time of the DEFIS was fourfold of ANFIS.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

High-performance hybrid modeling chemical reactors using differential evolution based fuzzy inference system

Babanezhad

Behroyan

Nakhjiri

et al. 2020

Sci Rep

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“…A good overview is given by Schlüter 9. For recent research, see Rollbusch et al 10 and Bhusare et al 11.…”

Section: Bubble Columnsmentioning

confidence: 99%

WattsApp in Multiphase Systems by Gravity‐Driven Buoyancy and Settling

Weber

2020

Chemie Ingenieur Technik

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Power and energy balances are described for simplified gravity‐driven multiphase flows. It is emphasized that the power (→ Watts) or energy input by gravity impulse must be found back as the sum of all power or energy consumptions from the flow processes (→ Application). Examples are given for bubble columns and for batch liquid‐liquid separation processes, based on simplified assumptions regarding the flow behavior. For large‐scale apparatus, the flow characteristic of the involved phases is more complex and needs to be modeled more precisely. That is still a key challenge for scaling‐up multiphase systems. Computational fluid dynamics is an adequate instrument but needs to be further developed and enhanced for multiphase flows in large‐scale processes. And of course, any rigorous model must then be verifiable by a power balance.

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“…Simulation of bubble column reactor makes such data as pressure, temperature, velocity, and the measure of gas content. These data provide a detailed understanding of the bubble column design and the two-phase fluid flow complexity 19 – 25 . Although several numerical and experimental methods estimate the multiphase flow pattern in BCRs 1 – 3 , 13 , 24 – 27 , there are still some obstacles to simulate the gas flow through a constant liquid fully.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor

Babanezhad

Marjani

Shirazian

2020

Sci Rep

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For understanding the complex behavior of fluids in a multiphase chemical bubble column reactor, a combination of the computational fluid dynamic (CFD) method and the adaptive network-based fuzzy inference system (ANFIS) method is used to predict bubble flow inside a reactor based on the function of column height. In this study, the Euler–Euler model is employed as a CFD method. In the Eulerian method, continuity and momentum governing equations are mathematically computed for each phase, while the equations are connected together by source terms. After calculating the flow pattern and turbulence flow in the reactor, all data sets are used to prepare a fully artificial method for further prediction. This algorithm contains different learning dimensions such as learning in different directions of reactor or large amount of input parameters and data set representing “big data”. The ANFIS method was evaluated in three steps by using one, two, and three inputs in each one to predict the liquid velocity in the x-direction (Ux). The x, y, and z coordinates of the location of the node of the liquid were considered as the inputs. Different percentages of data and various iterations and membership functions were used for training in the ANFIS method. The ANFIS method showed the best prediction using three inputs. This combination also shows the ability of computer science and computational methods in learning physical and chemical phenomena.

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Mixing in a co‐current upflow bubble column reactors with and without internals

Cited by 21 publications

References 18 publications

High-performance hybrid modeling chemical reactors using differential evolution based fuzzy inference system

High-performance hybrid modeling chemical reactors using differential evolution based fuzzy inference system

WattsApp in Multiphase Systems by Gravity‐Driven Buoyancy and Settling

Multidimensional machine learning algorithms to learn liquid velocity inside a cylindrical bubble column reactor

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