Developing Intelligent Algorithm as a Machine Learning Overview over the Big Data Generated by Euler–Euler Method To Simulate Bubble Column Reactor Hydrodynamics

Babanezhad, Meisam; Nakhjiri, Ali Taghvaie; Rezakazemi, Mashallah; Shirazian, Saeed

doi:10.1021/acsomega.0c02784

Cited by 40 publications

(22 citation statements)

References 33 publications

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“…FIS is based on training process, and is capable to utilize other algorithms as a trainer for example using ant colont optimization algorithm called ACOFIS and using artificial neural network as a trainer called ANFIS 30 . There are three types of fuzzy sets proposed and implemneted by Takagi and Sugeno 42 where the rth rule function is stated as 42,43 :…”

Section: Fuzzy Inference System (Fis)mentioning

confidence: 99%

Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

Babanezhad

Behroyan

Nakhjiri

et al. 2021

Sci Rep

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Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

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Section: Fuzzy Inference System (Fis)mentioning

confidence: 99%

Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

Babanezhad

Behroyan

Nakhjiri

et al. 2021

Sci Rep

Self Cite

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“…Bubble column (BC) reactors are usually utilized in industrial work like heterogeneous churn turbulent flow pattern 4 . Thereby, it is imperative to examine such columns under such operational circumstances for better performance and optimization.…”

Section: Introductionmentioning

confidence: 99%

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm

Babanezhad

Zabihi

Behroyan

et al. 2021

Sci Rep

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In this investigation, differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process. Considered data in this study were extracted from simulation of a 2D two-phase reactor in which gas was sparged from bottom of reactor, and the injected gas velocities were between 0.05 to 0.11 m/s. After doing a couple of training by making some changes in DE parameters and FIS parameters, the greatest percentage of FIS capacity was achieved. By applying the optimized model, the gas phase velocity in x direction inside the reactor was predicted when the injected gas velocity was 0.08 m/s.

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“…CFD is appropriately perceived as a trustworthy procedure to model the refining processes such as NCR and analyzing their corresponding momentum and mass transport equations [ 8 , 9 ]. In this paper, COMSOL is applied as the governing software to provide a computational simulation for the radial flow catalytic reactor of the naphtha reforming unit.…”

Section: Introductionmentioning

confidence: 99%

Mechanistic modeling and numerical simulation of axial flow catalytic reactor for naphtha reforming unit

et al. 2020

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Naphtha catalytic reforming (NCR) process has been of tremendous attention all over the world owing to the significant requirement for high-quality gasoline. Industrialized naphtha reforming unit at oil refineries applies a series of fixed bed reactors (FBRs) to improve the quality of the low-octane hydrocarbons and convert them to more valuable products. The prominent purpose of this research is to understand the catalytic reactor of naphtha reforming unit. For this aim, an appropriate mechanistic modeling and its related CFD-based computational simulation is presented to predict the behavior of the system when the reactors are of the axial flow type. Also, the triangular meshing technique (TMT) is performed in this paper due to its brilliant ability to analyze the results of model’s predictions along with improving the computational accuracy. Additionally, mesh independence analysis is done to find the optimum number of meshes needed for reaching the results convergence. Moreover, suitable kinetic and thermodynamic equations are derived based on Smith model to describe the NCR process. The results proved that the proceeding of NCR process inside the reactor significantly increased the concentration amount of aromatic materials, lighter ends and hydrogen, while deteriorated the concentration amount of naphthene and paraffin. Moreover, the pressure drop along the reactor length was achieved very low, which can be considered as one of the momentous advantages of NCR process.

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Developing Intelligent Algorithm as a Machine Learning Overview over the Big Data Generated by Euler–Euler Method To Simulate Bubble Column Reactor Hydrodynamics

Cited by 40 publications

References 33 publications

Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm

Mechanistic modeling and numerical simulation of axial flow catalytic reactor for naphtha reforming unit

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