Multi-objective optimization of microchannel reactor for Fischer-Tropsch synthesis using computational fluid dynamics and genetic algorithm

Na, Jonggeol; Kshetrimayum, Krishnadash S.; Lee, Ung; Han, Chonghun

doi:10.1016/j.cej.2016.11.040

Cited by 40 publications

(16 citation statements)

References 35 publications

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“…However, this algorithm was used in a wide range of heat transfer optimization problems in recent years. Liu, Bu and Xu [39] performed an optimization study of a plate-fin heat exchanger using CFD and multi-objective GA. Na et al [40] performed a multiobjective optimization of a micro-channel reactor for Fischer-Tropsch synthesis using CFD and GA. Ghorbaniet al…”

Section: Studies Of Heat Transfer Enhancement In Mini/ Micro-channelsmentioning

confidence: 99%

Calibration of κ-ε turbulence model for thermal–hydraulic analyses in rib-roughened narrow rectangular channels using genetic algorithm

2021

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Nowadays, applications of turbulent fluid flow in removing high heat flux in rib-roughened narrow channels are drawing much interest. In this work, an improved version of the κ-ε turbulence model is proposed for better prediction of thermal–hydraulic characteristics of flow inside rib-roughened (pitch-to-rib height (p/k) ratio = 10 and 20) narrow channels (channel height, H = 1.2 mm and 3.2 mm). For this, the four turbulence model parameters, Cμ, Cε1, Cε2, and σk, are calibrated. These parameters are adjustable empirical constants provided for controlling the accuracy of the turbulence model results when needed. The simulated data are used to develop correlations between the relative errors in predicting the friction factor (f), Nusselt number (Nu), and the model parameters using a multivariate nonlinear regression method. These correlations are used to optimize the errors using genetic algorithm. Results reveal that the calibrated parameters are not the same for all the narrow channel configurations. After calibration, the overall predictive improvements are up to 35.83% and 27.30% for p/k = 10 and p/k = 20 respectively when H = 1.2 mm. Also, up to 15.48% and 18.05% improvements are obtained for p/k = 10 and p/k = 20 respectively when H = 3.2 mm. The role of the two parameters Cε1 and Cε2 are found to be of primary importance. Furthermore, three types of nanofluids i.e. Al2O3-water, CuO-water, and TiO2-water are studied using the calibrated model to check the potentiality of heat transfer enhancement. Among them, CuO-water nanofluid is predicted to have around 1.32 times higher value of Nu than pure water for the same narrow channel configuration. Article Highlights κ-ε turbulence model is calibrated for rib-roughened narrow rectangular channels using genetic algorithm. Cε1 and Cε2 are the most influential parameters on the performance of the model inside rib-roughened narrow channel. Suggested calibration process is more effective for channel height of 1.2 mm than 3.2 mm.

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Section: Studies Of Heat Transfer Enhancement In Mini/ Micro-channelsmentioning

confidence: 99%

Calibration of κ-ε turbulence model for thermal–hydraulic analyses in rib-roughened narrow rectangular channels using genetic algorithm

2021

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“…For example, Aghajani used an artificial neural network to research the size of synthesised nano-iodine in microreactors; it was found that the relationships between flow rate of solvent, flow rate of antisolvent, and size of the synthesised nano-iodine are in inverse relation [21]. Na researched the optimisation of catalyst loading in Fischer-Tropsch microchannel reactors, using the distribution of catalyst loading in microchannel reactors as a variable and considering C5+ productivity and temperature rise in microchannels as optimisation objects by using computational fluid dynamics, it was found that C5+ productivity was increased to 22% and ΔTmax was decreased to 63.2% by using a genetic algorithm (GA) [22]. Recently, Jung researched the structure optimisation of Fischer-Tropsch microchannel reactors, considering such structure parameters as the length, width, and height of microchannels in microreactor as variables, using reactor core volume and reaction temperature rise were used as optimisation objects by utilising the coupling method and artificial neural networks [23].…”

Section: Nomenclaturementioning

confidence: 99%

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

Zheng

Zhou

et al. 2019

Chemical Engineering Journal

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To optimise methanol steam reforming performance of cylindrical microreactor for hydrogen production, an error backpropagation algorithm was used to build a mathematical model for reaction performance of different microreactors for hydrogen production. Additionally, a genetic algorithm (GA) was utilised to process the computational model to obtain the optimum reaction parameters. The reliability of the optimum reaction parameters of cylindrical microreactor for hydrogen production was verified by experiments. Firstly, take plate microreactor as an example, the porosity of porous copper fiber sintered sheet (PCFSS), reaction temperature of methanol steam reforming for hydrogen production, injection velocity of the methanol and water mixture, and catalyst loading of PCFSS were considered as input data, whereas methanol conversion was used as output data. The computational model for specific testing system was gained by utilising input and output data from specific testing system to train the mathematical model for different microreactors, combining with matrix laboratory (MATLAB) neural network toolbox and designed MATLAB program. The E max of 5% for plate microreactor and E max of 3.2% for cylindrical microreactor verified the good predictive ability and reliability of the computational model for plate and cylindrical microreactor, indicating the reliability and universal applicability of the mathematical model for different microreactors. Secondly, the effects and mechanisms of PPI, reaction temperature, injection velocity, and catalyst loading on methanol conversion were studied, relying on the computational model. Finally, the optimum reaction parameters were acquired using GA, MATLAB neural network toolbox and designed MATLAB program. The validity of the optimum reaction parameters of cylindrical microreactor for hydrogen production was confirmed by experiments. This study provides a reference method for methanol steam reforming performance optimisation for hydrogen production.Highlights >Error backpropagation algorithm is used to built general mathematical model.>Computational model is established based on general mathematical model.>Computational model shows good reliability and predictive ability. >Relations between reaction parameters and performance are studied. > Optimum reaction parameters are obtained by using genetic algorithm.Graphical abstract

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“…Tarafder et al 16 tried to optimize the design and operating variables of an industrial ethylene reactor by GA multiobjective optimization. Na et al 17 proposed a multiobjective optimization procedure using GA and CFD for Fischer‐Tropsch synthesis in microchannels.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Helical Microreactors by a Genetic Algorithm Technique

2020

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Micromixing in coiled microreactors is attained by a higher pressure drop and more pumping power. The optimum geometries of helically coiled microreactors were determined by multiobjective optimization based on a genetic algorithm (GA). The segregation index (X s) values of the Villermaux/Dushman reaction were measured in twelve coiled microchannels. The effects of the geometries including curvature diameter and coil pitch on the mixing performance and pressure drop were investigated. The mixing performance of the microreactors and the pressure drop were considered as the GA objectives. The optimum geometries of the studied coiled microchannels with a trade-off between X s and friction factors were obtained using GA-based multiobjective optimization.

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Multi-objective optimization of microchannel reactor for Fischer-Tropsch synthesis using computational fluid dynamics and genetic algorithm

Cited by 40 publications

References 35 publications

Calibration of κ-ε turbulence model for thermal–hydraulic analyses in rib-roughened narrow rectangular channels using genetic algorithm

Calibration of κ-ε turbulence model for thermal–hydraulic analyses in rib-roughened narrow rectangular channels using genetic algorithm

Methanol steam reforming performance optimisation of cylindrical microreactor for hydrogen production utilising error backpropagation and genetic algorithm

Optimization of Helical Microreactors by a Genetic Algorithm Technique

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