Separator performance modeling and analysis using artificial neural network and response surface method

Ouyang, Shuo; Xiong, Zhenqin; Zhao, Jiyun; Li, Zhen

doi:10.1016/j.anucene.2022.109139

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Many optimization studies are using the response surface method, which shows that it is feasible to optimize structural parameters by using the response surface method through numerical simulation. Based on the application of the response surface method, Ouyang et al [18] combined the advantages of the response surface method and artificial neural network to analyze the performance of the separator. Their result shows that the combination of these two methods can not only analyze the multivariate interaction of separator but also predict the performance of the separator quickly and accurately.…”

Section: Introductionmentioning

confidence: 99%

Simulation Analysis of Ejector Optimization for High Mass Entrainment under the Influence of Multiple Structural Parameters

et al. 2022

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As an energy-saving technology, the ejector is widely used in the heating, aerospace, and chemical industry. The ejector performance is closely related to its structure, so the structure of the ejector needs to be optimized. In the present study, single-factor optimization is first carried out, and the main structural parameters affecting the ejector performance are screened out. Then, the response surface method is used to analyze the combined effect of the multiple structural parameters of the ejector, find out the optimal structure, and analyze the flow field inside the ejector. This study shows that, through numerical simulation, the ejector performance obtained by the response surface method is better than that obtained by the single-factor optimization method or theoretically designed ejector, and the ejector performance is 35.4% higher than that of the theoretically designed ejector. Moreover, the optimal structure of the ejector obtained by the response surface method has high reliability, and the difference between the simulation result and the prediction result of the response surface method is 0.96%.

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Section: Introductionmentioning

confidence: 99%