A mechanism-embedded neural network model for predicting performance parameters of centrifugal pump

Min Chai, Qing Huang, Weiwei Zhang, Yun Ren and Shuihua Zheng

Energy, 2025, vol. 326, issue C

Abstract: A novel mechanism-embedded neural network (MENN) model is proposed to ensure the prediction accuracy of centrifugal pump performance under limited data conditions. The MENN model, embedded into the neural network module through a ‘Transition layer’, offers a versatile solution applicable to various fluid-mechanical performance prediction problems. Structural optimization using a genetic algorithm (GA) enhances the model's reliability and robustness across different datasets and application scenarios. Comparative analysis with traditional models in predicting centrifugal pump performance demonstrates superior performance, with a significantly smaller average relative error and higher determination coefficient. Specifically, the mean relative errors for predicting head and efficiency are only 0.796 % and 1.240 %, respectively, showcasing a marked improvement in prediction reliability compared to single-model approaches. The method's practicality is validated through successful predictions on a dataset of 63 commercial centrifugal pumps, achieving a determination coefficient greater than 0.997 in the performance prediction model. In summary, this study represents a notable advancement in fluid mechanics, providing a reliable and applicable solution for accurate performance predictions in scenarios with limited sensor and data resources, with broad engineering utility.

Keywords: Centrifugal pump; Performance prediction; Multi-pump; Empirical formula; Neural network; Genetic algorithm (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225019322
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:326:y:2025:i:c:s0360544225019322

DOI: 10.1016/j.energy.2025.136290

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().