Performance change prediction of stoner pipeline simulator simulation centrifugal compressor based on Whale Optimization Algorithm-enhanced back-propagation neural network

Abstract

Centrifugal compressors are the primary energy-consuming equipment in long-distance gas pipelines, accounting for approximately 40%–50% of the total operational costs. Thus, an accurate understanding of the performance parameters, specifically the shaft power, of centrifugal compressors is crucial for reducing operational expenses. Current research primarily focuses on predicting performance parameters such as the pressure ratio, with limited studies on shaft power prediction. This research aims to enhance the accuracy of centrifugal compressor performance prediction by establishing four prediction models: support vector machine (SVM), back-propagation neural network (BPNN), Bayesian optimization back-propagation neural network (BO-BPNN), and Whale Optimization Algorithm back-propagation neural network (WOA-BPNN). Eight sets of training data and two sets of testing data are utilized for interpolation and extrapolation, while the stoner pipeline simulator (SPS) simulation software is employed for validation purposes. The results demonstrate that the WOA-BPNN model exhibits higher precision and accuracy in predicting performance. By improving the weights and thresholds of the BPNN based on the WOA, this model avoids the risk of falling into local optima and demonstrates good generalization performance and applicability. Compared to the SVM and BPNN models, the WOA-BPNN model showcases lower mean absolute errors, root mean square errors, and mean absolute percentage errors in predictions, be it interpolation, extrapolation, or simulation of actual operating conditions using SPS software. The predicted results show minimal differences from the actual operating conditions, while the absolute average percentage error can be controlled around 1.24%, which is acceptable. The established WOA-BPNN model outperforms the other models across all evaluation metrics. This method enhances the accuracy and applicability of performance prediction for centrifugal compressors.

Keywords

Centrifugal compressor performance prediction BPNN SVM WOA

Get full access to this article

View all access options for this article.

References

Tian

Yang

, et al. Hybrid ANN-PLS approach to scroll compressor thermodynamic performance prediction. Appl Therm Eng 2015; 77: 113–120.

Tsoutsanis

Meskin

Benammar

, et al. A component map tuning method for performance prediction and diagnostics of gas turbine compressors. Appl Energ 2014; 135: 572–585.

Galvas

. Computer program for predicting off-design performance of centrifugal compressors. In: 1973.

Shao

Yang

, et al. Investigation on one-dimensional loss models for predicting performance of multistage centrifugal compressors in supercritical CO2 Brayton cycle. J Therm Sci 2021; 30: 133–148.

Ghorbanian

Gholamrezaei

. An artificial neural network approach to compressor performance prediction. Appl Energ 2009; 86: 1210–1221.

Fei

Zhao

Shi

, et al. Compressor performance prediction using a novel feed-forward neural network based on Gaussian kernel function. Adv Mech Eng 2016; 8: 1687814016628396.

Jiang

Dong

Liu

, et al. Performance prediction of the centrifugal compressor based on a limited number of sample data. Math Probl Eng 2019; 2019: 5954128.

Cutrina Vilalta

Wan

S. Patnaik

. Centrifugal Compressor Performance Prediction Using Gaussian Process Regression and Artificial Neural Networks. In: ASME 2019 International Mechanical Engineering Congress and Exposition 2019, V008T09A045. American Society of Mechanical Engineers.

Zhong

Liu

Miao

, et al. Compressor performance prediction based on the interpolation method and support vector machine. Aerospace-Basel 2023; 10: 558.

10.

Liu

Karimi

. Gas turbine performance prediction via machine learning. Energy 2020; 192: 116627.

11.

Chu

Wang

, et al. A model for parameter estimation of multistage centrifugal compressor and compressor performance analysis using genetic algorithm. Sci China Technol Sci 2012; 55: 3163–3175.

12.

Peng

Bao

, et al. Centrifugal compressor performance prediction and dynamic simulation of natural gas hydrogen blended. Int J Hydrogen Energ 2024; 52: 872–893.

13.

Yao

Wang

, et al. Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis. Measurement 2020; 153: 107419.

14.

Chen

Liu

Yin

, et al. Predict the effect of meteorological factors on haze using BP neural network. Urban Clim 2023.

15.

Mahmood

. Enhanced human face recognition using LBPH descriptor, multi-KNN, and back-propagation neural network. IEEE Access 2018; 6: 20641–20651.

16.

Wang

Zhang

Ding

, et al. Estimation of soil salt content (SSC) in the Ebinur Lake Wetland national nature reserve (ELWNNR), Northwest China, based on a bootstrap-BP neural network model and optimal spectral indices. Sci Total Environ 2018; 615: 918–930.

17.

Deng

Zhou

Shen

, et al. New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water. Sci Total Environ 2021; 772: 145534.

18.

Lan

, et al. A method of using back propagation neural network to estimate orbital lifetime of LEO satellites. Adv Space Res 2023; 72: 1961–1969.

19.

Ding

Xia

Yuan

, et al. Performance prediction for a fuel cell air compressor based on the combination of backpropagation neural network optimized by genetic algorithm (GA-BP) and support vector machine (SVM) algorithms. Therm Sci Eng Prog 2023; 44: 102070.

20.

Huang

. Research and application of wavelet neural networks of particle swarm optimization algorithm in the performance prediction of centrifugal compressor. Adv Mat Res 2011; 187: 271–276.

21.

Guo

, et al. Application of state parameter learning for fault diagnosis on the large reciprocating compressor. Proc Instit Mech Eng E-J Process Mech Eng 2024; 238: 931–944.

22.

Wei

Zheng

Yan

, et al. Dynamic particle swarm optimization-radial function extremum neural network method of HCF probability analysis for compressor blade. Int J Fatigue 2023; 176: 107900.

23.

Mirjalili

Lewis

. The whale optimization algorithm. Adv Eng Softw 2016; 95: 51–67. Article.

24.

Petrovic

Miljkovic

Jokic

. A novel methodology for optimal single mobile robot scheduling using Whale Optimization Algorithm. Appl Soft Comput 2019; 81: 105520.

25.

Yue

. An improved Whale Optimization Algorithm based on multilevel threshold image segmentation using the Otsu method. Eng Appl Artif Intel 2022; 113: 104960.

26.

Liu

, et al. Modified Whale Optimization Algorithm for solar cell and PV module parameter identification. Complexity 2021; 2021: 8878686.

27.

Suthaharan

. Support vector machine. In: Suthaharan

(eds) Machine learning models and algorithms for big data classification: thinking with examples for effective learning. Boston, MA: Springer US, 2016, pp.207–235.

28.

Buscema

. Back propagation neural networks. Subst Use Misuse 1998; 33: 233–270.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

3.09 MB