A GA-LSSVM approach for predicting and controlling in screw chiller

Tian, Chengcheng; Xing, Ziwen; Pan, Xiaogang; Wang, Haojie

doi:10.1177/0957650920983102

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Tian et al [52] applied the back propagation neural network model trained by BigData to predict the transient coefficient of performance of an on-site screw chiller applied in cinema, whose error was almost within ±5.0%. They [53] further presented the least square support vector machine model with a genetic algorithm for high precision prediction and control of a screw chiller. Ping et al [54] applied a combined back propagation neural network with a genetic algorithm model to predict and optimize the maximum power output of a SSE in organic Rankine cycle (ORC) for diesel engine waste heat recovery.…”

Section: Empirical and Semi-empirical Modelmentioning

confidence: 99%

A Review of Recent Research and Application Progress in Screw Machines

Wang

Liu

et al. 2022

Machines

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Screw machines, mainly including single-screw type and twin-screw type, have gone through significant development and improvement during the past decade. This paper reviews the relevant studies available in the open literature for acquiring insight into and to establish the state of the art of the research and application status of screw machines. The related research on different aspects, which would affect the performance and reliability of screw machines includes rotor profile and geometric characteristics, thermodynamic modelling, vibration and noise, lubrication and wear, control of capacity and built-in volume ratio, and liquid injection technology. In the aspect of thermodynamic modelling, the available methods, i.e., empirical or semi-empirical model, lump model, and 3D CFD model, adopted for the performance prediction and optimal design of screw machines are summarized. Then, the review covers the application status of screw machines in the fields of air compression and expansion, refrigeration and heat pump, organic Rankine cycle (ORC), and other popular applications, with an emphasis on the reported performance and progress in technologies of screw machines. Finally, conclusions and perspectives for future research in the area of screw machines are presented. The review provides readers with a good understanding of the research focus and progress in the field of screw machines.

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Section: Empirical and Semi-empirical Modelmentioning

confidence: 99%

A Review of Recent Research and Application Progress in Screw Machines

Wang

Liu

et al. 2022

Machines

Self Cite

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“…Some scholars have also tried to use immune algorithm to optimize the parameters of LS-SVM [33] to reduce the blindness of parameter selection and improve the prediction accuracy of LS-SVM, but the implementation of this method is complicated. Besides, some scholars proposed to use genetic algorithm to determine LS-SVM parameters [34], but genetic algorithm needs to perform crossover and mutation operations, and many parameters need to be adjusted, which is computationally complex and inefficient.…”

Section: Optimization Of Ls-svm Model Parameters Based On Psomentioning

confidence: 99%

Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

2022

Complexity

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According to the definition of nonlinear cointegration, this article studies the small sample nonlinear cointegration test and NECM (Nonlinear Error Correction Model) based on the LS-SVM (Least Squares Support Vector Machine) optimized by PSO (Particle Swarm Optimization). And the logical process of this method is also designed. Then, we carry out empirical research on the ship maintenance cost index and the several price indexes. Based on the judgment of the type of cointegration test relationship, the test of the nonlinear cointegration test relationship of small samples is realized, and the NECM for predicting the ship maintenance cost index is established as well, which is compared with the VAR model of linear vector. The research result shows that the small sample nonlinear cointegration test and modeling method based on the LS-SVM Optimized by PSO can describe the nonlinear cointegration test relationship of the small sample system. And the NECM has better performance. The prediction effect can effectively predict small sample nonlinear systems. We also compare the prediction results with the wavelet neural network algorithm, and the results show that the generalization ability of LS-SVM Optimized by PSO is better, and the prediction accuracy of small samples is higher.

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“…Reciprocating compressors (RCs) are extensively used in chemical and petroleum industries, such as chemical fertilizer synthesis process, natural gas transportation, etc., [1]. To ensure the compressor reliability and performance, the fault detection based on automatic condition monitoring systems for the compressor is necessary [2]. In fact, benefiting from the development of online monitoring technique, the automatic monitoring system for RCs tends to be mature [3].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN

Liu

Cai

et al. 2022

Applied Sciences

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The health management of the reciprocating compressor is crucial for its long term steady operation and safety. Online condition monitoring technology for the reciprocating compressor is almost mature, whereas the fault diagnosis technologies are still insufficient to meet the need. Therefore, in this paper, a novel fault detection method for the reciprocating compressor based on digital image processing and artificial neural network (ANN) was proposed. This method is implemented to the sectionalized pressure–volume (p–V) curves, which are obtained by dividing a working cycle in the cylinder into four thermal processes, including expansion, suction, compression and discharge. Hit-or-miss transform is adopted to extract the comprehensive gradients of expansion and compression curves, and vertical projection transform is applied to extract the vertical projection features. Finally, all of the features are fed to an ANN to do classification. To validate the proposed method, a seeded fault testing was conducted to collect real running data. The results showed that the new approach shows a good performance, with a high classification accuracy of 97.9%.

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A GA-LSSVM approach for predicting and controlling in screw chiller

Cited by 4 publications

References 27 publications

A Review of Recent Research and Application Progress in Screw Machines

A Review of Recent Research and Application Progress in Screw Machines

Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN

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