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Applied Sciences
Special Issues
Intelligent Operation and Maintenance of Refrigeration Systems
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Intelligent Operation and Maintenance of Refrigeration Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 972

Special Issue Editors

Dr. Chuang Wang

E-Mail Website
Guest Editor
School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: refrigeration systems; screw compressors; energy saving; intelligent control; fault diagnosis
Prof. Dr. Zhilong He

E-Mail Website
Guest Editor
School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: compressors; energy saving; Internet of Things; big data

Special Issue Information

Dear Colleagues,

This Special Issue aims to keep track of the current state-of-the-art research on “Intelligent Operation and Maintenance of Refrigeration Systems”, collecting high-quality research and review papers in the various fields within refrigeration technology research. We encourage researchers from various fields within the journal’s scope to contribute research and review papers highlighting the latest developments in their research field, or to invite relevant experts and colleagues to do so. Topics of interest for this Special Issue include, but are not limited to:

  • Big data processing and analysis;
  • System modelling based on artificial intelligence algorithms;
  • Real-time optimization method for energy saving;
  • Advanced control method;
  • Intelligent fault diagnosis;
  • Predictive maintenance;
  • Platform construction for Intelligent Operation and Maintenance.                                                   

Dr. Chuang Wang
Prof. Dr. Zhilong He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • refrigeration systems
  • artificial intelligence
  • intelligent control
  • fault diagnosis
  • predictive maintenance
  • energy-saving operation

Published Papers (1 paper)

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Research

16 pages, 3381 KiB  
Article
Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
by Yanpeng Li, Chuang Wang, Zengqun Li, Dawei Ren, Ziwen Xing, Dawei Wu and Huagen Wu
Appl. Sci. 2023, 13(21), 11855; https://doi.org/10.3390/app132111855 - 30 Oct 2023
Cited by 2 | Viewed by 679
Abstract
The energy consumption in the cold store is growing day by day, 70% of which is consumed by the refrigeration system. Meanwhile, a significant amount of electricity generated by power plants is wasted during off-peak periods. Demand-side management (DSM) provides a viable solution [...] Read more.
The energy consumption in the cold store is growing day by day, 70% of which is consumed by the refrigeration system. Meanwhile, a significant amount of electricity generated by power plants is wasted during off-peak periods. Demand-side management (DSM) provides a viable solution for addressing the problem of the time and space inconsistency between energy supply and consumption, hence improving overall system efficiency. In this paper, an artificial intelligence model is developed for accurate cooling load forecasting. On this basis, a peak shifting control strategy with two optional modes combining temperature setpoint control and operation mode control is then proposed to realize cost reductions. Taking a large-scale cold store as a case study, the cooling capacity supply and temperature variation within two typical working days are investigated to illustrate the feasibility and applicability of the strategy. Detailed thermodynamic and thermo-economic analyses of the proposed strategy are then carried out to demonstrate the control effect. The results show that both modes have good peaking performances and the average cost reduction rate of the two modes reaches 40% and 13.4%, respectively. Full article
(This article belongs to the Special Issue Intelligent Operation and Maintenance of Refrigeration Systems)
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