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Article

Improving the Energy Efficiency of Industrial Refrigeration Systems by Means of Data-Driven Load Management

Department of Electronic Engineering, Technical University of Catalonia, 08034 Barcelona, Spain
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Author to whom correspondence should be addressed.
Processes 2020, 8(9), 1106; https://doi.org/10.3390/pr8091106
Received: 4 August 2020 / Revised: 28 August 2020 / Accepted: 2 September 2020 / Published: 5 September 2020
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
A common denominator in the vast majority of processes in the food industry is refrigeration. Such systems guarantee the quality and the requisites of the final product at the expense of high amounts of energy. In this regard, the new Industry 4.0 framework provides the required data to develop new data-based methodologies to reduce such energy expenditure concern. Focusing in this issue, this paper proposes a data-driven methodology which improves the efficiency of the refrigeration systems acting on the load side. The solution approaches the problem with a novel load management methodology that considers the estimation of the individual load consumption and the necessary robustness to be applicable in highly variable industrial environments. Thus, the refrigeration system efficiency can be enhanced while maintaining the product in the desired conditions. The experimental results of the methodology demonstrate the ability to reduce the electrical consumption of the compressors by 17% as well as a 77% reduction in the operation time of two compressors working in parallel, a fact that enlarges the machines life. Furthermore, these promising savings are obtained without compromising the temperature requirements of each load. View Full-Text
Keywords: data-driven; load management; multi-layer perceptron; partial load ratio; refrigeration systems; compressors; energy efficiency; energy disaggregation; NILM; optimization data-driven; load management; multi-layer perceptron; partial load ratio; refrigeration systems; compressors; energy efficiency; energy disaggregation; NILM; optimization
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MDPI and ACS Style

Cirera, J.; Carino, J.A.; Zurita, D.; Ortega, J.A. Improving the Energy Efficiency of Industrial Refrigeration Systems by Means of Data-Driven Load Management. Processes 2020, 8, 1106. https://doi.org/10.3390/pr8091106

AMA Style

Cirera J, Carino JA, Zurita D, Ortega JA. Improving the Energy Efficiency of Industrial Refrigeration Systems by Means of Data-Driven Load Management. Processes. 2020; 8(9):1106. https://doi.org/10.3390/pr8091106

Chicago/Turabian Style

Cirera, Josep, Jesus A. Carino, Daniel Zurita, and Juan A. Ortega. 2020. "Improving the Energy Efficiency of Industrial Refrigeration Systems by Means of Data-Driven Load Management" Processes 8, no. 9: 1106. https://doi.org/10.3390/pr8091106

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