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Open AccessFeature PaperArticle

A Data-Driven-Based Industrial Refrigeration Optimization Method Considering Demand Forecasting

Department of Electronic Engineering, Technical University of Catalonia, 08034 Barcelona, Spain
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Processes 2020, 8(5), 617; https://doi.org/10.3390/pr8050617
Received: 22 April 2020 / Revised: 13 May 2020 / Accepted: 18 May 2020 / Published: 21 May 2020
(This article belongs to the Special Issue Synergies in Combined Development of Processes and Models)
One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this type of process consume a huge amount of electricity that can be reduced with an optimal compressor configuration. In this paper, a novel data-driven methodology is presented, which employs self-organizing maps (SOM) and multi-layer perceptron (MLP) to deal with the (PLR) issue of refrigeration systems. The proposed methodology takes into account the variables that influence the system performance to develop a discrete model of the operating conditions. The aforementioned model is used to find the best PLR of the compressors for each operating condition of the system. Furthermore, to overcome the limitations of the historical performance, various scenarios are artificially created to find near-optimal PLR setpoints in each operation condition. Finally, the proposed method employs a forecasting strategy to manage the compressor switching situations. Thus, undesirable starts and stops of the machine are avoided, preserving its remaining useful life and being more efficient. An experimental validation in a real industrial system is performed in order to validate the suitability and the performance of the methodology. The proposed methodology improves refrigeration system efficiency up to 8%, depending on the operating conditions. The results obtained validates the feasibility of applying data-driven techniques for the optimal control of refrigeration system compressors to increase its efficiency. View Full-Text
Keywords: data-driven; self-organizing maps; multi-layer perceptron; partial load ratio; refrigeration systems; compressors; energy efficiency; industrial process modelling data-driven; self-organizing maps; multi-layer perceptron; partial load ratio; refrigeration systems; compressors; energy efficiency; industrial process modelling
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Cirera, J.; Carino, J.A.; Zurita, D.; Ortega, J.A. A Data-Driven-Based Industrial Refrigeration Optimization Method Considering Demand Forecasting. Processes 2020, 8, 617.

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