A data-driven-based industrial refrigeration optimization method considering demand forecasting
| Autor: | Juan Antonio Ortega, Josep Cirera, Daniel Zurita, Jesus A. Carino |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Enginyeria Electrònica, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group |
| Rok vydání: | 2020 |
| Předmět: |
Artificial intelligence
Computer science 020209 energy self-organizing maps Bioengineering 02 engineering and technology lcsh:Chemical technology Data-driven lcsh:Chemistry multi-layer perceptron Partial load ratio Control de processos 0202 electrical engineering electronic engineering information engineering Chemical Engineering (miscellaneous) lcsh:TP1-1185 energy efficiency Self-organizing maps Process Chemistry and Technology Intel·ligència artificial Process (computing) Refrigeration Multi-layer perceptron Demand forecasting Optimal control Reliability engineering partial load ratio Energy efficiency lcsh:QD1-999 compressors Multilayer perceptron refrigeration systems Industrial process modelling data-driven industrial process modelling Refrigeration systems Process control 020201 artificial intelligence & image processing Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC] Gas compressor Efficient energy use Compressors |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Processes Volume 8 Issue 5 Processes, Vol 8, Iss 617, p 617 (2020) |
| Popis: | 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. The authors would like to thank the support of Corporación Alimentaria Guissona S.A. for providing access to their refrigeration system dataset and their expert advice. |
| Databáze: | OpenAIRE |
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