Prediction of Compressed Air Demand Depending on the Type of Production with the Use of Neural Networks

• Autorzy: Kasprzyk Kamil , Gałuszka Adam

Identyfikatory

DOI

10.12913/22998624/165989

• Języki publikacji: EN

Abstrakty

EN

Compressed air systems are commonly used in industrial plants to produce the compressed air required for the facility's daily operations. Since air compressors consume more electricity than any other type of facility equipment, an optimization of the efficiency of compressed air system operation cycles is essential for energy savings. In this article the demand for compressed air in production plants with different operating characteristics is analyzed. It is checked how the neural network identified for a given plant would work in the case of another plant with a different needs while predicting compressed air demand, which is understood as a prediction of compressor on/offs. The simulation results based on real data indicate possible decisions that improves system efficiency.

Słowa kluczowe

EN

optimization; deep learning; neural networks; compressed air; demand prediction

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2023
• Tom: Vol. 17, no 3
• Strony: 154--159
• Opis fizyczny: Bibliogr. 11 poz., fig., tab.

Twórcy

autor

Kasprzyk Kamil

• Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-4792-2112

autor

Gałuszka Adam

• Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland

• https://orcid.org/0000-0002-6176-0500

Bibliografia

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• 5. Dragan D.Š., Ivana M.M., Slobodan P.D. and Jovan I.Š. Improving energy efficiency in compressed air systems – practical experiences. Thermal Science, 2016, 20, Suppl. 2: 355-370.
• 6. Šešlija D., Ignjatovic I. and Dudic S., Increasing the energy efficiency in compressed air systems. In: Energy Efficiency - The Innovative Ways for Smart Energy, the Future Towards Modern Utilities. InTech. DOI: 10.5772/47873, 2012.
• 7. Kasprzyk K. and Gałuszka A. LSTM networks in prediction of the demand for compressed air depending on the type of production. In: M. Karaboyacı, K. Taşdelen, A. Beram, H. Kandemir, E. Kala, S. Özdemir (Eds.), International Conferences on Science and Technology. Engineering Science A, 2022.
• 8. Da-Chun W., Asl B.B. and Ali Razban J.C. Air compressor load forecasting using artificial neural network, Expert Systems with Applications, 2021, 168: 114209. https://doi.org/10.1016/j eswa.2020.114209.
• 9. Raschka S. and Mirjalili V., Python Machine learning and deep learning Libraries scikit-learn and TensorFlow 2nd Edition III, Translation: Krzysztof Sawka, Helion SA, 2021: 533-576.
• 10. Gałuszka A., Dzida T. and Klimczak K. Modelling and simulation 2020: The European Simulation and Modelling Conference 2020. ESM ‘2020, October 21-23, 2020, Toulouse, France. In: LSTM network with reinforced learning in short and medium term Warsaw Stock Market index forecast, 2020.
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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).