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

• Autorzy: Kasprzyk, Kamil , Gałuszka, Adam
• 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

• 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,

autor

Gałuszka, Adam

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

Bibliografia

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• 3. Eras J.J.C., Sagastume A., Santos V.S. and Ulloa M.C. Energy management of compressed air systems. Assessing the production and use of compressed air in industry, Energy 2020, 213: 118662.
• 4. Zahlan J. and Asfour S.S. A multi-objective approach for determining optimal air compressor location in a manufacturing facility. Journal of Manufacturing Systems, 2015, 35: 176-190.
• 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.
• 11. Gałuszka A. and Świerniak A. Optimization methods and decision making: Lecture notes, Gliwice, 2003.

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).

Identyfikatory

DOI

10.12913/22998624/165989