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Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing

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  • Cassettari, Lucia
  • Bendato, Ilaria
  • Mosca, Marco
  • Mosca, Roberto

Abstract

At a historic time when the eco-sustainability of industrial manufacturing is considered one of the cornerstones of relations between people and the environment, the use of energy from Renewable Energy Sources (RES) has become a fundamental element of this new vision. After years of vain attempts to hammer out an agreement to significantly reduce CO2 emissions produced by the burning of fossil fuels, a binding global accord was finally reached (Paris December 2015 - New York April 2016).

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  • Cassettari, Lucia & Bendato, Ilaria & Mosca, Marco & Mosca, Roberto, 2017. "Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing," Applied Energy, Elsevier, vol. 190(C), pages 841-851.
  • Handle: RePEc:eee:appene:v:190:y:2017:i:c:p:841-851
    DOI: 10.1016/j.apenergy.2017.01.009
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    Cited by:

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    3. He, Yan & Wu, Pengcheng & Li, Yufeng & Wang, Yulin & Tao, Fei & Wang, Yan, 2020. "A generic energy prediction model of machine tools using deep learning algorithms," Applied Energy, Elsevier, vol. 275(C).
    4. Jingjing Xu & Lei Wang, 2017. "A Feedback Control Method for Addressing the Production Scheduling Problem by Considering Energy Consumption and Makespan," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    5. Athar Ajaz Khan & János Abonyi, 2022. "Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy," Sustainability, MDPI, vol. 14(15), pages 1-40, August.
    6. Hsiao, Kai-Long, 2017. "To promote radiation electrical MHD activation energy thermal extrusion manufacturing system efficiency by using Carreau-Nanofluid with parameters control method," Energy, Elsevier, vol. 130(C), pages 486-499.
    7. Gibb, Duncan & Johnson, Maike & Romaní, Joaquim & Gasia, Jaume & Cabeza, Luisa F. & Seitz, Antje, 2018. "Process integration of thermal energy storage systems – Evaluation methodology and case studies," Applied Energy, Elsevier, vol. 230(C), pages 750-760.
    8. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
    9. Favi, Claudio & Marconi, Marco & Mandolini, Marco & Germani, Michele, 2022. "Sustainable life cycle and energy management of discrete manufacturing plants in the industry 4.0 framework," Applied Energy, Elsevier, vol. 312(C).
    10. Caro-Ruiz, C. & Lombardi, P. & Richter, M. & Pelzer, A. & Komarnicki, P. & Pavas, A. & Mojica-Nava, E., 2019. "Coordination of optimal sizing of energy storage systems and production buffer stocks in a net zero energy factory," Applied Energy, Elsevier, vol. 238(C), pages 851-862.
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