IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v286y2024ics0360544223028426.html
   My bibliography  Save this article

Strategic management of energy consumption and reduction of specific energy consumption using modern methods of artificial intelligence in an industrial plant

Author

Listed:
  • Ebrahimzadeh Sarvestani, Maryam
  • Hoseiny, Saeed
  • Tavana, Davood
  • Di Maria, Francesco

Abstract

Given the importance of energy and its role in sustainable gas production, paying special attention to its consumption indicators in all industries, especially oil & gas is very important. The energy consumption index parameter, which equals the ratio of energy consumption to gas production, is one of the most significant indicators of the gas refinery. Artificial intelligence can be used to evaluate the effects of different parameters and find the factors influencing energy performance. Important variables such as inlet gas from various sources, the energy consumption of each part, and the amount of waste refinery gas are collected on different days of the year, then using the least square support vector machine method and networking and sensitivity analysis of the impact of each variable separately. The role of each in this parameter is determined so that it can be used to provide appropriate solutions to reduce energy consumption and the energy consumption index parameter for sustainable gas production. According to the results of this study, the steam unit has a 14 % impact on the energy consumption index, and the gas sweetening unit has an 18 % impact on gas consumption. So, more research on the optimization of these two units is necessary.

Suggested Citation

  • Ebrahimzadeh Sarvestani, Maryam & Hoseiny, Saeed & Tavana, Davood & Di Maria, Francesco, 2024. "Strategic management of energy consumption and reduction of specific energy consumption using modern methods of artificial intelligence in an industrial plant," Energy, Elsevier, vol. 286(C).
  • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223028426
    DOI: 10.1016/j.energy.2023.129448
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223028426
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.129448?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Arghira, Nicoleta & Hawarah, Lamis & Ploix, Stéphane & Jacomino, Mireille, 2012. "Prediction of appliances energy use in smart homes," Energy, Elsevier, vol. 48(1), pages 128-134.
    2. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    3. Shengjun, Zhang & Huaixin, Wang & Tao, Guo, 2011. "Performance comparison and parametric optimization of subcritical Organic Rankine Cycle (ORC) and transcritical power cycle system for low-temperature geothermal power generation," Applied Energy, Elsevier, vol. 88(8), pages 2740-2754, August.
    4. Wang, E.H. & Zhang, H.G. & Fan, B.Y. & Ouyang, M.G. & Zhao, Y. & Mu, Q.H., 2011. "Study of working fluid selection of organic Rankine cycle (ORC) for engine waste heat recovery," Energy, Elsevier, vol. 36(5), pages 3406-3418.
    5. Izadyar, Nima & Ghadamian, Hossein & Ong, Hwai Chyuan & moghadam, Zeinab & Tong, Chong Wen & Shamshirband, Shahaboddin, 2015. "Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption," Energy, Elsevier, vol. 93(P2), pages 1558-1567.
    6. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    7. Esen, Hikmet & Inalli, Mustafa & Sengur, Abdulkadir & Esen, Mehmet, 2008. "Modeling a ground-coupled heat pump system by a support vector machine," Renewable Energy, Elsevier, vol. 33(8), pages 1814-1823.
    8. Al-Shammari, Eiman Tamah & Keivani, Afram & Shamshirband, Shahaboddin & Mostafaeipour, Ali & Yee, Por Lip & Petković, Dalibor & Ch, Sudheer, 2016. "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm," Energy, Elsevier, vol. 95(C), pages 266-273.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dong, Shengming & Zhang, Yufeng & He, Zhonglu & Deng, Na & Yu, Xiaohui & Yao, Sheng, 2018. "Investigation of Support Vector Machine and Back Propagation Artificial Neural Network for performance prediction of the organic Rankine cycle system," Energy, Elsevier, vol. 144(C), pages 851-864.
    2. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    3. Yang, Min-Hsiung & Yeh, Rong-Hua, 2015. "Thermo-economic optimization of an organic Rankine cycle system for large marine diesel engine waste heat recovery," Energy, Elsevier, vol. 82(C), pages 256-268.
    4. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    5. Lecompte, S. & Huisseune, H. & van den Broek, M. & De Paepe, M., 2015. "Methodical thermodynamic analysis and regression models of organic Rankine cycle architectures for waste heat recovery," Energy, Elsevier, vol. 87(C), pages 60-76.
    6. Alijanpour sheshpoli, Mohamad & Mousavi Ajarostaghi, Seyed Soheil & Delavar, Mojtaba Aghajani, 2018. "Waste heat recovery from a 1180 kW proton exchange membrane fuel cell (PEMFC) system by Recuperative organic Rankine cycle (RORC)," Energy, Elsevier, vol. 157(C), pages 353-366.
    7. Jung, Hyung-Chul & Taylor, Leighton & Krumdieck, Susan, 2015. "An experimental and modelling study of a 1 kW organic Rankine cycle unit with mixture working fluid," Energy, Elsevier, vol. 81(C), pages 601-614.
    8. Xue, Guixiang & Qi, Chengying & Li, Han & Kong, Xiangfei & Song, Jiancai, 2020. "Heating load prediction based on attention long short term memory: A case study of Xingtai," Energy, Elsevier, vol. 203(C).
    9. Chintala, Venkateswarlu & Kumar, Suresh & Pandey, Jitendra K., 2018. "A technical review on waste heat recovery from compression ignition engines using organic Rankine cycle," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 493-509.
    10. Safarian, Sahar & Saboohi, Yadollah & Kateb, Movaffaq, 2013. "Evaluation of energy recovery and potential of hydrogen production in Iranian natural gas transmission network," Energy Policy, Elsevier, vol. 61(C), pages 65-77.
    11. Bao, Junjiang & Zhao, Li, 2013. "A review of working fluid and expander selections for organic Rankine cycle," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 325-342.
    12. Shu, Gequn & Li, Xiaoning & Tian, Hua & Liang, Xingyu & Wei, Haiqiao & Wang, Xu, 2014. "Alkanes as working fluids for high-temperature exhaust heat recovery of diesel engine using organic Rankine cycle," Applied Energy, Elsevier, vol. 119(C), pages 204-217.
    13. Murray, D.M. & Liao, J. & Stankovic, L. & Stankovic, V., 2016. "Understanding usage patterns of electric kettle and energy saving potential," Applied Energy, Elsevier, vol. 171(C), pages 231-242.
    14. Kim, Kyeongsu & Lee, Ung & Kim, Changsoo & Han, Chonghun, 2015. "Design and optimization of cascade organic Rankine cycle for recovering cryogenic energy from liquefied natural gas using binary working fluid," Energy, Elsevier, vol. 88(C), pages 304-313.
    15. Fuhaid Alshammari & Apostolos Karvountzis-Kontakiotis & Apostolos Pesyridis & Muhammad Usman, 2018. "Expander Technologies for Automotive Engine Organic Rankine Cycle Applications," Energies, MDPI, vol. 11(7), pages 1-36, July.
    16. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    17. Cavazzini, G. & Bari, S. & Pavesi, G. & Ardizzon, G., 2017. "A multi-fluid PSO-based algorithm for the search of the best performance of sub-critical Organic Rankine Cycles," Energy, Elsevier, vol. 129(C), pages 42-58.
    18. Yang, Min-Hsiung & Yeh, Rong-Hua, 2016. "Economic performances optimization of an organic Rankine cycle system with lower global warming potential working fluids in geothermal application," Renewable Energy, Elsevier, vol. 85(C), pages 1201-1213.
    19. Lecompte, S. & Huisseune, H. & van den Broek, M. & De Schampheleire, S. & De Paepe, M., 2013. "Part load based thermo-economic optimization of the Organic Rankine Cycle (ORC) applied to a combined heat and power (CHP) system," Applied Energy, Elsevier, vol. 111(C), pages 871-881.
    20. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223028426. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.