IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i15p12030-d1211325.html
   My bibliography  Save this article

Management of Natural Gas Consumption during the Manufacturing of Lead-Acid Batteries

Author

Listed:
  • Alexis Sagastume Gutiérrez

    (Energy Department, Universidad de la Costa, Calle 50 No. 55-66, PBX 336 22 00, Barranquilla 080002, Colombia)

  • Juan Jose Cabello Eras

    (Departamento de Ingeniería Mecánica, Universidad de Córdoba, Carrera 6 No. 77-305, Montería 230002, Colombia)

  • Jorge Mario Mendoza Fandiño

    (Departamento de Ingeniería Mecánica, Universidad de Córdoba, Carrera 6 No. 77-305, Montería 230002, Colombia)

  • Humberto Carlos Tavera Quiroz

    (Departamento de Ingeniería Mecánica, Universidad de Córdoba, Carrera 6 No. 77-305, Montería 230002, Colombia)

Abstract

The production of lead-acid batteries is an energy-intensive process where 28 to 35% of the energy is used in the form of heat, usually obtained from the combustion of fossil fuels. Regardless of the importance of heat consumption during battery manufacturing, there is no discussion available in the specialized literature that assesses heat during battery manufacturing. This study assessed natural gas consumption in a battery plant based on historical data, the thermographic evaluation of different equipment, and measurements of the combustion processes and combustion gases. Heat transfer models were used to calculate surface heat losses in the various assessed processes, while combustion theory was used to identify other saving potentials. Saving potentials equivalent to 16.6% of the plant’s total natural gas consumption were identified. Replacing the ingot casting system accounts for a potential saving equivalent to 13.6% of the plant gas consumption, improving the grid casting systems for 2.8%, and the leady oxide accounts for a low 0.1%. Implementing the saving measures related to surface heat loss and poor operational practice reduced natural gas consumption by an estimated 1.2% monthly. Savings could be increased to 3.2% by expanding the saving measures to the remaining grid casting systems. Overall, natural gas consumption was reduced by an estimated 777 m 3 /month, GHG emissions by 1.6 tCO 2eq. /month, and fuel costs by 1603 USD/month.

Suggested Citation

  • Alexis Sagastume Gutiérrez & Juan Jose Cabello Eras & Jorge Mario Mendoza Fandiño & Humberto Carlos Tavera Quiroz, 2023. "Management of Natural Gas Consumption during the Manufacturing of Lead-Acid Batteries," Sustainability, MDPI, vol. 15(15), pages 1-27, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:12030-:d:1211325
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/15/12030/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/15/12030/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carpenter, Joseph & Woodbury, Keith A. & O'Neill, Zheng, 2018. "Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison," Applied Energy, Elsevier, vol. 213(C), pages 415-425.
    2. Milen Balbis Morejon & Juan Jose Cabello Eras & Alexis Sagastume Gutierrez & Vladimir Sousa Santos & Yabiel Perez Gomez & Juan Gabriel Rueda Bayona, 2019. "Factors Affecting the Electricity Consumption and Productivity of the Lead Acid Battery Formation Process. The Case of a Battery Plant in Colombia," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 103-112.
    3. Cabello Eras, Juan José & Sagastume Gutiérrez, Alexis & Sousa Santos, Vladimir & Cabello Ulloa, Mario Javier, 2020. "Energy management of compressed air systems. Assessing the production and use of compressed air in industry," Energy, Elsevier, vol. 213(C).
    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. Victor A. Alcal Abraham & Elkin D. Alem n Causil & Vladimir Sousa Santos & Eliana Noriega Angarita & Julio R. G mez Sarduy, 2021. "Identification of Savings Opportunities in a Steel Manufacturing Industry," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 43-50.
    2. Massimo Borg & Paul Refalo & Emmanuel Francalanza, 2023. "Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review," Energies, MDPI, vol. 16(7), pages 1-36, March.
    3. Fábio de Oliveira Neves & Henrique Ewbank & José Arnaldo Frutuoso Roveda & Andrea Trianni & Fernando Pinhabel Marafão & Sandra Regina Monteiro Masalskiene Roveda, 2022. "Economic and Production-Related Implications for Industrial Energy Efficiency: A Logistic Regression Analysis on Cross-Cutting Technologies," Energies, MDPI, vol. 15(4), pages 1-19, February.
    4. Fan Yang & Qian Mao, 2023. "Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    5. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    6. Leonardo Leoni & Alessandra Cantini & Filippo De Carlo & Marcello Salvio & Chiara Martini & Claudia Toro & Fabrizio Martini, 2021. "Energy-Saving Technology Opportunities and Investments of the Italian Foundry Industry," Energies, MDPI, vol. 14(24), pages 1-29, December.
    7. Czopek, Dorota & Gryboś, Dominik & Leszczyński, Jacek & Wiciak, Jerzy, 2022. "Identification of energy wastes through sound analysis in compressed air systems," Energy, Elsevier, vol. 239(PB).
    8. Yan, Li & Wen, Hu & Liu, Wenyong & Jin, Yongfei & Liu, Yin & Li, Chuansheng, 2022. "Adiabatic spontaneous coal combustion period derived from the thermal effect of spontaneous combustion," Energy, Elsevier, vol. 239(PB).
    9. Yayuan Feng & Youxian Huang & Haifeng Shang & Junwei Lou & Ala deen Knefaty & Jian Yao & Rongyue Zheng, 2022. "Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression," Energies, MDPI, vol. 15(13), pages 1-19, June.
    10. Díaz, Julián Arco & Ramos, José Sánchez & Delgado, M. Carmen Guerrero & García, David Hidalgo & Montoya, Francisco Gil & Domínguez, Servando Álvarez, 2018. "A daily baseline model based on transfer functions for the verification of energy saving. A case study of the administration room at the Palacio de la Madraza, Granada," Applied Energy, Elsevier, vol. 224(C), pages 538-549.
    11. Doner, Nimeti & Ciddi, Kerem, 2022. "Regression analysis of the operational parameters and energy-saving potential of industrial compressed air systems," Energy, Elsevier, vol. 252(C).
    12. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).

    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:gam:jsusta:v:15:y:2023:i:15:p:12030-:d:1211325. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.