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Estimating the potential for thermal energy efficiency in the industrial sector: A hybrid model integrating exergy analysis and long-term technology diffusion scenarios

Author

Listed:
  • Lima, Ricardo G.A.
  • Calili, Rodrigo F.
  • Almeida, Maria Fatima L.
  • Silva, Felipe L.
  • Santos, Samuel
  • Velásquez, Roberto

Abstract

Thermal energy for heating and cooling accounts for a significant portion of the total energy consumed in the industrial sector, and is predominantly provided by fossil fuels. Thus, effective thermal energy management is essential for reducing overall energy consumption, operational costs, and emissions in the industrial sector. While previous studies have explored energy efficiency in this sector, there is a lack of research specifically addressing the challenges of modeling thermal energy efficiency in industry. Therefore, this study proposes a novel hybrid methodological approach that integrates macroeconomic perspectives with detailed end-use analysis and exergy considerations, allowing for a more accurate assessment of recoverable energy in industrial processes in alternative technology diffusion scenarios over a long-term horizon. An empirical study focusing on the pulp and paper subsector in Brazil demonstrated the applicability of the proposed model and revealed significant potential for thermal energy efficiency improvements. The model identified that through optimal technology diffusion scenarios, the sector could achieve up to 25 % reduction in thermal energy consumption by 2050, with heat recovery systems accounting for approximately 40 % of these savings. Cost-effective technology adoption curves indicated that 60 % of this potential could be realized through economically viable investments with payback periods under 3 years. Overall, this study contributes to the field of industrial energy efficiency by offering a hybrid methodological approach that can be adapted to different industrial settings, and can help policymakers and industry stakeholders develop effective strategies to improve thermal energy efficiency and reduce emissions in the industrial sector.

Suggested Citation

  • Lima, Ricardo G.A. & Calili, Rodrigo F. & Almeida, Maria Fatima L. & Silva, Felipe L. & Santos, Samuel & Velásquez, Roberto, 2024. "Estimating the potential for thermal energy efficiency in the industrial sector: A hybrid model integrating exergy analysis and long-term technology diffusion scenarios," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224036028
    DOI: 10.1016/j.energy.2024.133824
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    References listed on IDEAS

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    1. Hasanuzzaman, M. & Rahim, N.A. & Hosenuzzaman, M. & Saidur, R. & Mahbubul, I.M. & Rashid, M.M., 2012. "Energy savings in the combustion based process heating in industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4527-4536.
    2. Chateau, Bertrand & Lapillonne, Bruno, 1990. "2.4. Accounting and end-use models," Energy, Elsevier, vol. 15(3), pages 261-278.
    3. Wene, C.-O., 1996. "Energy-economy analysis: Linking the macroeconomic and systems engineering approaches," Energy, Elsevier, vol. 21(9), pages 809-824.
    4. Ong, Benjamin H.Y. & Bhadbhade, Navdeep & Olsen, Donald G. & Wellig, Beat, 2023. "Characterizing sector-wide thermal energy profiles for industrial sectors," Energy, Elsevier, vol. 282(C).
    5. Eduardo Correia & Rodrigo Calili & José Francisco Pessanha & Maria Fatima Almeida, 2023. "Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions," Energies, MDPI, vol. 16(6), pages 1-22, March.
    6. Klinge Jacobsen, Henrik, 1998. "Integrating the bottom-up and top-down approach to energy-economy modelling: the case of Denmark," Energy Economics, Elsevier, vol. 20(4), pages 443-461, September.
    7. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
    8. McMillan, Colin A. & Ruth, Mark, 2019. "Using facility-level emissions data to estimate the technical potential of alternative thermal sources to meet industrial heat demand," Applied Energy, Elsevier, vol. 239(C), pages 1077-1090.
    9. Bass, Frank M, 1980. "The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations," The Journal of Business, University of Chicago Press, vol. 53(3), pages 51-67, July.
    10. Koopmans, Carl C. & te Velde, Dirk Willem, 2001. "Bridging the energy efficiency gap: using bottom-up information in a top-down energy demand model," Energy Economics, Elsevier, vol. 23(1), pages 57-75, January.
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