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Optimization of Solar-Assisted CCHP Systems: Enhancing Efficiency and Reducing Emissions Through Harris Hawks-Based Mathematical Modeling

Author

Listed:
  • Uchechi Ukaegbu

    (Department of Mechanical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg P.O. Box 2028, South Africa)

  • Lagouge Tartibu

    (Department of Mechanical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg P.O. Box 2028, South Africa)

  • C. W. Lim

    (Department of Mechanical Engineering, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg P.O. Box 2028, South Africa
    Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China)

Abstract

The increasing demand for energy, driven by technological advances, population growth, and economic expansion, has intensified the focus on efficient energy management. Tri-generation systems, such as Combined Cooling, Heating, and Power (CCHP) systems, are of particular interest due to their efficiency and sustainability. Integrating renewable energy sources like solar power with traditional fossil fuels further optimizes CCHP systems. This study presents a novel method for enhancing the CCHP system efficiency by identifying the optimal design parameters and assisting decision makers in selecting the best geometric configurations. A mathematical programming model using the Harris Hawks optimizer was developed to maximize the net power and exergy efficiency while minimizing CO 2 emissions in a solar-assisted CCHP system. The optimization resulted in 100 Pareto optimal solutions, offering various choices for performance improvement. This method achieved a higher net power output, satisfactory exergy efficiency, and lower CO 2 emissions compared to similar studies. The study shows that the maximum net power and exergy efficiency, with reduced CO 2 emissions, can be achieved with a system having a low compression ratio and low combustion chamber inlet temperature. The proposed approach surpassed the response surface method, achieving at least a 4.2% reduction in CO 2 emissions and improved exergy values.

Suggested Citation

  • Uchechi Ukaegbu & Lagouge Tartibu & C. W. Lim, 2024. "Optimization of Solar-Assisted CCHP Systems: Enhancing Efficiency and Reducing Emissions Through Harris Hawks-Based Mathematical Modeling," Sustainability, MDPI, vol. 16(23), pages 1-21, December.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10694-:d:1537923
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    References listed on IDEAS

    as
    1. Azaza, Maher & Wallin, Fredrik, 2017. "Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden," Energy, Elsevier, vol. 123(C), pages 108-118.
    2. Wang, Jiangjiang & Liu, Yi & Ren, Fukang & Lu, Shuaikang, 2020. "Multi-objective optimization and selection of hybrid combined cooling, heating and power systems considering operational flexibility," Energy, Elsevier, vol. 197(C).
    3. Costa, E. & Almeida, M.F. & Alvim-Ferraz, C. & Dias, J.M., 2021. "Otimization of Crambe abyssinica enzymatic transesterification using response surface methodology," Renewable Energy, Elsevier, vol. 174(C), pages 444-452.
    4. Tholkappiyan Ramachandran & Abdel-Hamid I. Mourad & Fathalla Hamed, 2022. "A Review on Solar Energy Utilization and Projects: Development in and around the UAE," Energies, MDPI, vol. 15(10), pages 1-27, May.
    5. Song, Zhihui & Liu, Tao & Lin, Qizhao, 2020. "Multi-objective optimization of a solar hybrid CCHP system based on different operation modes," Energy, Elsevier, vol. 206(C).
    6. Wang, Lang & Lu, Jianfeng & Wang, Weilong & Ding, Jing, 2016. "Energy, environmental and economic evaluation of the CCHP systems for a remote island in south of China," Applied Energy, Elsevier, vol. 183(C), pages 874-883.
    Full references (including those not matched with items on IDEAS)

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