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Swarm Intelligence-Based Multi-Objective Optimization Applied to Industrial Cooling Towers for Energy Efficiency

Author

Listed:
  • Nadia Nedjah

    (Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 20550-013, Brazil
    These authors contributed equally to this work.)

  • Luiza de Macedo Mourelle

    (Department of Systems Engineering and Computation, State University of Rio de Janeiro, Rio de Janeiro 20550-013, Brazil
    These authors contributed equally to this work.)

  • Marcelo Silveira Dantas Lizarazu

    (Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 20550-013, Brazil
    These authors contributed equally to this work.)

Abstract

Cooling towers constitute a fundamental part of refrigeration systems in power plants and large commercial buildings. Their main function is to treat the heat emitted by other equipment to cool down the temperature of the environment and/or processes. In the considered refrigeration system, cooling towers are coupled with compression chillers. The serious world-wide concerns with regard to environmental wear and water scarcity are now common knowledge. One way to mitigate their impact is to reach a state of maximum energy efficiency in industrial processes. For this purpose, this work proposes the application of multi-objective optimization algorithms to find out the optimal operational setpoints of the studied refrigeration system. Here, we exploit swarm intelligence strategies to offer the best trade-offs. This consists of finding solutions that maximize the cooling tower’s effectiveness and yet minimize the global power requirement of the system. Additionally, the solutions must also respect operational constraints for the safe operation of the equipment. In this investigation, we apply two algorithms, multi-objective particle swarm optimization and multi-objective TRIBES, using two different models. The achieved results are compared considering two different scenarios and two different models of the refrigeration system. This allows for the selection of the best algorithm and best equipment model for energy efficiency of the refrigeration system. For the studied configuration, we achieve an energy efficiency factor of 1.78, allowing power savings of 9.48% with tower effectiveness reduction of only 5.32%.

Suggested Citation

  • Nadia Nedjah & Luiza de Macedo Mourelle & Marcelo Silveira Dantas Lizarazu, 2022. "Swarm Intelligence-Based Multi-Objective Optimization Applied to Industrial Cooling Towers for Energy Efficiency," Sustainability, MDPI, vol. 14(19), pages 1-43, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11881-:d:920685
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    References listed on IDEAS

    as
    1. Nadia Nedjah & Luiza de Macedo Mourelle & Marcelo Silveira Dantas Lizarazu, 2022. "Evolutionary Multi-Objective Optimization Applied to Industrial Refrigeration Systems for Energy Efficiency," Energies, MDPI, vol. 15(15), pages 1-27, August.
    2. Ma, Zhenjun & Wang, Shengwei, 2011. "Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm," Applied Energy, Elsevier, vol. 88(1), pages 198-211, January.
    3. Liang, Gin-Shuh, 1999. "Fuzzy MCDM based on ideal and anti-ideal concepts," European Journal of Operational Research, Elsevier, vol. 112(3), pages 682-691, February.
    4. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    5. Kusiak, Andrew & Li, Mingyang & Tang, Fan, 2010. "Modeling and optimization of HVAC energy consumption," Applied Energy, Elsevier, vol. 87(10), pages 3092-3102, October.
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