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Evolutionary Multi-Objective Optimization Applied to Industrial Refrigeration Systems for Energy Efficiency

Author

Listed:
  • Nadia Nedjah

    (Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 20550-900, 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 20550-900, 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 20550-900, Brazil
    These authors contributed equally to this work.)

Abstract

Refrigeration systems based on cooling towers and chillers are widely used equipment in industrial buildings, such as shopping centers, gas and oil refineries and power plants, among many others. Cooling towers are used to recover the heat rejected by the refrigeration system. In this work, the refrigeration is composed of cooling towers dotted with ventilators and compression chillers. The growing environmental concerns and the current scenario of scarce water and energy resources have lead to the adoption of actions to obtain the maximum energy efficiency in such refrigeration equipment. This backs up the application of computational intelligence to optimize the operating conditions of the involved equipment and cooling processes. In this context, we utilize multi-objective optimization algorithms to determine the optimal operational setpoints of the cooling system regarding the cooling towers, its fans and the included chillers. We use evolutionary multi-objective optimization to provide the best trade-offs between two conflicting objectives: maximization of the effectiveness of the cooling towers and minimization of the overall power requirement of the refrigeration system. The optimization process respects the constraints to guarantee the correct and safe operation of the equipment when the evolved solution is implemented. In this work, we apply three evolutionary multi-objective algorithms: Non-dominated Sorting Genetic Algorithm (NSGA-II), Micro-Genetic Algorithm (Micro-GA) and Strength Pareto Evolutionary Algorithm (SPEA2). The results obtained are analyzed under different scenarios and models of the cooling system’s equipment, allowing for the selection of the best algorithm and best equipment’s model to achieve energy efficiency of the studied refrigeration system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5575-:d:877485
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Carlos D. Zuluaga-Ríos & Alejandro Villa-Jaramillo & Sergio D. Saldarriaga-Zuluaga, 2022. "Evaluation of Distributed Generation and Electric Vehicles Hosting Capacity in Islanded DC Grids Considering EV Uncertainty," Energies, MDPI, vol. 15(20), pages 1-17, October.
    2. 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.
    3. Grzegorz Dudek & Paweł Piotrowski & Dariusz Baczyński, 2023. "Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications," Energies, MDPI, vol. 16(7), pages 1-11, March.

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