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Energy Recovery Maximisation Modelling Subject to Constrained Cooling

Author

Listed:
  • Johannes Petrus Bester

    (School of Mechanical Engineering, North-West University, Potchefstroom 2520, South Africa)

  • Martin Van Eldik

    (School of Mechanical Engineering, North-West University, Potchefstroom 2520, South Africa)

  • Philip van Zyl Venter

    (School of Industrial Engineering, University of Stellenbosch, Stellenbosch 7602, South Africa)

Abstract

The primary heat rejection cycle, which is critical for the stability and integrity of the metal production process and equipment, involves the transfer of heat from flue gas to a fluid circulated through a gas-cooler. The rate of heat transfer from the flue gas is influenced by several parameters, including the temperature of the cooling fluid. Heat transfer rates that are too high or too low can negatively impact equipment’s life, emphasising the need for a temperature operational envelope in the cooling fluid prior to entering the gas-cooler. Rejected heat is used for power generation, transferred to the environment, or both. This study examines the impact of control philosophies on both temperature and power generation, while maintaining the exit temperature within the desired range as the highest priority. A more advanced philosophy that combines bypass control with feedforward parameters can maintain temperatures within safe operating limits at all times, while improving the power generation, compared to a typical works approach which is used as a baseline. This study presents a formulation that increased power generation from an average of 6.11 MW for a typical works philosophy to 10.68 MW, while maintaining the temperature within the operating temperature envelope.

Suggested Citation

  • Johannes Petrus Bester & Martin Van Eldik & Philip van Zyl Venter, 2023. "Energy Recovery Maximisation Modelling Subject to Constrained Cooling," Energies, MDPI, vol. 17(1), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:131-:d:1307488
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    References listed on IDEAS

    as
    1. Wang, Xuan & Wang, Rui & Jin, Ming & Shu, Gequn & Tian, Hua & Pan, Jiaying, 2020. "Control of superheat of organic Rankine cycle under transient heat source based on deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    2. Xu, Bin & Li, Xiaoya, 2021. "A Q-learning based transient power optimization method for organic Rankine cycle waste heat recovery system in heavy duty diesel engine applications," Applied Energy, Elsevier, vol. 286(C).
    3. Rathod, Dhruvang & Xu, Bin & Filipi, Zoran & Hoffman, Mark, 2019. "An experimentally validated, energy focused, optimal control strategy for an Organic Rankine Cycle waste heat recovery system," Applied Energy, Elsevier, vol. 256(C).
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