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Efficient Polynomial PID controller and nonlinear autoregressive with Exogenous for increasing the Efficiency of Combined Gas Turbine (CGT) Plant

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  • Surase, Ravindra S.
  • Ramakrishna, Konijeti
  • Ramchandra P., Chopade

Abstract

The Combined Gas Turbine (CGT) extracts energy from natural gas, producing electricity with higher thermal efficiency and operational flexibility. However, attaining better thermal efficiency faces issues with factors like pressure drop, component efficiency NOx emission, and fuel conditions. To analyze these factors effectively, the proposed research involves two works, in which work 1 exhibits constraints over the prediction and monitoring of the air filter pressure drop, automatic cleaning, thermal efficiency, and inaccurate fuel control. While the work 2 involves issues with higher exergy destruction within the condenser and incomplete NOx reduction. Hence, to overcome these issues and to validate the two works, a novel Polynomial adaptive swarm Parallel Genetic Optimization PID (PAD-PID) controller and Nonlinear Bayesian Kalman Least Squares autoregressive exogenous (BK-NARX) controller-based models are proposed in this work. The novel PAD-PID controller aids in reducing the exergy destruction within the condenser and also maximizes the NOx reduction efficiency. Moreover, the novel BK-NARX controller effectively predicts the turbine performance and also responds to variations in exhaust gas temperature effectively. The defined work is compared with different conventional techniques, to identify its significance. The analyzation results show that both the controllers attains an overall Efficiency of 91 % and 91.1 %, Exergy Efficiency of about 60.5 % and 59 %, thermal Efficiency of about 69 % and 69.5 %, Energy destruction of 8 kW, NOx reduction of 0.98 kg/kW-h, and NOx concentration of 39.52, ensuring stable and accurate performance across all evaluated metrics.

Suggested Citation

  • Surase, Ravindra S. & Ramakrishna, Konijeti & Ramchandra P., Chopade, 2025. "Efficient Polynomial PID controller and nonlinear autoregressive with Exogenous for increasing the Efficiency of Combined Gas Turbine (CGT) Plant," Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:energy:v:327:y:2025:i:c:s0360544225019760
    DOI: 10.1016/j.energy.2025.136334
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    References listed on IDEAS

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    1. Rahmoune, Mohamed Ben & Hafaifa, Ahmed & Kouzou, Abdellah & Chen, XiaoQi & Chaibet, Ahmed, 2021. "Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 23-47.
    2. Mohammad Alsarayreh & Omar Mohamed & Mustafa Matar, 2022. "Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning," Sustainability, MDPI, vol. 14(2), pages 1-25, January.
    3. Ibrahem, Ibrahem M.A. & Akhrif, Ouassima & Moustapha, Hany & Staniszewski, Martin, 2021. "Nonlinear generalized predictive controller based on ensemble of NARX models for industrial gas turbine engine," Energy, Elsevier, vol. 230(C).
    4. Zhen Wang & Liqiang Duan, 2021. "Thermoeconomic Optimization of Steam Pressure of Heat Recovery Steam Generator in Combined Cycle Gas Turbine under Different Operation Strategies," Energies, MDPI, vol. 14(16), pages 1-20, August.
    5. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
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