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Co-optimizing NOx emission and power output of a natural gas engine-ORC combined system through neural networks and genetic algorithms

Author

Listed:
  • Wang, Chongyao
  • Wang, Xin
  • Wang, Huaiyu
  • Xu, Yonghong
  • Ge, Yunshan
  • Tan, Jianwei
  • Hao, Lijun
  • Wang, Yachao
  • Zhang, Mengzhu
  • Li, Ruonan

Abstract

Organic Rankine cycle (ORC) can improve engine power by recovering exhaust energy. This paper co-optimizes the engine-ORC combined system's power and NOx emission, with decision variables of the engine's excess air ratio, spark advance angle, as well as ORC's pump and expander speeds. Firstly, a simulation model of the combined system is established and validated. Then, the initial dataset is generated by the D-optimum Latin hypercube method and simulation model. The artificial neural network (ANN) prediction models of NOx emission and power are established based on these datasets. Finally, the co-optimization is conducted using the ANN prediction model and genetic algorithm. Focusing on maximizing the combined system's power results in an 18.30 % increase in power, and a significant reduction in brake-specific fuel consumption (BSFC) and brake-specific NOx (BSNOx) by 10.10 % and 71.30 %, respectively, compared to the unoptimized basis. Targeting the lowest BSNOx leads to a limited 1.20 % increase in power output; however, it results in a 19.50 % increase in BSFC. When optimizing for both system output and BSNOx, the output remains 13.5 % above the unoptimized basis. Meanwhile, up to 89.8 % of BSNOx can be eliminated with negligible deterioration in BSFC. This study could be used for engine performance enhancements.

Suggested Citation

  • Wang, Chongyao & Wang, Xin & Wang, Huaiyu & Xu, Yonghong & Ge, Yunshan & Tan, Jianwei & Hao, Lijun & Wang, Yachao & Zhang, Mengzhu & Li, Ruonan, 2024. "Co-optimizing NOx emission and power output of a natural gas engine-ORC combined system through neural networks and genetic algorithms," Energy, Elsevier, vol. 289(C).
    • Handle: RePEc:eee:energy:v:289:y:2024:i:c:s0360544223034667
    DOI: 10.1016/j.energy.2023.130072
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