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

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

Energy, 2024, vol. 289, issue C

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.

Keywords: NG engine; Organic rankine cycle; NOx emission; Artificial neural network; Genetic algorithm (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:289:y:2024:i:c:s0360544223034667

DOI: 10.1016/j.energy.2023.130072

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