IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v263y2023ipds036054422202847x.html
   My bibliography  Save this article

Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm

Author

Listed:
  • Wang, Huaiyu
  • Ji, Changwei
  • Shi, Cheng
  • Yang, Jinxin
  • Wang, Shuofeng
  • Ge, Yunshan
  • Chang, Ke
  • Meng, Hao
  • Wang, Xin

Abstract

Hydrogen is a promising way to achieve high efficiency and low emissions for Wankel rotary engines. In this paper, the intake and exhaust phases and excess air ratios (λ) were optimized using machine learning (ML) and genetic algorithm (GA). Firstly, a one-dimensional model was built and verified under various λ. Secondly, the variables were determined using sensitivity analysis method, and the sample for training models was generated using the Latin hypercube sampling. Finally, a prediction model for performance and emissions was built using ML and combined with GA for multi-objective optimization. The results show that the timing of intake port full closing (IPFC) and exhaust port start opening (EPSO) exhibits the most significant influence on performance and emissions, while the other phases are less influential. Both indicated mean effective pressure (IMEP) and indicated specific nitrogen oxides (ISNOx) increase as the IPFC timing is advanced, while indicated specific fuel consumption (ISFC) decreases as EPSO timing is delayed. Compared with the original engine, the optimized IMEP is improved by 0.18%, ISFC is reduced by 2.39%, and ISNOx is reduced by up to 65.43%. It is an efficient way to use ML combined with GA to improve performance and reduce emissions simultaneously.

Suggested Citation

  • Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan & Chang, Ke & Meng, Hao & Wang, Xin, 2023. "Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm," Energy, Elsevier, vol. 263(PD).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s036054422202847x
    DOI: 10.1016/j.energy.2022.125961
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422202847X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125961?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Yaopeng & Jia, Ming & Han, Xu & Bai, Xue-Song, 2021. "Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)," Energy, Elsevier, vol. 225(C).
    2. Wang, Huaiyu & Ji, Changwei & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan, 2022. "Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm," Energy, Elsevier, vol. 261(PB).
    3. Sui, Zengguang & Sui, Yunren & Wu, Wei, 2022. "Multi-objective optimization of a microchannel membrane-based absorber with inclined grooves based on CFD and machine learning," Energy, Elsevier, vol. 240(C).
    4. Shi, Cheng & Chai, Sen & Di, Liming & Ji, Changwei & Ge, Yunshan & Wang, Huaiyu, 2023. "Combined experimental-numerical analysis of hydrogen as a combustion enhancer applied to wankel engine," Energy, Elsevier, vol. 263(PC).
    5. Qin, Zhaoju & Jia, Minghui & Yang, Huadong, 2020. "Study on vortex characteristics and velocity distribution in small rotary engine," Energy, Elsevier, vol. 206(C).
    6. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Ge, Yunshan & Meng, Hao & Yang, Jinxin & Chang, Ke & Wang, Shuofeng, 2022. "Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine," Energy, Elsevier, vol. 248(C).
    7. Rajkumar, Sundararajan & Das, Arnab & Thangaraja, Jeyaseelan, 2022. "Integration of artificial neural network, multi-objective genetic algorithm and phenomenological combustion modelling for effective operation of biodiesel blends in an automotive engine," Energy, Elsevier, vol. 239(PA).
    8. Jaliliantabar, Farzad & Ghobadian, Barat & Najafi, Gholamhassan & Mamat, Rizalman & Carlucci, Antonio Paolo, 2019. "Multi-objective NSGA-II optimization of a compression ignition engine parameters using biodiesel fuel and exhaust gas recirculation," Energy, Elsevier, vol. 187(C).
    9. Osman Akin Kutlar & Fatih Malkaz, 2019. "Two-Stroke Wankel Type Rotary Engine: A New Approach for Higher Power Density," Energies, MDPI, vol. 12(21), pages 1-22, October.
    10. Liu, Jinlong & Huang, Qiao & Ulishney, Christopher & Dumitrescu, Cosmin E., 2021. "Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine," Applied Energy, Elsevier, vol. 300(C).
    11. Gharehghani, Ayat & Abbasi, Hamid Reza & Alizadeh, Pouria, 2021. "Application of machine learning tools for constrained multi-objective optimization of an HCCI engine," Energy, Elsevier, vol. 233(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Liming Di & Zhuogang Sun & Fuxiang Zhi & Tao Wan & Qixin Yang, 2023. "Assessment of an Optimal Design Method for a High-Energy Ultrasonic Igniter Based on Multi-Objective Robustness Optimization," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    3. Shi, Ting & Wang, Huaiyu & Yang, Wenming & Peng, Xueyuan, 2024. "Mathematical modeling and optimization of gas foil bearings-rotor system in hydrogen fuel cell vehicles," Energy, Elsevier, vol. 290(C).
    4. Hai, Tao & Hussein Kadir, Dler & Ghanbari, Afshin, 2023. "Modeling the emission characteristics of the hydrogen-enriched natural gas engines by multi-output least-squares support vector regression: Comprehensive statistical and operating analyses," Energy, Elsevier, vol. 276(C).
    5. Yang, Jinxin & Wang, Huaiyu & Ji, Changwei & Chang, Ke & Wang, Shuofeng, 2023. "Investigation of intake closing timing on the flow field and combustion process in a small-scaled Wankel rotary engine under various engine speeds designed for the UAV application," Energy, Elsevier, vol. 273(C).
    6. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Xiao, Qiuhong & Liu, Yongzheng & Ma, Fanhua, 2024. "Performance, emissions and combustion analysis of hydrogen-enriched compressed natural gas spark ignition engine by optimized Gaussian process regression and neural network at low speed on different l," Energy, Elsevier, vol. 302(C).
    7. Meng, Qinglong & Wei, Ying'an & Fan, Jingjing & Li, Yanbo & Zhao, Fan & Lei, Yu & Sun, Hang & Jiang, Le & Yu, Lingli, 2024. "Peak regulation strategies for ground source heat pump demand response of based on load forecasting: A case study of rural building in China," Renewable Energy, Elsevier, vol. 224(C).
    8. Chen, Guisheng & Sun, Min & Li, Junda & Wang, Jiguang & Shen, Yinggang & Liang, Daping & Xiao, Renxin, 2024. "Study on high-altitude ceiling strategy of compression ignition aviation piston engines based on BP-NSGA II algorithm optimization," Energy, Elsevier, vol. 294(C).
    9. Zhu, Yizi & He, Zhixia & Xuan, Tiemin & Shao, Zhuang, 2024. "An enhanced automated machine learning model for optimizing cycle-to-cycle variation in hydrogen-enriched methanol engines," Applied Energy, Elsevier, vol. 362(C).
    10. Zhai, Yifan & Wang, Shuofeng & Wang, Zhe & Zhang, Tianyue & Ji, Changwei, 2023. "Experimental and numerical study on laminar combustion characteristics of by-product hydrogen coke oven gas," Energy, Elsevier, vol. 278(C).
    11. Wang, Huaiyu & Ji, Changwei & Wang, Du & Wang, Zhe & Yang, Jinxin & Meng, Hao & Shi, Cheng & Wang, Shuofeng & Wang, Xin & Ge, Yunshan & Yang, Wenming, 2023. "Investigation on the potential of using carbon-free ammonia and hydrogen in small-scaled Wankel rotary engines," Energy, Elsevier, vol. 283(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Huaiyu & Ji, Changwei & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan, 2022. "Towards a comprehensive optimization of the intake characteristics for side ported Wankel rotary engines by coupling machine learning with genetic algorithm," Energy, Elsevier, vol. 261(PB).
    2. Cesar de Lima Nogueira, Silvio & Och, Stephan Hennings & Moura, Luis Mauro & Domingues, Eric & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2023. "Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering," Energy, Elsevier, vol. 280(C).
    3. Yang, Jinxin & Wang, Huaiyu & Ji, Changwei & Chang, Ke & Wang, Shuofeng, 2023. "Investigation of intake closing timing on the flow field and combustion process in a small-scaled Wankel rotary engine under various engine speeds designed for the UAV application," Energy, Elsevier, vol. 273(C).
    4. 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).
    5. Bo Zhang & Huaiyu Wang & Shuofeng Wang, 2023. "Computational Investigation of Combustion, Performance, and Emissions of a Diesel-Hydrogen Dual-Fuel Engine," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    6. Shi, Ting & Wang, Huaiyu & Yang, Wenming & Peng, Xueyuan, 2024. "Mathematical modeling and optimization of gas foil bearings-rotor system in hydrogen fuel cell vehicles," Energy, Elsevier, vol. 290(C).
    7. Farhan, Muhammad & Chen, Tianhao & Rao, Anas & Shahid, Muhammad Ihsan & Xiao, Qiuhong & Liu, Yongzheng & Ma, Fanhua, 2024. "Performance, emissions and combustion analysis of hydrogen-enriched compressed natural gas spark ignition engine by optimized Gaussian process regression and neural network at low speed on different l," Energy, Elsevier, vol. 302(C).
    8. Jiao, Huichao & Ye, Xianlei & Zou, Run & Wang, Nana & Liu, Jinxiang, 2022. "Comparative study on ignition and combustion between conventional spark-ignition method and near-wall surface ignition method for small-scale Wankel rotary engine," Energy, Elsevier, vol. 255(C).
    9. Shi, Cheng & Chai, Sen & Di, Liming & Ji, Changwei & Ge, Yunshan & Wang, Huaiyu, 2023. "Combined experimental-numerical analysis of hydrogen as a combustion enhancer applied to wankel engine," Energy, Elsevier, vol. 263(PC).
    10. Wang, Huaiyu & Ji, Changwei & Wang, Du & Wang, Zhe & Yang, Jinxin & Meng, Hao & Shi, Cheng & Wang, Shuofeng & Wang, Xin & Ge, Yunshan & Yang, Wenming, 2023. "Investigation on the potential of using carbon-free ammonia and hydrogen in small-scaled Wankel rotary engines," Energy, Elsevier, vol. 283(C).
    11. Cao, Jiale & Li, Tie & Huang, Shuai & Chen, Run & Li, Shiyan & Kuang, Min & Yang, Rundai & Huang, Yating, 2023. "Co-optimization of miller degree and geometric compression ratio of a large-bore natural gas generator engine with novel Knock models and machine learning," Applied Energy, Elsevier, vol. 352(C).
    12. Ma, Dingyuan & Li, Xiaodong & Lin, Borong & Zhu, Yimin, 2023. "An intelligent retrofit decision-making model for building program planning considering tacit knowledge and multiple objectives," Energy, Elsevier, vol. 263(PB).
    13. Gharehghani, Ayat & Abbasi, Hamid Reza & Alizadeh, Pouria, 2021. "Application of machine learning tools for constrained multi-objective optimization of an HCCI engine," Energy, Elsevier, vol. 233(C).
    14. Chang, Ke & Ji, Changwei & Wang, Shuofeng & Yang, Jinxin & Wang, Huaiyu & Xin, Gu & Meng, Hao, 2022. "Numerical investigation of the combined effect of injection angle and injection pressure in a gasoline direct injection rotary engine," Energy, Elsevier, vol. 254(PB).
    15. Wenbo Ai & Haeng Muk Cho, 2024. "Predictive Models for Biodiesel Performance and Emission Characteristics in Diesel Engines: A Review," Energies, MDPI, vol. 17(19), pages 1-25, September.
    16. Kale, Aneesh Vijay & Krishnasamy, Anand, 2023. "Experimental study of homogeneous charge compression ignition combustion in a light-duty diesel engine fueled with isopropanol–gasoline blends," Energy, Elsevier, vol. 264(C).
    17. Tehseen Johar & Chiu-Fan Hsieh, 2023. "Design Challenges in Hydrogen-Fueled Rotary Engine—A Review," Energies, MDPI, vol. 16(2), pages 1-22, January.
    18. Murthy, Anarghya Ananda & Krishan, Gopal & Shenoy, Praveen & Patil, Ishwaragouda S, 2024. "Theoretical, CFD modelling and experimental investigation of a four-intersecting-vane rotary expander," Applied Energy, Elsevier, vol. 353(PB).
    19. Sungur, Bilal & Basar, Cem & Kaleli, Alirıza, 2023. "Multi-objective optimisation of the emission parameters and efficiency of pellet stove at different supply airflow positions based on machine learning approach," Energy, Elsevier, vol. 278(PA).
    20. Shi, Cheng & Zhang, Zheng & Wang, Huaiyu & Wang, Jingyi & Cheng, Tengfei & Zhang, Liang, 2024. "Parametric analysis and optimization of the combustion process and pollutant performance for ammonia-diesel dual-fuel engines," Energy, Elsevier, vol. 296(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:263:y:2023:i:pd:s036054422202847x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.