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Multi-Objective Optimization of Organic Rankine Cycle (ORC) for Tractor Waste Heat Recovery Based on Particle Swarm Optimization

Author

Listed:
  • Wanming Pan

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)

  • Junkang Li

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)

  • Guotao Zhang

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Le Zhou

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)

  • Ming Tu

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Agricultural Equipment in Mid-lower Reaches of the Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China)

Abstract

Diesel engines are widely used in agricultural tractors. During field operations, the tractors operate at low speed and high load for a long time, the fuel efficiency is only about 15% to 35%, and the exhaust waste heat accounts for 38% to 45% of the energy released from the fuel. The use of tractor exhaust waste heat can effectively reduce fuel consumption and pollutant emissions, of which the organic Rankine cycle (ORC)-based waste heat recovery conversion efficiency is the highest. First, the diesel engine map is achieved through the test rig, a plate-fin evaporator is trial-produced based on the tractor size, and the thermodynamic and economic performance model of the ORC are established. Then, taking the thermal efficiency of ORC and the specific investment cost (SIC) as the objective function, the particle swarm optimization (PSO) algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) decision method were used to obtain the optimal operating parameter set under all working conditions. Finally, the results showed that the ORC thermal efficiency could reach a maximum of 12.76% and the corresponding SIC value was 8539.66 $/kW; the ORC net output power could be up to 8.31 kW compared with the system without ORC; and the maximum brake specific fuel consumption (BSFC) could be reduced by 8.3%. The improvement in the thermodynamic performance will lead to a sacrifice in economic performance, and at high speeds, the economic benefits and thermal efficiency reach a balance and show a better thermal economic performance. Recovering exhaust heat energy through ORC can reduce tractor fuel consumption and pollution emissions, which is one of the effective technical means to achieve “carbon neutrality” in agricultural production. At the same time, through the PSO algorithm, the optimal combination of ORC operating parameters is obtained, which ensures that the exhaust heat energy can be effectively recovered during the tractor field operation, and provides a basis for the adjustment of real-time work strategies for future research.

Suggested Citation

  • Wanming Pan & Junkang Li & Guotao Zhang & Le Zhou & Ming Tu, 2022. "Multi-Objective Optimization of Organic Rankine Cycle (ORC) for Tractor Waste Heat Recovery Based on Particle Swarm Optimization," Energies, MDPI, vol. 15(18), pages 1-24, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6720-:d:914791
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    References listed on IDEAS

    as
    1. Jahedul Islam Chowdhury & Bao Kha Nguyen & David Thornhill & Yukun Hu & Payam Soulatiantork & Nazmiye Balta-Ozkan & Liz Varga, 2018. "Fuzzy Nonlinear Dynamic Evaporator Model in Supercritical Organic Rankine Cycle Waste Heat Recovery Systems," Energies, MDPI, vol. 11(4), pages 1-24, April.
    2. Zhao, Mingru & Canova, Marcello & Tian, Hua & Shu, Gequn, 2019. "Design space exploration for waste heat recovery system in automotive application under driving cycle," Energy, Elsevier, vol. 176(C), pages 980-990.
    3. Jahedul Islam Chowdhury & Bao Kha Nguyen & David Thornhill, 2015. "Modelling of Evaporator in Waste Heat Recovery System using Finite Volume Method and Fuzzy Technique," Energies, MDPI, vol. 8(12), pages 1-20, December.
    4. Steven Lecompte & Sanne Lemmens & Henk Huisseune & Martijn Van den Broek & Michel De Paepe, 2015. "Multi-Objective Thermo-Economic Optimization Strategy for ORCs Applied to Subcritical and Transcritical Cycles for Waste Heat Recovery," Energies, MDPI, vol. 8(4), pages 1-28, April.
    5. Youcef Redjeb & Khatima Kaabeche-Djerafi & Anna Stoppato & Alberto Benato, 2021. "The IRC-PD Tool: A Code to Design Steam and Organic Waste Heat Recovery Units," Energies, MDPI, vol. 14(18), pages 1-37, September.
    6. Antonina Kalinichenko & Valerii Havrysh & Vasyl Hruban, 2018. "Heat Recovery Systems for Agricultural Vehicles: Utilization Ways and Their Efficiency," Agriculture, MDPI, vol. 8(12), pages 1-18, December.
    7. Hongjin Wang & Hongguang Zhang & Fubin Yang & Songsong Song & Ying Chang & Chen Bei & Kai Yang, 2015. "Parametric Optimization of Regenerative Organic Rankine Cycle System for Diesel Engine Based on Particle Swarm Optimization," Energies, MDPI, vol. 8(9), pages 1-26, September.
    8. Shuozhuo Hu & Zhen Yang & Jian Li & Yuanyuan Duan, 2021. "A Review of Multi-Objective Optimization in Organic Rankine Cycle (ORC) System Design," Energies, MDPI, vol. 14(20), pages 1-36, October.
    9. Zhou, Yuhong & Li, Shanshan & Sun, Lei & Zhao, Shupeng & Ashraf Talesh, Seyed Saman, 2020. "Optimization and thermodynamic performance analysis of a power generation system based on geothermal flash and dual-pressure evaporation organic Rankine cycles using zeotropic mixtures," Energy, Elsevier, vol. 194(C).
    10. Huican Luo & Peijian Zhou & Lingfeng Shu & Jiegang Mou & Haisheng Zheng & Chenglong Jiang & Yantian Wang, 2022. "Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model," Energies, MDPI, vol. 15(9), pages 1-19, May.
    11. Vaupel, Yannic & Huster, Wolfgang R. & Mhamdi, Adel & Mitsos, Alexander, 2021. "Optimal operating policies for organic Rankine cycles for waste heat recovery under transient conditions," Energy, Elsevier, vol. 224(C).
    12. Liya Ren & Jianyu Liu & Huaixin Wang, 2020. "Thermodynamic Optimization of a Waste Heat Power System under Economic Constraint," Energies, MDPI, vol. 13(13), pages 1-23, July.
    13. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Zhang, Jian & Zhang, Wujie & Song, Gege, 2021. "Introducing machine learning and hybrid algorithm for prediction and optimization of multistage centrifugal pump in an ORC system," Energy, Elsevier, vol. 222(C).
    14. Wang, Lingbao & Bu, Xianbiao & Li, Huashan, 2020. "Multi-objective optimization and off-design evaluation of organic rankine cycle (ORC) for low-grade waste heat recovery," Energy, Elsevier, vol. 203(C).
    15. Shiyang Teng & Yong-Qiang Feng & Tzu-Chen Hung & Huan Xi, 2021. "Multi-Objective Optimization and Fluid Selection of Different Cogeneration of Heat and Power Systems Based on Organic Rankine Cycle," Energies, MDPI, vol. 14(16), pages 1-22, August.
    16. 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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