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Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System

Author

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  • Yongchao Sun

    (College of Engineering, Ocean University of China, Qingdao 266100, China
    These authors contributed equally to this work.)

  • Pengyuan Sun

    (College of Energy, Xiamen University, Xiamen 361005, China
    These authors contributed equally to this work.)

  • Zhixiang Zhang

    (College of Engineering, Ocean University of China, Qingdao 266100, China)

  • Shuchao Zhang

    (Dezhou State Owned Sports Industry Development Limited, Dezhou 253300, China)

  • Jian Zhao

    (College of Engineering, Ocean University of China, Qingdao 266100, China
    These authors contributed equally to this work.)

  • Ning Mei

    (College of Engineering, Ocean University of China, Qingdao 266100, China
    College of Mechanical & Electrical Engineering, Qingdao City University, Qingdao 266106, China
    These authors contributed equally to this work.)

Abstract

The output of the absorption refrigeration system driven by exhaust gas is unstable and the efficiency is low. Therefore, it is necessary to keep the performance of absorption refrigeration systems in a stable state. This will help predict the dynamic parameters of the system and thus control the output of the system. This paper presents a machine-learning algorithm for predicting the key parameters of an ammonia–water absorption refrigeration system. Three new machine-learning algorithms, Elman, BP neural network (BPNN), and extreme learning machine (ELM), are tested to predict the system parameters. The key control parameters of the system are predicted according to the exhaust gas parameters, and the cooling system is adjusted according to the predicted values to achieve the goal of stable cooling output. After comparison, the ELM algorithm has a fast learning speed, good generalization performance, and small test set error sum, so it is selected as the final optimal prediction algorithm.

Suggested Citation

  • Yongchao Sun & Pengyuan Sun & Zhixiang Zhang & Shuchao Zhang & Jian Zhao & Ning Mei, 2022. "Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System," Energies, MDPI, vol. 15(19), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7070-:d:925680
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    References listed on IDEAS

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    Cited by:

    1. Apostolos Pesyridis & Muhammad Suleman Asif & Sadegh Mehranfar & Amin Mahmoudzadeh Andwari & Ayat Gharehghani & Thanos Megaritis, 2023. "Design of the Organic Rankine Cycle for High-Efficiency Diesel Engines in Marine Applications," Energies, MDPI, vol. 16(11), pages 1-17, May.

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