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

Yongchao Sun, Pengyuan Sun, Zhixiang Zhang, Shuchao Zhang, Jian Zhao () and Ning Mei ()
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Yongchao Sun: College of Engineering, Ocean University of China, Qingdao 266100, China
Pengyuan Sun: College of Energy, Xiamen University, Xiamen 361005, China
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
Ning Mei: College of Engineering, Ocean University of China, Qingdao 266100, China

Energies, 2022, vol. 15, issue 19, 1-22

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.

Keywords: exhaust gas heat recovery; ammonia–water-based absorption refrigeration; quantitative control of refrigeration output; machine-learning algorithms; prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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