Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System
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 ou...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/19/7070 |
| _version_ | 1797479580154462208 |
|---|---|
| author | Yongchao Sun Pengyuan Sun Zhixiang Zhang Shuchao Zhang Jian Zhao Ning Mei |
| author_facet | Yongchao Sun Pengyuan Sun Zhixiang Zhang Shuchao Zhang Jian Zhao Ning Mei |
| author_sort | Yongchao Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:47:49Z |
| format | Article |
| id | doaj.art-47c103da56ca44cbb43217902a93f2c4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:47:49Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-47c103da56ca44cbb43217902a93f2c42023-11-23T20:12:35ZengMDPI AGEnergies1996-10732022-09-011519707010.3390/en15197070Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration SystemYongchao Sun0Pengyuan Sun1Zhixiang Zhang2Shuchao Zhang3Jian Zhao4Ning Mei5College of Engineering, Ocean University of China, Qingdao 266100, ChinaCollege of Energy, Xiamen University, Xiamen 361005, ChinaCollege of Engineering, Ocean University of China, Qingdao 266100, ChinaDezhou State Owned Sports Industry Development Limited, Dezhou 253300, ChinaCollege of Engineering, Ocean University of China, Qingdao 266100, ChinaCollege of Engineering, Ocean University of China, Qingdao 266100, ChinaThe 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.https://www.mdpi.com/1996-1073/15/19/7070exhaust gas heat recoveryammonia–water-based absorption refrigerationquantitative control of refrigeration outputmachine-learning algorithmsprediction |
| spellingShingle | Yongchao Sun Pengyuan Sun Zhixiang Zhang Shuchao Zhang Jian Zhao Ning Mei Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System Energies exhaust gas heat recovery ammonia–water-based absorption refrigeration quantitative control of refrigeration output machine-learning algorithms prediction |
| title | Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System |
| title_full | Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System |
| title_fullStr | Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System |
| title_full_unstemmed | Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System |
| title_short | Performance Prediction for a Marine Diesel Engine Waste Heat Absorption Refrigeration System |
| title_sort | performance prediction for a marine diesel engine waste heat absorption refrigeration system |
| topic | exhaust gas heat recovery ammonia–water-based absorption refrigeration quantitative control of refrigeration output machine-learning algorithms prediction |
| url | https://www.mdpi.com/1996-1073/15/19/7070 |
| work_keys_str_mv | AT yongchaosun performancepredictionforamarinedieselenginewasteheatabsorptionrefrigerationsystem AT pengyuansun performancepredictionforamarinedieselenginewasteheatabsorptionrefrigerationsystem AT zhixiangzhang performancepredictionforamarinedieselenginewasteheatabsorptionrefrigerationsystem AT shuchaozhang performancepredictionforamarinedieselenginewasteheatabsorptionrefrigerationsystem AT jianzhao performancepredictionforamarinedieselenginewasteheatabsorptionrefrigerationsystem AT ningmei performancepredictionforamarinedieselenginewasteheatabsorptionrefrigerationsystem |