机器学习在喷射器研究中的应用进展

doi:10.16085/j.issn.1000-6613.2024-0375

化工进展 ›› 2024, Vol. 43 ›› Issue (S1): 1-12.DOI: 10.16085/j.issn.1000-6613.2024-0375

机器学习在喷射器研究中的应用进展

戴征舒¹^,²(), 左元浩¹, 陈孝罗¹, 张犁³, 赵根¹, 张学军², 张华¹

^1.上海理工大学能源与动力工程学院，上海 200093
^2.浙江大学制冷与低温研究所，浙江杭州 310027
^3.浙江大学先进技术研究院，浙江杭州 310027

收稿日期:2024-03-07 修回日期:2024-04-08 出版日期:2024-11-20 发布日期:2024-12-06
通讯作者: 戴征舒
作者简介:戴征舒（1984—），女，副教授，硕士生导师，研究方向为低品位热驱动喷射制冷系统及关键部件喷射器。E-mail：zsdai-hvacr@163.com。
基金资助:
国家自然科学基金(51906152);上海市浦江人才计划(22PJ1411200);浙江省“领雁”研发攻关计划(2024C03117)

Process in the application of machine learning in ejector research

DAI Zhengshu¹^,²(), ZUO Yuanhao¹, CHEN Xiaoluo¹, ZHANG Li³, ZHAO Gen¹, ZHANG Xuejun², ZHANG Hua¹

^1.School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
^2.Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou 310027, Zhejiang, China
^3.Institute of Advanced Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China

Received:2024-03-07 Revised:2024-04-08 Online:2024-11-20 Published:2024-12-06
Contact: DAI Zhengshu

摘要/Abstract

摘要：

喷射器是一种用途广泛的机械装置，具有结构简单、初投资低、维护容易、运行可靠等优点，广泛应用于制冷、海水淡化、化工、燃料电池、航空航天等领域。喷射器不直接消耗机械能，可实现节能的目的，在我国“双碳”背景下备受关注。机器学习方法作为一种基于数据的自动化分析方法，可以用于喷射器内部流动特性的分析及喷射器性能的优化。近年来，已有部分学者将机器学习方法应用于喷射器的研究中，以提升喷射器性能和系统性能。但是目前文献中的研究方向较为分散，研究现状和研究水平尚不明晰。本文对采用机器学习方法对喷射器进行研究的文献进行了全面的梳理，分析现状、总结方法，并指出未来可以将机器学习方法应用于喷射器内部流动特征的研究中，为提升喷射器效率和性能提供依据和指导；将机器学习方法应用于喷射器变工况性能研究中，构建喷射器从自动化设计到真实应用的通路；构建更加合适的算法，提出一系列具有针对性的解决方案。

关键词: 喷射制冷, 喷射器, 机器学习, 人工神经网络, 预测, 优化

Abstract:

The ejector is a widely used mechanical device with advantages such as simple structure, low initial cost, easy maintenance, and reliable operation. It is widely applied in fields such as refrigeration, desalination, chemical engineering, fuel cells, aerospace, etc. The ejector does not directly consume mechanical energy, which allows for energy-saving purposes, making it more important and attractive with the national goals of“carbon peaking and carbon neutrality”in China. Machine learning methods, as a data-driven automated analysis approach, can be used for analyzing the internal flow characteristics of ejectors and optimizing ejector performance. In recent years, a small number of scholars have already applied machine learning methods to the study of ejectors in various applications, aiming at improving the ejector performance and the system performance. But the research in the open literature is currently scattered, and the state of the art is not yet clear. The present work comprehensively reviewed the literature on the application of machine learning methods in the study of ejectors for different applications, analyzed the current research status, summarized the machine learning methods utilized in the open literature, and pointed out that in the future machine learning methods can be applied to the study of internal flow characteristics of ejectors, providing a basis and guidance for improving the efficiency and performance of ejectors. Machine learning methods can be applied to the study of ejector performance under variable operating conditions, constructing a pathway from automated design to real-world application of ejectors. Constructing more suitable algorithms and proposing a series of targeted solutions.

Key words: ejector refrigeration, ejector, machine learning, artificial neural network, prediction, optimization

中图分类号:

TB65

戴征舒, 左元浩, 陈孝罗, 张犁, 赵根, 张学军, 张华. 机器学习在喷射器研究中的应用进展[J]. 化工进展, 2024, 43(S1): 1-12.

DAI Zhengshu, ZUO Yuanhao, CHEN Xiaoluo, ZHANG Li, ZHAO Gen, ZHANG Xuejun, ZHANG Hua. Process in the application of machine learning in ejector research[J]. Chemical Industry and Engineering Progress, 2024, 43(S1): 1-12.

图/表 5

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作者	工质	模型/网络结构	训练算法/优化器	优化方法	数据集
Ahmed和Chen^[41]	水蒸气	4-102-2	Adam，跟随正则化领导者法（follow the regularized leader, Ftrl）自适应学习率法（adaptive delta，Adadelta）	—	实验216
Ringstad等^[42]	CO₂	GPR	—	—	模拟200
Shukla等^[43]	R141b	5-(5-5-5-5)-1	SCG	遗传算法	模拟1008
Liu等^[44-45]	CO₂	5-10-1	LM	遗传算法	模拟350
Zhang等^[46]	水蒸气	4-9-1	—	粒子群优化算法	模拟97
Maghsoodi等^[47]	工作流体为纯氢气，引射流体为氢气和水蒸气	4-4-1	—	遗传算法	模拟167
Jahingir和Huque^[48]	工作流体为氧气与氢气，引射流体为空气	—	—	满意度函数优化	模拟27
杨飞飞^[49]	工作流体为空气，引射流体为乳胶制品	4-(6-1)-1	动量梯度下降算法（gradient descent with momentum，GDM）	控制步长方法	模拟16
高孝良^[50]	一氯二氟甲烷（R22）	2-(10-20)-1	LM	控制步长方法	—

作者	工质	模型/网络结构	训练算法/优化器	优化方法	数据集
Ahmed和Chen^[41]	水蒸气	4-102-2	Adam，跟随正则化领导者法（follow the regularized leader, Ftrl）自适应学习率法（adaptive delta，Adadelta）	—	实验216
Ringstad等^[42]	CO₂	GPR	—	—	模拟200
Shukla等^[43]	R141b	5-(5-5-5-5)-1	SCG	遗传算法	模拟1008
Liu等^[44-45]	CO₂	5-10-1	LM	遗传算法	模拟350
Zhang等^[46]	水蒸气	4-9-1	—	粒子群优化算法	模拟97
Maghsoodi等^[47]	工作流体为纯氢气，引射流体为氢气和水蒸气	4-4-1	—	遗传算法	模拟167
Jahingir和Huque^[48]	工作流体为氧气与氢气，引射流体为空气	—	—	满意度函数优化	模拟27
杨飞飞^[49]	工作流体为空气，引射流体为乳胶制品	4-(6-1)-1	动量梯度下降算法（gradient descent with momentum，GDM）	控制步长方法	模拟16
高孝良^[50]	一氯二氟甲烷（R22）	2-(10-20)-1	LM	控制步长方法	—

作者	工质	模型/网络结构	训练算法/优化器	优化方法	数据集
Rashidi等^[51]	R600	2-3-5	—	PSO和ACO_R	模拟1000
Sangesaraki 等^[52]	R600a	6-(9-9)-1	—	GA	模拟1000
Ebrahimi-Moghadam等^[53]	氨	6-(4-4)-1	LM	NSGA-Ⅱ	模拟32140
Hai等^[54]	—	6隐藏层	—	NSGA-Ⅱ和TOPSIS	模拟1500
Soltani等^[55]	溴化锂-水	5-15-2	—	GA	模拟1300
史棋棋^[59]	R141b	2-(3隐藏层)-1	Adam，RMSprop，SGD，AdaGrad	—	模拟1640
Hao等^[56]	HFC-245fa HCFO-1233zd(E) HCFO-1224yd(Z) R600 HFO-1336mzz(Z)	SVR，KRR，RFR	—	—	—
Wang等^[57]	R134a	—	—	—	—
Sözen等^[60]	甲醇-溴化锂	3-(3-8)-1	SCG，CGP，LM	—	实验174
Sözen等^[60]	甲醇-溴化锂	4-8-3	SCG	—	实验（大小未知）
Sözen和Akçayol ^[61-62]	氨-水	4-20-3	GDR	—	模拟（大小未知）
Sözen等^[63]	甲醇-氯化锂	3-(3-8)-1	SCG，CGP，LM	—	实验174
Sözen等^[63]	甲醇-氯化锂	4-6-5	LM	—	实验（大小未知）
Sözen等^[64]	氨-水	4-7-3	SCG，LM	—	模拟（大小未知）
Sözen和Arcaklioğlu^[65]	氨-水	4-(5,6,7)-4	SCG，LM	—	模拟（大小未知）
Shen等^[58]	燃油	SVR	—	—	实验15

作者	工质	模型/网络结构	训练算法/优化器	优化方法	数据集
Rashidi等^[51]	R600	2-3-5	—	PSO和ACO_R	模拟1000
Sangesaraki 等^[52]	R600a	6-(9-9)-1	—	GA	模拟1000
Ebrahimi-Moghadam等^[53]	氨	6-(4-4)-1	LM	NSGA-Ⅱ	模拟32140
Hai等^[54]	—	6隐藏层	—	NSGA-Ⅱ和TOPSIS	模拟1500
Soltani等^[55]	溴化锂-水	5-15-2	—	GA	模拟1300
史棋棋^[59]	R141b	2-(3隐藏层)-1	Adam，RMSprop，SGD，AdaGrad	—	模拟1640
Hao等^[56]	HFC-245fa HCFO-1233zd(E) HCFO-1224yd(Z) R600 HFO-1336mzz(Z)	SVR，KRR，RFR	—	—	—
Wang等^[57]	R134a	—	—	—	—
Sözen等^[60]	甲醇-溴化锂	3-(3-8)-1	SCG，CGP，LM	—	实验174
Sözen等^[60]	甲醇-溴化锂	4-8-3	SCG	—	实验（大小未知）
Sözen和Akçayol ^[61-62]	氨-水	4-20-3	GDR	—	模拟（大小未知）
Sözen等^[63]	甲醇-氯化锂	3-(3-8)-1	SCG，CGP，LM	—	实验174
Sözen等^[63]	甲醇-氯化锂	4-6-5	LM	—	实验（大小未知）
Sözen等^[64]	氨-水	4-7-3	SCG，LM	—	模拟（大小未知）
Sözen和Arcaklioğlu^[65]	氨-水	4-(5,6,7)-4	SCG，LM	—	模拟（大小未知）
Shen等^[58]	燃油	SVR	—	—	实验15

作者	工质	模型/网络结构	训练算法/优化器	优化方法	数据集
Chen和Cai^[66]	R134a	7-10-1	BP	—	实验396
Bencharif等^[67]	HFC-245fa	12-10-2	LM	—	实验233
Bencharif等^[38]	R134a HFC-245fa R141b R1234ze(E) R1233zd(E)	12-10-2	LM	—	实验959
Bencharif等^[38]	R134a HFC-245fa R141b R1234ze(E) R1233zd(E)	7-10-4	LM	—	实验959
Gupta等^[68]	R113，R141b R134a，R718 HFC-245fa，R600a R290,R152a R123，R717 R1234ze R744，空气	7-84-2	Adam	—	实验(大小未知)
Gupta等^[32]	空气	5-17-2	Adam	—	实验543
Abbady等^[69]	R1234yf	8-20-1	LM	—	模拟1170
黄亮亮等^[72]	—	5-10-1	SA-BP	—	实验1348
黄亮亮和曹家枞^[73]	—	5-12-1	CACS-BP, ACO_R-BP	—	实验1131
Zhu等^[74]	水，R134a，R141b	6-10-1	PSO-BPNN和动态误差补偿方法	—	实验107
Gupta等^[70]	空气	4-17-1	Adam	—	实验541
Gupta等^[70]	空气	5-18-2	Adam	—	实验541
Zhang等^[75]	水	7-18-1	LM，RBP，SCG	—	实验489
谷迎港^[76]	水	7-18-1	LM，BR，SCG	—	实验516
Xu等^[77]	工作流体为H₂，引射流体为H₂、水和N₂的混合物	LASSO回归和弹性网络回归	—	—	模拟343
Petrovic等^[71]	工作流体为天然气或R2气体，引射流体为工业中产生的废气	混合专家模型	—	—	实验12（天然气）实验22（R2气体）
António等^[78]	—	4-10-2	LM	—	模拟5000
Jeon等^[79]	R600a	4-(3隐藏层)-1	LM	—	实验(大小未知)
Jamali等^[80]	CO₂	11-12-2	LM	多目标遗传算法	模拟>600

机器学习在喷射器研究中的应用进展

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