ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse

JOURNAL OF CLEANER PRODUCTION

卷:260

Article

Opalic, Sven Myrdahl^1,2,4  Goodwin, Morten^1,2  Jiao, Lei^1,2  Nielsen, Henrik Kofoed²  Pardinas, Angel Alverz³  Hafner, Armin³  Kolhe, Mohan Lal² 

[1] Univ Agder, Ctr Artificial Intelligence Res, N-4879 Grimstad, Norway

[2] Univ Agder, Fac Engn & Sci, N-4879 Grimstad, Norway

[3] Norwegian Univ Sci & Technol, Dept Energy & Proc Engn, Kolbjorn Hejes Vei 1B, N-7034 Trondheim, Norway

[4] Login Eiendom AS, Kongens Gata 16, N-7011 Trondheim, Norway

关键词: Industrial cooling systems;    Carbon dioxide refrigerant;    Artificial neural networks;    Coefficient of performance;    Energy storage;    Smart warehouse;   

DOI  :  10.1016/j.jclepro.2020.120887

来源: Elsevier

【 摘 要 】

Industrial cooling systems consume large quantities of energy with highly variable power demand. To reduce environmental impact and overall energy consumption, and to stabilize the power requirements, it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To control these operations continuously in a complex energy system, an intelligent energy management system can be employed using operational data and machine learning. In this work, we have developed an artificial neural network based technique for modelling operational CO2 refrigerant based industrial cooling systems for embedding in an overall energy management system. The operating temperature and pressure measurements, as well as the operating frequency of compressors, are used in developing operational model of the cooling system, which outputs electrical consumption and refrigerant mass flow without the need for additional physical measurements. The presented model is superior to a generalized theoretical model, as it learns from data that includes individual compressor type characteristics. The results show that the presented approach is relatively precise with a Mean Average Percentage Error (MAPE) as low as 5%, using low resolution and asynchronous data from a case study system. The developed model is also tested in a laboratory setting, where MAPE is shown to be as low as 1.8%. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free