ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse. (1st July 2020)

• Record Type: Journal Article
• Authors: Opalic, Sven Myrdahl; Goodwin, Morten; Jiao, Lei; Nielsen, Henrik Kofoed; Pardiñas, Ángel Álvarez; Hafner, Armin; Kolhe, Mohan Lal
• Is Part Of: Journal of cleaner production. Volume 260(2020)
• Publication Date: 2020-07-01
• Subjects: Industrial cooling systems -- Carbon dioxide refrigerant -- Artificial neural networks -- Coefficient of performance -- Energy storage -- Smart warehouse; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.120887
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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%. Highlights: ANN based CO2 refrigerant compressor model for refrigerant flow and compressor power consumption. Experimental validation of ANN based CO2 refrigerant compressor model. Intelligent energy management system to control electrical and thermal energy storage in a smart warehouse.