دانلود مقاله انگلیسی رایگان:مدل سازی ANN سیستم خنک کننده کولر CO2 COP در یک انبار هوشمند - 2020
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  • ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse
    ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

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


    ترجمه فارسی عنوان مقاله:

    مدل سازی ANN سیستم خنک کننده کولر CO2 COP در یک انبار هوشمند


    منبع:

    Sciencedirect - Elsevier - Journal of Cleaner Production, 260 (2020) 120887. doi:10.1016/j.jclepro.2020.120887


    نویسنده:

    Sven Myrdahl Opalic a, b, d, Morten Goodwin, PhD a, b, Lei Jiao, PhD a, b, Henrik Kofoed Nielsen, PhD b, Angel Alvarez Pardi~nas c, Armin Hafner c, Mohan Lal Kolhe, PhD


    چکیده انگلیسی:

    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%.
    Keywords: Industrial cooling systems | Carbon dioxide refrigerant | Artificial neural networks | Coefficient of performance | Energy storage | Smart warehouse


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 10
    حجم فایل: 1793 کیلوبایت

    قیمت: رایگان


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