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    Wyświetlanie 1-3 z 3
    Tytuł:
    Experience in modelling of a single-stage silica gel-water adsorption chiller
    Autorzy:
    Krzywański, J.
    Szyc, M.
    Nowak, W.
    Kolenda, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/298154.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Warmińsko-Mazurski w Olsztynie
    Tematy:
    low grade thermal energy
    waste heat recovery
    adsorption chillers
    silica gel
    coefficient of performance
    cooling capacity
    Opis:
    Heat utilization for cooling capacity production is nowadays a desirable challenge in several industrial applications. There are lots of industrial processes with low parameters of heat generated as by-product, which utilization is very important to improve theirs total energy efficiency. Waste heat driven chillers seem to be great competitors for mechanical chillers. Among them special attention should be paid to adsorption chillers, since they can be powered with low – temperature heat sources. The paper presents a model of a single-stage adsorption chiller with silica gel as adsorbent and water, acting as a refrigerant. The performed model allows to predict the behaviour of the adsorption chiller, among others the main energy efficiency factors, such as coefficient of performance (COP) and cooling capacity (CC) for different working conditions.
    Źródło:
    Technical Sciences / University of Warmia and Mazury in Olsztyn; 2016, 19(4); 367-386
    1505-4675
    2083-4527
    Pojawia się w:
    Technical Sciences / University of Warmia and Mazury in Olsztyn
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks
    Autorzy:
    Krzywanski, Jarosław
    Sztekler, Karol
    Bugaj, Marcin
    Kalawa, Wojciech
    Grabowska, Karolina
    Chaja, Patryk Robert
    Sosnowski, Marcin
    Nowak, Wojciech
    Mika, Łukasz
    Bykuć, Sebastian
    Powiązania:
    https://bibliotekanauki.pl/articles/2173577.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    adsorption heat pump
    polygeneration
    cooling capacity
    low grade thermal energy
    artificial neural networks
    soft computing
    absorpcyjna pompa ciepła
    poligeneracja
    wydajność chłodnicza
    energia cieplna niskiej jakości
    sztuczne sieci neuronowe
    przetwarzanie miękkie
    Opis:
    Adsorption cooling and desalination technologies have recently received more attention. Adsorption chillers, using eco-friendly refrigerants, provide promising abilities for low-grade waste heat recovery and utilization, especially renewable and waste heat of the near ambient temperature. However, due to the low coefficient of performance (COP) and cooling capacity (CC) of the chillers, they have not been widely commercialized. Although operating in combined heating and cooling (HC) systems, adsorption chillers allow more efficient conversion and management of low-grade sources of thermal energy, their operation is still not sufficiently recognized, and the improvement of their performance is still a challenging task. The paper introduces an artificial intelligence (AI) approach for the optimization study of a two-bed adsorption chiller operating in an existing combined HC system, driven by low-temperature heat from cogeneration. Artificial neural networks are employed to develop a model that allows estimating the behavior of the chiller. Two crucial energy efficiency and performance indicators of the adsorption chiller, i.e., CC and the COP, are examined during the study for different operating sceneries and a wide range of operating conditions. Thus this work provides useful guidance for the operating conditions of the adsorption chiller integrated into the HC system. For the considered range of input parameters, the highest CC and COP are equal to 12.7 and 0.65 kW, respectively. The developed model, based on the neurocomputing approach, constitutes an easy-to-use and powerful optimization tool for the adsorption chiller operating in the complex HC system.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137054
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks
    Autorzy:
    Krzywanski, Jarosław
    Sztekler, Karol
    Bugaj, Marcin
    Kalawa, Wojciech
    Grabowska, Karolina
    Chaja, Patryk Robert
    Sosnowski, Marcin
    Nowak, Wojciech
    Mika, Łukasz
    Bykuć, Sebastian
    Powiązania:
    https://bibliotekanauki.pl/articles/2128167.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    adsorption heat pump
    polygeneration
    cooling capacity
    low grade thermal energy
    artificial neural networks
    soft computing
    absorpcyjna pompa ciepła
    poligeneracja
    wydajność chłodnicza
    energia cieplna niskiej jakości
    sztuczne sieci neuronowe
    przetwarzanie miękkie
    Opis:
    Adsorption cooling and desalination technologies have recently received more attention. Adsorption chillers, using eco-friendly refrigerants, provide promising abilities for low-grade waste heat recovery and utilization, especially renewable and waste heat of the near ambient temperature. However, due to the low coefficient of performance (COP) and cooling capacity (CC) of the chillers, they have not been widely commercialized. Although operating in combined heating and cooling (HC) systems, adsorption chillers allow more efficient conversion and management of low-grade sources of thermal energy, their operation is still not sufficiently recognized, and the improvement of their performance is still a challenging task. The paper introduces an artificial intelligence (AI) approach for the optimization study of a two-bed adsorption chiller operating in an existing combined HC system, driven by low-temperature heat from cogeneration. Artificial neural networks are employed to develop a model that allows estimating the behavior of the chiller. Two crucial energy efficiency and performance indicators of the adsorption chiller, i.e., CC and the COP, are examined during the study for different operating sceneries and a wide range of operating conditions. Thus this work provides useful guidance for the operating conditions of the adsorption chiller integrated into the HC system. For the considered range of input parameters, the highest CC and COP are equal to 12.7 and 0.65 kW, respectively. The developed model, based on the neurocomputing approach, constitutes an easy-to-use and powerful optimization tool for the adsorption chiller operating in the complex HC system.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137054, 1--11
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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