Autor: Kalawa, Wojciech - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Możliwości wykorzystania małych układów kogeneracyjnych w instalacjach Prosumenckich
Autorzy:: Sztekler, Karol
Górski, Jan
Nowak, Wojciech
Siwek, Tomasz
Kalawa, Wojciech
Żak, Łukasz
Powiązania:: https://bibliotekanauki.pl/articles/89521.pdf
Data publikacji:: 2017
Wydawca:: Nowa Energia
Opis:: W niniejszej pracy zaprezentowano możliwości zastosowania małych układów kogeneracyjnych do produkcji energii elektrycznej i ciepła łącznie, na przykładzie Laboratorium Układów Kogeneracji (LUK) w Centrum Energetyki - AGH oraz możliwości badawcze laboratorium LUK.
Źródło:: Nowa Energia; 2017, 2; 56-59
1899-0886
Pojawia się w:: Nowa Energia
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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

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Skocz do pozycji: 3.

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

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