Szczegóły publikacji
Opis bibliograficzny
Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks / Jarosław Krzywanski, Karol SZTEKLER, Marcin Bugaj, Wojciech KALAWA, Karolina Grabowska, Patryk Robert Chaja, Marcin Sosnowski, Wojciech NOWAK, Łukasz MIKA, Sebastian Bykuć // Bulletin of the Polish Academy of Sciences. Technical Sciences ; ISSN 0239-7528. — 2021 — vol. 69 no. 3 art. no. e137054, s. 1-11. — Bibliogr. s. 9-11, Abstr. — Publikacja dostępna online od: 2021-06
Autorzy (10)
- Krzywanski Jarosław
- AGHSztekler Karol
- Bugaj Marcin
- AGHKalawa Wojciech
- Grabowska Karolina
- Chaja Patryk Robert
- Sosnowski Marcin
- AGHNowak Wojciech Janusz
- AGHMika Łukasz
- Bykuć Sebastian
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 134990 |
|---|---|
| Data dodania do BaDAP | 2021-07-05 |
| Tekst źródłowy | URL |
| DOI | 10.24425/bpasts.2021.137054 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Bulletin of the Polish Academy of Sciences, Technical Sciences |
Abstract
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.
