Modelling of energy and exergy analysis of ORC integrated systems in terms of sustainability by applying artificial neural network

Basit Öğe Kaydı
dc.contributor.authorUtlu, Zafer
dc.contributor.authorTolon, Mert
dc.contributor.authorKarabuğa, Arif
dc.date.accessioned2024-06-13T20:18:14Z
dc.date.available2024-06-13T20:18:14Z
dc.date.issued2021
dc.departmentFakülteler, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractThe present study focuses on the organic Rankine cycle (ORC) integrated into an evacuated tube heat pipe (ETHP), whose systems are an alternative solar energy system to low-efficiency planary collectors. In this work, a detailed thermodynamic and artificial neural network (ANN) analysis was conducted to evaluate the solar energy system. One of the key parameters of sustainable approaches focused on exergy efficiency is application of thermal engineering. In addition to this, sustainable engineering approaches nowadays are a necessity for improving the efficiency of all of the engineering research areas. For this reason, the ANN model is used to forecast different types of energy efficiency problems in thermodynamic literature. The examined system consists of two main parts such as the ETHP system and the ORC system used for thermal energy production. With this system, it is aimed to evaluate energy and exergy analysis results by the ANN method in the case of integrating the ORC system to ETHP, which is one of the planar collectors suitable for the roofs of the buildings. Within the scope of this study, the exergy efficiency was evaluated on the developed ANN algorithm. The effect rates of parameters such as pressure, temperature and ambient temperature affecting the exergy efficiency of ORC integrated ETHP were calculated. Ambient temperature was found to be the most influential parameter on exergy efficiency. The exergy efficiency of the whole system has been calculated as similar to 23.39%. The most suitable BPNN architecture for this case study is recurrent networks with dampened feedback (Jordan-Elman nets). The success rate of the developed BPNN model is 95.4%.
dc.identifier.doi10.1093/ijlct/ctaa033
dc.identifier.endpage164
dc.identifier.issn1748-1317
dc.identifier.issn1748-1325
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85112130635
dc.identifier.scopusqualityQ1
dc.identifier.startpage156
dc.identifier.urihttps://doi.org/10.1093/ijlct/ctaa033
dc.identifier.urihttps://hdl.handle.net/11501/1267
dc.identifier.volume16
dc.identifier.wosWOS:000648944700014
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTolon, Mert
dc.language.isoen
dc.publisherOxford University Press
dc.relation.ispartofInternational Journal of Low-Carbon Technologies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectEnergy and Exergy Analysis
dc.subjectSustainability
dc.subjectArtificial Neural Network
dc.subjectORC System
dc.titleModelling of energy and exergy analysis of ORC integrated systems in terms of sustainability by applying artificial neural network
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Tam Metin / Full Text
Boyut:
957.24 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu