Improvement of Marine Steam Turbine Conventional Exergy Analysis by Neural Network Application

Baressi Šegota, Sandi; Lorencin, Ivan; Anđelić, Nikola; Mrzljak, Vedran; Car, Zlatan

doi:10.3390/jmse8110884

Pregled bibliografske jedinice broj: 1089629

Improvement of Marine Steam Turbine Conventional E xergy Analysis by Neural Network Application

Baressi Šegota, Sandi; Lorencin, Ivan; Anđelić, Nikola; Mrzljak, Vedran; Car, Zlatan

Improvement of Marine Steam Turbine Conventional E xergy Analysis by Neural Network Application // Journal of marine science and engineering, 8 (2020), 11; 884, 38 doi:10.3390/jmse8110884 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1089629 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Improvement of Marine Steam Turbine Conventional E xergy Analysis by Neural Network Application

Autori
Baressi Šegota, Sandi ; Lorencin, Ivan ; Anđelić, Nikola ; Mrzljak, Vedran ; Car, Zlatan

Izvornik
Journal of marine science and engineering (2077-1312) 8 (2020), 11; 884, 38

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
exergy destruction ; exergy efficiency ; marine steam turbine ; MLP neural network ; turbine cylinders

Sažetak
This article presented an improvement of marine steam turbine conventional exergy analysis by application of neural networks. The conventional exergy analysis requires numerous measurements in seven different turbine operating points at each load, while the intention of MLP (Multilayer Perceptron) neural network‐based analysis was to investigate the possibilities for measurements reducing. At the same time, the accuracy and precision of the obtained results should be maintained. In MLP analysis, six separate models are trained. Due to a low number of instances within the data set, a 10‐fold cross‐validation algorithm is performed. The stated goal is achieved and the best solution suggests that MLP application enables reducing of measurements to only three turbine operating points. In the best solution, MLP model errors falling within the desired error ranges (Mean Relative Error) MRE < 2.0% and (Coefficient of Correlation) R2 > 0.95 for the whole turbine and each of its cylinders.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Strojarstvo

POVEZANOST RADA

Projekti:
--KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Šmuc, Tomislav; Lončarić, Sven; Petrović, Ivan; Jokić, Andrej; Palunko, Ivana) ( CroRIS)
IP-2018-01-3739 - Sustav potpore odlučivanju za zeleniju i sigurniju plovidbu brodova (DESSERT) (Prpić-Oršić, Jasna, HRZZ - 2018-01) ( CroRIS)
NadSve-Sveučilište u Rijeci-UNIRI_TEHNIC‐18‐18‐1146 - Nesigurnosti procjene brzine broda u pri realnim vremenskim uvjetima (Prpić-Oršić, Jasna, NadSve ) ( CroRIS)
Ostalo-CEI - 305.6019-20 - Use of regressive artificial intelligence (AI) and machine learning (ML) methods in modelling of COVID-19 spread (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( CroRIS)
--KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( CroRIS)
InoUstZnVO-CIII-HR-0108-10 - Concurrent Product and Technology Development - Teaching, Research and Implementation of Joint Programs Oriented in Production and Industrial Engineering (Car, Zlatan, InoUstZnVO - CEEPUS) ( CroRIS)