A contribution to the modelling of fouling resistance in heat exchanger-condenser by direct and inverse artificial neural network
The aim of this study was to predict the fouling resistance (FR) using the artificial neural networks (ANN) approach. An experimental database collected from the literature regarding the fouling of condenser tubes cooling seawater of a nuclear power plant was used to build the ANN model. All models...
| Permalink: | http://skupnikatalog.nsk.hr/Record/nsk.NSK01001144932/Details |
|---|---|
| Matična publikacija: |
Kemija u industriji (Online) 70 (2021), 11/12 ; str. 639-650 |
| Glavni autori: | Benyekhlef, Ahmed (Author), Mohammedi, Brahim, Hanini, Salah, Boumahdi, Mouloud, Rezrazi, Ahmed, Laidi, Maamar |
| Vrsta građe: | e-članak |
| Jezik: | eng |
| Predmet: | |
| Online pristup: |
https://doi.org/10.15255/KUI.2020.076 Kemija u industriji (Online) Hrčak |
| LEADER | 04543naa a22004334i 4500 | ||
|---|---|---|---|
| 001 | NSK01001144932 | ||
| 003 | HR-ZaNSK | ||
| 005 | 20221206111038.0 | ||
| 006 | m d | ||
| 007 | cr|||||||||||| | ||
| 008 | 220803s2021 ci d |o |0|| ||eng | ||
| 024 | 7 | |2 doi |a 10.15255/KUI.2020.076 | |
| 035 | |a (HR-ZaNSK)001144932 | ||
| 040 | |a HR-ZaNSK |b hrv |c HR-ZaNSK |e ppiak | ||
| 041 | 0 | |a eng |b hrv | |
| 042 | |a croatica | ||
| 044 | |a ci |c hr | ||
| 080 | 1 | |a 66 |2 2011 | |
| 080 | 1 | |a 004 |2 2011 | |
| 100 | 1 | |a Benyekhlef, Ahmed |4 aut |9 HR-ZaNSK | |
| 245 | 1 | 2 | |a A contribution to the modelling of fouling resistance in heat exchanger-condenser by direct and inverse artificial neural network |h [Elektronička građa] / |c Ahmed Benyekhlef, Brahim Mohammedi, Salah Hanini, Mouloud Boumahdi, Ahmed Rezrazi, Maamar Laidi. |
| 300 | |b Graf. prikazi. | ||
| 504 | |a Bibliografija: 31 jed. | ||
| 504 | |a Abstract ; Sažetak. | ||
| 520 | |a The aim of this study was to predict the fouling resistance (FR) using the artificial neural networks (ANN) approach. An experimental database collected from the literature regarding the fouling of condenser tubes cooling seawater of a nuclear power plant was used to build the ANN model. All models contained 7 inputs: dimensionless condenser cooling seawater temperature, dimensionless inside overall heat transfer coefficient, dimensionless outside overall heat transfer coefficient, dimensionless condenser temperature, dimensionless condenser pressure, dimensionless output power, and dimensionless overall thermal efficiency. Dimensionless fouling resistance was the output. The accuracy of the model was confirmed by comparing the predicted and experimental data. The results showed that ANN with a configuration of 7 input neurons, 7 hidden neurons, and 1 output neuron presented an excellent agreement, with the root mean squared error RMSE = 3.6588 ∙ 10–7, average absolute percentage error MAPE = 0.1295 %, and high determination coefficient of R2 = 0.99996. After conducting the sensitivity analysis (all input variables had strong effect on the estimation of the fouling resistance), in order to control the fouling, an inverse artificial neural network (ANNi) model was established, and showed good agreement in the case of different values of dimensionless condenser cooling seawater temperature. | ||
| 520 | |a Cilj ovog istraživanja bio je predvidjeti otpor prljanja primjenom umjetnih neuronskih mreža (ANN). Baza podataka za ANN modeliranje preuzeta je iz dostupne literature i sadrži podatke vezane uz prljanje kondenzacijskih cijevi u sustavu hlađenja morskom vodom u nuklearnoj elektrani. Sedam parametara korišteno je kao ulaz u neuronske mreže: bezdimenzijska temperatura morske vode, bezdimenzijski unutarnji ukupni koeficijent prijenosa topline, bezdimenzijski vanjski ukupni koeficijent prijenosa topline, bezdimenzijska temperatura kondenzatora, bezdimenzijski tlak u kondenzatoru, bezdimenzijska izlazna snaga i bezdimenzijska ukupna toplinska efikasnost. Kao izlaz uzet je bezdimenzijski otpor prljanja. Točnost modela potvrđena je statističkom analizom podudarnosti predviđenih i eksperimentalno dobivenih podataka. Rezultati su pokazali izvrsno slaganje u slučaju neuronske mreže sa 7 ulaza, 7 neurona u skrivenom sloju i 1 izlazom, uz korijen srednje kvadratne pogreške (RMSE) od 3,6588 ∙ 10–7, srednju apsolutnu postotnu pogrešku (MAPE) od 0,1295 % te visoki koeficijent determinacije (R2 = 0,99996). Nakon provedene analize osjetljivosti (sve ulazne varijable imale su snažan utjecaj na procjenu otpora prljanja), s ciljem kontrole prljanja, uspostavljen je model inverzne umjetne neuronske mreže (ANNi); model je pokazao dobro slaganje za različite vrijednosti bezdimenzijske temperature morske vode. | ||
| 653 | 0 | |a Modeliranje |a Umjetna neuronska mreža |a Izmjenjivač topline |a Kondenzator |a Grafičko korisničko sučelje | |
| 700 | 1 | |a Mohammedi, Brahim |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Hanini, Salah |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Boumahdi, Mouloud |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Rezrazi, Ahmed |4 aut |9 HR-ZaNSK | |
| 700 | 1 | |a Laidi, Maamar |4 aut |9 HR-ZaNSK | |
| 773 | 0 | |t Kemija u industriji (Online) |x 1334-9090 |g 70 (2021), 11/12 ; str. 639-650 |w nsk.(HR-ZaNSK)000530475 | |
| 981 | |b Be2021 |b B03/21 | ||
| 998 | |b tino2212 | ||
| 856 | 4 | 0 | |u https://doi.org/10.15255/KUI.2020.076 |
| 856 | 4 | 0 | |u http://silverstripe.fkit.hr/kui/issue-archive/article/814 |y Kemija u industriji (Online) |
| 856 | 4 | 0 | |u https://hrcak.srce.hr/264636 |y Hrčak |
| 856 | 4 | 1 | |y Digitalna.nsk.hr |