The neural network approach for estimation of heat transfer coefficient in heat exchangers considering the fouling formation dynamic

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F24%3APU156178" target="_blank" >RIV/00216305:26210/24:PU156178 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2451904924002336#gp030" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2451904924002336#gp030</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.tsep.2024.102615" target="_blank" >10.1016/j.tsep.2024.102615</a>

Result language

angličtina

Original language name

The neural network approach for estimation of heat transfer coefficient in heat exchangers considering the fouling formation dynamic

Original language description

Routine maintenance for plate heat exchanger (PHE) cleaning improves the effectiveness of heat exchange network operation. Until recently, complex mathematical modelling was used to predict the value of the heat transfer coefficient after a certain period of operation of the heat exchanger, as well as the point in time when the coefficient reached the allowable limit. The applied mathematical tools included the systems of differential equations, matrices of heuristic coefficients, which needed a lot of computer resources. This paper offers an artificial neural network (ANN) technique for forecasting the following values: heat transfer coefficient at any time points during the operating period of PHEs; the time point, when the heat transfer coefficient reaches its lower permitted value. In this method, ANN uses the fuzzy logic techniques to expand the set of training parameters for the model, working with data of industrial measurements and data obtained from the mathematical modelling of the process. It allowed to train the developed feed-forward neural network (FFNN) with the coefficient of determination R2 equal to 0.99 and can predict the thermal resistance in PHE based on measurement data. To adequately predict the time-point to reach the limiting value of the heat transfer coefficient, it was proposed a recurrent neural network (RNN) with a hidden layer of long short-term memory (LSTM), where R2 value came up to 0.89.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20300 - Mechanical engineering

Project

—

Continuities

R - Projekt Ramcoveho programu EK

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Thermal science and engineering progress

ISSN

2451-9049

e-ISSN

—

Volume of the periodical

neuveden

Issue of the periodical within the volume

51

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

102615-102615

UT code for WoS article

001241432000001

EID of the result in the Scopus database

2-s2.0-85192675622