Naslov
Process monitoring and predictive diagnostics

Autori
Brzović, Adriana

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, preddiplomski

Fakultet
Fakultet kemijskog inženjerstva i tehnologije

Mjesto
Zagreb

Datum
19.09

Godina
2019

Stranica
45

Mentor
Bolf, Nenad

Ključne riječi
machine learning ; heat exchanger ; fouling ; neural network ; random forest

Sažetak
As technology advances, particularly in the IT sector, more and more companies running large-scale industrial operations look to computer based solutions to replace expensive monitoring and maintenance methods. Machine learning is one such solution gaining popularity in the last couple of years. This paper explores how machine learning models can be used for preventative and predictive maintenance of an industrial heat exchanger by continuously detecting fouling formation. The results of the neural network model show successful prediction of fouling formation in the heat exchanger. Random forest model does not successfully predict fouling formation as it cannot extrapolate on new data. The results of this study show great potential in application of machine learning methods in the industry for improving production processes in terms of greater efficiency, energy and raw material saving.

Izvorni jezik
Engleski

Znanstvena područja
Kemijsko inženjerstvo

POVEZANOST RADA