Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks

Srečko Herceg, Željka Ujević Andrijić (), Nikola Rimac and Nenad Bolf
Additional contact information
Srečko Herceg: Department of Measurements and Process Control, Faculty of Chemical Engineering and Technology, University of Zagreb, Savska cesta 16/5a, 10000 Zagreb, Croatia
Željka Ujević Andrijić: Department of Measurements and Process Control, Faculty of Chemical Engineering and Technology, University of Zagreb, Savska cesta 16/5a, 10000 Zagreb, Croatia
Nikola Rimac: Department of Measurements and Process Control, Faculty of Chemical Engineering and Technology, University of Zagreb, Savska cesta 16/5a, 10000 Zagreb, Croatia
Nenad Bolf: Department of Measurements and Process Control, Faculty of Chemical Engineering and Technology, University of Zagreb, Savska cesta 16/5a, 10000 Zagreb, Croatia

Mathematics, 2023, vol. 11, issue 21, 1-17

Abstract: Dynamic neural networks (DNNs) are a type of artificial neural network (ANN) designed to work with sequential data where context in time is important. Unlike traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if the dynamic of a certain process is emphasized. They are commonly used in natural language processing, speech recognition, and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial isomerization process, it is crucial to measure the quality attributes that affect the octane number of gasoline. Process analyzers commonly used for this purpose are expensive and subject to failure. Therefore, to achieve continuous production in the event of a malfunction, mathematical models for estimating product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNNs), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM), and dynamic polynomial models. The obtained results are satisfactory, suggesting a good possibility of application.

Keywords: dynamic neural networks; industrial process; recurrent neural networks; long short-term memory (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/21/4518/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/21/4518/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:21:p:4518-:d:1272769

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().