Herceg, S.; Ujević Andrijić, Ž.; Rimac, N.; Bolf, N. Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks. Mathematics2023, 11, 4518.
Herceg, S.; Ujević Andrijić, Ž.; Rimac, N.; Bolf, N. Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks. Mathematics 2023, 11, 4518.
Herceg, S.; Ujević Andrijić, Ž.; Rimac, N.; Bolf, N. Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks. Mathematics2023, 11, 4518.
Herceg, S.; Ujević Andrijić, Ž.; Rimac, N.; Bolf, N. Development of Mathematical Models for Industrial Processes Using Dynamic Neural Networks. Mathematics 2023, 11, 4518.
Abstract
Dynamic neural networks (DNN) are types of artificial neural networks (ANN) that are designed to work with sequential data where context in time is important. In contrast to traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if dynamic of a certain process is emphasized. They are widely 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 process of isomerization, it is crucial to measure the quality attributes affecting the octane number of gasoline. Process analyzers that are commonly used for this purpose are expensive and subject to failures, therefore, in order to achieve continuous production in case of malfunction, mathematical models for estimating the product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNN), 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, which suggests a good possibility of application.
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