Maintenance of industrial reactors supported by deep learning driven ultrasound tomography
Author(s) -
Grzegorz Kłosowski,
Tomasz Rymarczyk,
Konrad Kania,
Antoni Świć,
Tomasz Cieplak
Publication year - 2019
Publication title -
eksploatacja i niezawodnosc - maintenance and reliability
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 27
eISSN - 2956-3860
pISSN - 1507-2711
DOI - 10.17531/ein.2020.1.16
Subject(s) - convolutional neural network , computer science , artificial intelligence , reliability (semiconductor) , deep learning , artificial neural network , pattern recognition (psychology) , support vector machine , field (mathematics) , pixel , machine learning , naive bayes classifier , data mining , mathematics , power (physics) , physics , quantum mechanics , pure mathematics
The basic device for the implementation of the batch processes are tank reactors. Batch processes are widely used in many branches of economy e.g. food, chemistry, pharmacy, semiconductors, biogas plants and so on. Due to the time-varying, non-linear and uneven nature of this process, it is very difficult to determine the exact mathematical model of these processes, which necessitates their monitoring. For this reason, to ensure a high level of reliability and trouble-free maintenance of tank reactors it is necessary to effectively monitor the processes taking place inside them. A tank chemical reactor is, in the simplest sense, a vessel adapted to carry out a specific chemical reaction in it. On an industrial scale, the construction of a reactor and the parameters of its process should ensure optimal economic results. Chemical reactors and the processes taking place in them are usually an essential element of a technological process aimed at producing a specific chemical product. Any other processes in such a sequence should be assigned a rather auxiliary role, consisting either in preparatory activities or in separating the products of the reaction and separating from them the component, the obtaining of which is the aim of production operations. ProducGrzegorz KłosowsKi Tomasz RymARczyK Konrad KAniA Antoni Świć Tomasz cieplAK
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