Learning and predicting operation strategies by sequence mining and deep learning

Gyula Dorgo, J. Abonyi

Research output: Article

2 Citations (Scopus)

Abstract

The operators of chemical technologies are frequently faced with the problem of determining optimal interventions. Our aim is to develop data-driven models by exploring the consequential relationships in the alarm and event-log database of industrial systems. Our motivation is twofold: (1) to facilitate the work of the operators by predicting future events and (2) analyse how consequent the event series is. The core idea is that machine learning algorithms can learn sequences of events by exploring connected events in databases. First, frequent sequence mining applications are utilised to determine how the event sequences evolve during the operation. Second, a sequence-to-sequence deep learning model is proposed for their prediction. The long short-term memory unit-based model (LSTM) is capable of evaluating rare operation situations and their consequential events. The performance of this methodology is presented with regard to the analysis of the alarm and event-log database of an industrial delayed coker unit.

Original languageEnglish
Pages (from-to)174-187
Number of pages14
JournalComputers and Chemical Engineering
Volume128
DOIs
Publication statusPublished - szept. 2 2019

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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