Diagnostics of failures of technological equipment of chemical industries using artificial intelligence

Abstract

The paper considers the problem of automated recognition of single emergencies in chemical and oil refining industries. Modern chemical and technological production facilities are maintained and managed by a small number of personnel, which increases the burden on each operator. To reduce the number of operator errors, their training is regularly conducted on simulators equipped with a set of both standard situations (routine start-up, shutdown, normal process management, switching from one mode to another) and emergency scenarios (column depressurization, pump failure, failure of the power supply system). Nevertheless, it is impossible to foresee all possible failures during operator training, and even a trained operator may not notice the first signs of an accident, and therefore it is necessary to create a decision support system that helps the operator to recognize failures of technological equipment in a timely manner. To recognize failures, it is proposed to use a neural network trained on an array of simulated accident data. An industrial simulator based on the RTsim platform was used to simulate typical accidents. The novelty of the research lies in the use of artificial intelligence methods to diagnose the property of the technological process according to the SCADA system and the use of data for training a neural network not from a real object (which will always be insufficient), but from a model that exactly corresponds to a specific technological site. The number of simulated scenarios used to train a neural network can be quite large, which reduces the proportion of erroneous system responses. The developed system confidently copes with the recognition of individual equipment failures. The results obtained can be used to help process operators and to improve emergency protection systems. The analysis of the time required by the system to recognize an emergency situation can be used to design new production facilities, modify the control and management system.