ARTIFICIAL TECHNICAL INTELLIGENCE AS A TOOL FOR THE PETROCHEMICAL INDUSTRY
Authors:
1.
OOO «Astraproekt»
2.
Kazan National Research Technological University
(Kafedra «Analiticheskoy himii, sertifikacii i menedzhmenta kachestva", professor)
employee
employee
3.
KNITU
4.
Kazan (Volga) Federal University
(kafedra sistemnogo analiza i informacionnyh tehnologiy, docent)
employee
Russian Federation
employee
Russian Federation
5.
KNITU
Type:
Article
Pages:
from 135
to 143
Status:
Published
Received:
28.01.2026
Accepted:
28.01.2026
Published:
28.01.2026
Language:
Russian
Keywords:
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, MODULAR ANALYTICAL SYSTEM, TECHNOLOGICAL PROCESS, SAFETY, ACCIDENT PREVENTION, CONVERGENT SYSTEM
Abstract:
Artificial technical intelligence is considered as a convergent system of humans and artificial intelligence, which is a comprehensive toolkit that combines advanced technologies for collecting, processing, analyzing data and human cognitive abilities (knowledge, experience, intuition). The key challenges facing modern industries are analyzed: increasing efficiency, optimizing technological processes, ensuring industrial safety. It is shown how artificial technical intelligence can contribute to solving these problems by providing opportunities for predictive equipment maintenance. A specific example of the use of artificial technical intelligence for identifying situations of depressurization of main pipelines is presented. The modular analytical system is based on artificial intelligence and deep machine learning technology, consists of five different neural network models of recurrent and convolutional types. The results of testing the analytical system indicate the high efficiency of the technical solutions used and the possibility of their application to optimize the technological process of pumping liquids. The key advantages of the analytical system are the ability to learn on large volumes of unstructured data, extract complex patterns and dependencies, identify relevant features to solve the task in order to further predict the state of the pipeline. In conclusion, it is concluded that artificial technical intelligence is a critical tool for ensuring the competitiveness and sustainable development of industrial production facilities in the context of digital transformation. The need for further research and development in the field of artificial technical intelligence, as well as the active implementation of these technologies in industrial enterprises is emphasized.
Artificial technical intelligence is considered as a convergent system of humans and artificial intelligence, which is a comprehensive toolkit that combines advanced technologies for collecting, processing, analyzing data and human cognitive abilities (knowledge, experience, intuition). The key challenges facing modern industries are analyzed: increasing efficiency, optimizing technological processes, ensuring industrial safety. It is shown how artificial technical intelligence can contribute to solving these problems by providing opportunities for predictive equipment maintenance. A specific example of the use of artificial technical intelligence for identifying situations of depressurization of main pipelines is presented. The modular analytical system is based on artificial intelligence and deep machine learning technology, consists of five different neural network models of recurrent and convolutional types. The results of testing the analytical system indicate the high efficiency of the technical solutions used and the possibility of their application to optimize the technological process of pumping liquids. The key advantages of the analytical system are the ability to learn on large volumes of unstructured data, extract complex patterns and dependencies, identify relevant features to solve the task in order to further predict the state of the pipeline. In conclusion, it is concluded that artificial technical intelligence is a critical tool for ensuring the competitiveness and sustainable development of industrial production facilities in the context of digital transformation. The need for further research and development in the field of artificial technical intelligence, as well as the active implementation of these technologies in industrial enterprises is emphasized.
Keywords:
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, MODULAR ANALYTICAL SYSTEM, TECHNOLOGICAL PROCESS, SAFETY, ACCIDENT PREVENTION, CONVERGENT SYSTEM
ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, MODULAR ANALYTICAL SYSTEM, TECHNOLOGICAL PROCESS, SAFETY, ACCIDENT PREVENTION, CONVERGENT SYSTEM
