An Approach to Intelligent Control of Complex Industrial Processes: An Example of Ferrous Metal Industry

Trofimov, V. B.

doi:10.1134/s0005117920100057

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…• converter steelmaking where the number of melting operations (cases) reaches a few thousands yearly, with making over 400 grades of steel. The information model of this case is described in the works [5][6][7];…”

Section: Cbr-technology Applicationmentioning

confidence: 99%

Application of case-based reasoning in hazard evaluation in complex process flow control

Trofimov,

Temkin,

Solodov

2023

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The article discusses the Case-Based Reasoning method which enables solving new problems that may arise during decision-making by using or adapting solutions of the similar known problems on the basis of accumulated data and knowledge on past situations or cases contained in a knowledge base. The metrics of similarity between the parameters of a current situation and previous cases, and the methods to retrieve and adapt the cases are described. The case information model used for the process management is presented and exemplified.The conditions and ranges of efficient case-based reasoning application in the socio-technical system control in case of nonstationary, nonlinear and sluggish processes are discussed.The authors propose the procedure for searching similar cases using the classical metrics and the Random Forest method, and describe the generalized control of a complex process or an object using the concepts of the industrial internet of things and the case-based reasoning.

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Section: Cbr-technology Applicationmentioning

confidence: 99%

Application of case-based reasoning in hazard evaluation in complex process flow control

Trofimov,

Temkin,

Solodov

2023

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“…The creation of modern intelligent technologies, systems for processing large amounts of data, machine learning, and artificial intelligence is an urgent trend in science and industry [5][6][7][8].…”

Section: Diagram Of An Intelligent Converter's Control Systemmentioning

confidence: 99%

“…The subsystem of the converter's software motion control is based on the approach presented in [6], using the CBR concept [7]. The precedent includes a description of the problematic situation (the chemical composition of cast iron; its temperature and weight; the type of scrap and its weight; the number and condition of the lance and lining; the specified steel grade; the type of ferromanganese and flux; the type of coal; the chemical composition of lime; slag rolling; slopping; and slag overflow); a description of the…”

Section: Diagram Of An Intelligent Converter's Control Systemmentioning

confidence: 99%

Designing an Intelligent Control System for a Basic Oxygen Furnace Based on Computer Vision

Trofimov

2021

J. Comput. Syst. Sci. Int.

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The structure of an intelligent control system for a converter, which includes a subsystem for recognizing and evaluating the slopping of a gas-slag-metallic emulsion in real time based on computer vision by explicit features and a subsystem for predicting slopping by indirect signs, which are most often used to monitor the oxygen-converter process, is proposed. Procedures for forecasting and detecting slopping based on artificial neural networks and a precedent approach are developed. The morphological signs of slopping and the factors influencing their occurrence are described.

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A Fault Diagnosis Method using Convolutional Neural Network with Visualized Samples

Guo

Liu

Pan

2021

2021 China Automation Congress (CAC)

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An Approach to Intelligent Control of Complex Industrial Processes: An Example of Ferrous Metal Industry

Cited by 4 publications

References 6 publications

Application of case-based reasoning in hazard evaluation in complex process flow control

Application of case-based reasoning in hazard evaluation in complex process flow control

Designing an Intelligent Control System for a Basic Oxygen Furnace Based on Computer Vision

A Fault Diagnosis Method using Convolutional Neural Network with Visualized Samples

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