Scientific Problems in Creating Intelligent Control Systems for Technological Processes in Pyrometallurgy Based on Industry 4.0 Concept

Спирин, Н. А.; Rybolovlev, V. Yu.; Лавров, В. В.; Гурин, И. А.; Schnayder, D. A.; Краснобаев, А. В.

doi:10.1007/s11015-020-01029-1

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…The entire production of blast furnace ironmaking is in a closed environment, and the internal state and parameters of the blast furnace are difficult to directly detect, which is a typical “black box” problem. − Due to the complexity of the blast furnace process and the lack of appropriate processing technology, a large amount of auxiliary process data is independent in “information silos”, and the information interoperability between various processes is poor. Many data have not been effectively integrated, analyzed, and processed into more valuable information, which has become a limiting link for the application of information and intelligent technology in blast furnaces. − The “Made in China 2025 Development Plan” clarifies the acceleration of traditional industrialization and the deep integration of information technology. − Combining information technology and Internet of Things technology, the intelligent and intensive production of blast furnaces in steel joint enterprises will become a new development direction. Blast furnace production has entered the era of intelligence from the digital era, and great progress has been made in the development and application of blast furnace mathematical models and expert systems.…”

Section: Introductionmentioning

confidence: 99%

“…Many data have not been effectively integrated, analyzed, and processed into more valuable information, which has become a limiting link for the application of information and intelligent technology in blast furnaces. 4 − 6 The “Made in China 2025 Development Plan” clarifies the acceleration of traditional industrialization and the deep integration of information technology. 7 − 12 Combining information technology and Internet of Things technology, the intelligent and intensive production of blast furnaces in steel joint enterprises will become a new development direction.…”

Section: Introductionmentioning

confidence: 99%

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Research and Development of a Big Data Application Platform for Intelligent Blast Furnace Intensive Management and Control

Fei,

Jiang,

Yang

et al. 2024

ACS Omega

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The blast furnaces of Anshan Iron and Steel have completed large-scale modernization, and a large amount of information technology has been popularized and applied to the process of blast furnaces. This paper takes the Anshan Iron and Steel blast furnace group as the research background. Based on big data and industrial Internet technology, combining the smelting process mechanism of blast furnace production and using artificial intelligence, cloud analysis, and other technologies, the data management platform was used to effectively integrate the data of each process of the blast furnace and design the data asset catalogue. The big data application platform for the intensive control of the blast furnace was established. The data were in multidimensional in-depth mining, and the intelligent application model of the blast furnace was established. The visual intelligent monitoring of the safe production and operation of the blast furnace was realized, and the production operation of the blast furnace was guided. The overall information and intelligent level of production operation and management of the blast furnace have been improved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research and Development of a Big Data Application Platform for Intelligent Blast Furnace Intensive Management and Control

Fei,

Jiang,

Yang

et al. 2024

ACS Omega

View full text Add to dashboard Cite

show abstract

“…Achieving this implies designing a control law adapted to the process model. To obtain this model means using physics and mathematical principles with parametrizing stage after modeling to achieve similar behavior to the process [1,2].…”

Section: Introductionmentioning

confidence: 99%

UPAFuzzySystems: A Python Library for Control and Simulation with Fuzzy Inference Systems

2023

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The main goal of control theory is input tracking or system stabilization. Different feedback-computed controlled systems exist in this area, from deterministic to soft methods. Some examples of deterministic methods are Proportional (P), Proportional Integral (PI), Proportional Derivative (PD), Proportional Integral Derivative (PID), Linear Quadratic (LQ), Linear Quadratic Gaussian (LQG), State Feedback (SF), Adaptative Regulators, and others. Alternatively, Fuzzy Inference Systems (FISs) are soft-computing methods that allow using the human expertise in logic in IF–THEN rules. The fuzzy controllers map the experience of an expert in controlling the plant. Moreover, the literature shows that optimization algorithms allow the adaptation of FISs to control different processes as a black-box problem. Python is the most used programming language, which has seen the most significant growth in recent years. Using open-source libraries in Python offers numerous advantages in software development, including saving time and resources. In this paper, we describe our proposed UPAFuzzySystems library, developed as an FISs library for Python, which allows the design and implementation of fuzzy controllers with transfer-function and state-space simulations. Additionally, we show the use of the library for controlling the position of a DC motor with Mamdani, FLS, Takagi–Sugeno, fuzzy P, fuzzy PD, and fuzzy PD-I controllers.

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“…At the moment, the digital transformation is actively changing the metallurgy. For example, in [3], the main directions for application of "Industry 4.0" concept at Magnitogorsk Iron & Steel Works PJSC are outlined, which allow creating a unified information basis for the implementation of a set of measures to optimize and increase the work efficiency of both separate units, workshops, production facilities and the enterprise as a whole.…”

Section: Introductionmentioning

confidence: 99%

Integration features of control systems in multi-complex distributed production

et al. 2021

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Different options of the structure of automated process control system (APCS) are considered using the example of the coal preparation plant with a single automation platform Omron Sysmac, as well as with heterogeneous devices connected by industrial networks Ethernet/IP at several levels with physical separation. The general APCS structure is proposed. It is suitable for the end-to-end control of objects with variable structure, built from various technological complexes of coal preparation plants.

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Scientific Problems in Creating Intelligent Control Systems for Technological Processes in Pyrometallurgy Based on Industry 4.0 Concept

Cited by 8 publications

References 7 publications

Research and Development of a Big Data Application Platform for Intelligent Blast Furnace Intensive Management and Control

Research and Development of a Big Data Application Platform for Intelligent Blast Furnace Intensive Management and Control

UPAFuzzySystems: A Python Library for Control and Simulation with Fuzzy Inference Systems

Integration features of control systems in multi-complex distributed production

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