Increasing the speed of information transfer and operational decision-making in metallurgical industry through an industrial bot

被引:2
|
作者
Bazhin, V. Yu. [1 ]
Masko, O. N. [2 ]
Nguyen, Huy H. [2 ]
机构
[1] St Petersburg Min Univ, Met Dept, St Petersburg, Russia
[2] St Petersburg Min Univ, St Petersburg, Russia
来源
NON-FERROUS METALS | 2023年 / 01期
基金
俄罗斯科学基金会;
关键词
metallurgical silicon; automated control; material balance; ore-thermal furnace; ICS; chatbot; MES system; SYSTEMS; CHATBOT;
D O I
10.17580/nfm.2023.01.10
中图分类号
TF [冶金工业];
学科分类号
0806 ;
摘要
In the production of non-ferrous metals, it is difficult to monitor technological parameters and to account for the mate-rial balance of the main and auxiliary components which leads to significant losses of raw materials and electricity. The metals industry is characterised by the need to account for, reduce and recycle large volumes of technogenic emissions. With global digitalisation and increased automation, the lack of a dedicated system for analysing and controlling shop floor data reduces the efficiency of environmentally hazardous operations with large quantities of material flows making them uncompetitive and environmentally damaging. Existing software-based material flow monitoring and control systems have a large import dependency. In pyrometallurgical and electrochemical production with multiple material streams the issue of data systematisation for effective control via a process control system needs to be addressed. As an adaptable example, this paper considers the feasibility of dedicated automated systems for accounting for material balances and generating appropriate process control actions through chatbots in metallurgical silicon production. Given the acute shortage of digital platforms for the implementation of MES-like production management systems, the use of application software interfaces chat-bots gaining popularity in services and education is promising. The paper presents a generic architecture of an industrial chatbot developed for the production of metallurgical silicon, describes the interaction of the application with the process control system, as well as an analysis of the results obtained and expected from the implementation. The system can be adapted to similar production facilities of non-ferrous metallurgy.
引用
收藏
页码:62 / 67
页数:6
相关论文
共 50 条
  • [31] Securing healthcare information system through fuzzy based decision-making methodology
    Alharbi, Abdullah
    Ahmad, Masood
    Alosaimi, Wael
    Alyami, Hashem
    Sarkar, Amal Krishna
    Agrawal, Alka
    Kumar, Rajeev
    Khan, Raees Ahamd
    HEALTH INFORMATICS JOURNAL, 2022, 28 (04)
  • [32] ENGAGING PATIENTS IN CANCER SCREENING DECISION-MAKING THROUGH HEALTH INFORMATION TECHNOLOGY
    Aycock, Rebecca
    Woolf, Steven
    Krist, Alexander
    Kashiri, Paulette Lail
    Sabo, Roy
    Jones, Resa M.
    Etz, Rebecca
    Lafata, Jennifer Elston
    Hochheimer, Camille
    ANNALS OF BEHAVIORAL MEDICINE, 2016, 50 : S328 - S328
  • [33] Health Information Systems Supporting Health and Resiliency Through Improved Decision-making
    Ring, David
    Tierney, William M.
    METHODS OF INFORMATION IN MEDICINE, 2017, 56 : E11 - E12
  • [34] OPERATIONAL DATA RECORDING AND PROCESSING SYSTEM FOR THE TEXTILE-INDUSTRY - EXPECTATIONS OF USERS, CRITERIA FOR DECISION-MAKING
    STENTENBACH, J
    MELLIAND TEXTILBERICHTE INTERNATIONAL TEXTILE REPORTS, 1986, 67 (07): : 477 - 478
  • [35] The Use of Cost Information in Decision-Making in Business: A Case Study in an Industrial Company of Flexible Plastics
    Herculano, Harlan de Azevedo
    Nobrega Cavalcante, Paulo Roberto
    REUNIR-REVISTA DE ADMINISTRACAO CONTABILIDADE E SUSTENTABILIDADE, 2011, 1 (02): : 18 - 33
  • [36] STUDY OF INFORMATION-GATHERING AND DECISION-MAKING ACTIVITIES AT A PULVERIZATION CONTROL POST IN A CHEMICAL INDUSTRY
    ASSENHEI.G
    TRAVAIL HUMAIN, 1968, 31 (3-4): : 309 - &
  • [37] Decision-making in a fast fashion company in the Industry 4.0 era: a Digital Twin proposal to support operational planning
    Carlos Henrique dos Santos
    Gustavo Teodoro Gabriel
    João Victor Soares do Amaral
    José Arnaldo Barra Montevechi
    José Antônio de Queiroz
    The International Journal of Advanced Manufacturing Technology, 2021, 116 : 1653 - 1666
  • [38] Decision-making in a fast fashion company in the Industry 4.0 era: a Digital Twin proposal to support operational planning
    dos Santos, Carlos Henrique
    Gabriel, Gustavo Teodoro
    do Amaral, Joao Victor Soares
    Montevechi, Jose Arnaldo Barra
    de Queiroz, Jose Antonio
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2021, 116 (5-6): : 1653 - 1666
  • [39] Reinforcement Learning-based Operational Decision-Making in the Process Industry Using Multi-View Data
    Liu, Chenliang
    Wang, Yalin
    Yang, Chunhua
    Gui, Weihua
    2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, 2023, : 3646 - 3651
  • [40] Unveiling the relation between the challenges and benefits of operational excellence and industry 4.0: a hybrid fuzzy decision-making approach
    Hajiagha, Seyed Hossein Razavi
    Mandiraji, Hannan Amoozad
    Moradi, Samin
    Garza-Reyes, Jose Arturo
    Alaei, Saeed
    TQM JOURNAL, 2024, 36 (01): : 51 - 70