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
  • [21] AN EXAMINATION OF DECISION-MAKING AS A FUNCTION OF INFORMATION TECHNOLOGY IN THE OIL AND GAS-INDUSTRY
    ONEIL, MD
    JOURNAL OF CANADIAN PETROLEUM TECHNOLOGY, 1993, 32 (06): : 5 - 5
  • [22] Asynchronous Updating Reinforcement Learning Algorithm for Decision-making Operational Indices of Uncertain Industrial Processes
    Li J.-N.
    Yuan L.
    Ding J.-L.
    Zidonghua Xuebao/Acta Automatica Sinica, 2023, 49 (02): : 461 - 472
  • [23] Parsing through paradigms: uncertainty and decision-making in human information behavior
    Mestre, Juliana
    JOURNAL OF DOCUMENTATION, 2024, 80 (01) : 39 - 53
  • [24] Conceptualizing and measuring "industry resilience": Composite indicators for postshock industrial policy decision-making
    Tommaso, Marco R. Di
    Prodi, Elena
    Pollio, Chiara
    Barbieri, Elisa
    SOCIO-ECONOMIC PLANNING SCIENCES, 2023, 85
  • [25] Investment decision-making and industrial performance: The British wool industry during the interwar years
    Bowden, Sue
    Higgins, David M.
    BUSINESS HISTORY, 2015, 57 (02) : 224 - 240
  • [26] AUTOMATIC INFORMATION-PROCESSING ACTIVITIES AND OPERATIONAL DECISION-MAKING - A CASE-STUDY OF CONSEQUENCE
    VANDERVLIST, R
    INFORMATION & MANAGEMENT, 1989, 16 (04) : 219 - 225
  • [27] DECISION-MAKING IN SOCCER TASKS - IS SPEED OF INFORMATION-PROCESSING A GOOD PREDICTOR OF PERFORMANCE
    HOFE, AV
    LERDA, R
    CAHIERS DE PSYCHOLOGIE COGNITIVE-CURRENT PSYCHOLOGY OF COGNITION, 1995, 14 (02): : 151 - 169
  • [28] Development and application of process industry equipment maintenance information system for intelligent decision-making
    Wang Q.
    Yang J.
    Liu W.
    Yuan Q.
    Ma H.
    Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2010, 46 (24): : 168 - 177
  • [29] Decision-making in the pharmaceutical industry: analysis of entrepreneurial risk and attitude using uncertain information
    Cowlrick, Ivor
    Hedner, Thomas
    Wolf, Roland
    Olausson, Michael
    Klofsten, Magnus
    R & D MANAGEMENT, 2011, 41 (04) : 321 - 336
  • [30] An Improvement of Consensus in Group Decision-Making Through an Optimal Distribution of Information Granularity
    Cabrerizo, Francisco Javier
    Gonzalez-Quesada, Juan Carlos
    Morente-Molinera, Juan Antonio
    Javier Perez, Ignacio
    Herrera-Viedma, Enrique
    Pedrycz, Witold
    2022 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2022, : 119 - 124