Application of an expert system to blast furnace operation

Yui, K.; Watanabe, Satoshi; Amano, Shigeru; Takarabe, Tsuyoshi; Nakamori, Takashi

doi:10.1016/0167-739x(89)90032-0

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…This came in a paper by Bowonder and Miyake (1992), discussing information management in Japan's Nippon Steel Corporation. They noted that Nippon Steel had been the first in the world to use an AI system for blast furnace control (see Yui, Watanabe, Amano, Takarabe, & Nakamori, 1989).…”

Section: Rule-based Inferencementioning

confidence: 99%

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Duan

Edwards

Dwivedi

2019

International Journal of Information Management

1,430

554

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Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.

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Section: Rule-based Inferencementioning

confidence: 99%

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Duan

Edwards

Dwivedi

2019

International Journal of Information Management

1,430

554

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“…Fuzziness, based on a heuristic grade, is used to enable judgment of the different hypotheses. 25 ALIS has been used for the control of the No.3 and No.4 blast furnaces at the Kimitsu Works since June 1988. 26 The system uses four types of knowledge:…”

Section: Iron Makingmentioning

confidence: 99%

Artificial intelligence and expert systems in the steel industry

Carayannis

1993

JOM

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This article was prepared in an effort to determine the state of the art with respect to the use of artificial intelligence and expert system technologies within the steel industry. A number of important developments have been reported and most of them resulted in significant savings. Mathematical modeling is quite important both for understanding and for controlling a process. However, most steelmaking operations are extremely complex and cannot be described mathematically. They are, however, adequately controlled by human operators on the basis of their knowledge and expertise. Because of this, artificial intelligence is an ideal technology for the automation of many steelmakingrelated processes.

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“…Expert systems are one rapidly growing area, and are a practical technique in the field of artificial intelligence [1][2][3][4][5][6].These are used in different fields as process control in the iron and steel industry [7][8][9]]. An expert system that is designed to emulate the expertise of experts and veteran operators in performing control activities is called an expert control system.…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

Application of Data Fusion on System of Measurement and Control for Grain Storage

Zhu

Tong

Zhi

2009

2009 IITA International Conference on Control, Automation and Systems Engineering (Case 2009)

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Data fusion was used to predict the safe value of grain environment in different operational conditions. The temperature, humidity, moisture, pest and etc, were used as inputs to the data fusion center and network. Feed-forward artificial neural networks with 4-10-5 arrangements were capable to estimate the safe value of grain environment as the outputs. This method, characterized by sufficiently utilizing the effective detected data, optimizing homogeneous data, and considering the complementation of the different data source makes possible an improvement in the reliability and entirety of detection system.

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Application of an expert system to blast furnace operation

Cited by 3 publications

References 1 publication

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Artificial intelligence and expert systems in the steel industry

Application of Data Fusion on System of Measurement and Control for Grain Storage

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