Neural network for decision support to determine the operating mode of lined equipment

The paper presents data on the problem of determining the operational mode of lined equipment at the iron and steel works. A neural network synthesis has been performed to determine the operational mode for lined equipment. The structure of the proposed neural network for decision support is described. The results of the modelling the neural network to determine the PM350 torpedo ladle car operational mode are presented.

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Automation of monitoring and diagnosing the technical condition of torpedo ladle cars

MATEC Web of Conferences ◽

10.1051/matecconf/201823904003 ◽

2018 ◽

Vol 239 ◽

pp. 04003

Author(s):

Vitaliy Yemelyanov ◽

Tatiana Tochilkina ◽

Alexey Nedelkin ◽

Evgeny Shved

Keyword(s):

Decision Making ◽

Information Processing ◽

Technical Condition ◽

The State ◽

Operational Mode ◽

Iron And Steel ◽

Automated Monitoring ◽

Iron And Steel Works ◽

Support Decision Making ◽

Steel Works

The paper presents data on the problem of monitoring and diagnosing the technical condition of torpedo ladle cars at the iron and steel works. The structure of technology for automated monitoring and diagnosing the technical condition of torpedo ladle cars has been developed and described, as a system-organized sequence of operations performed with the information on the state of the torpedo ladle cars applying the proposed methods. There has been developed software to implement the operations of information processing for torpedo ladle cars and to support decision-making on selecting their operational mode.

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Supply and Demand Forecasting of Blast Furnace Gas Based on Artificial Neural Network in Iron and Steel Works

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.443-444.183 ◽

2012 ◽

Vol 443-444 ◽

pp. 183-188 ◽

Cited By ~ 2

Author(s):

Qi Zhang ◽

Yan Liang Gu ◽

Wei Ti ◽

Jiu Ju Cai

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Blast Furnace ◽

Supply And Demand ◽

Demand Forecasting ◽

Iron And Steel ◽

Blast Furnace Gas ◽

Artificial Neural ◽

Iron And Steel Works ◽

Steel Works

Abstract.Blast Furnace Gas (BFG) system of an iron and steel works was considered. The relationship of gas amount and factors about BFG generation and consumption was analyzed by grey correlationand the BP neural network prediction model of blast furnace gaswas established based on artificial neural network for forecasting thesupply and demandof BFGinthe iron and steel-making processes.The scientific forecasting of BFG generation and consumption in each process was discussed undernormal production and accidental maintenance condition. The results show that established forecasting model is high precision, small errors, and can solve effectively actual production of BFG prediction problem and decreasing BFG flare, providing theoretical basis for establishing reasonable plans in the iron and steel works.

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Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

Methods of Information in Medicine ◽

10.1055/s-0038-1636858 ◽

1997 ◽

Vol 36 (04/05) ◽

pp. 349-351

Author(s):

H. Mizuta ◽

K. Kawachi ◽

H. Yoshida ◽

K. Iida ◽

Y. Okubo ◽

...

Keyword(s):

Neural Network ◽

Decision Support ◽

Psychiatric Diagnosis ◽

Psychiatric Patients ◽

Bayesian Classifier ◽

Neural Network Classifier ◽

Correct Decision ◽

Neurotic Disorders ◽

The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

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