PI-controller parameters tuning method to reject disturbances acting on heating furnaces

Eremenko, Y. I.; Glushchenko, Anton; Fomin, Andrey

doi:10.1109/icieam.2016.7910910

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Discussion Of Results Of the Study Into Using A Neuralnetwor1

Application Of a Neural-network Tuner On The Model Of Gas Fu1

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Cited by 4 publications

(2 citation statements)

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“…Present study is continuation of a larger study into development and application of a neural-network tuner [14][15][16].…”

Section: Discussion Of Results Of the Study Into Using A Neuralnetwormentioning

confidence: 83%

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Applying a neural tuner of the PIcontroller parameters to control gas heating furnaces

Eremenko

Glushchenko

Fomin

2017

EEJET

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“…Present study is continuation of a larger study into development and application of a neural-network tuner [14][15][16].…”

Section: Discussion Of Results Of the Study Into Using A Neuralnetwormentioning

confidence: 83%

“…Much of the previous research by authors of the present study [14][15][16] was conducted for the model of muffle furnace SNOL 1,6.2,5.1/11-I, shown as (1).…”

Section: Application Of a Neural-network Tuner On The Model Of Gas Fumentioning

confidence: 99%

Applying a neural tuner of the PIcontroller parameters to control gas heating furnaces

Eremenko

Glushchenko

Fomin

2017

EEJET

View full text Add to dashboard Cite

Improving safety of the operation of pipe heaters oil and gas production

Баширов

Luneva

Evdokimova

et al. 2019

J. Phys.: Conf. Ser.

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In technological processes of modern enterprises of oil and gas production flammable and explosive substances turn, which, combined with a wide range of changes and extreme values of technological parameters - temperature, pressure, flow rate, corrosivity, causes high rates of accidents risk. Pipe heating furnaces are among the most dangerous objects of oil and gas production, more than 10% of all accidents occur on them. Cause most of these accidents is leakage of pipe coil and the ingress of the process medium into the combustion space of the pipe heater. During operation, irreversible damage accumulates in the pipe coil metal, there is an uneven stress-strain state due to thermal force loading, there is a carbon deposition on the inner surface. This article discusses the process of carbon-producing in the pipe heaters on the example of a pipe heater box type using a computer predictive model of the production process. According to the results of the study, a new evaluation criterion for the level of coking of the pipe coil – the rate of carbon-producing is proposed and its value corresponding to the maximum permissible level of coking is determined. Calculation of the rate of carbon-producing is carried out by virtual quality analyzers using the process parameters of the carbon-producing.

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On PI-Controller neural tuner implementation in programmable logic controller to improve rejection of disturbances effecting heating plant

Eremenko

Glushchenko

Fomin

2017

2017 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

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PI-controller parameters tuning method to reject disturbances acting on heating furnaces

Cited by 4 publications

References 17 publications

Applying a neural tuner of the PIcontroller parameters to control gas heating furnaces

Applying a neural tuner of the PIcontroller parameters to control gas heating furnaces

Improving safety of the operation of pipe heaters oil and gas production

On PI-Controller neural tuner implementation in programmable logic controller to improve rejection of disturbances effecting heating plant

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PI-controller parameters tuning method to reject disturbances acting on heating furnaces

Cited by 4 publications

References 17 publications

Applying a neural tuner of the PI­controller parameters to control gas heating furnaces

Applying a neural tuner of the PI­controller parameters to control gas heating furnaces

Improving safety of the operation of pipe heaters oil and gas production

On PI-Controller neural tuner implementation in programmable logic controller to improve rejection of disturbances effecting heating plant

Contact Info

Product

Resources

About

Applying a neural tuner of the PIcontroller parameters to control gas heating furnaces

Applying a neural tuner of the PIcontroller parameters to control gas heating furnaces