Dust preparation systems with pulverizing fans for the boilers of large power units

Klepikov, N. S.; Abydennikov, V. V.; Volkov, A. B.; Simonov, V. V.; Veksler, F. M.; Monakhova, V. I.; Ogurtsova, L. V.

doi:10.1134/s0040601508090085

Search citation statements

Order By: Relevance

Paper Sections

Select...

Mill Fan Diagnostics1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2012

2016

Publication Types

Select...

Article2

Other1

Book1

Relationship

Self Cite1

Independent3

Authors

Journals

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2 and 3), which is attributed to a smaller value of K grVTI for coal used at the Tszyutai TPS. The above mentioned dependence of R 90 on the grindability coefficient was pointed out in [2] and confirmed by the data presented above.…”

supporting

confidence: 79%

Results from tests of MV3600/1000/490 mill-ventilators in milling Chinese brown coals

Klepikov¹,

Nyrtsov²

2012

Therm. Eng.

Self Cite

View full text Add to dashboard Cite

supporting

confidence: 79%

Results from tests of MV3600/1000/490 mill-ventilators in milling Chinese brown coals

Klepikov¹,

Nyrtsov²

2012

Therm. Eng.

Self Cite

View full text Add to dashboard Cite

“… Asymmetric wear of operative wheel blades, variable fan and grinding capacity between two successive repairs, Fig.2 [1,2,4,5]. -The measurements of the necessary mode quantities for mill fan in the DCS or SCADA system are rather inaccurate due to the significant changeability of the conditions for measurements (wear, slagging, sensor pollution) and also due to the great amount of external disturbances (dust and humidity of fuel, imprecise dosing of coal, stohasticity of temperature of the intake oven gases due to non-stationarity of the torch position) [1,2,7,9,10].…”

Section: Mill Fan Diagnosticsmentioning

confidence: 99%

Intelligent diagnostic on mill fan system

Hadjiski

Doukovska

Koprinkova–Hristova

2012

2012 6th IEEE INTERNATIONAL CONFERENCE INTELLIGENT SYSTEMS

View full text Add to dashboard Cite

The mill fans (MF) are centrifugal fans of the simplest type with flat radial blades adapted for simultaneous operation both like fans and also like mills. The key variable that could be used for diagnostic purposes is vibration amplitude of MF corpse. However its mode values include a great deal of randomness. Therefore the application of deterministic dependencies with correcting coefficients is non-effective for MF predictive modeling. Standard statistical and probabilistic (Bayesian) approaches are also inapplicable to estimate MF vibration state due to non-stationarity, non-ergodicity and the significant noise level of the monitored vibrations. Adequate for the case methods of computational intelligence (fuzzy logic, neural networks and more general AI techniques -the precedents' method or machine learning (ML)) must be used. The present paper describes promising initial results on applying the Case Based Reasoning (CBR) approach for intelligent diagnostic of the mill fan working capacity using its vibration state.

show abstract

Intelligent Technical Fault Condition Diagnostics of Mill Fan

Hadjiski

Doukovska

2016

Studies in Computational Intelligence

View full text Add to dashboard Cite

Dust preparation systems with pulverizing fans for the boilers of large power units

Cited by 4 publications

References 0 publications

Results from tests of MV3600/1000/490 mill-ventilators in milling Chinese brown coals

Results from tests of MV3600/1000/490 mill-ventilators in milling Chinese brown coals

Intelligent diagnostic on mill fan system

Intelligent Technical Fault Condition Diagnostics of Mill Fan

Contact Info

Product

Resources

About