Boiler Flame Image Classification Based on Hidden Markov Model

Han, Pu; Zhang, Xin; Zhen, Chenggang; Wang, Bing

doi:10.1109/isie.2006.295522

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…The flames generated are turbulent and can be looked straight and well-defined, which also refers to the flame oscillation. The temperatures of flame measured by thermocouples are the average values (Han et al, 2006) and the images of the flame will give instantaneous temperatures. Table 1 gives the specifications of the boiler at Neyveli Lignite Corporation (NLC) Ltd.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Soft Sensors for Monitoring Combustion Quality and Flue Gas Emissions in Power Station Boilers

Bhavani¹,

Sujatha²,

Rajendran³

2018

American Journal of Applied Sciences

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This research work includes a combination of Fisher's Linear Discriminant (FLD) analysis by merging Radial Basis Function (RBF) Network and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler. The CCD Camera is used to capture the two dimensional flame images. The features such as images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size for faster learning of the RBF network. Also video processing has been done to extract three classes of images corresponding to different burning conditions of the flames. For various flame conditions, the corresponding temperatures and flue gas emissions are obtained using analyzers and sensors. The combustion quality indicates the air/fuel ratio which can be varied automatically. The proposed feed forward control scheme presents an alternative for the existing setup for measuring SOx, NO x , CO and CO 2 emissions that are detected from the samples collected at regular intervals of time in the laboratory or by using gas analyzers. Further training and testing of Parallel architecture of Radial Basis Function and Back Propagation Algorithm (PRBFBPA) with the data obtained has been done and the performance of the algorithms is presented.

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Section: Methodsmentioning

confidence: 99%

“…Differences from one burner to the next in combustion conditions are due to factors such as imbalances in air/fuel ratio and maintenance problems at individual burners. The existing set-up indicates only the presence or absence of the flame in the furnace (Han et al, 2006). The schematic diagram for the proposed work is shown in the Fig.…”

Section: Methodsmentioning

confidence: 99%

Soft Sensors for Monitoring Combustion Quality and Flue Gas Emissions in Power Station Boilers

Bhavani¹,

Sujatha²,

Rajendran³

2018

American Journal of Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Fisher's linear discriminant function reduces the dimensions of extracted features [3] into 2 dimensions [2]. The RBF [4,6] is trained with the 4 feature vectors generated from 3 groups of images and similarly the BPA is also trained with remaining 3 feature vectors. The final output from the two networks is once again given as input to another RBN network.…”

Section: Methodsmentioning

confidence: 99%

Intelligent Parallel Networks for Combustion Quality Monitoring in Power Station Boilers

Sujatha

Pappa

Nambi

et al. 2013

AMR

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This research work includes a combination of Fisher’s Linear Discriminant (FLD) analysis by combining Radial Basis Function Network (RBF) and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler so as to control the air/fuel ratio. For this two dimensional flame images are required which was captured with CCD camera whose features of the images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size for faster learning of the RBF. Also three classes of images corresponding to different burning conditions of the flames have been extracted from a continuous video processing. In this the corresponding temperatures, the Carbon monoxide (CO) emissions and other flue gases have been obtained through measurement. Further the training and testing of Parallel architecture of Radial Basis Function and Back Propagation Algorithm (PRBFBPA) with the data collected have been done and the performance of the algorithms is presented.

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“…The tubes are interconnected to common channels or headers and eventually to a steam outlet for distribution to the plant system. A boiler is designed to absorb the maximum amount of heat released in the process of combustion [3]. This heat is transferred to the boiler water through radiation, conduction and convection.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Power Station Boilers Using ANN and Image Processing

Sujatha

Pappa²,

Kumar

et al. 2013

AMR

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This project deals with the monitoring the combustion quality of the power station boilers using Artificial Intelligence for improvement in the combustion quality in the power station boiler. The colour of the flame indicates whether the combustion taking place is complete, partial or incomplete. When complete combustion takes place the flue gases released are within the permissible limits otherwise its level is high which is out of limit. By analyzing the flame color which is captured using infrared camera and displayed on CCTV the quality of combustion is estimated. If combustion is partial or incomplete the flue gases released will create air pollution. So this work includes enhancement in the quality of combustion, saving of energy as well as check on the pollution level. The features are extracted from the flame images such as average intensity, area, brightness and orientation are obtained after preprocessing. Three classes of images corresponding to different burning conditions are taken from continuous video. Further training, testing and validation with the data collected have been carried out and performance of the various intelligent algorithms is presented.

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Boiler Flame Image Classification Based on Hidden Markov Model

Cited by 10 publications

References 11 publications

Soft Sensors for Monitoring Combustion Quality and Flue Gas Emissions in Power Station Boilers

Soft Sensors for Monitoring Combustion Quality and Flue Gas Emissions in Power Station Boilers

Intelligent Parallel Networks for Combustion Quality Monitoring in Power Station Boilers

Monitoring Power Station Boilers Using ANN and Image Processing

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