Combustion monitoring of a water tube boiler using a discriminant radial basis network

Sujatha, K.; Pappa, N.

doi:10.1016/j.isatra.2010.08.006

Cited by 26 publications

(12 citation statements)

References 8 publications

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“…The features are extracted (Sujatha et al, 2014) from each image. Fisher's linear discriminant function reduces the dimensions of extracted features (Sujatha and Pappa, 2011a) into 2 dimensions (Sujatha, 2012). The RBF (Sujatha and Pappa, 2010;Sujatha and Pappa, 2011) 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.…”

Section: Methodsmentioning

confidence: 99%

“…The main objective is to design a expert flame monitoring system with progressive cameras, along with artificial intelligence techniques to identify flame features (Sujatha and Pappa, 2011a) that can be correlated with air/fuel ratio, NO x , CO, CO 2 emission levels, temperature, etc. The 3D temperature profiler is designed to provide control of furnace and flame temperature which also reduces the flue gas emissions which is the key in achieving high combustion quality (Sujatha and Pappa, 2011a). The system is also designed to provide guidance for balancing air/fuel ratio so as to ensure complete combustion.…”

Section: Previous Researchmentioning

confidence: 99%

“…The goal ofon-line monitoring and controlled combustion is to address everincreasing demands for higher furnace efficiency, reduced flue gas emissions and improved combustion quality (Sujatha and Pappa, 2011a). These systems, are based on the latest optical sensing and digital image processing techniques (Sujatha and Pappa, 2011a), are capable of determining geometry (size and location), i.e., the geometry of the burner (fixed), luminous (brightness and uniformity) and fluid-dynamics parameters (temperature) of a flame. In the current set up, based on the oxygen content in the exhaust gas the air/fuel ratio of the ratio controller is varied manually in a feedback manner.…”

Section: Previous Researchmentioning

confidence: 99%

“…The features extracted are filtered (median filter, average filter and self adaptive filters) to enhance the flame images that are used for testing the performance of the Self-Organizing feature Maps (SOM) which classifies the flame images as proposed by Fan Jiang et al (2009). Advanced flame and temperature measurement techniques include Laser Raman (LR)/Laser Rayleigh Scattering (RS), Fourier Transform Infrared (FTIR) Spectroscopy and interferometry along with the traces of smoke, small particles, gas streams and bubbles were used to visualize combustion phenomenon (Sujatha and Pappa, 2011a).…”

Section: Previous Researchmentioning

confidence: 99%

See 3 more Smart Citations

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%

Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

Section: Previous Researchmentioning

confidence: 99%

See 2 more Smart Citations

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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“…These images are preprocessed for using Gaussian filter for noise removal. Edges are detected using fuzzy logic [9,10]. Thereafter, features like entropy, correlation and average intensity are extracted from the edge detected images.…”

Section: Proposed Strategy For Phylum Identification Of Various mentioning

confidence: 99%

Identification and Classification of Animal Kingdom using Image Processing and Artificial Neural Networks

Sujatha*¹,

Srividhya²,

Aruna³

2019

IJRTE

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The biological kingdom ‘Animalia’ is composed of multi cellular eukaryotic organisms. Most of the animal species exhibit bilateral symmetry. The hierarchy of biological classification has eight taxonomy ranks. The top position in the hierarchy is occupied by the ‘domain’ and ending with the lowest position occupied by ‘species’. The classification of animal kingdom includes, Porifera, Coelenterata, Platyhelminthes, Aschelminthes, Annelida, Arthropoda, Mollusca, Echinodermata and Chordata. Manual identification of Phylum or class for each and every species, is very tedious, because there exists nearly a millions of species categorized under various classes. Hence an automated system is proposed to be developed using image segmentation and Artificial Neural Networks (ANN) trained with Back Propagation Algorithm (BPA) which is capable of assisting the scientists and researchers for class identification. This system will be useful in Museums and Archeological departments, where a huge variety of species are maintained. The classification efficiency of the proposed system is 89.1%.

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Cloud Computing for Image Based Condition Monitoring of Road Surface

Sujatha

Babu

Ganesan³

et al. 2018

International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018

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Combustion monitoring of a water tube boiler using a discriminant radial basis network

Cited by 26 publications

References 8 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

Identification and Classification of Animal Kingdom using Image Processing and Artificial Neural Networks

Cloud Computing for Image Based Condition Monitoring of Road Surface

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