Automation of combustion monitoring in boilers using discriminant radial basis network

Sujatha, K.; Pappa, N.; Nambi, U. Siddharth; Kumar, K. Senthil; Dinakaran, C.R. Raja

doi:10.1504/ijaisc.2013.053406

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…They represent the characteristics of the images. The features extracted are centroid x and centroid y of the flame in the image, orientation of the flame, average intensity of the flame image, area (Sujatha et al, 2014) of the flame and the discriminant vectors Φ 1 and Φ 2 (Sujatha et al, 2013). The target outputs are the temperatures, measured for set of images, the CO emissions measured in ppm, NO x in mg/Nm 3 , CO 2 in Nm 3 /hr, SO x in mg/Nm 3 , in the rate of air supply in t/hr and the fuel supplied in t/hr (Sujatha et al, 2014).…”

Section: Feature Extractionmentioning

confidence: 99%

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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: Feature Extractionmentioning

confidence: 99%

“…The input to a perceptron is the summation of input pattern vectors by weight vectors. Information flows in a feed-forward manner from input layer to the output layer through hidden layers (Sujatha and Pappa, 2011b;Fisher, 1936). The number of nodes in the input layer and output layer is fixed.…”

Section: Classificationmentioning

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

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“…Various architectures of such networks have been developed, and many of them achieve excellent performance in image recognition tasks, including flame images. Examples include deep convolutional neural networks [ 2 , 3 , 4 , 5 , 6 ], deep belief networks [ 7 , 8 ], deep convolutional auto-encoder [ 9 ], deep convolutional auto-encoder connected with the principal component analysis and the hidden Markov model [ 10 ], deep, fully connected neural networks [ 11 ], deep convolutional selective autoencoder [ 12 ] and various architectures followed by a symbolic time series analysis [ 13 , 14 ]. Each of the architectures mentioned above plays a vital role in a specific application area.…”

Section: Introductionmentioning

confidence: 99%

Flame Image Processing and Classification Using a Pre-Trained VGG16 Model in Combustion Diagnosis

Omiotek

Kotyra

2021

Sensors

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Nowadays, despite a negative impact on the natural environment, coal combustion is still a significant energy source. One way to minimize the adverse side effects is sophisticated combustion technologies, such as, e.g., staged combustion, co-combustion with biomass, and oxy-combustion. Maintaining the combustion process at its optimal state, considering the emission of harmful substances, safe operation, and costs requires immediate information about the process. Flame image is a primary source of data which proper processing make keeping the combustion at desired conditions, possible. The paper presents a method combining flame image processing with a deep convolutional neural network (DCNN) that ensures high accuracy of identifying undesired combustion states. The method is based on the adaptive selection of the gamma correction coefficient (G) in the flame segmentation process. It uses the empirically determined relationship between the G coefficient and the average intensity of the R image component. The pre-trained VGG16 model for classification was used. It provided accuracy in detecting particular combustion states on the ranging from 82 to 98%. High accuracy and fast processing time make the proposed method possible to apply in the real systems.

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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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Automation of combustion monitoring in boilers using discriminant radial basis network

Cited by 11 publications

References 10 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

Flame Image Processing and Classification Using a Pre-Trained VGG16 Model in Combustion Diagnosis

Cloud Computing for Image Based Condition Monitoring of Road Surface

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