Abstract
Combustion condition monitoring is a fundamental and critical issue that needs to be addressed in the wide-load operation of coal-fired boilers. In this paper, an unsupervised classification framework based on the convolutional auto-encoder (CAE), the principal component analysis (PCA), and the hidden Markov model (HMM) is proposed to monitor the combustion condition with the uniformly spaced flame images, which are collected from the furnace combustion monitoring system. First, CAE is adopted to extract the features from the flame images, which obtain the sparse representations in the images. Then, PCA is applied to project the feature vectors into the orthogonal space for robustness and computation efficiency. Finally, a HMM is built to calculate the corresponding optimal states by learning the temporal behaviors in the compressed representations. A coal combustion adjustment experiment was conducted in a 660 MW opposed-firing boiler, and the sequential 14,400 flame images with three different combustion states were obtained to evaluate the effectiveness of the proposed approach. We tested six different compression dimensions of the latent variable z in the CAE model and ensured that the appropriate compress parameter was 1024. The proposed framework is compared with five other methods: the CAE + Gaussian mixture model (GMM), CAE + Kmean, the CAE + fuzzy c-mean method, CAE + HMM, and the traditional handcraft feature extraction method (TH) + HMM. The results show that the proposed framework has the highest classification accuracy (95.25% for the training samples and 97.36% for the testing samples) and has the best performance in recognizing the semi-stable state (85.67% for the training samples and 77.60% for the testing samples), indicating that the proposed framework is capable of identifying the combustion condition, changing when the combustion deteriorates as the coal feed rate falls.
Highlights
Fossil power plants in China are facing more peak-shaving requests for the growth of renewable energies
The results show that the proposed framework has the highest classification accuracy (95.25% for the training samples and 97.36% for the testing samples) and has the best performance in recognizing the semi-stable state (85.67% for the training samples and 77.60% for the testing samples), indicating that the proposed framework is capable of identifying the combustion condition, changing when the combustion deteriorates as the coal feed rate falls
We propose an unsupervised classification framework based on the convolutional auto-encoder (CAE), principal component analysis (PCA), and the hidden Markov model (HMM) for pulverized coal combustion status recognition with the uniformly spaced flame images
Summary
Fossil power plants in China are facing more peak-shaving requests for the growth of renewable energies. Reducing the minimum unit technique output is one of the goals of flexible transformation, which means the boilers in power plants should be operated below the designed minimum output. When the boiler runs in a low load, the changeable quality of coal used in practice makes the combustion unstable, which directly affects the safety and economics of the boiler operation. The identification of combustion condition has received extensive attention by researchers. Flame visualization and characterization techniques are some of the research tools for understanding the Energies 2019, 12, 2585; doi:10.3390/en12132585 www.mdpi.com/journal/energies. Current research mainly includes feature-based machine learning, statistical-based process monitoring, and deep learning methods
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