Multimode Monitoring of Oxy-Gas Combustion Through Flame Imaging, Principal Component Analysis, and Kernel Support Vector Machine

Bai, Xiaojing; Lü, Gang; Hossain, Moinul; Yan, Yong; Liu, Shixia

doi:10.1080/00102202.2016.1250749

Cited by 11 publications

(6 citation statements)

References 28 publications

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“…Therefore, to identify differences in the parameter data of the flame image depending on the CER, detailed information regarding brightness, saturation, and luminance is required. In previous studies, image data ranging from resolutions of 488 × 582 to 1280 × 1024 were obtained using a CCD camera [15,25]. However, because of insufficient information, characteristics cannot be asserted to be solely based on flame shape.…”

Section: Collection Of Flame Imagesmentioning

confidence: 99%

“…The use of instantaneous combustion state images for predicting air pollutants can address the aforementioned issues. Xia proposed a learning model that combines principle component analysis (PCA) and Kernel-SVM for flame images from a gas-fired boiler (GFB) [15]. Yang developed an algorithm for predicting exhaust gas emissions by applying Garbor filters to gray-level co-occurrence matrix (GLCM) features in flame images from an industrial circulating fluidized bed (CFB) [16].…”

Section: Introductionmentioning

confidence: 99%

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Experimental Study on Prediction for Combustion Optimal Control of Oil-Fired Boilers of Ships Using Color Space Image Feature Analysis and Support Vector Machine

Lee,

Jung,

Choi

2023

JMSE

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The International Maritime Organization strives to improve the atmospheric environment in oceans and ports by regulating ship emissions of air pollutants and promoting energy efficiency. This study deals with the prediction of eco-friendly combustion in boilers to reduce air pollution emissions. Accurately measuring air pollutants from ship boilers in real-time is crucial for optimizing boiler combustion. However, using data obtained through an exhaust gas analyzer for real-time control is challenging due to combustion process delays. Therefore, a real-time predictive modeling approach is proposed to enhance the accuracy of prediction models for NOx, SO2, CO2, and O2 by analyzing the color spectrum of flame images in a quasi-instantaneous combustion state. Experimental investigations were carried out on an oil-fired boiler installed on an actual ship, where the air damper was adjusted to create various combustion conditions. This algorithm is a saturation-based feature extraction filter (SEF) through color spectrum analysis using RGB (red, green, and blue) and HSV (hue, saturation, and value). The prediction model applying the proposed method was verified against exhaust gas analyzer data using a new data set, and real-time prediction performance and generalization were confirmed.

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Section: Collection Of Flame Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Study on Prediction for Combustion Optimal Control of Oil-Fired Boilers of Ships Using Color Space Image Feature Analysis and Support Vector Machine

Lee,

Jung,

Choi

2023

JMSE

View full text Add to dashboard Cite

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“…Ye et al [18] propose a new flame segmentation approach with wavelet analysis to detect smoke and flame simultaneously for color dynamic video sequences obtained. Bai et al [19] use PCA and kernel support vector machine (KSVM) techniques to segment the combustion flame pixels, and the experimental results show that the PCA-KSVM model is feasible and effective in monitoring a combustion process. Han et al [20] propose multicolor-based detection combining the RGB, HSI, and YUV color space which takes full advantage of the motion feature and color information of combustion flame.…”

Section: Introductionmentioning

confidence: 99%

A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid‐Coded HLO

et al. 2021

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In recent years, the combustion furnace has been widely applied in many different fields of industrial technology, and the accurate detection of combustion states can effectively help operators adjust combustion strategies to improve combustion utilization and ensure safe operation. However, the combustion states inside the industrial furnace change according to the production needs, which further challenges the optimal set of model parameters. To effectively segment the flame pixels, a novel segmentation method for furnace flame using adaptive color model and hybrid-coded human learning optimization (AHcHLO) is proposed. A new adaptive color model with mixed variables (NACMM) is designed for adapting to different combustion states, and the AHcHLO is developed to search for the optimal parameters of NACMM. Then, the best NACMM with optimal parameters is adopted to segment the combustion flame image more precisely and effectively. Finally, the experiment results show that the developed AHcHLO obtains the best-known overall results so far on benchmark functions and the proposed NACMM outperforms state-of-the-art flame segmentation approaches, providing a high detection accuracy and a low false detection rate.

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“…Especially, with the advance of optical sensing and digital image processing methods, digital imaging devices have been employed in on-line combustion-state monitoring systems [14][15][16][17][18][19][20][21][22][23]. For example, Lu et al extracted from flame images a variety of handcrafted features such as the ignition point and area, and the flickering of flame [14].…”

Section: Introductionmentioning

confidence: 99%

Combustion Instability Monitoring through Deep-Learning-Based Classification of Sequential High-Speed Flame Images

Choi

Kim

et al. 2020

Electronics

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In this study, novel deep learning models based on high-speed flame images are proposed to diagnose the combustion instability of a gas turbine. Two different network layers that can be combined with any existing backbone network are established—(1) An early-fusion layer that can learn to extract the power spectral density of subsequent image frames, which is time-invariant under certain conditions. (2) A late-fusion layer which combines the outputs of a backbone network at different time steps to predict the current combustion state. The performance of the proposed models is validated by the dataset of high speed flame images, which have been obtained in a gas turbine combustor during the transient process from stable condition to unstable condition and vice versa. Excellent performance is achieved for all test cases with high accuracy of 95.1–98.6% and a short processing time of 5.2–12.2 ms. Interestingly, simply increasing the number of input images is as competitive as combining the proposed early-fusion layer to a backbone network. In addition, using handcrafted weights for the late-fusion layer is shown to be more effective than using learned weights. From the results, the best combination is selected as the ResNet-18 model combined with our proposed fusion layers over 16 time-steps. The proposed deep learning method is proven as a potential tool for combustion instability identification and expected to be a promising tool for combustion instability prediction as well.

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Multimode Monitoring of Oxy-Gas Combustion Through Flame Imaging, Principal Component Analysis, and Kernel Support Vector Machine

Cited by 11 publications

References 28 publications

Experimental Study on Prediction for Combustion Optimal Control of Oil-Fired Boilers of Ships Using Color Space Image Feature Analysis and Support Vector Machine

Experimental Study on Prediction for Combustion Optimal Control of Oil-Fired Boilers of Ships Using Color Space Image Feature Analysis and Support Vector Machine

A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid‐Coded HLO

Combustion Instability Monitoring through Deep-Learning-Based Classification of Sequential High-Speed Flame Images

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