A new camera-based method for measuring the flame stability of non-oscillating and oscillating combustions

Matthes, Jörg; Waibel, Patrick; Vogelbacher, Markus; Gehrmann, Hans‐Joachim; Keller, Hubert B.

doi:10.1016/j.expthermflusci.2019.03.008

Cited by 21 publications

(15 citation statements)

References 21 publications

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“…A high dynamic ranged µEye UI-5240SE-M-GL camera of the manufacturer Imaging Development Systems GmbH (IDS) was aimed vertically at the flame from the coal burner A high dynamic ranged µEye UI-5240SE-M-GL camera of the manufacturer Imaging Development Systems GmbH (IDS) was aimed vertically at the flame from the coal burner above the post burner chamber in order to analyse the flame image during the experiments. The analysis of the image material was carried out by the Institute for Automation and Applied Informatics [15].…”

Section: Pilot-scale Power Plant Brendamentioning

confidence: 99%

“…experiments. The analysis of the image material was carried out by the Institute for Automation and Applied Informatics [15].…”

Section: Pilot-scale Power Plant Brendamentioning

confidence: 99%

“…If the flame shapes of the images coincide, a relative flame stability of 100% is calculated, if the flame shapes differ strongly from each other, a relative flame stability of 0% is obtained. The exact calculation bases for the optical analysis as well as the made assumptions for the calculations are to be taken from [15].…”

Section: Test Campaign Bmentioning

confidence: 99%

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Analysis of Oscillating Combustion for NOx−Reduction in Pulverized Fuel Boilers

et al. 2021

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Thermal power plants in different fields are regularly adapted to the state-of-the-art emissions standards, applying “The Best Available Techniques Reference”. Since 2016 in the power plant area new, more stringent limits for power plant units with a thermal output of more than 300 MW operated with black coal are valid. Usually, in order to reach the new limits e.g., for NOX emissions, downstream reduction processes (Selective Non-Catalytic Reduction, SNCR or Selective Catalytic Reduction) are applied, which use of operating resources (essentially ammonia water) thereby increase. By the means of an experimentally validated process, by which pulverized fuel is fed by oscillation through a swirl burner into a pilot combustion chamber with a thermal output of 2.5 MW, nitrogen oxides can be reduced without further activities, for instance from 450 mg/mN3 in non-oscillation operation mode (0 Hz) to 280 mg/mN3 in oscillation operation mode (3.5 Hz), normalized to an O2–content of 6% each. These findings were patented in EP3084300. Particularly promising are the experiments which utilize oscillation of a large portion of the burn out air instead of the fuel in order to minimize the fatigue of the pulverized fuel oscillator, amongst others. Thereby, the nitrogen conversion rate, which describes the ratio of NOX to fuel nitrogen, including thermal NOX can be reduced from 26% for non-oscillation operation mode down to 16%. The present findings show that fuel oscillation alone is not sufficient to achieve nitrogen oxides concentrations below the legislative values. Therefore, a combination of different primary (and secondary) measures is required. This paper presents the experimental results for oscillating coal-dust firing. Furthermore, an expert model based on a multivariate regression is developed to evaluate the experimental results.

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Section: Pilot-scale Power Plant Brendamentioning

confidence: 99%

“…experiments. The analysis of the image material was carried out by the Institute for Automation and Applied Informatics [15].…”

Section: Pilot-scale Power Plant Brendamentioning

confidence: 99%

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Analysis of Oscillating Combustion for NOx−Reduction in Pulverized Fuel Boilers

et al. 2021

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“…The segmentation of flames is motivated by applications that require geometric information about flames in image or video data, see, e.g., in [1], where the authors use growing fire regions as a criterion to identify dangerous fire in outdoor and indoor scenery. Similarly, [2] use segmentations of burner flame images to derive a geometric stability measure for combustion processes. The flame stability can be used to analyze combustion and optimize process control.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [2] use the Otsu threshold method for flame segmentation in an industrial context. Other methods channel multiple features into a more complex classification process such as SVM [7], Bayes classifier [4,6], neural network [3], or fuzzy logic classifier [5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Learning-Based Segmentation Methods for Industrial Burner Flames

et al. 2021

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The energetic usage of fuels from renewable sources or waste material is associated with controlled combustion processes with industrial burner equipment. For the observation of such processes, camera systems are increasingly being used. With additional completion by an appropriate image processing system, camera observation of controlled combustion can be used for closed-loop process control giving leverage for optimization and more efficient usage of fuels. A key element of a camera-based control system is the robust segmentation of each burners flame. However, flame instance segmentation in an industrial environment imposes specific problems for image processing, such as overlapping flames, blurry object borders, occlusion, and irregular image content. In this research, we investigate the capability of a deep learning approach for the instance segmentation of industrial burner flames based on example image data from a special waste incineration plant. We evaluate the segmentation quality and robustness in challenging situations with several convolutional neural networks and demonstrate that a deep learning-based approach is capable of producing satisfying results for instance segmentation in an industrial environment.

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