Camera-based detection of properties of biogenic solid fuels to determine fuel quality categories - Lukas, Johannes; Kolb, Sebastian; Plankenbühler, Thomas; Karl, Jürgen (6th Doctoral Colloquium Bioenergy, 2023)

Early Detection of Agglomeration in Fluidized Beds by Means of Frequency Analysis of Pressure Fluctuations - Leimbach, Steffen; Lukas, Johannes; Kolb, Sebastian; Yang, Lei; Plankenbühler, Thomas; Sega, Marcello; Harting, Jens; Karl, Jürgen (Energy & Fuels, 2022)
For description of the fluidization state of fluidized beds, both time-domain and frequency-domain analyses of high-frequency pressure fluctuations are established approaches. Common methods for the detection of agglomeration or defluidization in fluidized beds use the variance or the standard deviation of the pressure signal or the maximum in its frequency spectrum. These methods are used, for example, in biomass combustion or gasification. However, these approaches lack the reliability for applications as an early agglomeration warning system in industrial applications. To address this issue, the present study introduces a robust methodology by means of extracting a characteristic frequency from the power spectral density of the pressure signal. A comparison of our developed approach with the commonly used frequency maximum and standard deviation for predicting the onset of agglomeration in laboratory experiments shows promising sensitivity on agglomeration formation. In order to evaluate the general applicability of this method on an industrial scale, this work investigates dependencies of possible influences, such as gas velocity, sand quantity, and temperature, on the characteristic frequency. The results indicate that the characteristic frequency can be a promising and robust method for the early detection of the onset of agglomeration in industrial plants.<br

Early Detection of Agglomeration in Fluidized Beds by Means ofFrequency Analysis of Pressure Fluctuations - Leimbach, Steffen; Lukas, Johannes; Kolb, Sebastian; Yang, Lei; Plankenbühler, Thomas; Sega, Marcello; Harting, Jens; Karl, Jürgen (Energy & Fuels, 2022)
For description of thefluidization state offluidizedbeds, both time-domain and frequency-domain analyses of high-frequency pressurefluctuations are established approaches.Common methods for the detection of agglomeration ordefluidization influidized beds use the variance or the standarddeviation of the pressure signal or the maximum in its frequencyspectrum. These methods are used, for example, in biomasscombustion or gasification. However, these approaches lack thereliability for applications as an early agglomeration warningsystem in industrial applications. To address this issue, the presentstudy introduces a robust methodology by means of extracting acharacteristic frequency from the power spectral density of thepressure signal. A comparison of our developed approach with thecommonly used frequency maximum and standard deviation for predicting the onset of agglomeration in laboratory experimentsshows promising sensitivity on agglomeration formation. In order to evaluate the general applicability of this method on an industrialscale, this work investigates dependencies of possible influences, such as gas velocity, sand quantity, and temperature, on thecharacteristic frequency. The results indicate that the characteristic frequency can be a promising and robust method for the earlydetection of the onset of agglomeration in industrial plants.

Image-based biomass characterization: Comparison of conventional image processing and a deep learning approach - Lukas, Johannes; Kolb, Sebastian; Heinbuch, Johann; Willig, Lukas; Plankenbühler, Thomas; Müller, Dominik; Karl, Jürgen (Fuel, 2023)
Due to the increased use of inconsistent low-grade biogenic solid fuels and simultaneously stricter regulatory limitations for biomass combustion, there is a rising demand for suitable methods for an online characterization of fuels to allow for optimal plant operation. Aim of this work is to evaluate two camera-based machine vision approaches using a conventional feature extraction method based classification and a deep learning approach with a convolutional neural network (CNN) to distinguish six types of biogenic solid fuels and 20 fuel mixtures thereof. A comparison of four machine-learning algorithms applied to classify the samples based on all extracted color and texture features reaches a prediction accuracy of 93.5 %. An evaluation of the feature performance of single features shows that the selected color features (color thresholds and histogram-based features) are more relevant for distinguishing the fuels than the textural features (Haralick features and features extracted from the frequency domain). Despite the small size of the image dataset, also the CNN achieves a good prediction accuracy of 73.7 % for the given classification task. An increase of the number of images by fragmentation leads to a slightly increasing prediction accuracy for the deep learning approach while the accuracy of the conventional features learning approach decreases. Both approaches are suitable to distinguish six typical biogenic solid fuels and achieve high accuracies for the classification of 26 fuels and mixtures. While the feature learning is more accurate for the mentioned classification task, the CNN does not require prior feature extraction and would benefit from a larger dataset. These are promising results for an implementation as an online fuel monitoring in various applications, with further development in the robustness and extensions by means of a broader range of fuel mixtures being required.
CRIS-Team Scopus Importer:2023-03-31
Keywords: Biomass combustion; Deep learning; Feature extraction; Fuel monitoring; Image processing; Machine vision