Comparison of the Depth Accuracy of a Plenoptic Camera and a Stereo Camera System in Spatially Tracking Single Refuse-derived Fuel Particles in a Drop Shaft

With the development of depth cameras in the last decades, several cameras are able to acquire 3D information of the captured scenes, such as plenoptic camera and stereo camera system. Because of the differences in principle and construction of various depth cameras, different cameras own particular advantages and disadvantages. Therefore, a comprehensive and detailed comparison of different cameras is essential to select the right camera for the application. Our research compared the depth accuracy and stability of a stereo camera system and a plenoptic camera by monitoring the settling processes of various refuse-derived fuel particles in a drop shaft. The particles are detected at first using detection approaches, and the particle detections are subsequently associated in accordance with data association algorithms. The spatial particle trajectories are obtained by the tracking-by-detection approach, based on which the performances of the cameras are evaluated

Oscillating Combustion - Primary Measure to Reduce Nitrogen Oxide in a Grate Furnace - Experiments and Simulations

The emission from industries and the mobility sector is under strong legal regulations in many countries worldwide. In Germany, the amendment to the 17th BlmSchV (Federal pollution control ordinance), which has been in force for waste incineration plants since 2013, has given rise to a new limit for nitrogen oxides of 150 mg/m3 as the daily mean level from 2019 on. A similar focus is on biomass-fired plants. According to the MCP (medium combustion plant) guideline of the EU, as a consequence, existing plants are required to either increase their consumption of ammonia water for nitrogen oxide reduction (SNCR process) or back fit SCR catalysts as secondary measures, which is a costly procedure. This paper presents a novel two-stage process in which an oscillating supply of secondary air allows nitrogen oxides to be reduced by approx. 50% at a good burnout level, which may obviate the need for secondary measures. Besides experimental investigations in a fixed bed reactor, CFD simulations confirm a high potential for reduction of nitrogen oxides. Together with the company POLZENITH, this process is under development for scale-up in a biomass incineration plant as a next step

A Novel Plenoptic Camera-Based Measurement System for the Investigation into Flight and Combustion Behavior of Refuse-Derived Fuel Particles

In the past several decades, refuse-derived fuels (RDFs) have been widely applied in industrial combustion processes, for instance, in cement production. Since RDF is composed of various waste fractions with complex shapes, its flight and combustion behaviors can be relatively complicated. In this paper, we present a novel plenoptic camera-based spatial measurement system that uses image processing approaches to determine the dwell time, the space-sliced velocity in the depth direction, and the ignition time of various applied RDF fractions based on the obtained images. The image processing approach follows the concept of tracking-by-detection and includes a novel combined detection method, a 2.5D multiple particle tracking algorithm, and a postprocessing framework to tackle the issues in the initial tracking results. The thereby obtained complete spatial fuel trajectories enable the analysis of the flight behaviors elaborated in the paper. The acquired particles’ properties (duration, velocity, and ignition time) reversely prove the availability and applicability of the developed measurement system. The adequacy and accuracy of the proposed novel measurement system are validated by the experiments of detecting and tracking burning and nonburning fuel particles in a rotary kiln. This new measurement system and the provided experimental results can benefit a better understanding of the RDF’s combustion for future research

Numerical modelling of the separation of complex shaped particles in an optical belt sorter using a DEM-CFD approach and comparison with experiments

In the growing field of bulk solids handling, automated optical sorting systems are of increasing importance. However, the initial sorter calibration is still very time consuming and the precise optical sorting of many materials still remains challenging. In order to investigate the impact of different operating parameters on the sorting quality, a numerical model of an existing modular optical belt sorter is presented in this study. The sorter and particle interaction is described with the Discrete Element Method (DEM) while the air nozzles required for deflecting undesired material fractions are modelled with Computation Fluid Dynamics (CFD). The correct representation of the resulting particle–fluid interaction is realized through a one–way coupling of the DEM with CFD. Complex shaped particle clusters are employed to model peppercorns also used in experimental investigations. To test the correct implementation of the utilized models, the particle mass flow within the sorter is compared between experiment and simulation. The particle separation results of the developed numerical model of the optical sorting system are compared with matching experimental investigations. The findings show that the numerical model is able to predict the sorting quality of the optical sorting system with reasonable accuracy

Improving optical sorting of bulk materials using sophisticated motion models

Visuelle Eigenschaften sind mächtige Merkmale zur Klassifikation von Schüttgütern, auf Basis derer man defekte oder unbrauchbare Teilchen erkennen kann. Die Verwendung optischer Bandsortieranlagen ist eine etablierte Technik zur Sortierung basierend auf diesen Merkmalen. Derartiger Sortierer leiden jedoch unter Verzögerungen zwischen der gleichzeitigen Klassifikation und Lokalisierung und der darauffolgenden Separation. Dadurch entsteht die Notwendigkeit für akkurate Modelle der Teilchenbewegung, mittels derer diese Lücke überbrücktwerden kann. In dieser Veröffentlichung stellen wir unser Konzept vor, mittels hochentwickelter Simulationen genaue Modelle herzuleiten und den Teilchenstrom durch Optimierungen im Design des Sortierers zu verbessern. Dies ermöglicht die Verbesserung der Sortiergüte und Kosteneffizienz. Abschließend präsentieren wir erste Ergebnisse

Real-time multitarget tracking for sensor-based sorting – A new implementation of the auction algorithm for graphics processing units

Utilizing parallel algorithms is an established way of increasing performance in systems that are bound to real-time restrictions. Sensor-based sorting is a machine vision application for which firm real-time requirements need to be respected in order to reliably remove potentially harmful entities from a material feed. Recently, employing a predictive tracking approach using multitarget tracking in order to decrease the error in the physical separation in optical sorting has been proposed. For implementations that use hard associations between measurements and tracks, a linear assignment problem has to be solved for each frame recorded by a camera. The auction algorithm can be utilized for this purpose, which also has the advantage of being well suited for parallel architectures. In this paper, an improved implementation of this algorithm for a graphics processing unit (GPU) is presented. The resulting algorithm is implemented in both an OpenCL and a CUDA based environment. By using an optimized data structure, the presented algorithm outperforms recently proposed implementations in terms of speed while retaining the quality of output of the algorithm. Furthermore, memory requirements are significantly decreased, which is important for embedded systems. Experimental results are provided for two different GPUs and six datasets. It is shown that the proposed approach is of particular interest for applications dealing with comparatively large problem sizes

Measurement of drag coefficients of non-spherical particles with a camera-based method

The current paper presents a novel experimental set-up which allows the automated determination of the drag coefficients of relatively large particles with complex shape. Typical examples of such types of particles are waste derived fuel (RDF) particles which are non-spherical and have a size up to a few centimeters. In contrast to conventional fossil fuel particles, where the particles may be considered as material points during the calculation of particle tracks in a reacting flow field, the spatial extent of RDF-particles and their lack of sphericity lead to pronounced self-induced movement and associated variations in the drag-coefficients. The experiments are based on a drop shaft equipped with two digital cameras. This allows to obtain time resolved stereo image sequences from which the settling velocity of particles, the self-induced velocity fluctuations and the corresponding drag and lift coefficients can be derived. As the system is automated, a large number of particles can be examined and statistical information on the distribution of drag coefficients can be obtained. In this publication, the methodology of these drop shaft measurements and their evaluation will be presented. Additionally drag coefficients of isometric spherical and non-spherical particle geometries (spheres, cubes, square plates and circular disks) were measured and compared with known correlations for drag coefficients. Probability density functions for the properties of typical RDF particles will be presented to highlight the potential of the new set-up