An experimental study is performed to investigate the ignition, devolatilization, and burnout of single biomass particles of various shapes and sizes under process conditions that are similar to those in an industrial combustor. A chargecoupled device (CCD) camera is used to record the whole combustion process. For the particles with similar volume (mass), cylindrical particles are found to lose mass faster than spherical particles and the burnout time is shortened by increasing the particle aspect ratio (surface area). The conversion times of cylindrical particles with almost the same surface area/volume ratio are very close to each other. The ignition, devolatilization, and burnout times of cylindrical particles are also affected by the oxidizer temperature and oxygen concentration, in which the oxygen concentration is found to have a more pronounced effect on the conversion times at lower oxidizer temperatures.

No abstract

How quickly large biomass particles can ignite and burn out when transported into a pulverized-fuel (pf) furnace and suddenly exposed to a hot gas flow containing oxygen is very important in biomass cofiring design and optimization. In this paper, the ignition and burnout of the largest possible biomass (pine wood) particles in a pf furnace (a few millimeters in diameter) are studied experimentally in a single particle combustion reactor rig, in which the ambient gas temperature and oxygen concentration can vary in the ranges 1473–1873 K and 5–20%, respectively. A one dimensional (1D) transient model is also developed to predict their conversion, in which the key processes inside the particle and in the boundary layer outside the particle are properly considered. For the pine wood particles in which large temperature gradients exist, the primary heterogeneous ignition is always detected for all the test conditions. As the particle is further heated and the volume-weighted average temperature reaches the onset of rapid decomposition of hemicellulose and cellulose, a secondary homogeneous ignition occurs. The model-predicted ignition delays and burnout times show a good agreement with the experimental results. Homogeneous ignition delays are found to scale with specific surface areas while heterogeneous ignition delays show less dependency on the areas. The ignition and burnout are also affected by the process conditions, in which the oxygen concentration is found to have a more pronounced impact on the ignition delays and burnout times at lower oxidizer temperatures.

Purpose Teeth are one of the most important anatomical components in ergonomic appearance of the face and crucial in craniofacial surgeries and dentistry. Besides, teeth have specific roles in forensic to identify death persons. In this paper, we propose a hybrid technique to classify teeth in computed tomography (CT) dental images. Method Our method consists of three steps: segmentation, feature extraction and classification. We segment the teeth by a hybrid approach including anatomical-based histogram thresholding, panaromic resampling and level-set techniques. In the second step, for each segmented tooth, we calculate the 2D discrete wavelet transform. We then determine 1D inverse discrete wavelet transform by the same mother wavelet, calculate energy of decomposition and approximation coefficients. Next, we utilize these coefficients as feature vectors for classification in the third step. Teeth classification is performed by a conventional feed forward neural network. Results The proposed method is evaluated in the presence of 180 teeth. Experimental results reveal that the technique is effective to automatically classify teeth, on an average, in more than 95% of the cases in both jaws. Conclusion Our method is independent of anatomical information such as the sequence and locality of the teeth in jaws. The techniques are applicable to both forensic and dentistry investigations.

A fully automated method for segmentation of neonatal skull in Magnetic Resonance (MR) images for source localization of electrical/magnetic encephalography (EEG/MEG) signals is proposed. Finding the source of these signals shows the origin of an abnormality. We propose a hybrid algorithm in which a Bayesian classifying framework is combined with a Hopfield Neural Network (HNN) for neonatal skull segmentation. Due to the non-homogeneity of skull intensities in MR images, local statistical parameters are used for adaptive training of Hopfield neural network based on Bayesian classifier error. The experimental results, which are obtained on high resolution T1-weighted MR images of nine neonates with gestational ages between 39 and 42 weeks, show 65% accuracy which consistently exhibits our scheme's superiority in comparison with previous neonatal skull segmentation methods.