No abstract

It is no doubt that the most important contributing cause of global efficiency of coal fired thermal systems is combustion efficiency. In this study, the relationship between the flame image obtained by a CCD camera and the excess air coefficient (k) has been modelled. The model has been obtained with a three-stage approach: 1) Data collection and synchronization: Obtaining the flame images by means of a CCD camera mounted on a 10 cm diameter observation port, k data has been coordinately measured and recorded by the flue gas analyzer. 2) Feature extraction: Gridding the flame image, it is divided into small pieces. The uniformity of each piece to the optimal flame image has been calculated by means of modelling with single and multivariable Gaussian, calculating of color probabilities and Gauss mixture approach. 3) Matching and testing: A multilayer artificial neural network (ANN) has been used for the matching of featureÀk.

Coal is still a strategic fuel for many developing countries. The environmental impact of emissions resulting from the widespread use of coal worldwide is a matter of serious debate. In this perspective, clean coal burning technologies are in demand. In this study, a measurement system that estimates emission from flame images in a domestic coal burner is proposed. The system consists of a charge-coupled device camera, image processing software (real time image acquisition, noise reduction and extracting features) and artificial intelligence elements (classification of features by neural networks). In feature extraction stage, only five flame region features (Gx, Gy , trace, L 2 and L ∞ norm) is extracted. Gcx and Gcy are the instantaneous change of the horizontal and vertical components of center mass of the flame image. These features are new concepts for emission estimation from the flame image. The proposed system makes a difference with its simpler structure and higher accuracy compared to its counterparts previously presented in the literature.

No abstract

In the present study, a new micellar nano LC-UV was, for the first time, reported for the separation and determination of five anions (chloride, nitrite, bromide, sulfate and nitrate) in 52 honey samples. Based on this approach, a graphene oxide-based monolithic column was prepared and applied for the samples. Various amounts of hexadecyltrimethyl-ammonium bromide (HTAB) in the mobile phase were used in order to optimize the separation conditions. The baseline separation was achieved using mobile phase with 25/75% (v/v) ACN/10 mM phosphate buffer at pH 3.4, while the amount of HTAB was optimized as 0.22 mM in the mobile phase. The whole method was validated and it leads to high sensitivity. The LOD values were found in the range of 0.02-0.22 µg/kg, while LOQ values were found in the range of 0.06-0.18 µg/kg. The method allowed to achieve sensitivity analyses of anionic content in 52 honey samples. All data were evaluated using a new algorithm for geographic origin discrimination. K-nearest neighbor algorithm (K-NN), cubic support vector classifier (K-DVS), and K-Mean cluster analysis were used for geographic origin discrimination of honeys. The accuracy of the whole model was calculated as 94.4% with the K-DVS method. The samples from five provinces were classified 100% correctly, while two of them were classified with one misclassification, with an accuracy of 89.9% and 83.3%, respectively.