scholarly journals Real-Time Characterization of Crystal Shape and Size Distribution Based on Moving Window and 3D Imaging in a Stirred Tank

2019 ◽  
Vol 09 (02) ◽  
pp. 13-38
Author(s):  
Rui Zhang ◽  
Xuezhong Wang ◽  
Yang Zhang ◽  
Tao Liu
1987 ◽  
Vol 113 ◽  
Author(s):  
I. Odler ◽  
K.-H. Zysk

ABSTRACTIn primary flue gas desulphurization lime is introduced into the flame, in addition to modifications of the burners and/or equipment. An ash is thus obtained in which the sulphur present in the fuel becomes chemically bound, rather than leaving the heat-generating unit as gaseous SO2, a constituent of the flue gases. In the present paper the composition, particle shape and size distribution of a series of ashes formed in the primary flue gas desulphurization are reported. The options for their utilization need to be evaluated.


Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.


Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2123
Author(s):  
Makuachukwu F. Mbaegbu ◽  
Puspa L. Adhikari ◽  
Ipsita Gupta ◽  
Mathew Rowe

Determining gas compositions from live well fluids on a drilling rig is critical for real time formation evaluation. Development and utilization of a reliable mass spectrometric method to accurately characterize these live well fluids are always challenging due to lack of a robust and effectively selective instrument and procedure. The methods currently utilized need better calibration for the characterization of light hydrocarbons (C1–C6) at lower concentrations. The primary goal of this research is to develop and optimize a powerful and reliable analytical method to characterize live well fluid using a quadruple mass spectrometer (MS). The mass spectrometers currently being used in the field have issues with detection, spectra deconvolution, and quantification of analytes at lower concentrations (10–500 ppm), particularly for the lighter (<30 m/z) hydrocarbons. The objectives of the present study are thus to identify the detection issues, develop and optimize a better method, calibrate and QA/QC the MS, and validate the MS method in lab settings. In this study, we used two mass spectrometers to develop a selective and precise method to quantitatively analyze low level lighter analytes (C1–C6 hydrocarbons) with masses <75 m/z at concentrations 10–500 ppm. Our results suggest that proper mass selection like using base peaks with m/z 15, 26, 41, 43, 73, and 87, respectively, for methane, ethane, propane, butane, pentane, and hexane can help detect and accurately quantify hydrocarbons from gas streams. This optimized method in quadrupole mass spectrometer (QMS) will be invaluable for early characterization of the fluid components from a live hydrocarbon well in the field in real time.


Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3322
Author(s):  
Sara Alonso ◽  
Jesús Lázaro ◽  
Jaime Jiménez ◽  
Unai Bidarte ◽  
Leire Muguira

Smart grid endpoints need to use two environments within a processing system (PS), one with a Linux-type operating system (OS) using the Arm Cortex-A53 cores for management tasks, and the other with a standalone execution or a real-time OS using the Arm Cortex-R5 cores. The Xen hypervisor and the OpenAMP framework allow this, but they may introduce a delay in the system, and some messages in the smart grid need a latency lower than 3 ms. In this paper, the Linux thread latencies are characterized by the Cyclictest tool. It is shown that when Xen hypervisor is used, this scenario is not suitable for the smart grid as it does not meet the 3 ms timing constraint. Then, standalone execution as the real-time part is evaluated, measuring the delay to handle an interrupt created in programmable logic (PL). The standalone application was run in A53 and R5 cores, with Xen hypervisor and OpenAMP framework. These scenarios all met the 3 ms constraint. The main contribution of the present work is the detailed characterization of each real-time execution, in order to facilitate selecting the most suitable one for each application.


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