کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6474954 1424971 2017 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prediction of elemental composition of coal using proximate analysis
ترجمه فارسی عنوان
پیش بینی ترکیب عنصری زغال سنگ با استفاده از تجزیه و تحلیل تقریبی
کلمات کلیدی
زغال سنگ، تجزیه و تحلیل نزدیک، تجزیه و تحلیل نهایی، همبستگی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
چکیده انگلیسی


- Select data points of four different rank coals.
- Understanding the relation between ultimate analysis and proximate analysis.
- Validate the fitted correlations with another set of data.
- Offer a valuable tool to set up a coal-thermal-conversion-process model.

Ultimate analysis is an important property for fuel utilization. The experimental determination of ultimate analysis is sophisticated, long time consumed, and expensive, on the contrary, the proximate analysis can be run rapidly and easily. A variety of correlations to predict the ultimate analysis of biomass using the proximate analysis have been appeared, while there exists a few number of correlations to estimate the elemental compositions of coal using proximate analysis in the literature but were focused on the predicted model or dependent on the heating value of coal. According to the proximate analysis of four different ranks of coal, this study proposes a series of correlations which are classified to predict carbon, hydrogen, and oxygen compositions through using 300 data points and validated further by another set of 40 data points. These correlations have the R2 of 0.95, 0.91, and 0.65 corresponding to the measured contents of C, H, and O in anthracite, 0.93, 0.83, and 0.67 of C, H, and O in high-rank bituminous, 0.86, 0.61, and 0.71 of C, H, and O in subbituminous, and 0.92, 0.67, and 0.66 of C, H, and O in lignite, respectively. The main merit of the correlations is the ability to estimate elemental composition of different rank coals using the proximate analysis and thus offers a valuable tool to set up a coal-thermal-conversion-process model.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fuel - Volume 193, 1 April 2017, Pages 315-321
نویسندگان
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