کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5476775 | 1521420 | 2017 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prediction models for chemical exergy of biomass on dry basis from ultimate analysis using available electron concepts
ترجمه فارسی عنوان
مدل های پیش بینی اگزرژی شیمیایی بیوماس به صورت خشک از تجزیه و تحلیل نهایی با استفاده از مفاهیم الکترون های موجود
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کلمات کلیدی
اگزرژی شیمیایی، زیست توده، تجزیه و تحلیل نهایی، مدل پیش بینی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
چکیده انگلیسی
Prediction models, based on ultimate analysis of biomass on dry basis (db) which is leveraged to predict chemical exergy, were proposed in this study. A new concept - chemical exergy per equivalent of available electrons transferred to oxygen (reductance degree) of model 1 was established. The result shows that chemical exergy per reductance degree of model 1 is relatively constant for the values of most biomass (db) beyond the±1% relative error range. A modified reductance degree of biomass was presented, whereas oxygen (O) content was neglected due to its inaccurate value and the high p-value for the coefficient of O variable. Chemical exergy per modified reductance degree of models 2 and 3 was approximated to be nearly a constant. Thus, two theoretical prediction models (model 2 and model 3) for the biomass (db) with and without sulfate (920.08(C/3 + H + S/8), 920.72(C/3 + H)) were established, respectively. The coefficients of the two models are of almost the same value, which indicates that the S content has also a negligible effect on chemical exergy. Model 3 (920.72(C/3 + H)) is also herein proposed for prediction of exergy of biomass. The average relative errors of model 1, model 2 and model 3 are 2.882%, 0.643% and 0.634%, respectively.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 131, 15 July 2017, Pages 251-258
Journal: Energy - Volume 131, 15 July 2017, Pages 251-258
نویسندگان
Hongliang Qian, Weiwei Zhu, Sudong Fan, Chang Liu, Xiaohua Lu, Zhixiang Wang, Dechun Huang, Wei Chen,