کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8072917 | 1521434 | 2016 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modeling of chemical exergy of agricultural biomass using improved general regression neural network
ترجمه فارسی عنوان
مدلسازی اگزرژی شیمیایی زیست توده کشاورزی با استفاده از شبکه عصبی رگرسیون عمومی بهبود یافته
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کلمات کلیدی
اگزرژی شیمیایی، زیست توده، مدل شبکه عصبی مصنوعی، ترکیب عنصر، الگوریتم پالایش خودکار،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
چکیده انگلیسی
A comprehensive evaluation for energy potential contained in agricultural biomass was a vital step for energy utilization of agricultural biomass. The chemical exergy of typical agricultural biomass was evaluated based on the second law of thermodynamics. The chemical exergy was significantly influenced by C and O elements rather than H element. The standard entropy of the samples also was examined based on their element compositions. Two predicted models of the chemical exergy were developed, which referred to a general regression neural network model based upon the element composition, and a linear model based upon the high heat value. An auto-refinement algorithm was firstly developed to improve the performance of regression neural network model. The developed general regression neural network model with K-fold cross-validation had a better ability for predicting the chemical exergy than the linear model, which had lower predicted errors (±1.5%).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 114, 1 November 2016, Pages 1164-1175
Journal: Energy - Volume 114, 1 November 2016, Pages 1164-1175
نویسندگان
Y.W. Huang, M.Q. Chen, Y. Li, J. Guo,