用于估计木质纤维素的较高热值的基于木质素和萃取物含量的模型的评估:模型混合物的使用

IF 0.4 4区 化学 Q4 CHEMISTRY, MULTIDISCIPLINARY
Sevilay DEMIRCI
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引用次数: 0

摘要

纤维素、木质素和萃取物分别从木质素纤维素材料中分离出来,按一定比例混合,如泽雷克茎、榛子壳和苏格兰松。它们的高热值(hhv)是通过使用炸弹量热计系统来确定的。通过将这些混合比率应用于文献中的一些多元(非线性)线性回归(M(N)LR)和人工神经网络(ANN)模型来计算估计的hhv。根据本研究数据建立MLR3模型,该模型显示出最高R2(0.974),最低MAPE(0.012)和RMSE(0.278)值。比较分析中,与MLR3模型估计精度最接近的是MLR2 (R2:0.972, MAPE:0.066, RMSE:1.714)。包含二次项的MNLR和ANN方程显示出高达132.6%的偏差(ANN3)。这是由于较小的规模和较差的同质性的个别组的样本,从模型方程开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of lignin and extractive content based models used in estimation of higher heating value of lignocellulosics: Use of model mixtures
Cellulose, lignin and extractive material are mixed in certain proportions by having isolated from lignocellulosic materials, such as Zeyrek stem, hazelnut shell and Scotch pine, respectively. Their higher heating values (HHVs) are determined by using a bomb calorimeter system. Estimated HHVs are calculated by applying these mixture ratios to some Multiple (Non)-Linear Regression (M(N)LR) and Artificial Neural Network (ANN) models from the literature. MLR3 model is developed from the data of this study and this model reveals the highest R2 (0.974), lowest MAPE (0.012) and RMSE (0.278) values. The closest estimation accuracy to the MLR3 model is obtained from MLR2 (R2:0.972, MAPE:0.066 and RMSE:1.714) in the comparative analysis. MNLR and ANN equations containing quadratic terms are found to show deviations up to 132.6% (ANN3). It is attributed to the lower size and poor homogeneity of the individual group of samples from which model equations are developed.
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来源期刊
Revue Roumaine De Chimie
Revue Roumaine De Chimie 化学-化学综合
CiteScore
0.80
自引率
20.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal Revue Roumaine de Chimie (Roumanian Journal of Chemistry) was founded in 1956 under the name Revue de Chimie. Acad. R. P. R. from 1964, the title was modified in Revue Roumaine de Chimie (preserving the numbering of the volumes started in 1956). In 1997, the English translation of the title – Roumanian Journal of Chemistry – was also included on each issue.
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