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基于原料组分的能源草厌氧发酵产气预测模型

  • 胡克勤 ,
  • 李连华 ,
  • 孙永明 ,
  • 孔晓英 ,
  • 张 毅 ,
  • 袁振宏
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  • 1. 中国科学院广州能源研究所,中国科学院可再生能源重点实验室,广州 510640;
    2. 中国科学院大学,北京 100049
胡克勤(1988-),男,硕士,研究实习员,主要从事生物质能源转化与利用集成技术研究。

收稿日期: 2015-12-01

  修回日期: 2016-01-25

  网络出版日期: 2016-04-29

基金资助

国家高技术研究发展计划(863计划)(2010AA101802);
农业科技成果转化资金项目(2014GB2E000034)

The Methane Yield Forecasting Model of Energy Crops in Anaerobic Digestion Based on Feedstock Components

  • HU Ke-qin ,
  • LI Lian-hua ,
  • SUN Yong-ming ,
  • KONG Xiao-ying ,
  • ZHANG Yi ,
  • YUAN Zhen-hong
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  • 1. Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2015-12-01

  Revised date: 2016-01-25

  Online published: 2016-04-29

摘要

基于原料的组分,运用线性回归的方法建立能源草厌氧发酵产气预测模型。以巴西象草、华南象草、矮象草、台牧B和七种不同月份收割的杂交狼尾草为样本,以组分C含量、N含量、C/N、纤维素含量、半纤维素含量以及木质素含量为自变量,以该能源草的累计产气率为因变量。一元线性分析结果显示,C元素含量、半纤维素含量和累积产气率之间的显著相关性较弱(R2 = 0.02,R2 = 0.03);C/N、纤维素含量与产气率之间有一定的显著相关性(R2 = 0.37,R2 = 0.313);N元素含量、木质纤维素含量和产气率之间的显著相关性较好(R2 = 0.461,R2 = 0.51)。通过多元线性回归分析,得出了两个置信度较高、相关性显著和误差较小的模型(R2 = 0.779,R2 = 0.783),并通过曲线拟合和标准误差计算,分析了模型的准确性,证实模型可靠。

本文引用格式

胡克勤 , 李连华 , 孙永明 , 孔晓英 , 张 毅 , 袁振宏 . 基于原料组分的能源草厌氧发酵产气预测模型[J]. 新能源进展, 2016 , 4(2) : 100 -104 . DOI: 10.3969/j.issn.2095-560X.2016.02.004

Abstract

The forecasting model of energy crops in anaerobic digestion was established based on feedstock component by using linear regression method. Taking brazil pennisetum purpureum, south pennisetum purpureum, dwarf pennisetum purpureum, Taiwan pasturage B and seven types of hybrid pennisetums harvested in different months as samples; the component of C, N, C/N, cellulose, hemicellulose, and lignin as independent variables; and the cumulative methane yield of energy crops as dependent variable. Linear regression find that methane yield have weak correlation against C and hemicellulose contents (R2 = 0.02, R2 = 0.03), certain correlation against C/N ratio and cellulose content (R2 = 0.37, R2 = 0.313), and strong correlation against N and lignin contents (R2 = 0.461, R2 = 0.51). By multiple linear regression analysis, we obtain two forecasting models which are high degree of confidence, significant correlation and small error (R2 = 0.779, R2 = 0.783). By curve fitting analysis and standard error calculation, the model is proved to be reliable.

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