Proximate analysis of sawdust using Near Infrared Spectroscopy and locally weighted partial least squares

被引：4
|
作者：
Sun Qi [1 ]
Yao Yan [1 ]
Cai Jinhui [1 ]
Zhu Yingying [2 ]
机构：
[1] China Jiliang Univ, Coll Metrol & Measurement Engn, Hangzhou, Zhejiang, Peoples R China
[2] Ningbo Univ, Maritime Coll, Ningbo, Zhejiang, Peoples R China
来源：
CUE 2015 - APPLIED ENERGY SYMPOSIUM AND SUMMIT 2015: LOW CARBON CITIES AND URBAN ENERGY SYSTEMS | 2016年 / 88卷
关键词：
NIRS; proximate analysis; sawdust; LW-PLS;
D O I：
10.1016/j.egypro.2016.06.085
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In recently years, biomass as a renewable and widely available source of energy has increasingly been used in power generation industry and the concept of bio-power is widely accepted. However, conventional methods for proximate analysis are time consuming and can only be performed in laboratory. In this paper, 110 biomass samples are collected and near infrared spectroscopy (NIRS) technology is applied to predict proximate analysis of samples. The data show that NIRS combined with the locally weighted partial least squares (LW-PLS) obtained better prediction results comparing to conventional methods like principal component regression (PCR) and partial least squares (PLS). (C) 2016 The Authors. Published by Elsevier Ltd.
引用
收藏
页码：600 / 607
页数：8
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