摘要
检测生物质原料的灰分含量是高效转化能源的基础,但传统高温灼烧法测试耗时长、成本高,而近红外光谱分析技术能够实现无损、快速及低成本对未知样本定性或定量的分析。以5个地点、10种类型的1465份生物质原料样品为研究对象,应用“筛选分类集合法”将样品划分9个集合,构建近红外光谱生物质样品灰分含量模型。主要结果为:玉米秸秆(M)、小麦秸秆+玉米秸秆+棉花秸秆(WCM)和小麦秸秆+杂草+园林叶(WWL)主因子数分别为5、6和6;M集合的交叉验证决定系数(R^(2)_(cv))为0.975,WCM集合的预测决定系数(R_(p)^(2))为0.983,模型拟合度最高;长白皮+棉花秸秆集合(WC)的均方根标准误差(RMSE)最小分别为0.5887和0.4864,M集合的交叉验证相对分析误差(RPD_(cv))最高为6.3,WCM集合的预测相对分析误差(RPD_(p))最高为7.8,模型预测精度最高;M集合的交叉验证平均相对偏差ARD_(cv)最小为6%,WCM集合预测平均相对偏差ARD_(p)最小为8%,木质(W)集合RMSECV/RMSEP为1.01,模型稳健性最高;9个生物质样品灰分含量集合模型的R^(2)范围为0.7538~0.9794,建模集与预测集偏差较小均具有较好的线性关系,其中,H集合(R^(2)=0.9425)、M集合(R^(2)=0.9794)和WCM集合(R^(2)=0.9787)其拟合度与线性关系最优;L集合(木材边角料)的R^(2)最低,其值为0.7538,判断影响的主要因素是样品中含有泥沙、粘合剂和油漆等杂质。为解决常见生物质发电厂原料检测评估问题,利用9个生物质灰分集合模型对11种生物质样品计算平均相对偏差(ARD)进行预测评估,草质样品模型预测效果好(ARD范围为3.7%~16.5%)。应用“筛选分类集合法”划分样品集合来建立近红外光谱生物质灰分含量模型,其拟合度、稳健性和精确度都较全样品集合模型性能更高。
Detecting the ash content of biomass raw materials was the basis for efficient energy conversion.However,the traditional high-temperature calcination method was time-consuming and costly,while the near-infrared spectroscopy analysis technology could achieve non-destructive,rapid and low-cost qualitative,and quantitative analysis of unknown samples.This study used 1465 biomass raw material samples of 5 locations and 10 types as the research object.The sample set was divided into 9 sample sets by the“screening classification set method”to construct the ash content model of biomass samples by near-infrared spectroscopy.The main results were as follows:the best principal components of corn straw(M),wheat straw+corn straw+cotton straw(WCM),and wheat straw+weeds+garden leaves(WWL)were 5,6,and 6,respectively.The R^(2)_(cv)of corn straw(M)was 0.975,the R_(p)^(2) of WCM was 0.983,and the model fitting degree was the highest.The RMSE of the set of Changbai+cotton straw(WC)was 0.5887 and 0.4864,respectively.The highest ratio of prediction to deviation(RPD_(cv))of M was 6.3,and the highest ratio of prediction to deviation(RPD_(p))of WCM was 7.8.The minimum average relative deviation(ARD_(cv))of maize straw(M)collection was 6%,the minimum average relative deviation(ARD_(p))of maize straw and WCM collection was 8%,and the RMSECV/RMSEP of wood(W)collection was 1.01.The R 2 range of the set model of ash content of 9 biomass samples was 0.7538~0.9794,and there was a good linear relationship between the predicted value and the measured value.Among them,H_(set)(R^(2)=0.9425),M_(set)(R^(2)=0.9794)and the WCM set(R^(2)=0.9787)had the best fitting degree and linear relationship.The R^(2)of the L set(wood scrap)was the lowest,and its value was 0.7538.The main factor in judge the influence was that the sample contained impurities such as sediment,adhesive,and paint.In order to solve the problem of raw material detection and evaluation of common biomass power plants,9 biomass ash collection models were used to predict and evaluate th
作者
郭歌
张梦玲
巩志杰
张世壮
王晓玉
周仲华
杨玉
谢光辉
GUO Ge;ZHANG Meng-ling;GONG Zhi-jie;ZHANG Shi-zhuang;WANG Xiao-yu;ZHOU Zhong-hua;YANG Yu;XIE Guang-hui(College of Agronomy,Hunan Agricultural University,Changsha 410128,China;Hunan Institute of Agricultural Information and Engineering,Changsha 410125,China;College of Agronomy and Biotechnology,China Agricultural University,Beijing 100193,China;National Energy R&D Center for Non-Food Biomass,China Agricultural University,Beijing 100193,China;Hunan Intelligent Agriculture Engineering Technology Research Center,Changsha 410125,China;Hunan Industrial Technology Basic Public Service Platform,Changsha 410125,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2023年第10期3143-3149,共7页
Spectroscopy and Spectral Analysis
基金
水稻大豆玉米高活力种子筛选技术攻关与推广应用项目(湘财农指[2022]67号)
湖南省农业科技创新资金项目(2022CX112)
湖南省棉花产业技术体系栽培与良种繁育岗位专家项目(湘农发[2022]31号)资助。
关键词
生物质样品
筛选分类集合法
近红外光谱技术
快速检测
模型构建
Biomass samples
Screening classification set method
Near-infrared spectroscopy
Rapid detection
Model construction
