测量甲烷体积分数的高灵敏度光纤传感器研究

基于气体的红外吸收原理,研究了一种能在复杂环境下测量甲烷气体体积分数的新方法.这种方法可以有效地减小由于光源的不稳定、环境的变化而引起的测量误差.在这种测量方法中设计了一条参考测量光路,此参考测量光路和测量光路同时随环境的变化而变化,通过对待测量气体光路与参考气体测量光路作对比而得出较为准确的待测甲烷气体体积分数.     

基于灰色模型的高炉炉温预测

以高炉炉温预测应用性为基本出发点,以灰色理论为基础,当实际系统的历史数据序列呈现增长过快或下降过快时,就不能盲目使用GM（1,1）模型,否则,预测效果会不令人满意。只有当满足建模的一定条件时,误差才能够被接受。对数据进行开方处理后能使平均相对误差在10%左右,效果不错。     

小兴安岭7种松叶挥发油GC-MS数据的分析

用化学计量学方法分析小兴安岭7种松科植物松叶挥发油气相色谱-质谱(GC-MS)数据,为松叶挥发油的药理活性与其它的应用研究提供依据。(1)用直观推导式演进特征投影(heuristic evolving latent projections,HELP)法分辨重叠色谱峰,得各组分的纯色谱峰和质谱;(2)用正交投影法比较不同挥发油的GC-MS数据;(3)用峰面积归一化法确定各化合物的相对含量;(4)用主要化学成分进行聚类分析。7种松叶挥发油的化学成分为单萜、倍半萜及其衍生物和酯类化合物,按化学成分7种松叶挥发油可分为3类。     

新型咔唑衍生物密度泛函理论的研究

运用量子化学中的密度泛函和含时密度泛函理论法,计算1种新型咔唑衍生物,以探讨其几何构型、电子结构、前线分子轨道和电子光谱性质。计算结果表明当2-(4-吡啶基)乙烯基的反式构型与咔唑环作用时比顺式稳定,引入取代基后使化合物的HOMO和LUMO之间的能隙降低,吸收光谱红移。电子被激发时,电子从咔唑环向吡啶环转移,说明新型咔唑类化合物具有很好的光学性能,可以作为空穴传输材料。     

在线质谱分析仪的研制及应用

针对工业过程在线监测需求,研制了一种基于四极杆质量分析器的在线质谱分析系统,详细描述了该仪器的主要组成结构及各模块的功能特点,对该仪器进行了性能测试,结果显示该仪器技术指标已达到国际同类产品的指标要求。在钢铁冶金、石油化工以及生物制药等行业的应用结果表明,该系统具有响应时间快(<1 s),测量精度高,动态范围宽,可同时进行多流路多组分分析,维护成本低和可靠性高等突出优势,能够适用于多种复杂、恶劣的应用环境。     

基于自适应噪声抵消的CZ单晶炉炉膛温度信号处理

为滤除CZ单晶炉炉膛温度信号在单晶生长过程中存在的低频干扰,提出一种基于噪声抵消技术的滤波方法.首先利用傅立叶级数构造出低频干扰的逼近信号,然后根据炉温信号的缓变特征建立能够获取低频干扰逼近信号的抵消器误差函数,最后利用一种改进的粒子群优化算法优化误差函数获得低频干扰的逼近信号,并用该逼近信号抵消低频干扰.实验结果表明,所提出的自适应噪声抵消滤波算法能够有效滤除CZ单晶炉炉膛温度信号中的低频干扰,并优于常用的滤波方法.     

复杂高炉炼铁过程的数据驱动建模及预测算法

高炉炼铁过程的控制意味着控制高炉铁水温度及成份在指定的范围. 本文以高炉炉内热状态的重要指示剂---高炉铁水硅含量为研究对象, 针对机理建模难以准确预测、控制高炉铁水硅含量的发展变化, 利用数据驱动建模的思想, 建立了基于多元时间序列的高炉铁水硅含量数据驱动预测模型. 实例分析表明, 建立的数据驱动预测模型能够很好地预测高炉铁水硅含量, 连续预测167炉高炉铁水硅含量, 命中率高达83.23%, 预测均方根误差为0.07260. 这些指标均优于基于单一硅时间序列所建立的数据驱动模型, 对实际生产具有很好的指导作用.     

Thermodynamic evaluation of Cu– system based on newly determined Gibbs energy of formation of

Thermodynamic evaluation of Cu–Cu₃P system has been conducted by applying subregular solution model with Gibbs energy of Cu₃P formation that was newly determined by means of triple Knudsen cell mass spectrometry. Both the calculated vapor pressure of phosphorus and phase diagram of Cu–P system are excellently consistent with the literature data in the composition range of Cu–Cu₃P, indicating that there is no significant thermodynamic inconsistency between the present work based on the Gibbs energy of Cu₃P formation determined and the literature data.     

Effects of soil composition and mineralogy on remote sensing of crop residue cover

The management of crop residues (non-photosynthetic vegetation) in agricultural fields influences soil erosion and soil carbon sequestration. Remote sensing methods can efficiently assess crop residue cover and related tillage intensity over many fields in a region. Although the reflectance spectra of soils and crop residues are often similar in the visible, near infrared, and the lower part of the shortwave infrared (400-1900 nm) wavelength region, specific diagnostic chemical absorption features are evident in the upper shortwave infrared (1900-2500 nm) region. Two reflectance band height indices used for estimating residue cover are the Cellulose Absorption Index (CAI) and the Lignin-Cellulose Absorption (LCA) index, both of which use reflectances in the upper shortwave infrared (SWIR). Soil mineralogy and composition will affect soil spectral properties and may limit the usefulness of these spectral indices in certain areas. Our objectives were to (1) identify minerals and soil components with absorption features in the 2000 nm to 2400 nm wavelength region that would affect CAI and LCA and (2) assess their potential impact on remote sensing estimates of crop residue cover. Most common soil minerals had CAI values ≤ 0.5, whereas crop residues were always > 0.5, allowing for good contrast between soils and residues. However, a number of common soil minerals had LCA values > 0.5, and, in some cases, the mineral LCA values were greater than those of the crop residues, which could limit the effectiveness of LCA for residue cover estimation. The LCA of some dry residues and live corn canopies were similar in value, unlike CAI. Thus, the Normalized Difference Vegetation Index (NDVI) or similar method should be used to separate out green vegetation pixels. Mineral groups, such as garnets and chlorites, often have wide ranges of CAI and LCA values, and thus, mineralogical analyses often do not identify individual mineral species required for precise CAI estimation. However, these methods are still useful for identifying mineral soils requiring additional scrutiny. Future advanced multi- and hyperspectral remote sensing platforms should include CAI bands to allow for crop residue cover estimation.