基于近红外光谱的发酵过程湿度软测量技术研究.doc
约38页DOC格式手机打开展开
基于近红外光谱的发酵过程湿度软测量技术研究,1.96万字我自己原创的毕业论文，仅在本站独家提交，大家放心使用摘要 固态发酵是一个多相多变量、强耦合的非线性系统。在实际的固态发酵生产过程中，由于硬件检测设备缺乏和价格过高的原因，一些关键变量的信息只能通过离线检测获得，往往造成信息滞后，这严重制约了固态发酵系统控制性能的提高...
内容介绍
此文档由会员 曹操55 发布
基于近红外光谱的发酵过程湿度软测量技术研究
1.96万字
我自己原创的毕业论文，仅在本站独家提交，大家放心使用
摘要 固态发酵是一个多相多变量、强耦合的非线性系统。在实际的固态发酵生产过程中，由于硬件检测设备缺乏和价格过高的原因，一些关键变量的信息只能通过离线检测获得，往往造成信息滞后，这严重制约了固态发酵系统控制性能的提高。为有效提高固态发酵过程检测与控制的效率，本文以蛋白饲料固态发酵为研究对象，开展了基于近红外光谱技术的固态发酵过程检测研究，着重探讨了基于近红外光谱技术的固态发酵过程湿度软测量方法。
首先对获取的固态发酵物样本的近红外光谱，采用离散小波变换（DWT）结合主成分分析（PCA）对其进行滤噪和特征提取；然后利用提取的特征变量建立基于支持向量机（SVM）的固态发酵过程湿度软测量模型。研究结果表明，利用近红外光谱技术来实现固态发酵过程湿度软测量是可行的。
本研究为固态发酵过程检测与控制带来新思路，旨在提高固体发酵过程参数检测和过程状态监测的准确度和时效性，研究成果可为固态发酵过程监控仪器装备的研发提供研究基础
关键词：固态发酵 湿度 近红外光谱 支持向量机 软测量 过程检测
Soft sensor of humidity based on near infrared spectroscopy for fermentation process
Abstract Solid-state fermentation is a multi-phase and multi-variable nonlinear system with high coupling. Due to the lack of hardware detection equipment and the over-high price in the actual production process of solid-state fermentation , some key variables can only be obtained through off-line testing , resulting in information lag. This has seriously hampered the control performance of solid-state fermentation system from being improved. In order to improve the efficiency of process detection and control of solid-state fermentation (SSF), this work attempted to the feasibility and method of the measurement of process parameters of SSF of protein feed by use of near-infrared spectroscopy (NIRS) techniques. In addition, soft sensor of humidity in solid-state fermentation was also focused on by use of NIRS in this work.
Firstly, the raw spectra of all fermented samples obtained were preprocessed by use of the discrete wavelet transform (DWT), and the feature vectors were extracted by use of principal component analysis (PCA) from the spectral data preprocessed. Then, the identified model was developed by use of support vector machine(SVM). The overall results demonstrate that SVM is a prominent approach, and NIRS technique combined with SVM has high potential to monitor the process state of SSF in a no-invasion way.
This study provides a new idea for the process detection and control of SSF. The main aim of improving the accuracy and timeliness for the measurement of process parameters and the monitoring of process state of SSF has been achieved. The results in this work can provide research foundation for developing instruments and equipment for the monitoring of SSF process.
Keywords solid-state fermentation; humidity; near-infrared spectroscopy; support vector machine; soft sensor; process detection
目 录
第一章 绪 论 1
1.1 本文研究背景及对象 1
1.2 近红外光谱技术研究 2
1.2.1 近红外光谱技术概述 2
1.2.2 近红外光谱技术发展历程 2
1.2.3 近红外光谱技术特点 5
1.2.4 基于近红外光谱技术的发酵过程研究现状 7
1.3 基于支持向量机的软测量方法 8
1.4 本文主要研究内容 8
第二章 固态发酵过程试验及数据采集 12
2.1 引言 12
2.2 样品制备 12
2.2.1 实验材料 12
2.2.2 发酵实验 12
2.3 光谱采集 13
2.4 湿度测定 13
2.5 本章小结 14
第三章 基于近红外光谱技术的固态发酵软测量建模方法研究 15
3.1 样本划分 15
3.2 近红外光谱预处理 16
3.2.1 平滑处理 16
3.2.2 导数计算 17
3.3 近红外光谱滤噪及特征提取 18
3.3.1 离散小波变换 18
3.3.2 特征提取 20
3.4 近红外光谱分析技术步骤 22
3.5 支持向量回归建模 24
3.5.1 模型评价标准 24
3.5.2 基于支持向量回归（SVR）的模型建立及预测 24
3.6 本章小结 28
第四章 总结与展望 29
4.1 工作总结 29
4.2 研究展望 29
参考文献 31
致 谢 34
1.96万字
我自己原创的毕业论文，仅在本站独家提交，大家放心使用
摘要 固态发酵是一个多相多变量、强耦合的非线性系统。在实际的固态发酵生产过程中，由于硬件检测设备缺乏和价格过高的原因，一些关键变量的信息只能通过离线检测获得，往往造成信息滞后，这严重制约了固态发酵系统控制性能的提高。为有效提高固态发酵过程检测与控制的效率，本文以蛋白饲料固态发酵为研究对象，开展了基于近红外光谱技术的固态发酵过程检测研究，着重探讨了基于近红外光谱技术的固态发酵过程湿度软测量方法。
首先对获取的固态发酵物样本的近红外光谱，采用离散小波变换（DWT）结合主成分分析（PCA）对其进行滤噪和特征提取；然后利用提取的特征变量建立基于支持向量机（SVM）的固态发酵过程湿度软测量模型。研究结果表明，利用近红外光谱技术来实现固态发酵过程湿度软测量是可行的。
本研究为固态发酵过程检测与控制带来新思路，旨在提高固体发酵过程参数检测和过程状态监测的准确度和时效性，研究成果可为固态发酵过程监控仪器装备的研发提供研究基础
关键词：固态发酵 湿度 近红外光谱 支持向量机 软测量 过程检测
Soft sensor of humidity based on near infrared spectroscopy for fermentation process
Abstract Solid-state fermentation is a multi-phase and multi-variable nonlinear system with high coupling. Due to the lack of hardware detection equipment and the over-high price in the actual production process of solid-state fermentation , some key variables can only be obtained through off-line testing , resulting in information lag. This has seriously hampered the control performance of solid-state fermentation system from being improved. In order to improve the efficiency of process detection and control of solid-state fermentation (SSF), this work attempted to the feasibility and method of the measurement of process parameters of SSF of protein feed by use of near-infrared spectroscopy (NIRS) techniques. In addition, soft sensor of humidity in solid-state fermentation was also focused on by use of NIRS in this work.
Firstly, the raw spectra of all fermented samples obtained were preprocessed by use of the discrete wavelet transform (DWT), and the feature vectors were extracted by use of principal component analysis (PCA) from the spectral data preprocessed. Then, the identified model was developed by use of support vector machine(SVM). The overall results demonstrate that SVM is a prominent approach, and NIRS technique combined with SVM has high potential to monitor the process state of SSF in a no-invasion way.
This study provides a new idea for the process detection and control of SSF. The main aim of improving the accuracy and timeliness for the measurement of process parameters and the monitoring of process state of SSF has been achieved. The results in this work can provide research foundation for developing instruments and equipment for the monitoring of SSF process.
Keywords solid-state fermentation; humidity; near-infrared spectroscopy; support vector machine; soft sensor; process detection
目 录
第一章 绪 论 1
1.1 本文研究背景及对象 1
1.2 近红外光谱技术研究 2
1.2.1 近红外光谱技术概述 2
1.2.2 近红外光谱技术发展历程 2
1.2.3 近红外光谱技术特点 5
1.2.4 基于近红外光谱技术的发酵过程研究现状 7
1.3 基于支持向量机的软测量方法 8
1.4 本文主要研究内容 8
第二章 固态发酵过程试验及数据采集 12
2.1 引言 12
2.2 样品制备 12
2.2.1 实验材料 12
2.2.2 发酵实验 12
2.3 光谱采集 13
2.4 湿度测定 13
2.5 本章小结 14
第三章 基于近红外光谱技术的固态发酵软测量建模方法研究 15
3.1 样本划分 15
3.2 近红外光谱预处理 16
3.2.1 平滑处理 16
3.2.2 导数计算 17
3.3 近红外光谱滤噪及特征提取 18
3.3.1 离散小波变换 18
3.3.2 特征提取 20
3.4 近红外光谱分析技术步骤 22
3.5 支持向量回归建模 24
3.5.1 模型评价标准 24
3.5.2 基于支持向量回归（SVR）的模型建立及预测 24
3.6 本章小结 28
第四章 总结与展望 29
4.1 工作总结 29
4.2 研究展望 29
参考文献 31
致 谢 34
