共查询到17条相似文献,搜索用时 203 毫秒
1.
2.
3.
基于PCA和LS-SVM的丁苯橡胶的门尼粘度预测 总被引:3,自引:0,他引:3
门尼粘度足合成橡胶生产的主要质量指标,如何在线监测门尼粘度,并实现质量的自动监控是橡胶生产工业亟待解决的问题.本文应用主元分析和最小二乘支持向量机法建立生产过程门尼粘度预测模型.结合工艺机理分析,找出影响橡胶门尼粘度的主要参数并做主元分析,确定最少辅助变量,简化支持向最机结构,建立基于PCA LS-SVM的门尼粘度预测模型.仿真结果,门尼粘度预测值与实际值最大相对误差为5.78%,预测模型精度高,泛化能力强,运行速度快,可以指导生产. 相似文献
4.
门尼粘度是合成橡胶生产的主要质量指标,如何在线监测门尼粘度,并实现质量的自动监控是橡胶生产工业亟待解决的问题;针对强非线性、时变和间歇的合成橡胶复杂系统,结合工艺机理分析,建立了门尼粘度的基于机理、小波神经网络和最小二乘支持向量机等多种建模方法的混合智能软测量模型,解决了门尼粘度的实时在线测量问题,并开发出基于Borland C++与MATLAB的门尼粘度软测量应用软件。乙烯厂试用结果:混合智能软测量模型预测精度高,泛化能力强,运行速度快;应用软件结构明晰,功能扩充灵活,操作界面友好,操作维护方便;该系统能稳定生产、指导操作、提高产品的优级品率,为合成橡胶生产装置的优化控制创造了条件。 相似文献
5.
时变过程在线辨识的即时递推核学习方法研究 总被引:3,自引:0,他引:3
为了及时跟踪非线性化工过程的时变特性, 提出即时递推核学习 (Kernel learning, KL)的在线辨识方法. 针对待预测的新样本点, 采用即时学习 (Just-in-time kernel learning, JITL)策略, 通过构造累积相似度因子, 选择与其相似的样本集建立核学习辨识模型. 为避免传统即时学习对每个待预测点都重新建模的繁琐, 利用两个临近时刻相似样本集的异同点, 采用递推方法有效添加新样本, 并删减旧模型的样本, 以快速建立新即时模型. 通过一时变连续搅拌釜式反应过程的在线辨识, 表明了所提出方法在保证计算效率的同时, 较传统递推核学习方法提高了辨识的准确程度, 能更好地辨识时变过程. 相似文献
6.
7.
混炼胶粘度测量仪的设计 总被引:1,自引:0,他引:1
提出了一种在线测量混炼胶粘度的新方法。该方法以对角递归神经网络为基础,综合考虑混炼过程各因素对胶料粘度的影响,建立起胶料粘度的软测量模型。根据此方法,设计了以数字信号处理器(DSP)为核心的在线测量仪,并进行了现场试验,试验结果表明:与数学建模方法相比,该仪器能更好地实现混炼胶粘度的实时测量。 相似文献
8.
9.
苛性碱溶液浓度是氧化铝生产过程中的重要运行指标,由于苛性碱溶液的温度和浓度频繁波动,导致目前的浓度检测仪表检测精度低,只能采用人工化验获得苛性碱浓度值,化验结果的严重滞后导致无法实现苛性碱浓度的自动控制,影响氧化铝产品质量.在分析苛性碱溶液浓度控制过程动态特性的基础上建立了由线性模型和未知非线性动态系统描述的苛性碱浓度预报模型,将参数辨识与自适应深度学习相结合,提出端边云协同的氧化铝生产过程苛性碱浓度智能预报方法,并采用氧化铝生产企业的实际生产数据对本文所提方法进行应用验证.应用结果表明,所提的苛性碱浓度智能预报方法可以实时、准确预报苛性碱浓度,为实现苛性碱浓度的闭环运行优化控制创造了条件. 相似文献
10.
11.
Multi-grade processes have played an important role in the fine chemical and polymer industries. An integrated nonlinear soft sensor modeling method is proposed for online quality prediction of multi-grade processes. Several single least squares support vector regression (LSSVR) models are first built for each product grade. For online prediction of a new sample, a probabilistic analysis approach using the statistical property of steady-state grades is presented. The prediction can then be obtained using the corresponding LSSVR model if its probability of the special steady-state grade is large enough. Otherwise, the query sample is considered located in the transitional mode because it is not similar to any steady-state grade. In this situation, a just-in-time LSSVR (JLSSVR) model is constructed using the most similar samples around it. To improve the efficiency of searching for similar samples of JLSSVR, a strategy combined with the characteristics of multi-grade processes is proposed. Additionally, the similarity factor and similar samples of JLSSVR can be determined adaptively using a fast cross-validation strategy with low computational load. The superiority of the proposed soft sensor is first demonstrated through a simulation example. It is also compared with other soft sensors in terms of online prediction of melt index in an industrial plant in Taiwan. 相似文献
12.
J. Smrekar D. Pandit M. Fast M. Assadi Sudipta De 《Neural computing & applications》2010,19(5):725-740
Accurate modeling of thermal power plant is very useful as well as difficult. Conventional simulation programs based on heat
and mass balances represent plant processes with mathematical equations. These are good for understanding the processes but
usually complicated and at times limited with large number of parameters needed. On the other hand, artificial neural network
(ANN) models could be developed using real plant data, which are already measured and stored. These models are fast in response
and easy to be updated with new plant data. Usually, in ANN modeling, energy systems can also be simulated with fewer numbers
of parameters compared to mathematical ones. Step-by-step method of the ANN model development of a coal-fired power plant
for its base line operation is discussed in this paper. The ultimate objective of the work was to predict power output from
a coal-fired plant by using the least number of controllable parameters as inputs. The paper describes two ANN models, one
for boiler and one for turbine, which are eventually integrated into a single ANN model representing the real power plant.
The two models are connected through main steam properties, which are the predicted parameters from boiler ANN model. Detailed
procedure of ANN model development has been discussed along with the expected prediction accuracies and validation of models
with real plant data. The interpolation and extrapolation capability of ANN models for the plant has also been studied, and
observed results are reported. 相似文献
13.
汽油属性的在线预测多采用无偏估计方法建立的近红外定量分析模型实现,累积预测误差的正负偏差范围难以控制,这会严重影响汽油调合优化控制的投运效果.针对这一问题,本文提出了一种采用有偏估计实现油品属性在线预测的方法.首先从最小最大概率学习机出发,提出了有偏最小最大概率回归模型.然后利用即时学习方法设计了有偏回归模型的局部建模与更新策略,用以提高回归模型的自适应能力.最后在国内某炼厂汽油调合过程中采集的工业数据上进行实验,结果表明该方法与传统方法相比具有明显优势,有利于大幅度提高调合优化控制的投运率. 相似文献
14.
瓦斯涌出量的混合pi-sigma模糊神经网络预测模型 总被引:1,自引:0,他引:1
提出了一种利用混合pi-sigma模糊神经推理方法建立瓦斯涌出量的预测模型。该模型采用高斯基函数作为模糊子集的隶属度函数, 可在线动态调整隶属度函数和结论参数。与神经网络预测模型比较, 该模型具有物理意义明确、原理清晰、收敛速度快、预测精度高等特点,在对某矿瓦斯涌出量数据的仿真结果表明,该方法预测准确度高、速度快,并且结果具有可重复性,证明该方法是有效的。为便于工程实际应用, 在Matlab环境中开发了基于图形用户界面(GUI)的仿真应用界面,给出了使用方法和预测结果。实验同时表明,对所采用的数据,模型的训练精度设置为0.001时网络的泛化能力最好,网络训练精度和预测精度之间不具有正比关系。 相似文献
15.
Changxin Liu Jinliang Ding Anthony J. Toprac Tianyou Chai 《Knowledge and Information Systems》2014,41(2):401-421
A data-based adaptive online prediction model is proposed for plant-wide production indices based on support vector regression, a general method which we customized specifically to model very large data sets that are generated dynamically and periodically. The proposed model can update its parameters online according to the statistical properties of the training samples. Further, in order to improve the prediction precision, each sample is weighted with a dynamic penalty factor that considers the effect of each sample on the prediction model accuracy. Moreover, a customized procedure is introduced to handle large training sets. After having been convincingly evaluated on benchmark data, effectiveness and performance of our approach for plant-wide production indices is demonstrated using industrial data from an operating ore dressing plant over a range of scale in training data set size. The higher accuracy and shorter computation times than existing methods suggest that it may prove advantageous in actual application to dynamic production processes. 相似文献
16.
A finite element method for the analysis of rubber parts, experimental and analytical assessment 总被引:1,自引:0,他引:1
A finite element method for the analysis of non linear rubber type parts is developed. Although using a penalty function approach, it is not based on reduced integration techniques but on a reduced constraint concept.
We compare computations to a closed form solution for a Mooney material. Finally computations of a test rubber part using a 9 coefficient Rivlin Law are compared to measurements. 相似文献
17.
烧结终点位置(BTP)是烧结过程至关重要的参数,直接决定着最终烧结矿的质量.由于BTP难以直接在线检测,因此,通过智能学习建模来实现BTP的在线预测并在此基础上进行操作参数调节对提高烧结矿质量具有重要意义.针对这一实际工程问题,首先提出一种基于遗传优化的Wrapper特征选择方法,可选取使后续预测建模性能最优的特征组合;在此基础上,为了解决单一学习器容易过拟合的问题,提出了基于随机权神经网络(RVFLNs)的稀疏表示剪枝(SRP)集成建模算法,即SRP-ERVFLNs算法.所提算法采用建模速度快、泛化性能好的RVFLNs作为个体基学习器,采用对基学习器基函数与隐层节点数等参数进行扰动的方式来增加集成学习子模型间的差异性;同时,为了进一步提高集成模型的泛化性能与计算效率,引入稀疏表示剪枝算法,实现对集成模型的高效剪枝;最后,将所提算法用于烧结过程BTP的预测建模.工业数据实验表明,所提方法相比于其他方法具有更好的预测精度、泛化性能和计算效率. 相似文献