检索结果-内蒙古大学图书馆

BATCH process MONITORING THROUGH THE INTEGRATION OF SPECTRAL AND process DATA

IFAC Proceedings Volumes 2005年第1期38卷 13-18页

作者： Julian Morris Elaine Martin David Stewart Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU UK

Most applications of MSPC have tended to focus upon the manufacture of a single product with separate models being developed to monitor individual recipes. With process manufacturing trends being influenced by customer demands there has been an increase in the manufacture of a wide variety of products, there is a real need for process models which allow a range of products, grades or recipes to be monitored using a single process model. With increasing attention now being paid to the FDA process Analytical Technologies (PAT) initiative, the use of spectro-chemical information for enhanced monitoring of reactions and is now gaining impetus. An application of the performance monitoring of a multi-recipe multi-reactor industrial batch polymer manufacturing is discussed in which NIR spectroscopic data is also integrated with process data to provide enhanced batch monitoring.

关键词： Multivariate Statistical process control Spectroscopic Data Multi-recipe Batch Reactors

来源：评论

学校读者我要写书评

暂无评论

Progressive PCA modeling for enhanced fault diagnosis in a batch process

Progressive PCA modeling for enhanced fault diagnosis in a b...

引用

International Conference on control, Automation and Systems ( ICCAS)

作者： Jeong Jin Hong Jie Zhang Centre for Process Analytics and Control Technology (CPACT) School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

The conventional process monitoring procedure using principal component analysis (PCA) can show which variable is highly related with the fault by looking at the contribution plots for the monitoring statistics, SPE (squared prediction errors) and T 2 . However, this procedure is not able to determine if the variable is just affected by the fault or the variable is the cause of the fault. In addition, it is not able to show fault propagation through the process variables during the process time. The proposed progressive PCA modeling procedure can identify all variables related to the fault through progressively removing the identified variables and PCA modeling with the remaining variables. It can also provide timing information of when abnormal behaviors are observed for the identified variables by using time series SPE plots with control limits estimated by weighted chi-squared distribution. Based on the timing information, it is able to build a flow chart showing the fault propagation paths. The proposed method is demonstrated on a benchmark fed-batch penicillin process simulator.

关键词： Principal component analysis Time series analysis Monitoring Substrates Data models Timing Feeds

来源：评论

学校读者我要写书评

暂无评论

Towards the Robust Application of PAT in Real Time control

引用

IFAC Proceedings Volumes 2010年第6期43卷 1-6页

作者： Julian Morris Zengping Chen David Lovett Centre for Process Analytics & Control Technology Newcastle University. UK State Key Laboratory of Chemo/Biosensing & Chemometrics Hunan University China Perceptive Engineering Daresbury Innovation Centre Daresbury Cheshire UK

process Analytical Technologies (PAT) are increasingly being explored and adopted by pharma-chem and bio-pharma companies for enhanced process understanding, Quality by Design (QbD) and Real-time-Release (RTR). To achieve these aspirations there is a critical need to extract the most information, and hence understanding, from complex and often very ‘messy’ spectroscopic data. A number of new approaches will be shown to overcome the limitations of existing calibration/modelling methodologies and algorithms and their use in some industrial applications will be presented.

关键词： process Analytical Technologies Spectroscopic Data Chemometrics Calibration process control

来源：评论

学校读者我要写书评

暂无评论

Data Filtering and Statistical process Modeling

引用

Tsinghua Science and technology 2000年第2期5卷 193-200页

作者：王京春金以慧 Department of Automation Tsinghua University Beijing 100084 China Foresight Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne NEI 7RU UK

A new filtering method is presented which extends the SureShrink algorithm by eliminating the peak noise in the wavelet transformed signal to improve the overall filtering properties. Data from industrial plants always contain some peak noise, but ‘denoise’ algorithms such as ‘SureShrink’ can have difficulty in handling sudden large excursions in the corrupting noise. In the new algorithm the peak noise is reduced prior to filtering using the SureShrink algorithm. The pre screened data can be used to build a number of projections to latent structures regression models. Data from an industrial fluidized bed reactor is used to evaluate the new algorithm, which demonstrates improved performance in terms of improved modeling capability through use of the new data pre filtering algorithm.

关键词： wavelet transform projection to latent structures modeling

来源：评论

学校读者我要写书评

暂无评论

State and Parameter Estimation Through Dynamic Bayesian Forecasting

引用

IFAC Proceedings Volumes 2004年第1期37卷 917-922页

作者： Zhen Lu Elaine Martin Julian Morris Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU England UK

Successful application of model based control depends on having good estimates for the system dynamic states and parameters. A multivariate dynamic linear model is developed for the estimation of the states from limited measurements in a non-linear system comprising model uncertainties. Since the noise statistics are rarely available a priori, the noise covariance matrix is treated as a tuning parameter and determined through repeated simulations. For non-linear, time varying processes, the assumption of a constant process noise covariance matrix does not realise accurate estimates. In this paper Monte Carlo simulations are used to obtain the time-varying noise covariance matrix. The methodology is demonstrated on a benchmark polymerisation process.

关键词： State estimation Parameter estimation Dynamic linear model Monte Carlo simulation Extended Kalman filter

来源：评论

学校读者我要写书评

暂无评论

Batch Monitoring Through Common Subspace Models

引用

IFAC Proceedings Volumes 2004年第1期37卷 287-292页

作者： S. Lane E.B. Martin A.J. Morris Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU England UK

Multi-way statistical projection techniques have typically been applied in the development of monitoring models for single recipe or single grade production. As defined, implementation of these techniques in multi-product applications necessitates the development of a large number of process models. This issue can be overcome through the use of common sub-space models constructed by pooling the individual variance-covariance matrices. A second issue with multi-way approaches is the difficulty of interpreting multiway contribution plots. An alternative approach is the U2 statistic. In this paper an extension is proposed, the V2 statistic, based on the cumulative contribution of variables at each sample point. The methodologies are demonstrated on two industrial applications.

关键词： Modelling Statistical Analysis Manufacturing processes

来源：评论

学校读者我要写书评

暂无评论

Monitoring Performance in Flexible process Manufacturing

引用

IFAC Proceedings Volumes 2004年第1期37卷 47-54页

作者： E.B. Martin A.J. Morris Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU England.

Partial Least Squares (PLS) is a popular method for the development of a framework for the detection and location of process deviations. A limitation of the approach is that it has generally been used to monitor one recipe, one product, for example, consequently applications may have been ignored because of the need for a large number of process models to monitor multi-product production. This paper introduces two extensions - multi-group and multi-group-multi-block PLS. These techniques enable a number of similar products, manufactured across different unit processes, to be monitored using a single model. The methodologies are demonstrated by application to a multi-recipe industrial manufacturing process.

关键词： Modelling Statistical Analysis Manufacturing processes

来源：评论

学校读者我要写书评

暂无评论

Adaptive Partial Least Squares with Application to process Monitoring

引用

IFAC Proceedings Volumes 2003年第5期36卷 789-794页

作者： S. Kumar S.N. Thennadil A.J. Morris E.B. Martin Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU England UK

Partial least squares has been applied in process modelling and process performance monitoring where a large number of correlated variables are recorded. The most commonly implemented algorithm for identifying a PLS model has been the batch algorithm. In this case, data from the process is first required to be stored. In this paper an algorithm is proposed that recursively updates the parameters of PLS as and when a sample of measurements is obtained thereby reducing the need for data storage. The algorithm was tested on an artificially generated data set. The approach was then used to derive a statistic for process monitoring that was applied for the detection of faults in a continuous stirred tank reactor.

关键词： Subspace methods Adaptive systems Fault detection

来源：评论

学校读者我要写书评

暂无评论

Investigation of Calibration-Free Resolution Techniques and Independent Component Analysis

引用

IFAC Proceedings Volumes 2004年第1期37卷 433-438页

作者： S. Triadaphillou A.J. Morris E.B. Martin I. Wells Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NEl 7RU UK Avecia Process Technology Department Grangemouth FK3 8XG UK

Calibration-free resolution techniques provide an alternative approach to the development of a calibration model. These combine spectroscopic measurement coupled with mathematical and statistical assumptions and give spectral profiles and non-quantitative concentration profiles for the unknown mixture. In this paper, a number of calibration free techniques including VARIMAX. ITTFA, EFA, FSWEFA, SIMPLISMA are described and applied to a synthetic spectral data set and the results are compared with the complementary technique of Independent Component Analysis (ICA) in particular FastICA and JADE. The results were comparable in all cases with ICA separating the signal from the constituent components successfully.

关键词： Reaction Monitoring and control Spectral Conjunction Spectral Deconvolution Multivariate Calibration Iterative Resolution Methods

来源：评论

学校读者我要写书评

暂无评论

Predicting the product yield profile and cracking degrees in an industrial ethylene pyrolysis furnace

Predicting the product yield profile and cracking degrees in...

引用

International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Qiang Zhou Jie Zhang Yongmao Xu Institute of Process Control Department of Automation Tsinghua University Beijing Beijing China Centre for process Analytics and Control Technology School of Chemical Engineering & Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

ISBN: (纸本)0780386531

This paper presents two soft-sensing models for predicting the product yields profile and the cracking degree of an ethylene pyrolysis furnace. The model based on single neural network with only one hidden layer trained by Levenberg-Marquardt algorithm with regularisation was first developed. It was found that the single neural network lack generalisation capability in that they can give undesirable performance when applied to unseen data. To improve the generalisation capability of the soft-sensing model, multi-model soft-sensors based on bootstrap aggregated neural networks with sequential training are used. In the sequential training of bootstrap aggregated networks, the first network is trained to minimise its prediction error whereas the rest of the networks are trained not only to minimise their prediction errors but also minimise the correlation among the trained networks. The overall output is obtained by combining all the individual networks. Application results show that the multi-model soft-sensors possess good generalisation capability in that they give good performance when applied to unseen data.

关键词： Furnaces Neural networks process control Predictive models Petrochemicals Chemical industry Automation Postal services Chemical analysis Automatic control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：