检索结果-内蒙古大学图书馆

Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant

process BIOCHEMISTRY 2006年第9期41卷 2050-2057页

作者： Lee, Dae Sung Lee, Min Woo Woo, Seung Han Kim, Young-Ju Park, Jong Moon Pohang Univ Sci & Technol Sch Environm Sci & Engn Dept Chem Engn Adv Environm Biotechnol Res Ctr Pohang 790784 Gyeongbuk South Korea Hanbat Natl Univ Dept Chem Engn Taejon 305719 South Korea Kyungpook Natl Univ Dept Environm Engn Taegu 702701 South Korea

Partial least squares (PLS) has been extensively used in process monitoring and modeling to deal with many, noisy, and collinear variables. However, the conventional linear PLS approach may be not effective due to the fundamental inability of linear regression techniques to account for nonlinearity and dynamics in most chemical and biological processes. A hybrid approach, by combining a nonlinear PLS approach with a dynamic modeling method, is potentially very efficient for obtaining more accurate prediction of nonlinear process dynamics. In this study, neural network PLS (NNPLS) were combined with finite impulse response (FIR) and auto-regressive with exogenous (ARX) inputs to model a full-scale biological wastewater treatment plant. It is shown that NNPLS with ARX inputs is capable of modeling the dynamics of the nonlinear wastewater treatment plant and much improved prediction performance is achieved over the conventional linear PLS model. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： multivariate statistical process control neural network partial least squares (PLS) dynamic system nonlinear system wastewater treatment plant

来源：评论

学校读者我要写书评

暂无评论

Non-parametric confidence bounds for process performance monitoring charts

引用

JOURNAL OF process control 1996年第6期6卷 349-358页

作者： Martin, EB Morris, AJ UNIV NEWCASTLE UPON TYNE DEPT ENGN MATHNEWCASTLE TYNE NE1 7RUTYNE & WEARENGLAND UNIV NEWCASTLE UPON TYNE DEPT CHEM & PROC ENGNNEWCASTLE TYNE NE1 7RUTYNE & WEARENGLAND

statistical process control (SPC) provides a tool for achieving and maintaining product quality. In today's climate of major data monitoring campaigns there has been an increase in interest in the multivariate statistical projection techniques of principal components analysis and projection to latent structures for process performance monitoring. Within univariate SPC, techniques for identifying when a process is moving out of control are well established. Similar guidelines are required for multivariate statistical process control (MSPC). Two approaches will be discussed - Hotelling's T-2 statistic and a new approach, the M(2) statistic. Both approaches will be illustrated by application to a high pressure low density polyethylene tubular reactor and to a batch methyl methacrylate polymerisation reactor. Copyright (C) 1996 Elsevier Science Ltd

关键词： multivariate statistical process control fault detection and diagnosis confidence bounds

来源：评论

学校读者我要写书评

暂无评论

An error-in-variable projection to latent structure framework for monitoring technical systems with orthogonal signal components

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2014年 133卷 70-83页

作者： Li, Zhe Kruger, Uwe Wang, Xun Xie, Lei Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310018 Zhejiang Peoples R China Sultan Qaboos Univ Dept Mech & Ind Engn Muscat Oman Petr Inst Dept Math Abu Dhabi U Arab Emirates

This article introduces an error-in-variable (EIV) data structure for process monitoring models, that assumes the presence of source variables that can be correlated and uncorrelated to the system response variables. To identify such models, the paper proposes a different objective function for the projection to latent structure (PLS) approach. Compared to existing work, this modified PLS formulation does not remove uncorrelated, or orthogonal, components from the predictor set prior to the identification of a PLS model nor identify an initial PLS model and carry out a subsequent extraction of various correlated and orthogonal components from the predictor set. The proposed PLS algorithms extract latent components to predict the system response variables as accurately as possible after estimating the error covariance matrices using a maximum likelihood algorithm that is also introduced in this article. A detailed analysis of this extended PLS framework yields that the objective function is an iterative maximum redundancy formulation. The article finally shows that the developed algorithms here are computationally more efficient than existing PLS-based approaches. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Projection to latent structures Maximum redundancy Error-in-variables Maximum likelihood estimation Orthogonal signal components multivariate statistical process control

来源：评论

学校读者我要写书评

暂无评论

Application of Kernel Partial Least Square to Predict Biological Hydrogen Production by Enterobacter aerogenes

引用

JOURNAL OF NANOELECTRONICS AND OPTOELECTRONICS 2010年第2期5卷 203-207页

作者： Jo, Ji Hye Jo, Eun Mi Park, Donghee Lee, Dae Sung Woo, Seung Han Hanbat Natl Univ Dept Chem Engn Taejon 305719 South Korea Natl Renewable Energy Lab Biosci Ctr Golden CO 80401 USA Kyungpook Natl Univ Dept Environm Engn Taegu 702701 South Korea

Hydrogen production by dark fermentation, where hydrogenase enzymes catalyze the oxidation or evolution of molecular hydrogen from two protons (H(+)) and electrons, is an economic and environmentally friendly technology for producing clean energy. However, the long-term operations of a continuous anaerobic reactor for fermentative hydrogen production were frequently unstable. In this study, a kernel partial least squares (KPLS) algorithm is employed to develop an online estimation of the key process variables in a biological hydrogen production process by Enterobacter aerogenes in minimal time and with minimal cost. The KPLS approach is potentially very efficient for predicting key quality variables of nonlinear processes by mapping an original input space into a high-dimensional feature space. The proposed kernel-based algorithm could effectively capture the nonlinear relationship in the process variables and show far better performance in the prediction of the key process variable compared with the conventional linear PLS and other nonlinear PLS methods.

关键词： Enterobacter aerogenes Hydrogen Production multivariate statistical process control Partial Least Squares Kernel-Based Algorithms

来源：评论

学校读者我要写书评

暂无评论

Local and global principal component analysis for process monitoring

引用

JOURNAL OF process control 2012年第7期22卷 1358-1373页

作者： Yu, Jianbo Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

In this paper, a novel data projection method, local and global principal component analysis (LGPCA) is proposed for process monitoring. LGPCA is a linear dimensionality reduction technique through preserving both of local and global information in the observation data. Beside preservation of the global variance information of Euclidean space that principal component analysis (PCA) does, LGPCA is characterized by capturing a good linear embedding that preserves local structure to find meaningful low-dimensional information hidden in the high-dimensional process data. LGPCA-based T-2 (D) and squared prediction error (Q) statistic control charts are developed for on-line process monitoring. The validity and effectiveness of LGPCA-based monitoring method are illustrated through simulation processes and Tennessee Eastman process (TEP). The experimental results demonstrate that the proposed method effectively captures meaningful information hidden in the observations and shows superior process monitoring performance compared to those regular monitoring methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： process monitoring multivariate statistical process control Manifold learning Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Reconstruction-based multivariate contribution analysis for fault isolation: A branch and bound approach

引用

JOURNAL OF process control 2012年第7期22卷 1228-1236页

作者： He, Bo Yang, Xianhui Chen, Tao Zhang, Jie Univ Surrey Div Civil Chem & Environm Engn Guildford GU2 7XH Surrey England Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Newcastle Univ Sch Chem Engn & Adv Mat Newcastle Upon Tyne NE1 7RU Tyne & Wear England

Identification of faulty variables is an important component of multivariate statistical process monitoring (MSPM);it provides crucial information for further analysis of the root cause of the detected fault. The main challenge is the large number of combinations of process variables under consideration, usually resulting in a combinatorial optimization problem. This paper develops a generic reconstruction based multivariate contribution analysis (RBMCA) framework to identify the variables that are the most responsible for the fault. A branch and bound (BAB) algorithm is proposed to efficiently solve the combinatorial optimization problem. The formulation of the RBMCA does not depend on a specific model, which allows it lobe applicable to any MSPM model. We demonstrate the application of the RBMCA to a specific model: the mixture of probabilistic principal component analysis (PPCA mixture) model. Finally, we illustrate the effectiveness and computational efficiency of the proposed methodology through a numerical example and the benchmark simulation of the Tennessee Eastman process. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Branch and bound Combinatorial optimization Fault detection and isolation multivariate contribution analysis multivariate statistical process control

来源：评论

学校读者我要写书评

暂无评论

Online monitoring of bioprocesses via multivariate sensor prediction within swarm intelligence decision making

引用

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2015年 145卷 48-59页

作者： Krause, D. Hussein, M. A. Becker, T. Tech Univ Munich Res Grp Bioproc Anal Technol Ctr Life & Food Sci Weihenstephan D-85354 Freising Weihenstephan Germany

In this work, a methodology combining process knowledge with computational efforts is presented. The aim is to create a self-organizing sensor network capable of getting over sensor failures. First, multivariate linear and nonlinear models are utilized creating a search space based on the multi-sensor data pool. Simple correlations between the raw data retrieved from several sensors are used for extracting multivariate statistical process control trajectories. Those different models are scored by swarm intelligence (particle swarm optimization) leading to the optimal sensor/model combination at certain time step aiming at determination of the fermentation trajectory in combination with sensor output validation. The results on online data indicate the possibility of more robust online monitoring using the swarm sensing idea for biotechnological processes to insure optimal and timely effective processing as well as sensor failure detection. Adjustments of the basic algorithms, cost function, accuracy of output as well as the dynamic behaviour are addressed. This methodology is not restricted to the number of sensor inputs as well as the use of specific sensor readings, which makes it beneficial over other approaches. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Swarm intelligence Particle swarm optimization Swarm sensors Fermentation Online monitoring multivariate statistical process control

来源：评论

学校读者我要写书评

暂无评论

Nonlinear multiscale modelling for fault detection and identification

引用

CHEMICAL ENGINEERING SCIENCE 2008年第8期63卷 2252-2266页

作者： Choi, Sang Wook Morris, Julian Lee, In-Beum Univ Newcastle Ctr Proc Analyt & Control Technol Sch Chem Engn & Adv Mat Newcastle Upon Tyne NE1 7RU Tyne & Wear England Samsung Elect Co Ltd Memory Div Semicond Business Hwasung 445701 South Korea Pohang Univ Sci & Technol Dept Chem Engn Pohang 790784 South Korea

In order to detect abnormal events at different scales, a number of multiscale multivariate statistical process control (MSPC) approaches which combine a multivariate linear projection model with multiresolution analysis have been suggested. In this paper, a new nonlinear multiscale-MSPC method is proposed to address multivariate process performance monitoring and in particular fault diagnostics in nonlinear processes. A kernel principal component analysis (KPCA) model, which not only captures nonlinear relationships between variables but also reduces the dimensionality of the data, is built with the reconstructed data obtained by performing wavelet transform and inverse wavelet transform sequentially on measured data. A guideline is given for both off-line and on-line implementations of the approach. Two monitoring statistics used in multiscale KPCA-based process monitoring are used for fault detection. Furthermore, variable contributions to monitoring statistics are also derived by calculating the derivative of the monitoring statistics with respect to the variables. An intensive simulation study on a continuous stirred tank reactor process and a comparison of the proposed approach with several existing methods in terms of false alarm rate, missed alarm rate and detection delay, demonstrate that the proposed method for detecting and identifying faults outperforms current approaches. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： multiresolution analysis kernel principal component analysis fault detection and diagnosis multivariate statistical process control multiscale kernel principal component analysis

来源：评论

学校读者我要写书评

暂无评论

process analytical technology beyond real-time analyzers: The role of multivariate analysis

引用

CRITICAL REVIEWS IN ANALYTICAL CHEMISTRY 2006年第3-4期36卷 257-278页

作者： Kourti, Theodora McMaster Univ Dept Chem Engn Hamilton ON L8S 4L7 Canada

process analytical chemistry was recognized by Callis et al. (Analytical Chemistry, 59 (1987): 624A-635A) (1) as a field that extends well beyond real time measurements of process parameters. process Analytical Technology is taking central stage with the 2004 guidance from the Food and Drug Administration, with a mandate much wider than real time measurements. The pharmaceutical industry is entering a new era. Chemometrics has played an integral part for the real time development of process analytical measurements (multivariate calibration) and it is ready to face the challenge of process Analytical Technology in this wider definition. The scope of this paper is to demonstrate that multivariate, data based statistical methods, can play a critical role in process understanding, multivariate statistical process control, abnormal situation detection, fault diagnosis, process control and process scale-up, as linked to process analytical technology.

关键词： process analytical technology multivariate statistical process control scale-up latent variables process understanding multivariate analysis

来源：评论

学校读者我要写书评

暂无评论

A phase I change-point method for high-dimensional process with sparse mean shifts

引用

NAVAL RESEARCH LOGISTICS 2023年第3期70卷 261-273页

作者： Huang, Wenpo Shu, Lianjie Li, Yanting Wang, Luyao Hangzhou Dianzi Univ Sch Management Hangzhou Zhejiang Peoples R China Univ Macau Fac Business Adm Taipa Macau Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Room 551Longbin BldgDongchuan Rd 800 Shanghai Peoples R China

Although Phase I analysis of multivariate processes has been extensively discussed, the discussion on techniques for Phase I monitoring of high-dimensional processes is still limited. In high-dimensional applications, it is common to observe that a large number of components but only a limited number of them change at the same time. The shifted components are often sparse and unknown a priori in practice. Motivated by this, this article studies Phase I monitoring of high-dimensional process mean vectors under an unknown sparsity level of shifts. The basic idea of the proposed monitoring scheme is to first employ the false discovery rate procedure to estimate the sparsity level of mean shifts, and then to monitor the mean changes based on the maximum of the directional likelihood ratio statistics over all the possible shift directions. The comparison results based on extensive simulations favor the proposed monitoring scheme. A real example is presented to illustrate the implementation of the new monitoring scheme.

关键词： monitoring scheme multivariate statistical process control nonparametric tests two-sample tests

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：