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7th IFAC Symposium on Dynamics and control of process Systems, DYCOPS 2004

作者： Chen, Zeng-Ping Morris, Julian Martin, Elaine Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle upon Tyne NE1 7RU United Kingdom

With a view to ensuring the validity of multivariate calibration models in the presence of temperature variations, a new methodology, individual contribution standardization (ICS), is proposed to correct for temperature-induced spectral variations. The methodology was applied to shortwave NIR spectral data sets recorded at different temperatures. The results showed that ICS can almost eliminate temperature effects resulting in calibration models that exhibit good predictive performance. Compared with other methods, such as continuous piecewise direct standardization, ICS has the advantages of easy implementation, simple selection of the calibration model and less restrictions on the training samples. © IFAC Dynamics and control of process Systems, Cambridge, Massachusetts, USA, 2004

关键词： Standardization

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PAT: The extraction of maximum information from messy spectral data 10th

PAT: The extraction of maximum information from messy spectr...

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10th IFAC Symposium on Computer Applications in Biotechnology, 2007

作者： Chen, Zeng-Ping Morris, Julian Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle upon TyneNE1 7RU United Kingdom

ISBN: (纸本)9783902661616

With the increasing take-up of PAT1 by the pharma- and bio-industries there is a critical need for robust spectral calibrations for processes which are subject to the variations in physical properties such as sample compactness, surface topology, etc. The variation in the optical path-length materializing from the physical differences between samples may result in multiplicative light scattering influencing spectra in a nonlinear manner leading to the poor calibration performance. A new approach "Optical Path Length Estimation and Correction" overcomes the limitations of existing light scattering correction methods. Copyright 2007 IFAC.

关键词： Light scattering

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Image-Based process Monitoring Using Deep Belief Networks

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IFAC-PapersOnLine 2018年第18期51卷 115-120页

作者： Lyu, Yuting Chen, Junghui Song, Zhihuan State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Department of Control Science and Engineering Zhejiang University HangzhouZhejiang310027 China Department of Chemical Engineering Chung-Yuan Christian University Chung-Li Taiwan

With the advances in optical sensing and image capture systems, process images certainly offer new perspectives to process monitoring. Compared to the process data collected by traditional sensors at local regions, process images, which can capture more significant variations in the whole space, enhance the monitoring performance in data-driven monitoring methods. In this paper, a popular deep learning method, namely deep belief network (DBN), is applied to effectively extract useful features from the images. Meanwhile, a new statistic is developed for the DBN model, which integrates feature extraction and fault detection into one model rather than separately accomplish them. The effectiveness of the proposed DBN based monitoring method is demonstrated in a real combustion system. © 2018

关键词： Monitoring Bayesian networks Deep learning Fault detection Feature extraction Image enhancement Learning systems process control process monitoring Combustion systems Deep belief network (DBN) Deep belief networks Image captures Learning methods Monitoring methods Monitoring performance Optical sensing

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Latent-Enhanced Variational Adversarial Active Learning Assisted Soft Sensor

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IEEE Sensors Journal 2023年第14期23卷 15762-15772页

作者： Dai, Yun Yang, Chao Liu, Yi Yao, Yuan Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering Hsinchu300044 Taiwan

As the acquisition of variables that measure quality is typically challenging, labeled samples for building a model for soft sensors are often inadequate. Additionally, owing to the installation of redundant sensors, high-dimensional process data with strong correlations are acquired. Therefore, in this study, a sample selection strategy for active learning (AL), referred to as latent-enhanced variational adversarial AL (LVAAL), is developed to enhance quality prediction performance with limited labeled data. The LVAAL method uses a minimax game to explore the latent representation of the original data. Specifically, a latent-enhanced variational autoencoder (LEVAE) is used to deceive the adversarial network into predicting all samples, both labeled and unlabeled, as labeled. However, the adversarial network attempts to indicate the difference between latent representations. Subsequently, Gaussian process regression (GPR) is adopted as a base model for LVAAL. With the proposed LVAAL selection strategy, informative unlabeled samples are incrementally selected, thereby enhancing the prediction performance. The application results for a numerical case and an industrial example demonstrate the advantages of LVAAL compared with existing AL strategies. © 2001-2012 IEEE.

关键词： Gaussian distribution

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Graph convolutional network soft sensor for process quality prediction

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Journal of process control 2023年第1期123卷 12-25页

作者： Jia, Mingwei Xu, Danya Yang, Tao Liu, Yi Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu30013 Taiwan

The nonlinear time-varying characteristics of the process industry can be modeled using numerous data-driven soft sensor methods. However, the intrinsic relationships among the variables, especially the localized spatial–temporal correlations that shed light on model behavior, have received little attention. In this study, a soft sensor based on a graph convolutional network is constructed by introducing the concept of graph to process modeling. The focus is on obtaining localized spatial–temporal correlations that aid in comprehending the intricate interactions among the variables included in the soft sensor. The model is trained by considering the regularization terms and it learns distinctive localized spatial–temporal correlations in an end-to-end manner. Furthermore, long-term dependence is established via temporal convolution. Thus, both the localized spatial–temporal correlations and time-series properties are captured. The feasibility of the proposed soft sensor is illustrated using two fermentation processes. The localized spatial–temporal correlations of this case study are visualized, and they demonstrate that the soft sensor is not a black-box model;instead, it is consistent with process knowledge. © 2023 Elsevier Ltd

关键词： Convolution

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Batch monitoring through common subspace models 7th

Batch monitoring through common subspace models

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7th International Symposium on Advanced control of chemical processes, ADCHEM 2003

作者： Lane, S. Martin, E.B. Morris, A.J. Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

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 vananee-co variance 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. Copyright © IFAC 2003

关键词： Manufacturing processes Modelling Statistical analysis

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Graph-guided masked autoencoder for process anomaly detection

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process Safety and Environmental Protection 2024年 186卷 1345-1357页

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu300044 Taiwan

Developing an accurate and reliable anomaly detection model is of great significance for safe operation in the process industry. To minimize false positives, it is crucial to accurately model the intricate topological and nonlinear connections among variables. In this study, a process anomaly detection method based on graph-guided masked autoencoder (GGMAE) is proposed by introducing the concept of the graph to the process industry. GGMAE first constructs a topology graph according to the process flowchart. Then, the patch and mask mechanism forces GGMAE to learn the process intrinsic information to reconstruct the input according to the variable topological relationship and temporal characteristics. Additionally, Kullback-Leibler divergence is used as the loss to ensure that the distribution of input and output is consistent. Experiments on two publicly available anomaly detection benchmarks demonstrate the superiority of GGMAE over existing methods. Visualization of the results demonstrates the physically compliant reconstruction logic. © 2024 The Institution of chemical Engineers

关键词： Anomaly detection

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Internal failure of anode materials for lithium batteriesd——A critical review

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Green Energy & Environment 2020年第1期5卷 22-36页

作者： Xiangqi Meng Yaolin Xu Hongbin Cao Xiao Lin Pengge Ning Yi Zhang Yaiza Gonzalez Garcia Zhi Sun School of Chemical Engineering and Technology Tianjin UniversityTianjin300350China Beijing Engineering Research Centre of Process Pollution Control Key Laboratory of Green Process and EngineeringInstitute of Process EngineeringChinese Academy of SciencesBeijing100190China Soft Matter and Functional Materials Helmholtz-Zentrum Berlin fur Materialien und Energie GmbHHahn-Meitner Platz 114109BerlinGermany Department of Materials Science and Engineering TU DelftDelft2628CDthe Netherlands

Prevention of mechanical and finally electrochemical failures of lithium batteries is a critical aspect to be considered during their design and performance, especially for those with high specific capacities. Internal failure is observed as one of the most serious factors, including loss of electrode materials, structure deformation and dendrite growth. It usually incubates from atomic/molecular level and progressively aggravates along with lithiation. Understanding the internal failure is of great importance for developing solutions of failure prevention and advanced anode materials. In this research, different internal failure processes of anode materials for lithium batteries are discussed. The progress on observation technologies of the anode failure is further summarized in order to understand their mechanisms of internal failure. On top of them, this review aims to summarize innovative methods to investigate the anode failure mechanisms and to gain new insights to develop advanced and stable anodes for lithium batteries.

关键词： Lithium battery Anode materials Internal failure

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Parameter estimation for batch processes using a Bayesian approach 7th

Parameter estimation for batch processes using a Bayesian ap...

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7th IFAC Symposium on Dynamics and control of process Systems, DYCOPS 2004

作者： Lu, Zhen Martin, Elaine B. Morris, Julian Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

In a changing operational environment, a major challenge that still exits is the assured state and parameter estimation of dynamic processes. The values or expressions of important parameters can be difficult to determine and initial errors may be present in some parameters as a result of changes in the initial operating conditions. Furthermore as a result of variations in the environmental and operational conditions or the dynamic characteristics of the process, parameters may be time-varying. In this paper, an on-line Bayesian parameter estimator is developed and evaluated on a simulation of a batch methyl methacrylate process. Copyright © IFAC Dynamics and control of process Systems, Cambridge, Massachusetts, USA, 2004

关键词： Parameter estimation

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Particle filters for dynamic data rectification and process change detection

Particle filters for dynamic data rectification and process ...

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作者： Chen, Tao Morris, Julian Martin, Elaine Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU United Kingdom

ISBN: (纸本)9783902661142

The objectives of dynamic data rectification are wide-ranging and include the estimation of the process states, process signal de-noising, and outlier detection and removal. One approach reported in the literature for dynamic data rectification is the conjunction of the extended Kalman filter (EKF) and the expectation-maximization algorithm. However, this approach is limited in terms of its applicability due to the EKF being less appropriate where the state and measurement functions are highly non-linear or where the posterior distribution of the states is non-Gaussian. This paper proposes an alternative approach whereby particle filters are utilized for dynamic data rectification. By formulating the rectification problem within a probabilistic framework, the particle filters generate Monte Carlo samples from the posterior distribution of the system states, and thus provide the basis for rectifying the process measurements. Furthermore, the proposed technique is capable of detecting changes in process operation and thus complements the task of process fault diagnosis. The appropriateness of particle filters for dynamic data rectification is demonstrated through its application to a benchmark pH neutralization process. Copyright © 2006 IFAC.

关键词： Extended Kalman filters

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