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Panagiotis D Christofides - One of the best experts on this subject based on the ideXlab platform.
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decentralized machine learning based predictive control of Nonlinear Processes
Chemical Engineering Research & Design, 2020Co-Authors: Scarlett Chen, Zhe Wu, Panagiotis D ChristofidesAbstract:Abstract This work focuses on the design of decentralized model predictive control (MPC) systems for Nonlinear Processes, where the Nonlinear Process is broken down into multiple, yet coupled subsystems and the dynamic behavior of each subsystem is described by a machine learning model. One decentralized MPC is designed and used to control each subsystem while accounting for the interactions between subsystems through feedback of the entire Process state. The closed-loop stability of the overall Nonlinear Process network and the performance properties of the decentralized model predictive control system using machine-learning prediction models are analyzed. More specifically, multiple recurrent neural network models suited for each different subsystem need to be trained with a sufficiently small modeling error from their respective actual Nonlinear Process models to ensure closed-loop stability. These recurrent neural network models are subsequently used as the prediction model in decentralized Lyapunov-based MPCs to achieve efficient real-time computation time while ensuring closed-loop state boundedness and convergence to the origin. The simulation results of a Nonlinear chemical Process network example demonstrate the effective closed-loop control performance when the Process is operated under the decentralized MPCs using the independently-trained recurrent neural network models, as well as the improved computational efficiency compared to the closed-loop simulation of a centralized MPC system.
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Process operational safety using model predictive control based on a Process safeness index
Computers & Chemical Engineering, 2017Co-Authors: Fahad Albalawi, Helen Durand, Panagiotis D ChristofidesAbstract:Abstract It has been repeatedly suggested that the common cause-and-effect approach to evaluating Process safety has deficiencies that could be addressed by a systems engineering perspective. A systems approach should consider safety as a system-wide property and thus would be required to integrate all aspects of the Process involved with monitoring or manipulating the Process dynamics, including the control, alarm, and emergency shut-down systems while operating them independently for redundancy. In this work, we propose initial steps in the first systems safety approach that coordinates the control and safety systems through a common metric (a Safeness Index) and develop a controller formulation that incorporates this index. Specifically, this work presents an economic model predictive control (EMPC) scheme that utilizes a Safeness Index function as a hard constraint to define a safe region of operation termed the safety zone. Under the proposed EMPC design, the closed-loop state of a Nonlinear Process is guaranteed to enter the safety zone in finite time in the presence of uncertainty while maximizing a stage cost that reflects the economics of the Process. Closed-loop stability is established for a Nonlinear Process under the proposed implementation strategy.
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economic model predictive control of Nonlinear Process systems using empirical models
Aiche Journal, 2015Co-Authors: Anas Alanqar, Matthew J Ellis, Panagiotis D ChristofidesAbstract:Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the Process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first-principles model of the Process. Motivated by this, in the present work, Lyapunov-based EMPC (LEMPC) is designed with a linear empirical model that allows for closed-loop stability guarantees in the context of Nonlinear chemical Processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed-loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical Process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages. © 2014 American Institute of Chemical Engineers AIChE J, 61: 816–830, 2015
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economic model predictive control with time varying objective function for Nonlinear Process systems
Aiche Journal, 2014Co-Authors: Matthew J Ellis, Panagiotis D ChristofidesAbstract:Economic model predictive control (EMPC) is a control scheme that combines real-time dynamic economic Process optimization with the feedback properties of model predictive control (MPC) by replacing the quadratic cost function with a general economic cost function. Almost all the recent work on EMPC involves cost functions that are time invariant (do not explicitly account for time-varying Process economics). In the present work, we focus on the development of a Lyapunov-based EMPC (LEMPC) scheme that is formulated with an explicitly time-varying economic cost function. First, the formulation of the proposed two-mode LEMPC is given. Second, closed-loop stability is proven through a theoretical treatment. Last, we demonstrate through extensive closed-loop simulations of a chemical Process that the proposed LEMPC can achieve stability with time-varying economic cost as well as improve economic performance of the Process over a conventional MPC scheme. © 2013 American Institute of Chemical Engineers AIChE J 60: 507–519, 2014
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integrating dynamic economic optimization and model predictive control for optimal operation of Nonlinear Process systems
Control Engineering Practice, 2014Co-Authors: Matthew J Ellis, Panagiotis D ChristofidesAbstract:Abstract In this work, we propose a conceptual framework for integrating dynamic economic optimization and model predictive control (MPC) for optimal operation of Nonlinear Process systems. First, we introduce the proposed two-layer integrated framework. The upper layer, consisting of an economic MPC (EMPC) system that receives state feedback and time-dependent economic information, computes economically optimal time-varying operating trajectories for the Process by optimizing a time-dependent economic cost function over a finite prediction horizon subject to a Nonlinear dynamic Process model. The lower feedback control layer may utilize conventional MPC schemes or even classical control to compute feedback control actions that force the Process state to track the time-varying operating trajectories computed by the upper layer EMPC. Such a framework takes advantage of the EMPC ability to compute optimal Process time-varying operating policies using a dynamic Process model instead of a steady-state model, and the incorporation of suitable constraints on the EMPC allows calculating operating Process state trajectories that can be tracked by the control layer. Second, we prove practical closed-loop stability including an explicit characterization of the closed-loop stability region. Finally, we demonstrate through extensive simulations using a chemical Process model that the proposed framework can both (1) achieve stability and (2) lead to improved economic closed-loop performance compared to real-time optimization (RTO) systems using steady-state models.
Sheng Chen - One of the best experts on this subject based on the ideXlab platform.
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deep principal component analysis based on layerwise feature extraction and its application to Nonlinear Process monitoring
IEEE Transactions on Control Systems and Technology, 2019Co-Authors: Xiaogang Deng, Xuemin Tian, Sheng Chen, C J HarrisAbstract:In order to deeply exploit intrinsic data feature information hidden among the Process data, an improved kernel principal component analysis (KPCA) method is proposed, which is referred to as deep principal component analysis (DePCA). Specifically, motivated by the deep learning strategy, we design a hierarchical statistical model structure to extract multilayer data features, including both the linear and Nonlinear principal components. To reduce the computation complexity in Nonlinear feature extraction, the feature-samples’ selection technique is applied to build the sparse kernel model for DePCA. To integrate the monitoring statistics at each feature layer, Bayesian inference is used to transform the monitoring statistics into fault probabilities, and then, two probability-based DePCA monitoring statistics are constructed by weighting the fault probabilities at all the feature layers. Two case studies involving a simulated Nonlinear system and the benchmark Tennessee Eastman Process demonstrate the superior fault detection performance of the proposed DePCA method over the traditional KPCA-based methods.
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Nonlinear Process fault diagnosis based on serial principal component analysis
IEEE Transactions on Neural Networks, 2018Co-Authors: Xiaogang Deng, Xuemin Tian, Sheng Chen, C J HarrisAbstract:Many industrial Processes contain both linear and Nonlinear parts, and kernel principal component analysis (KPCA), widely used in Nonlinear Process monitoring, may not offer the most effective means for dealing with these Nonlinear Processes. This paper proposes a new hybrid linear-Nonlinear statistical modeling approach for Nonlinear Process monitoring by closely integrating linear principal component analysis (PCA) and Nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the Nonlinear PCs as Nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and Nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and Nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and Nonlinear PCs simultaneously, and therefore, it can better exploit the underlying Process’s structure to enhance fault diagnosis performance. Two case studies involving a simulated Nonlinear Process and the benchmark Tennessee Eastman Process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of Nonlinear Process fault detection and identification.
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modified kernel principal component analysis based on local structure analysis and its application to Nonlinear Process fault diagnosis
Chemometrics and Intelligent Laboratory Systems, 2013Co-Authors: Xiaogang Deng, Xuemin Tian, Sheng ChenAbstract:Traditional kernel principal component analysis (KPCA) concentrates on the global structure analysis of data sets but omits the local information which is also important for Process monitoring and fault diagnosis. In this paper, a modified KPCA, referred to as the local KPCA (LKPCA), is proposed based on local structure analysis for Nonlinear Process fault diagnosis. In order to extract data feature better, local structure analysis is integrated within the KPCA, and this results in a new optimisation objective which naturally involves both global and local structure information. With the application of usual kernel trick, the optimisation problem is transformed into a generalised eigenvalue decomposition on the kernel matrix. For the purpose of fault detection, two monitoring statistics, known as the T2 and Q statistics, are built based on the LKPCA model and confidence limit is computed by kernel density estimation. In order to identify fault variables, contribution plots for monitoring statistics are constructed based on the idea of sensitivity analysis to locate the fault variables. Simulation using the Tennessee Eastman benchmark Process shows that the proposed method outperforms the traditional KPCA, in terms of fault detection performance. The results obtained also demonstrate the potential of the proposed fault identification approach.
Xiaogang Deng - One of the best experts on this subject based on the ideXlab platform.
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deep principal component analysis based on layerwise feature extraction and its application to Nonlinear Process monitoring
IEEE Transactions on Control Systems and Technology, 2019Co-Authors: Xiaogang Deng, Xuemin Tian, Sheng Chen, C J HarrisAbstract:In order to deeply exploit intrinsic data feature information hidden among the Process data, an improved kernel principal component analysis (KPCA) method is proposed, which is referred to as deep principal component analysis (DePCA). Specifically, motivated by the deep learning strategy, we design a hierarchical statistical model structure to extract multilayer data features, including both the linear and Nonlinear principal components. To reduce the computation complexity in Nonlinear feature extraction, the feature-samples’ selection technique is applied to build the sparse kernel model for DePCA. To integrate the monitoring statistics at each feature layer, Bayesian inference is used to transform the monitoring statistics into fault probabilities, and then, two probability-based DePCA monitoring statistics are constructed by weighting the fault probabilities at all the feature layers. Two case studies involving a simulated Nonlinear system and the benchmark Tennessee Eastman Process demonstrate the superior fault detection performance of the proposed DePCA method over the traditional KPCA-based methods.
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Nonlinear Process fault diagnosis based on serial principal component analysis
IEEE Transactions on Neural Networks, 2018Co-Authors: Xiaogang Deng, Xuemin Tian, Sheng Chen, C J HarrisAbstract:Many industrial Processes contain both linear and Nonlinear parts, and kernel principal component analysis (KPCA), widely used in Nonlinear Process monitoring, may not offer the most effective means for dealing with these Nonlinear Processes. This paper proposes a new hybrid linear-Nonlinear statistical modeling approach for Nonlinear Process monitoring by closely integrating linear principal component analysis (PCA) and Nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the Nonlinear PCs as Nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and Nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and Nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and Nonlinear PCs simultaneously, and therefore, it can better exploit the underlying Process’s structure to enhance fault diagnosis performance. Two case studies involving a simulated Nonlinear Process and the benchmark Tennessee Eastman Process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of Nonlinear Process fault detection and identification.
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Nonlinear Process fault pattern recognition using statistics kernel pca similarity factor
Neurocomputing, 2013Co-Authors: Xiaogang Deng, Xuemin TianAbstract:Data-driven fault diagnosis technique has exhibited its wide applications in industrial Process monitoring. However, how to recognize fault pattern based on Process dataset is still a difficult problem in data-driven fault diagnosis field. In this paper, a novel Nonlinear fault recognition method is proposed based on statistics kernel principal component analysis similarity factor (SKPCASF), which combines Nonlinear similarity factor and statistics pattern analysis. Principal component analysis similarity factor (PCASF) is firstly reviewed which measures the similar degree of two datasets by comparing their principal component subspaces. In order to deal with Nonlinear characteristics of Process dataset, kernel principal component analysis (KPCA) is applied to build a Nonlinear similarity factor, referred to as KPCA similarity factor (KPCASF). Moreover, for well utilizing the statistical information of data variables, statistics pattern analysis is used to compute data statistics for substituting for the original measured variables in fault recognition. Based on the statistics, a new similarity factor method called as statistics KPCA similarity factor (SKPCASF) is lastly built for fault pattern recognition. Simulations on a simple Nonlinear system and the benchmark Tennessee Eastman Process show that the proposed SKPCASF method is more effective than PCASF and KPCASF in terms of fault pattern recognition performance.
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modified kernel principal component analysis based on local structure analysis and its application to Nonlinear Process fault diagnosis
Chemometrics and Intelligent Laboratory Systems, 2013Co-Authors: Xiaogang Deng, Xuemin Tian, Sheng ChenAbstract:Traditional kernel principal component analysis (KPCA) concentrates on the global structure analysis of data sets but omits the local information which is also important for Process monitoring and fault diagnosis. In this paper, a modified KPCA, referred to as the local KPCA (LKPCA), is proposed based on local structure analysis for Nonlinear Process fault diagnosis. In order to extract data feature better, local structure analysis is integrated within the KPCA, and this results in a new optimisation objective which naturally involves both global and local structure information. With the application of usual kernel trick, the optimisation problem is transformed into a generalised eigenvalue decomposition on the kernel matrix. For the purpose of fault detection, two monitoring statistics, known as the T2 and Q statistics, are built based on the LKPCA model and confidence limit is computed by kernel density estimation. In order to identify fault variables, contribution plots for monitoring statistics are constructed based on the idea of sensitivity analysis to locate the fault variables. Simulation using the Tennessee Eastman benchmark Process shows that the proposed method outperforms the traditional KPCA, in terms of fault detection performance. The results obtained also demonstrate the potential of the proposed fault identification approach.
Xuemin Tian - One of the best experts on this subject based on the ideXlab platform.
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deep principal component analysis based on layerwise feature extraction and its application to Nonlinear Process monitoring
IEEE Transactions on Control Systems and Technology, 2019Co-Authors: Xiaogang Deng, Xuemin Tian, Sheng Chen, C J HarrisAbstract:In order to deeply exploit intrinsic data feature information hidden among the Process data, an improved kernel principal component analysis (KPCA) method is proposed, which is referred to as deep principal component analysis (DePCA). Specifically, motivated by the deep learning strategy, we design a hierarchical statistical model structure to extract multilayer data features, including both the linear and Nonlinear principal components. To reduce the computation complexity in Nonlinear feature extraction, the feature-samples’ selection technique is applied to build the sparse kernel model for DePCA. To integrate the monitoring statistics at each feature layer, Bayesian inference is used to transform the monitoring statistics into fault probabilities, and then, two probability-based DePCA monitoring statistics are constructed by weighting the fault probabilities at all the feature layers. Two case studies involving a simulated Nonlinear system and the benchmark Tennessee Eastman Process demonstrate the superior fault detection performance of the proposed DePCA method over the traditional KPCA-based methods.
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Nonlinear Process fault diagnosis based on serial principal component analysis
IEEE Transactions on Neural Networks, 2018Co-Authors: Xiaogang Deng, Xuemin Tian, Sheng Chen, C J HarrisAbstract:Many industrial Processes contain both linear and Nonlinear parts, and kernel principal component analysis (KPCA), widely used in Nonlinear Process monitoring, may not offer the most effective means for dealing with these Nonlinear Processes. This paper proposes a new hybrid linear-Nonlinear statistical modeling approach for Nonlinear Process monitoring by closely integrating linear principal component analysis (PCA) and Nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the Nonlinear PCs as Nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and Nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and Nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and Nonlinear PCs simultaneously, and therefore, it can better exploit the underlying Process’s structure to enhance fault diagnosis performance. Two case studies involving a simulated Nonlinear Process and the benchmark Tennessee Eastman Process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of Nonlinear Process fault detection and identification.
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Nonlinear Process fault pattern recognition using statistics kernel pca similarity factor
Neurocomputing, 2013Co-Authors: Xiaogang Deng, Xuemin TianAbstract:Data-driven fault diagnosis technique has exhibited its wide applications in industrial Process monitoring. However, how to recognize fault pattern based on Process dataset is still a difficult problem in data-driven fault diagnosis field. In this paper, a novel Nonlinear fault recognition method is proposed based on statistics kernel principal component analysis similarity factor (SKPCASF), which combines Nonlinear similarity factor and statistics pattern analysis. Principal component analysis similarity factor (PCASF) is firstly reviewed which measures the similar degree of two datasets by comparing their principal component subspaces. In order to deal with Nonlinear characteristics of Process dataset, kernel principal component analysis (KPCA) is applied to build a Nonlinear similarity factor, referred to as KPCA similarity factor (KPCASF). Moreover, for well utilizing the statistical information of data variables, statistics pattern analysis is used to compute data statistics for substituting for the original measured variables in fault recognition. Based on the statistics, a new similarity factor method called as statistics KPCA similarity factor (SKPCASF) is lastly built for fault pattern recognition. Simulations on a simple Nonlinear system and the benchmark Tennessee Eastman Process show that the proposed SKPCASF method is more effective than PCASF and KPCASF in terms of fault pattern recognition performance.
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modified kernel principal component analysis based on local structure analysis and its application to Nonlinear Process fault diagnosis
Chemometrics and Intelligent Laboratory Systems, 2013Co-Authors: Xiaogang Deng, Xuemin Tian, Sheng ChenAbstract:Traditional kernel principal component analysis (KPCA) concentrates on the global structure analysis of data sets but omits the local information which is also important for Process monitoring and fault diagnosis. In this paper, a modified KPCA, referred to as the local KPCA (LKPCA), is proposed based on local structure analysis for Nonlinear Process fault diagnosis. In order to extract data feature better, local structure analysis is integrated within the KPCA, and this results in a new optimisation objective which naturally involves both global and local structure information. With the application of usual kernel trick, the optimisation problem is transformed into a generalised eigenvalue decomposition on the kernel matrix. For the purpose of fault detection, two monitoring statistics, known as the T2 and Q statistics, are built based on the LKPCA model and confidence limit is computed by kernel density estimation. In order to identify fault variables, contribution plots for monitoring statistics are constructed based on the idea of sensitivity analysis to locate the fault variables. Simulation using the Tennessee Eastman benchmark Process shows that the proposed method outperforms the traditional KPCA, in terms of fault detection performance. The results obtained also demonstrate the potential of the proposed fault identification approach.
Uwe Kruger - One of the best experts on this subject based on the ideXlab platform.
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block adaptive kernel principal component analysis for Nonlinear Process monitoring
Aiche Journal, 2016Co-Authors: Zhe Li, Jiusun Zeng, Uwe KrugerAbstract:On-line modeling of multivariate Nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing with the existing work, the proposed algorithm (1) does not rely on iterative computation in the calculating Process, (2) combines the up- and downdating operations to become a single one (3) and describes the adaptation of the Gram matrix as a series of rank-1 modification. In addition, (4) the updation of the eigenvalues and eigenvectors is of O(N) and high-precision. The computational complexity analysis and the numerical study show that the derived strategy possesses better ability to model the time-varying Nonlinear variable interrelationships in Process monitoring. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4334–4345, 2016
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block adaptive kernel principal component analysis for Nonlinear Process monitoring
Aiche Journal, 2016Co-Authors: Lei Xie, Jiusun Zeng, Uwe KrugerAbstract:On-line modeling of multivariate Nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing with the existing work, the proposed algorithm (1) does not rely on iterative computation in the calculating Process, (2) combines the up- and downdating operations to become a single one (3) and describes the adaptation of the Gram matrix as a series of rank-1 modification. In addition, (4) the updation of the eigenvalues and eigenvectors is of O(N) and high-precision. The computational complexity analysis and the numerical study show that the derived strategy possesses better ability to model the time-varying Nonlinear variable interrelationships in Process monitoring. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4334–4345, 2016
