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检索条件"任意字段=Symposium on Dynamics and Control of Process Systems"

共 1794 条记录，以下是21-30 订阅

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Dynamic mode decomposition based MPC of fluidized bed spray agglomeration 12

Dynamic mode decomposition based MPC of fluidized bed spray ...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Otto, E. Duerr, R. Bueck, A. Kienle, A. Otto von Guericke Univ D-39106 Magdeburg Germany Magdeburg Stendal Univ Appl Sci D-39114 Magdeburg Germany Friedrich Alexander Univ Erlangen Nuremberg D-91058 Erlangen Germany Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany

Fluidized bed spray agglomeration (FBSA) is an efficient particle formation process for the production of granules extensively used in the food, agricultural and pharmaceutical industry. Specifications on agglomerate properties such as the agglomerate size determine the quality of the product and can be controlled by varying different process conditions. In this contribution data-driven model predictive control (MPC) of the average agglomerate size is presented. Dynamic mode decomposition (DMD) is used to identify a linear model of the process dynamics from snapshot measurements of the particle size distribution. Using DMD as system identification technique eliminates the complex process of identifying a mechanistic process model and at the same time includes advantageous model order reduction for the MPC application. The DMD model is obtained from simulated data and validated against a second, independent, data set. Subsequently, the model is deployed in an MPC controller, which is tested in a simulation study, showing promising performance in set point tracking and disturbance rejection scenarios.

关键词： control of particulate processes Data-driven control Model-predictive control Linear system identification Model order reduction

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Structural Modeling and Intervention of Gene Regulatory Networks for Ultra-Early Disease Prevention

Structural Modeling and Intervention of Gene Regulatory Netw...

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SICE International symposium on control systems (SICE ISCS) part of the 10th SICE Multi-symposium on control systems (MSCS)

作者： Naga, Ryou Inoue, Masaki Keio Univ Dept Appl Phys & Physicoinformat Yokohama Kanagawa Japan

ISBN: (纸本)9784907764760

Prediction and prevention of complex diseases are essential to secure human health and social benefits. It is known that gene regulatory networks (GRNs) have critical roles in many biological activities and also in the process of disease progression. Identifying the structure of GRNs leads to accurate prediction and proper intervention for preventing complex diseases. In this paper, we develop a structural modeling method for GRNs. First, the dynamics of each gene's expression are described as a discrete-time linear state equation. Then, we develop model-based and data-based robust intervention methods for GRNs such that the system matrix in the state equation is manipulated, aiming at improved stability. Finally, the presented structural modeling and intervention methods are demonstrated in a numerical experiment.

关键词： Ultra-Early Disease Prevention Gene Regulatory Networks Network Structural Modeling Model-Based control Robust control

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Recursive Dynamic inner Principal Component Analysis for Adaptive process Modeling 12

Recursive Dynamic inner Principal Component Analysis for Ada...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Qu, Qilin Dong, Yining Zheng, Ying Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

Dynamic latent variable (DLV) methods, represented by dynamic-inner principal component analysis (DiPCA), take into account the high dimensionality and auto-correlation of industrial process data to successfully extract and model the dynamic components. Meanwhile, the time-varying dynamics involved in industrial processes motivate us to explore adaptive DLV methods. In this paper, we propose a recursive DiPCA (RDiPCA) for time-varying dynamic process modeling. Specifically, a recursive autocovariance matrices updating method and the corresponding deflation method are given to achieve low computational costs. The computational efficiency is further improved by a recursive parameter initialization approach in the iterative optimization algorithm solving procedure. Finally, the effectiveness of the proposed algorithm is demonstrated with experiments on a numerical dataset and a wastewater treatment plant dataset.

关键词： Dynamic latent variable methods Recursive dynamic-inner principal component analysis Adaptive dynamic process modeling

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Model order reduction for the TASEP Master equation 26

Model order reduction for the TASEP Master equation

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26th International symposium on Mathematical Theory of Networks and systems (MTNS)

作者： Pioch, K. Kriecherbauer, T. Margaliot, M. Gruene, L. Univ Bayreuth Inst Math D-95440 Bayreuth Germany Tel Aviv Univ Sch Elect Engn IL-6997801 Tel Aviv Israel

The totally asymmetric simple exclusion process (TASEP) is a stochastic model for the unidirectional dynamics of interacting particles on a 1D-lattice that is much used in systems biology and statistical physics. Its master equation describes the evolution of the probability distribution on the state space. The size of the master equation grows exponentially with the length of the lattice. It is known that the complexity of the system may be reduced using mean field approximations. We provide a rigorous derivation and a stochastic interpretation of these approximations and present numerical results on their accuracy for a number of relevant cases. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Stochastic systems systems biology model reduction Markov process interacting particle systems moment closure pair-approximation

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control-Relevant Input Signal Design For Integrating processes: Application to a Microalgae Raceway Reactor 20

Control-Relevant Input Signal Design For Integrating Process...

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20th IFAC symposium on System Identification (SYSID)

作者： Banerjee, Sarasij Otalora, Pablo El Mistiri, Mohamed Khan, Owais Luis Guzman, Jose Rivera, Daniel E. Arizona State Univ Sch Engn Matter Transport & Energy Control Syst Engn Lab Tempe AZ 85287 USA Univ Almeria Dept Informat CeiA3 CIESOL Ctra Sacramento S-N Almeria 04120 Spain

Plants with integrators possess control-relevant modeling requirements that are typically ignored in the literature. Desired closed-loop speeds of response for such systems can differ significantly from their open-loop dynamics, causing conventional system identification guidelines to fail or be inaccurate. One such issue relates to experimental design. This paper presents guidelines for the design of excitation signals for system identification of plants with integrators, with application to the modeling and control of a microalgae raceway reactor. The concept is to excite the system through optimized test signals with control-relevant shaping of their power spectra. This facilitates a shift in emphasis of the identification objective from estimating a model with good open-loop performance to having a model possessing desired closed-loop characteristics. Such a consideration is particularly important when generating informative databases for estimating predictive models for closed-loop control. An illustration for this experimental design procedure is accomplished in this paper through the estimation of ARX-based models and, subsequently, model predictive control of the pH dynamics of an experimental raceway photobioreactor facility hosting sustainable microalgae production. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： identification for control input and excitation design robust control model predictive control process control

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State estimation of a carbon capture process through POD model reduction and neural network approximation 12

State estimation of a carbon capture process through POD mod...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Liu, Siyu Zhang, Xiao Pan, Zhichao Yin, Xunyuan Liu, Jinfeng Zhejiang Normal Univ Coll Engn Jinhua 321004 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China Nanyang Technol Univ Sch Chem Chem Engn & Biotechnol 62 Nanyang Dr Singapore Singapore Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

This paper presents an efficient approach for state estimation of post-combustion CO2 capture plants (PCCPs). The approach involves extracting lower-dimensional feature vectors from the high-dimensional operational data of PCCPs and constructing a reduced-order process model through proper orthogonal decomposition (POD). Multi-layer perceptron (MLP) neural network is then constructed and trained to approximate the dynamics of reduced-order process. For state estimation, a reduced-order extended Kalman filtering scheme, grounded in the POD-MLP model, is developed. Our simulations demonstrate that the proposed POD-MLP modeling reduces computational complexity in comparison to the POD -only model when applied to nonlinear systems. Additionally, the proposed algorithm can accurately reconstruct complete state information of PCCPs while markedly improving computational efficiency.

关键词： Carbon capture and utilization

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Integrated scheduling and control with closed -loop prediction 12

Integrated scheduling and control with closed -loop predicti...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Dering, D. Swartz, C. L. E. McMaster Univ Hamilton ON L8S 3L8 Canada

Dynamic market conditions as a consequence of increased globalization, coupled with fluctuations in electricity prices brought about by the deregulation of energy markets, require process manufacturing plants to operate in a responsive manner in order to remain competitive. In particular, the quasi steady-state assumption that is typically applied in optimal scheduling does not hold in a highly dynamic operating environment, where the dynamics of transitions have an increasingly significant impact. This has led to a research thrust on the integration of scheduling and control. In this paper, we provide an overview of this topic, highlighting assumptions and formulations related to the plant control system. We then focus on a class of `controller aware' scheduling formulations, in which the predicted closed-loop response of the plant under the action of the plant control system is taken into account. A case study illustrating key concepts is presented.

关键词： Scheduling and control closed-loop prediction dynamic real-time optimization

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Slow-Varying dynamics Supervised Data Decomposition for Batch process Quality Prediction 12

Slow-Varying Dynamics Supervised Data Decomposition for Batc...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Qin, Yan Xu, Xiaodong Yin, Xunyuan Singapore Univ Technol & Design 8 Somapah Rd Singapore 487372 Singapore Cent South Univ Sch Automat Changsha 410083 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada Nanyang Technol Univ Sch Chem Chem Engn & Biotechnol 62 Nanyang Dr Singapore 637459 Singapore

Ensuring high prediction accuracy is essential for maintaining high quality of products in batch processes, given their inherent multi-phase characteristics and dynamic variations in real-world applications. Identifying quality-relevant process variations is crucial to address these challenges and produce interpretable and accurate predictions. This work aims to uncover critical quality-relevant process variables from raw measurements collected from batch processes. The proposed method consists of two key components. First, a dynamic subspace is designed for batch processes to extract the slow-varying features that are relevant to the quality index. Second, the quality-relevant features have been employed to achieve the reliable prediction of the performance index. Through the simulated experiment on a concentration batch production process, the proposed method is illustrated. Copyright (C) 2024 The Authors.

关键词： Quality prediction data decomposition slow feature analysis batch processes

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Actuator Fault Tolerant control for a Remotely Operated Vehicle based on Adaptive Extended Kalman Filter 12

Actuator Fault Tolerant Control for a Remotely Operated Vehi...

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12th IFAC symposium on Fault Detection, Supervision and Safety for Technical processes (SAFEprocess)

作者： Baldini, Alessandro Felicetti, Riccardo Freddi, Alessandro Monteriu, Andrea Univ Politecn Marche Dept Informat Engn Via Brecce Bianche I-60131 Ancona Italy

In this paper, we propose a strategy for detecting and isolating actuator faults in an overactuated Remotely Operated Vehicle (ROV) with six degrees of freedom. Fault detection and isolation are based on the Adaptive Extended Kalman Filter (AEKF), which is a recent extension of the well-known Kalman Filter designed for nonlinear dynamics and additive disturbances estimation. The residuals generated by the AEKF act as directional residuals, and fault isolation is performed by calculating the cosine similarity between the residuals and the columns of the control effectiveness matrix. The decision is then used in a simple fault-tolerant control allocation algorithm, without the need to alter the control law. Simulation results show the effectiveness of the estimation method in the presence of thruster failures. Copyright (c) 2024 The Authors.

关键词： Safety of marine systems Reconfigurable control, sensor and actuator faults

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An Observer-Based Approach to Mitigation of Integrity Cyberattacks 12

An Observer-Based Approach to Mitigation of Integrity Cybera...

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12th IFAC symposium on Fault Detection, Supervision and Safety for Technical processes (SAFEprocess)

作者： Zhou, Liutao Salah, Abd A. Ding, Steven X. Liu, Tianyu Univ Duisburg Essen Inst Automat Control & Complex Syst AKS D-47057 Duisburg Germany

This article presents a novel integrity cyberattack mitigation strategy within the unified control and detection framework. Specifically, an observer-based system configuration is leveraged to achieve resilient control under additive integrity attacks in the input and output channels. It is demonstrated that the integrity attacks can be systematically mitigated by feeding back the residual signals derived from the controller dynamics. The internal stability of the resulting feedback system is investigated. Subsequently, we study the resilient controller synthesis with both static and dynamic attack mitigation mechanisms. Finally, the proposed scheme is verified on a networked robot system. Copyright (c) 2024 The Authors.

关键词： Cyber-physical systems cybersecurity integrity cyberattack mitigation resilient control Youla parameterization

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