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

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Visual Analytics: A New Paradigm for process Monitoring 13th

Visual Analytics: A New Paradigm for Process Monitoring

引用

13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Yousef, Ibrahim Shah, Sirish L. Gopaluni, R. Bhushan Univ British Columbia Dept Chem & Biol Engn Vancouver BC Canada Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada

As a result of recent breakthroughs in computer vision technologies, significant research interest has emerged to encode process data into visual clues and treat process monitoring problems as computer vision tasks. Imaging time-series signals as a feature engineering step forms a new branch of data analytics called "visual analytics". In the context of process monitoring, we define visual analytics as the integration of visual representation of the data combined with the use of computer vision tools and analytical reasoning to support decision-making and knowledge extraction from the data. In this work, a novel end-to-end visual analytics pipeline for industrial process fault detection using 1D and 2D convolution operations is proposed. The proposed approach presents a visual representation of data that captures temporal and local features from historical time-series signals. Next, the learned features in a 2D format are visually recognized and classified using 2D convolution operations. Our experimental results demonstrate that this approach achieves better performance on an industrial multivariate dataset compared to other state-of-art signals imaging tools such as Gramian Angular Field (GAF) and Recurrence Plots (RP).

关键词： process Monitoring Signal processing Deep Learning Image Analysis Fault Detection Vision Applications

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Effect of the demethanizer improved control strategy on the separation train for the NGL separation process 13th

Effect of the demethanizer improved control strategy on the ...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Mandis, Marta Chebeir, Jorge A. Romagnoli, Jose A. Baratti, Roberto Tronci, Stefania Univ Cagliari Dip Ingn Meccan Chim & Mat I-09123 Cagliari Italy Louisiana State Univ Dept Chem Engn Baton Rouge LA 70809 USA

In recent years the attention on natural gas production and utilization is growing due to different fundamental aspects. First, the availability of natural gas has increased thanks to technological improvements in the extraction techniques that have made possible the production from unconventional reservoirs. Second, the interest in clean energy is growing, aiming to reduce CO2 emission and thus global warming. Natural gas is a cleaner fossil fuel compared with other traditional energy sources such as oil and coal. Another reason that drives the attention on this fossil fuel is the increasing economic interest of recovering the heavier hydrocarbon fractions contained in it. The fractionation of natural gas liquids (NGL) is an energy-demanding process, often conducted with a separation train that includes cryogenic distillation columns. This work is intended to show how to achieve an energy-efficient recovery of NGLs with a proper control strategy and without composition analyzers. In particular, the effects of a combined cascade control with boilup approximation plus a pressure compensator control for the demethanizer section, on the desired NGL extraction product targets are investigated and compared under feed flowrate disturbances with conventional direct temperature controllers. Overall, it is shown that the cascade control plus pressure compensator provides the best performance and the lowest energy consumption. Copyright (C) 2022 The Authors.

关键词： Natural gas liquids recovery Dynamic process simulation Pressure compensated temperature Multicomponent distillation

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Actuator Cyberattack Handling Using Lyapunov-based Economic Model Predictive control 13th

Actuator Cyberattack Handling Using Lyapunov-based Economic ...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Rangan, Keshav Kasturi Oyama, Henrique Durand, Helen Wayne State Univ Dept Chem Engn & Mat Sci Detroit MI 48202 USA

Cybersecurity has gained increasing interest as a consequence of the potential impacts of cyberattacks on profits and safety. While attacks can affect various components of a plant, prior work from our group has focused on the impact of cyberattacks on control components such as process sensors and actuators and the development of detection strategies for cybersecurity derived from control theory. In this work, we provide greater focus on actuator attacks;specifically, we extend a detection and control strategy previously applied for sensor attacks and based on an optimization-based control technique called Lyapunov-based economic model predictive control (LEMPC) to detect attacks impacting the control action applied by the actuators when the state measurements provided to the controller are accurate. Closed-loop stability guarantees are rigorously derived. A continuous stirred tank reactor is simulated to elucidate aspects of the detection strategy proposed. Copyright (c) 2022 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： Nonlinear processes model predictive control cybersecurity nonlinear control actuators

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Physics Constrained Learning in Neural Network based Modeling 13th

Physics Constrained Learning in Neural Network based Modelin...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Patel, Rahul S. Bhartiya, Sharad Gudi, Ravindra D. Indian Inst Technol Dept Chem Engn Mumbai 400076 Maharashtra India

Neural Network (NN) models based on training solely using data are limited in their use due to issues related to extrapolability and interpretability. On the other hand, while mechanistic models based on governing physical laws can overcome these limitations, the unavailability of accurate mechanistic models render them unsuitable for critical applications. In this paper, we propose an approach to develop an NN model that is trained to exploit available data while also being regularized by physics based information;in other words the loss function of NN is augmented by constraints associated with the system physics. This approach, also known as PINNs (Raissi et al. 2019) has been applied to representative problems in process systems engineering (PSE) to evaluate its efficacy to represent the knowledge about the physics of the system while also exploiting the information in the data. It has been shown that in the presence of noisy data and partially known physics model, this approach can give better predictions compared to the conventional training methodology. It has also been shown that the constraints given by the physics based model are also satisfied to a greater extent as compared to models trained only on data. Copyright (C) 2022 The Authors.

关键词： Artificial intelligence and machine learning Modeling and identification Dynamic modelling and simulation for control and operation

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Modeling and Simulation of Upstream and Downstream processes for Monoclonal Antibody Production 13th

Modeling and Simulation of Upstream and Downstream Processes...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Wahlgreen, Morten Ryberg Meyer, Kristian Ritschel, Tobias K. S. Engsig-Karup, Allan Peter Gernaey, Krist, V Jorgensen, John Bagterp Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark Tech Univ Denmark Dept Chem & Biochem Engn PROSYS DK-2800 Lyngby Denmark MCT Bioseparat ApS DK-2800 Lyngby Denmark 2 Control Aps DK-7400 Herning Denmark

We present a numerical case study for modeling and simulation of upstream and downstream processes for monoclonal antibody (mAb) production. We apply a systematic and intuitive modeling methodology for an existing upstream process and downstream process. The resulting models are based on differential mass balances and kinetic expressions for the reactions and adsorption. Mass balances for the fedbatch reactor yield a model consisting of five ordinary differential equations (ODEs). The downstream process is conducted batchwise in a chromatographic column for capture of mAbs. Mass balances of the chromatographic column yield a system of partial differential equations (PDEs). The chromatographic model applies the nonlinear shrinking core adsorption isotherm model for transition between the mobile phase and the stationary phase. We apply a high-order spectral nodal continuous Galerkin scheme for spatial discretization of the chromatographic column, which result in a semi-discrete ODE formulation. The resulting simulation model, coupling the upstream and downstream processes in batchwise mAb production, can be used as a benchmark for numerical estimation, control and optimization studies. Copyright (C) 2022 The Authors.

关键词： Monoclonal antibody production fedbatch reactor capture chromatography shrinking core adsorption isotherm process modeling

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On the Implementation of a Preconditioned Riccati Recursion based Primal-Dual Interior-Point Algorithm for Input Constrained Optimal control Problems 13th

On the Implementation of a Preconditioned Riccati Recursion ...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Wahlgreen, Morten Ryberg Jorgensen, John Bagterp Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark

We present a preconditioned interior-point algorithm tailored for input constrained quadratic programmings (QPs) arising in optimal control problems (OCPs). The implicit approach to OCPs results in large sparse QPs, which we utilized by a tailored Riccati recursion algorithm. The Riccati recursion algorithm requires the solution of a set of small dense linear sub-systems of equations. The proposed preconditioner is an easily invertible diagonal matrix, which we apply in every linear sub-system of equations. We solve a target tracking OCP for a linearized modified quadruple tank system in Matlab. The computational results indicate that ill-conditioning in the sub-systems are reduced and that the additional CPU time for preconditioning is negligible. Additionally, the paper presents a detailed description of the proposed algorithm and serves as an implementation guide for the algorithm. Copyright (c) 2022 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： Interior-point method Quadratic programming Optimal control Problem Riccati recursion Preconditioning

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Data-Driven Geometric control Method to the Attitude control of a Carrier Rocket

Data-Driven Geometric Control Method to the Attitude Control...

引用

International symposium on Autonomous systems (ISAS)

作者： Hongru Jiang Xiangyun Meng Tongna Kang Yue Guo Chao Deng Quan-Yong Fan School of Automation Northwestern Polytechnical University Xi’an China Institude of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

The focus of this paper is to obtain a data-driven model of the rocket using a manifold-based approach and designing the controller for closed-loop rocket dynamics system. To meet the requirements of the optimal model, appropriate orthogonal decomposition is utilized to select low-order subspaces, and the state space model is fitted to the subspaces using the least squares method. On this basis, iterative algebraic equations for the construction of optimal feedback under Hamiltonian cohomology conditions are derived by designing gradient-based minimisation algorithms in convex positive definite matrix manifold functions. Finally, we demonstrate the reliability of the proposed manifold optimisation algorithm for a rocket attitude control model.

关键词： Manifolds Rockets State feedback Parameter estimation Attitude control process control Minimization

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Data-Efficient Reinforcement Learning from controller Guidance with Integrated Self-Supervision for process control 13th

Data-Efficient Reinforcement Learning from Controller Guidan...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Bougie, Nicolas Onishi, Takashi Tsuruoka, Yoshimasa Natl Inst Adv Ind Sci & Technol NEC AIST AI Cooperat Res Lab Tokyo Japan NEC Corp Ltd Data Sci Res Labs Kawasaki Kanagawa Japan Univ Tokyo Dept Informat & Commun Engn Tokyo Japan

Model-free reinforcement learning methods have achieved significant success in a variety of decision-making problems. In fact, they traditionally rely on large amounts of data generated by sample-efficient simulators. However, many process control industries involve complex and costly computations, which limits the applicability of model-free reinforcement learning. In addition, extrinsic rewards are naturally sparse in the real world, further increasing the amount of necessary interactions with the environment. This paper presents a sample-efficient model-free algorithm for process control, which massively accelerates the learning process even when rewards are extremely sparse. To achieve this, we leverage existing controllers to guide the agent's learning - controller guidance is used to drive exploration towards key regions of the state space. To further mitigate the above-mentioned challenges, we propose a strategy for self-supervision learning that lets us improve the agent's policy via its own successful experience. Notably, the method we develop is able to leverage guidance that does not include the actions and remains effective when the existing controllers are suboptimal. We present an empirical evaluation on a vinyl acetate monomer (VAM) chemical plant under disturbances. The proposed method exhibits better performance than baselines approaches and higher sample efficiency. Besides, empirical results show that our method outperforms the existing controllers for controlling the plant and canceling disturbances, mitigating the drop in the production load. Copyright (C) 2022 The Authors.

关键词： Reinforcement learning control process control Chemical plant control Co-Learning and self-learning Artificial intelligence

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Lexicographic Optimization control Method Based Phase Recognition for Multiphase Batch Polymerization Reaction 13th

Lexicographic Optimization Control Method Based Phase Recogn...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Dong, Chao Bo, Cuimei Gao, Shida Cui, Chengtian Li, Jun Jin, Xiaoming Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Jiangsu Peoples R China Shanghai Jiao Tong Univ Ningbo Artificial Intelligence Inst Ningbo 315012 Zhejiang Peoples R China Nanjing Tech Univ Inst Intelligent Mfg Nanjing 211816 Jiangsu Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

To solve the problem that the correlation of variables varies with phase in multiphase batch polymerization reaction, this paper proposes an optimization method for lexicographic order switching based on phase recognition. This method uses kernel principal component analysis to analyze the correlation of each sampling time in a sliding time window, and analyze the phase of each batch of historical data. According to the analysis results, each batch is divided into individual phases, and the sub-phase data is used to identify the phase of real-time production. This method sets the corresponding lexicographic control target according to the characteristics of each phase of the process. According to the lexicographical order of real-time phase location, the optimal operation variable trajectory is obtained, so as to achieve the best optimization effect in phase, and ensure the safe production process and continuous and stable product quality. Copyright (C) 2022 The Authors.

关键词： Multiphase batch process Kernel Principle Component Analysis(KPCA) Uneven-length Phase Partition Lexicographic Optimization

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Online Feedback-based Optimization with Multi-input Direct Constraint control 13th

Online Feedback-based Optimization with Multi-input Direct C...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Dirza, Risvan Skogestad, Sigurd Norwegian Univ Sci Technol NTNU Dept Chem Engn NO-7491 Trondheim Norway

The starting point of this paper is to solve a steady-state optimization problem with simple feedback controllers using the Primal-Dual approach. However, this approach controls the constraint indirectly and on a slow time scale by manipulating the Dual variables (Lagrange multipliers). This control strategy may lead to unacceptable dynamic violations. The main contribution of this paper is to reduce the violations by introducing direct constraint control on the fast time scale using a selector. To get consistency with the steady-state optimization, we need to change the constraints in the Primal-Dual approach to involve the input used for direct constraint control. As a further refinement of this idea, we propose a multi-input direct constraint control for cases where many inputs affect the constraint value, and where pairing with a single input may lead to input rate saturation. The proposed control structure is applied to a subsea oil production network, showing that the proposed approach can minimize the dynamic violation during transient, and can achieve optimal steady-state conditions using simple feedback controllers. Copyright (C) 2022 The Authors.

关键词： process optimization Scheduling coordination and optimization Plantwide control

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