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

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On-line estimation of the reaction rates in bioreactors from multirate sampled outputs with multiple delays 12

On-line estimation of the reaction rates in bioreactors from...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Treangle, C. Farza, M. M'Saad, M. Normandie Univ UNICAEN ENSICAEN LAC F-14000 Caen France

This paper provides a simple high gain continuous-discrete time observer to handle the estimation problem of the reaction rates in bioreactors using delayed sampled measurements of the component concentrations in the context of multirate sampling of the outputs each one of which is affected by a constant delay. The rational behind the proposed observer design consists in the consideration of an appropriate corrective term provided by a linear Delay Differential Equation (DDE), which accounts for different sampling periods associated to the outputs as well as the underlying delays. The convergence analysis is performed thanks to a comprehensive Lyapunov approach under a well-defined condition on the sum of the maximum value of the sampling periods and of the output delays. The exponential convergence to zero of the underlying convergence error is established in the case of constant reaction rates. In the case where these rates are time-varying, the asymptotic estimation error admits an ultimate bound which can be made as small as desired by choosing appropriate values of the observer design parameters. The performance of the proposed observer and its main properties are highlighted through a fermentation process dealing with ethanol production. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： High gain observer Continuous-discrete time observer Multirate sampling process Output delay Reaction rates Bioreactor

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Economics Optimizing control with Model Mismatch Based on Modifier Adaptation 12

Economics Optimizing Control with Model Mismatch Based on Mo...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Hernandez, Reinaldo Engell, Sebastian TU Dortmund Proc Dynam & Operat Grp Emil Figge Str 70 D-44227 Dortmund Germany

It is well-know that the use of an inaccurate model in any model-based control scheme might lead to a suboptimal operation and to violation of the constraints. In this contribution, an economics optimizing control scheme is proposed with the aim of achieving closed-loop optimality despite the presence of structural model-plant mismatch. Model uncertainties are handled by augmenting the nominal model with bias and gradients correction terms which are iteratively updated using plant information. The basic idea is to enforce the matching of the optimality conditions between the plant and the augmented model by linear correction terms similar to modifier adaptation. The proposed scheme is discussed and illustrated by simulation studies of a benchmark problem. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Economic Model Predictive control Economics Optimizing control Model-plant Mismatch Modifier Adaptation

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Model Predictive control with Adaptive Strategy Applied to an Electric Submersible Pump in a Subsea Environment 12

Model Predictive Control with Adaptive Strategy Applied to a...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Delou, Pedro de A. de Azevedo, Julia P. A. Krishnamoorthy, Dinesh de Souza Jr, Mauricio B. Secchi, Argimiro R. Univ Fed Rio de Janeiro Chem Engn Program COPPE Ctr Tecnol BR-21941914 Rio De Janeiro RJ Brazil Norwegian Univ Sci & Technol Dept Chem Engn Trondheim Norway Univ Fed Rio de Janeiro Escola Quim Dept Engn Quim BR-21941909 Rio De Janeiro RJ Brazil

An automatic control of Electric Submersible Pumps (ESPs), employed for artificial lift oil production, is desired in order to ensure safe operation while satisfying pump and well constraints. In this context, the use of an advanced control strategy, such as model predictive control (MPC), can be beneficial in order to deal with the process constraints, variables coupling and optimal operation. In this paper, a linear MPC strategy was applied to an ESP model in order to keep the pump inlet pressure in a desired reference value, minimizing the pump power and respecting the variables constraints. Model adaptation based on homotopic transition between models was proposed for plant nonlinear response regions broadening the range of control. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Electric Submersible Pump (ESP) Model predictive control (MPC) QDMC (Quadratic/Dynamic Matrix control) Homotopy Adaptive MPC

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Data-driven Online Adaptation of the Scenario-tree in Multistage Model Predictive control 12

Data-driven Online Adaptation of the Scenario-tree in Multis...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Thombre, Mandar Krishnamoorthy, Dinesh Jaschke, Johannes Norwegian Univ Sci & Technol Dept Chem Engn NO-7491 Trondheim Norway

Multistage model predictive control (MPC) is based on the enumeration of scenarios that represent the uncertainty in the system. Scenario selection is important in multistage MPC since the choice of scenarios determines the degree of conservativeness of the optimal solution. We propose a data-driven approach based on principal component analysis (PCA) to dynamically select the scenarios, leading to reduced conservativeness. When time-varying uncertainty is considered, PCA can be performed online to select new scenarios whenever the uncertainty data is updated. The results of the approach are demonstrated for a two-plant system with a thermal storage tank. The solution obtained is less conservative than with standard multistage MPC. This is because the online PCA-based approach accounts for the most recent, and thus more representative, uncertainty realizations. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Multistage MPC online PCA scenario selection parametric uncertainty

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Effective Recursive Set-membership State Estimation for Robust Linear MPC 12

Effective Recursive Set-membership State Estimation for Robu...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Valero, Carlos E. Paulen, Radoslav Slovak Univ Technol Bratislava Fac Chem & Food Technol Bratislava Slovakia

In this paper the problem of robust output-feedback Model Predictive control (MPC) is considered. Uncertainty in the state estimates obtained from noisy measurements is bounded using set-membership techniques as we consider the noise to be bounded. Robustness of the MPC controller is achieved in a min-max sense. We use parallelotopic bounding for the state estimates. We propose enhancements to a well-known Recursive Optimal Parallelotopic Outbounding (ROPO) algorithm such that the resulting closed-loop cost is improved. All methodologies are tested using a simple linear case study. The results obtained show the benefits of the developed methods. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Predictive control Bounded noise State estimation

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A new qualitative control strategy for the genetic Toggle Switch 12

A new qualitative control strategy for the genetic Toggle Sw...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Chambon, Lucie Gouze, Jean-Luc Univ Cote dAzur INRIA INRA CNRSSorbonne UnivBiocore Team Sophia Antipolis France

Genetic positive feedback loops are essential for cell decision making and cell differentiation. They are traditionally described by a two-dimensional smooth non-linear differential system composed of Hill functions. The bistability of this classic model properly captures the decision properties of these biological motifs. This paper designs a new control strategy based on the measurement and control of a unique gene within the loop, in order to stabilize the system around its unstable fixed point. The quantized nature of genetic measurements and the new synthetic control approaches available in biology encourage the use of a piecewise constant control law. A specific partitioning of the state space and the study of successive repelling regions allow to prove global convergence and global stability for the resulting system. The same strategy is shown to be efficient as well in the more realistic context in which measurements are considered possibly uncertain. This new control strategy is compared with a real biological experiment that was implemented for the Toggle Switch with the same stabilization objective, but for which both genes were measured and controlled. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Gene regulatory networks Qualitative control Non-linear systems Quantized measurements Global stability

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Tube-based Stochastic Nonlinear Model Predictive control: A Comparative Study on Constraint Tightening 12

Tube-based Stochastic Nonlinear Model Predictive Control: A ...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS-CAB)

作者： Bonzanini, Angelo D. Santos, Tito L. M. Mesbah, Ali Univ Calif Berkeley Dept Chem & Biomol Engn Berkeley CA 94720 USA Univ Fed Bahia Dept Elect Engn BR-40210630 Salvador BA Brazil

This paper presents a comparative study between two constraint-tightening approaches for tube-based stochastic nonlinear model predictive control (SNMPC) with and without terminal constraints. A simple constraint-tightening method based on the exponential decay rate of a (5-Lyapunov function is extended to the stochastic setting. This method uses the notion of incremental stabilizability to alleviate the need for offline, but involved computation of terminal constraints. The proposed method is compared to a SNMPC formulation that employs terminal constraints and Lipschitz constant-based constraint tightening. A comparative analysis is presented on a benchmark continuous stirred-tank reactor problem. Practical approximations for computing terminal sets are discussed in the context of this comparison. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Tube-based MPC chance constraints constraint tightening incremental stabilizability

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Investigation of the diagnostic properties of sensors and features in a multiphase flow facility case study 12

Investigation of the diagnostic properties of sensors and fe...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS-CAB)

作者： Stief, Anna Ottewill, James R. Baranowski, Jerzy ABB Corp Res Ctr Ul Starowislna 13a PL-31038 Krakow Poland ACH Univ Sci & Technol Fac Elect Engn Automat Comp Sci & Biomed Engn Dept Automat Control & Robot Al A Mickiewicza 30 PL-30059 Krakow Poland

Feature design and selection is one of the first steps towards successful fault detection and diagnosis. Data from different sources can contain complimentary information about a monitored system. Hence methods which fuse features from multiple sources can often detect and diagnose a greater number of fault modes with higher confidence. However, solutions that require data from multiple sensors as inputs can be susceptible to failure if one or more of those sensors cease to function. Optimally, a solution will fuse data from a sufficient number of sensors so that the advantages of sensor fusion are realized, while the robustness of the system is retained. In this paper the authors investigate how the best subset of features might differ for fault detection and fault severity diagnosis in a multiphase flow facility case study. ReliefF, which is a K-nearest neighbors-based feature selection filter, is used to rank the features for different problems. The dataset used for the analysis contains data from various operating conditions and induced faults with various severities. It was found that the optimal subset of features varied for different monitoring problems. It was also shown that including features that are ranked as being uninformative into a fault classifier can also impact the robustness of the classifier to sensor failures. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Signal processing Feature extraction Feature selection and process monitoring

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Dynamic Real-Time Optimisation of a CO2 Capture Facility 12

Dynamic Real-Time Optimisation of a CO2 Capture Facility

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Hotvedt, Mathilde Hauger, Svein O. Gjertsen, Fredrik Imsland, Lars NTNU Engn Cybernet Dept Trondheim Norway Cybernet AS Trondheim Norway

This work investigates economic optimisation of an energy-intensive amine regeneration process in a post-combustion CO2 capture plant, subject to a minimum CO2 capture ratio over 24 hours. A Dynamic Real-Time Optimisation algorithm is implemented as a single-level Nonlinear Model Predictive control scheme by utilising the infeasible soft-constraint method to include economic objectives in an industrial tracking NMPC package. A time-varying price of electricity is exploited to enhance cost minimisation by adjusting the regeneration according to the peaks of the price curve. This flexible mode of operation is compared to a fixed mode of operation with constant amine regeneration. Simulation results indicate a cost reduction of 10.9% for a reference accumulated capture ratio of 91%. Robustness of the optimisation to abrupt changes in CO2 feed composition and electricity price is also investigated in simulations and results are promising. The NMPC controller uses a reduced, control-oriented model of the capture plant developed from first principle conservation laws with control volumes to discretize the model equations in space. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： dynamic real-time optimisation nonlinear model predictive control model-based optimisation and control CO2 management

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Statistical Monitoring of processes with Multiple Operating Modes 12

Statistical Monitoring of Processes with Multiple Operating ...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS-CAB)

作者： Tan, Ruomu Cong, Tian Thornhill, Nina F. Ottewill, James R. Baranowski, Jerzy Imperial Coll London Ctr Proc Syst Engn London England AGH Univ Sci & Technol Dept Automat Control & Robot Krakow Poland ABB Corp Res Ctr Krakow Poland

Varying production regimes and loading conditions on equipment often result in multiple operating modes in process operations. The data recorded from such processes will typically be multimodal in nature leading to challenges in applying standard data-driven process monitoring approaches. Moreover, even if a monitoring approach is able to account for the variability present in a training set comprised of historical process data, in order to be robust and reliable the method will need to account for any new operating modes which might emerge during production. Therefore, it is desirable to have a monitoring algorithm that can both handle data multimodality in off-line training and, when implemented on-line, can actively update in order to incorporate new operating modes. This paper proposes a monitoring framework which combines an unsupervised clustering approach with a kernel-based Multivariate Statistical process Monitoring (MSPM) algorithm. A monitoring model is trained off-line and is subsequently used to detect anomalies on-line. An anomaly might be indicative of either a developing fault or a change in the process to a new operating mode. In the latter case, the monitoring model can be updated to account for the new mode whilst still being able to detect faults under this framework. The advantages of the off-line training procedure relative to a standard kernel-based method are demonstrated via a numerical simulation. Additionally, the monitoring performance in the presence of faults and the capability of updating the model in the presence of new operating modes is demonstrated using a benchmark data set from an experimental pilot plant. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Fault detection unsupervised learning process monitoring multimode process kernel method

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