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Robust control and observation of nonlinear processes using discontinuities 12

Robust control and observation of nonlinear processes using ...

引用

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

作者： Moreno, Jaime A. UNAM Inst Ingn Ciudad De Mexico 04510 DF Mexico

In this overview paper we illustrate, using four simple nonlinear and uncertain processes, the advantages of using discontinuities for robust observation or control. In particular, we show that in some applications a discontinuous term behaves as a universal model for a large class of signals, and it allows the exact estimation, rejection or tracking of these signals, what is impossible using continuous control or estimation algorithms. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Discontinuos control Nonlinear Observers Robust control and observation

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A Polytopic Invariant Set Based Iterative Learning Model Predictive control 12

A Polytopic Invariant Set Based Iterative Learning Model Pre...

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

作者： Lu, Jingyi Gao, Furong Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Hong Kong Peoples R China

Model predictive control (MPC) is often combined with iterative learning control (ILC), which results in the so-called iterative learning model predictive control (ILMPC), to control batch processes with constraints. It is a long standing and challenging problem that how to simultaneously guarantee system stability and constraint satisfaction in ILMPC design. Several invariant set-based methods, such as the zero-terminal state and the ellipsoidal invariant set, have been proposed to solve this problem. However, these methods are often restrictive with conservative control performance and limited applicability. In this paper, we propose a polytopic invariant set based ILMPC method to reduce conservativeness. Specifically, a polytopic invariant set is designed based on geometric computation and proved to be convex and compact. An iterative algorithm is proposed to compute the maximal one. Numerical simulations are provided to demonstrate its effectiveness. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： batch process control model predictive control iterative learning control two-dimensional system robust control invariant set constrained systems

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Extension of the do-mpc Development Framework to Real-time Simulation Studies 12

Extension of the do-mpc Development Framework to Real-time S...

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

作者： Tatulea-Codrean, Alexandru Lindscheid, Clemens Farrera-Saldana, Rafael Engell, Sebastian TU Dortmund Dept Dynam Simulat & Control Fac Bio & Chem Engn Dortmund Germany

Nonlinear Model Predictive control (NMPC) has become a serious option for industrial applications, with advances in software and algorithms making economics optimizing control suitable even for large scale systems. However, the industrial applications are posing a number of engineering challenges, related to the performance of NMPC in real plant environments. In this paper we address the issue of validating NMPC solutions in a real-time simulation environment, which mimics precisely the behavior of the controlled plant. We propose a validation strategy and describe the simulation framework that was developed in order to support the validation process. The software platform used for this purpose is based on the do-mpc framework, benefiting from its modularity and efficient implementation of NMPC algorithms. The focus of this work is the extension of the software environment to a real-application oriented platform where control scenarios can be simulated in real time. The necessity of the proposed validation strategy is demonstrated for a semi-batch polymerization example, where it is shown that feedback delays have a significant impact on the real-time performance. The necessary steps for a transition from simulation studies to the real application are discussed and a method for improving the NMPC performance is proposed based on the real-time simulation studies. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： NMPC real-time systems simulation framework real-time validation

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Optimal Coordination Of Batch processes with Shared Resources 12

Optimal Coordination Of Batch Processes with Shared Resource...

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

作者： Palacin, Carlos G. de Prada, Cesar Univ Valladolid Dept Syst Engn & Automat Control Escuela Ingn Ind Sede Mergelina C Real Burgos S-N E-47011 Valladolid Spain Univ Valladolid Inst Sustainable Proc IPS C Real Burgos S-N E-47011 Valladolid Spain

This paper deals with the scheduling of batch processes that share common resources having consumption profiles that change over time and are subjected to some global constraints. In order to approach the problem, the formulation mixes three different time basis. Scheduling is performed in continuous time, but the description of the shared resources and the global constraints uses discrete values. The method is applied to a challenging problem in a tuna canning factory that mixes continuous flows of cans with the batch operation of a set of sterilizers sharing the steam supply line. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： process scheduling shared resources tuna cans sterilization process optimization continuous-batch process scheduling

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Classification of Profit-Based Operating Regions for the Tennessee Eastman process using Deep Learning Methods 12

Classification of Profit-Based Operating Regions for the Ten...

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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： Agarwal, Piyush Budman, Hector Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada

The focus of this work is on the classification of an input-design space into different regions of input conditions that result in different corresponding ranges of productivity costs in the Tennessee Eastman process (TEP). Although similar classification tasks had been previously carried out using linear multivariate statistical data analysis methods, these are of limited efficacy when dealing with highly non-linear dynamics. In this work, we present two Deep Learning Tools for classification: using either supervised learning or un-supervised learning. For classification with supervised learning a Recurrent Neural Network (RNN) known as Long Short Term Memory (LSTM) is trained on normalized training data. Since deep learning networks generally involve a large number of nodes and parameters an algorithm named Sequential Layer wise Relevance Propagation (SLRPFP) is proposed for selecting the relevant inputs and for pruning the LSTM network such that the test accuracy at each step is maintained or even improved. For classification with unsupervised learning, main features from the input dataset are first extracted using an Autoencoder. Then a Multi-dimensional Support Vector Machines (MSVM) model is applied to the features identified by the autoencoder. The performance of the proposed supervised and unsupervised deep learning approaches are compared to an approach that combines linear Dynamic Principal Component Analysis (DPCA) and a MSVM based classification and conclusions are drawn on the relative advantages of the deep learning methods. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： recurrent neural network long short-term memory (LSTM) neural network classification Tennessee Eastman deep learning SVM

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An Optimal control Strategy Separating Two Species of Microalgae in Photobioreactors 12

An Optimal Control Strategy Separating Two Species of Microa...

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

作者： Djema, Walid Giraldi, Laetitia Bernard, Olivier Cote dAzur Univ UCA BIOCORE Project Team Inria Sophia Antipolis Nice France Cote dAzur Univ UCA McTAO Project Team Inria Sophia Antipolis Mediterranean Nice France

We investigate a minimal-time control problem in a chemostat continuous photobioreactor model that describes the dynamics of two distinct microalgae populations. More precisely, our objective is to optimize the time of selection - or separation - between two species of microalgae. We focus in this work on Droop's model which takes into account an internal quota storage for each microalgae species. Using Pontryagin's principle, we develop a dilution-based control strategy that steers the model trajectories to a suitable target in minimal time. Our study reveals that singular arcs play a key role in the optimization problem. A numerical optimal-synthesis, based on direct optimal control tools, is performed throughout the paper, thereby confirming the optimality of the provided feedback-control law, which is of type bang singular. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Optimization feedback control nonlinear Droop's model microalgae chemostat

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Oscillation Detection and Diagnosis in process Industries by Pattern Recognition Technique 12

Oscillation Detection and Diagnosis in Process Industries by...

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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： Dambros, Jonathan W., V Farenzena, Marcelo Trierweiler, Jorge O. Fed Univ Rio Grande do Sul UFRGS Chem Engn Dept GIMSCOP R Eng Luiz Englert S-NCampus Cent Porto Alegre RS Brazil Univ Kaiserslautern Dept Comp Sci D-67663 Kaiserslautern Germany

Oscillation in control loops is a frequent problem faced in process industries. It deviates the process variables from their desired condition, affecting negatively plant productivity. To guarantee profitability, oscillation must be detected, diagnosed, and, finally, eliminated. Dozens of automatic detection and diagnosis techniques have been proposed over the last years. However, the application to real industrial data reveals low efficiencies, which indicates that the techniques require improvement. This work presents a new method for oscillation detection and diagnosis. The technique classifies the loops based on the shape of the PV(OP) diagram by a pattern recognition approach, where the model is trained with simulated examples that cover a large range of processes and different conditions found in industrial data, such as noise and disturbance presence. The performance of the proposed technique is compared to well-established oscillation detection and diagnosis methods, returning better results. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Oscillation detection and diagnosis process control fault detection and diagnosis pattern recognition process monitoring

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Integration between Dynamic Optimization and Scheduling of Batch processes under Uncertainty: A Back-off Approach 12

Integration between Dynamic Optimization and Scheduling of B...

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

作者： Valdez-Navarro, Yael, I Ricardez-Sandoval, Luis A. Univ Waterloo Waterloo ON N2L 3G1 Canada

The aim of this study is to present a decomposition algorithm that employs a new back-off methodology to consider stochastic-based parameter uncertainty for the integration of dynamic optimization and scheduling of multi-unit batch plants. This is achieved by solving a series of optimization problems involving scheduling and control decisions. Simulations based on Monte Carlo sampling techniques are used to propagate uncertainty into the system and determine back-off terms for the process operational constraints. At each step in the algorithm, back-off terms are updated such that the system moves away from the nominal solution until a convergence criterion is met, obtaining a solution that satisfies constraints up to a user-defined probability limit. This algorithm, when applied to a multi-product multi-unit batch plant, produces an optimal schedule and control profiles that remain dynamically feasible in the presence of stochastic based uncertain parameters. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Model-based optimization process scheduling batch processes uncertainty

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control of Self-Assembly with Dynamic Programming 12

Control of Self-Assembly with Dynamic Programming

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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： Grover, Martha A. Griffin, Daniel J. Tang, Xun Georgia Inst Technol Atlanta GA 30332 USA

This conference paper updates a previously-reported methodology for establishing feedback control of self-assembly (Griffin et al. (2016b)). The methodology combines dimension reduction, supervised learning, and dynamic programming to obtain an optimal feedback control policy for reaching a desired assembled state. The strategy is further demonstrated, with both simulation and experimental results, for two applications: control of colloidal assembly (to produce perfect colloidal crystals) and control of crystallization from solution (to produce crystals of desired average size). (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Dynamic programming material systems Markov decision processes closed-loop control reduced-order models learning

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Predictive Simulation Applied to Refinery Hydrogen Networks for Operators' Decision Support 12

Predictive Simulation Applied to Refinery Hydrogen Networks ...

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

作者： Galan, Anibal De Prada, Cesar Gutierrez, Gloria Sarabia, Daniel Gonzalez, Rafael Univ Valladolid Dept Syst Engn & Automat Control E-47011 Valladolid Spain Univ Burgos Dept Electromech Engn Escuela Politecn Super Avda Cantabria S-N Burgos 09006 Spain Petroleos Norte SA Pefronor 48550 Muskiz Spain

Hydrogen networks are essential in modern oil refineries for they supply the main reactant for hydrodesulphurisation processes. These networks are usually integrated in a complex fashion, and sensitive to changes in any of their process units. In addition, plant measurements and prediction tools available, tend to be insufficient to make educated decisions when unexpected changes realize, especially on-line. This study presents an embedded dynamic estimator within a simulation framework, which uses plant data to estimate states and parameters of a hydrogen network and predicts its future behavior. A representative process network of three consumers and two sources is the case study utilised to demonstrate the usefulness in their decision-making process of operators. For this purpose, a brief what-if-analysis is discussed. In the last section, future challenges and directions of this predictive simulation tool are highlighted. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： MHE parameter estimation process simulation what-if-analysis

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