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Mixed Modifier-Adaptation for RTO in a Continuous Bioreactor

IFAC Proceedings Volumes 2014年第3期47卷 7635-7640页

作者： Daniel Navia Gloria Gutiérrez César de Prada Dpt. Ingeniería Química y Ambiental Universidad Técnica Federico Santa María Santiago Chile Dpt. Systems Engineering and Automatic Control University of Valladolidc/ Real de Burgos s/n 47011

This paper deals with the problem of optimizing the performance of a process using Real Time Optimization (RTO) considering the unavoidable errors in the process models. It implements a new architecture within the modifier-adaptation methodology, presenting a nested optimization problem with two layers. With this methodology, it is possible to find a point that satisfies the KKT conditions of a process using an inaccurate model in the optimization, without the need to estimate directly the experimental gradients of the process. The suggested methodology has been tested in a continuous bioreactor example that present a washout closer to the real optimum of the simulated process. The results show that the proposed methodology is able to find the optimum of the process smoothly, avoiding unstable operating points.

关键词： Real time optimization Nested modifier-adaptation Modifier adaptation uncertainty

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Incubator system identification of humidity and temperature: Comparison between two identification environments 9

Incubator system identification of humidity and temperature:...

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9th International Renewable Energy Congress, IREC 2018

作者： El Hadj Ali, J. Feki, E. Zermani, M.A. De Prada, C. Mami, A. Tunis Tunisia Systems Engineering and Automatic Control DPT EII Universidad de Valladolid C/Real de Burgos s/n Valladolid47011 Spain

ISBN: (纸本)9781538609989

The preterm infant are estimated to be around 15 million cases worldwide every year, according to the World Health Organization (WHO) that is more than 1 in 10 babies worldwide 60% of them are born in Sub-Saharan Africa and South Asia. So the babies born prematurely are generally kept in special chamber or sensitive unit care called 'incubators' which are designed to help this preterm babies to keeping their bodies warm for the first several weeks of life. Because the infant incubator provides a stable level of temperature, relative humidity and oxygen concentration. Temperature control system is the most important part of the baby incubator which is around (37°C). This paper focuses on the comparison of two identification methods to get the best model from the experimental data and chooses the one that is nearest to the real process. The two methods are the identification system toolbox from Matlab environment and a software called HIDEN or Hito IDENTification. © 2018 IEEE.

关键词： MATLAB

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Nested Modifier-Adaptation for RTO in the Otto Williams Reactor

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IFAC Proceedings Volumes 2013年第32期46卷 123-128页

This paper deals with the problem of uncertainty management in real time optimization (RTO). It proposes a new architecture in the modifier-adaptation methodology, reformulating the algorithm as a nested optimization problem with two layers. Using this approach, it is possible to find a point that satisfies the KKT conditions of a process using an inaccurate model, but unlike the original modifier method, with no need to estimate the experimental gradients of the process. The proposed method has been tested in the Otto Williams Reactor considering structural mismatches and perfect and noisy measurements. The results are compared with the previous modifier adaptation methodology using dual control optimization showing that the method finds a KKT point of the process with the advantage that no experimental gradient information is required and with less sensitivity to process noise.

关键词： Process optimization Real Time Optimization modifier-adaptation direct search uncertainty

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Hybrid control of a Four Tanks System

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IFAC Proceedings Volumes 2004年第1期37卷 749-754页

作者： C. de Prada S. Cristea D. Megías J. Serrano Dpt. Systems Engineering and Automatic Control University of Valladolid Faculty of Sciences c/ Real de Burgos s/n 47011 Valladolid Spain Dpt. Systems Engineering and Automatic Control ETSE Universitat Autónoma de Barcelona 08193 Bellaterra Barcelona Spain Tel. (+34) 935 813 027 Fax (+34) 935 813 033

Many processes, even being of a continuous nature, involve in its operation signals or rules different from the classical continuous variables represented by real variables and modelled by DAE. In practice they include on/off valves or other binary actuators, are subjected to logical operational rules, or are mixed with sequential operations. As a result, classical control does not fit very well with the overall operation of the plant. In this paper we consider the problem of hybrid control from a predictive control perspective, showing in a practical non trivial example with changing process structure, how the problem can be stated and solved.

关键词： Hybrid control predictive control four tank system

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Optimizing crude oil operations scheduling considering blending in tanks

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IFAC-PapersOnLine 2024年第14期58卷 67-72页

作者： Tomas Garcia Garcia-Verdier Gloria Gutierrez Carlos Mendez Cesar de Prada Dpt. of Systems Engineering and Automatic Control University of Valladolid Valladolid Spain Institute of Sustainable Processes University of Valladolid Valladolid Spain Center for Advanced Process Systems Engineering (CAPSE) INTEC (UNL - CONICET) Argentina

This paper focuses on the optimization of crude oil operations scheduling in a refinery that is supplied with crude oil by ship. One of the main challenges associated with the crude oil operations scheduling problem is the management of crude storage in tanks. Since storage capacity is limited and there are several types of crude oil depending on their composition, it is necessary to store mixtures of crude oil in tanks. This feature makes necessary the inclusion of nonlinear, non-convex constraints, which complicates the resolution of mathematical programming models. To address this problem, we have developed a mathematical programming model based on a continuous-time formulation using time slots, along with a strategy based on piecewise McCormick relaxation that allows us to efficiently handle the nonlinear constraints generated by blending crude oils in tanks.

关键词： Modelling decision making in complex systems Efficient strategies for large scale complex systems Production planning control Optimization control of large-scale network systems Complex logistic systems

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Simplified modelling and validation of an industrial diesel hydrodesulfurization plant

Simplified modelling and validation of an industrial diesel ...

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9th International Symposium on Dynamics and control of Process systems, DYCOPS 2010

作者： Gómez, E. Sarabia, D. Cristea, S. Gutiérrez, G. Méndez, C.A. Sola, J.M. Unzueta, E. González, R. De Prada, C. Dpt. of Systems Engineering and Automatic Control Faculty of Sciences University of Valladolid c/ Real de Burgos 47011 Spain Güemes 3450-3000 Santa Fe Argentina Edificio Muñatones c/ San Martín 5 48550 Muskiz Vizcaya Spain

ISBN: (纸本)9783902661692

A dynamic reduced model of an industrial diesel hydrodesulfurization plant has been developed and validated against plant data. Its purpose is the prediction of the rate of hydrogen consumption, as well as some other critical variables, as a function of the hydrocarbon feed, so as to be integrated in a decision support system aimed at the operation optimization in real time of the refinery hydrogen network. A model which combines first physical-chemical principles with black box elements is proposed. Model structure and calibration procedure are described, and validation results are presented. © 2009 IFAC.

关键词： Optimization

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Optimizing the monthly scheduling of crudes in a terminal-refinery system

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IFAC-PapersOnLine 2023年第2期56卷 7414-7419页

This paper focuses on solving the optimization of crude oil operations scheduling carried out in a real system composed of a refinery and a marine terminal, over a monthly horizon. In the present article, we introduce a large-scale mixed-integer non-linear programming (MINLP) model that faithfully represents the operation and characteristics of the system. Considering the model's complexity and its non-linear and non-convex nature, the challenge lies in solving the model in a time frame that meets the user's needs. To tackle this problem, we develop a temporal decomposition method in conjunction with a linear approximation.

关键词： Modelling and decision making in complex systems Production planning and control Continuous-time representation Optimization and control of large-scale network systems Temporal decomposition method

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Modifier-Adaptation methodology for RTO applied to Distillation Columns using a simplified steady-state model

Modifier-Adaptation methodology for RTO applied to Distillat...

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IEEE Multi-Conference on systems and control

作者： T. Rodriguez-Blanco D. Sarabia C. de Prada Dpt of Systems Engineering and Automatic Control University of Valladolid Dpt of Electromechanical Engineering University of Burgos

ISBN: (纸本)9781509007561

Real-Time Optimization (RTO) is not always able to achieve optimal process operation due to different reasons, among them the presence of significant uncertainty in the plant models that are used to make decisions or the differences between control architecture layers which operate at different time-scales and use different kind of models. To overcome these issues the economic optimization problem, solved in the RTO layer, is modified following the Modifier Adaptation methodology (MA) to bring the process to the real optimum despite the presence of uncertainty by using plant measurements. This paper presents the implementation of MA methodology to the optimal management of distillation columns as a depropanizer column. Specifically, two approaches have been implemented starting from the traditional gradient-based technique called Dual Modifier Adaptation (DMA) to the recent approach Nested Modifier Adaptation (NMA), updating modifiers at the steady state of the process using static information. The RTO layer is based on a really simplified static model of the process which implies the presence of strong structural plant-model mismatch.

关键词： Distillation columns Modifiers static models real time operating system Steady state Mission Assurance

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Gross error management in data reconciliation

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IFAC-PapersOnLine 2015年第8期48卷 623-628页

作者： M.J. Fuente G. Gutierrez E. Gomez D. Sarabia C. de Prada Dpt. of Systems Engineering and Automatic Control EII University of Valladolid c/ Real de Burgos 47011 Spain Dpt of Electromechanical Eng. University of Burgos Avda. Cantabria s/n 09006 Burgos

This paper reviews the problems associated to the presence of gross errors in data reconciliation problems as well as the main approaches to avoid their undesirable effects: detection and removal of the faulty measurements and minimization of their effect on the reconciled results by using robust estimators that modify the cost function. In the first case, Principal Component Analysis is used to detect the gross errors while, in the second, the Fair function is used. A combined approach is also presented that improves the PCA results. The methods are evaluated in a realistic large-scale problem with plant data corresponding to the hydrogen network of a petrol refinery.

关键词： Data reconciliation Gross error detection PCA robust estimators hydrogen networks

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Checking orthogonal transformations and genetic algorithms for selection of fuzzy rules based on interpretability-accuracy concepts

Checking orthogonal transformations and genetic algorithms f...

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IEEE International Conference on Fuzzy systems

作者： Rey, M. Isabel Galende, Marta Sainz, Gregorio I. Fuente, Maria J. INDOMAUT S.L. Pol. Ind. San Cristóbal 47012 Valladolid Spain Spain Dpt. of Systems Engineering and Control School of Industrial Engineering University of Valladolid 47011 Valladolid Spain

ISBN: (纸本)9781424473175

Fuzzy modeling is one of the most known and used techniques in different areas to emulate the behavior of systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high performance from the point of view of accuracy, but from other points of view, such as complexity or interpretability, the models can present a poor performance. Several approaches are found in the specialized literature to reduce the complexity and improve the interpretability of the fuzzy models. Here, a post-processing approach is taken into account via the definition of the rules selection criterion that aims to choose the most relevant rules according to the well-known accuracy-interpretability trade-off. This criterion is based on Orthogonal Transformations, here the QRP transformation is taking into consideration, and its parameters are tuned genetically. The main objective is to check the true significance, drawbacks and advantages the firing matrix of the rules, that is the foundation of the most usual approaches based on orthogonal transformations for the complexity reduction of the fuzzy models. A neuro-fuzzy system, FasArt (Fuzzy Adaptive System ART based), and several case studies, data sets from the KEEL Project Repository, are used to tune and check this approach. This neuro-fuzzy system generates Mamdani fuzzy rule based systems (FRBSs), each with its own particularities and complexities from the point of view of fuzzy sets and rule generation. NSGA-II is the MOEA tool used to tune the criterion parameters based on accuracy-interpretability ideas. © 2011 IEEE.

关键词： Genetic algorithms

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