Dynamic optimization with a simultaneou method: Application to a heat exchanger

In this work we use a numeric method based on a combined discretization and simultaneous dynamic optimization approach to solve a system consisting of partial differential and algebraic equations. The spatial derivatives are discretized by finite differences while the resulting DAE (Differential-Algebraic Equations) optimization problem is transformed into a large-scale NLP (Nonlinear Programming) problem through collocation over finite elements. This method is implemented in a computer package resident in a remote computer located at the Department of Chemical Engineering Carnegie Mellon University, which is accessed via a high-speed internet connection (Internet 2) from a client computer at the Centro Regional de Investigaciones Basicas y Aplicadas Bahia Blanca (CRIBABB). We have applied this strategy to the resolution of the dynamic optimization model of a gas - gas heat exchanger, which is part of a larger model under development. The goal is to minimize the transient between two set points of an outlet stream temperature. The dynamic model provides profiles of controlled and manipulated variables which are in agreement with available data, and the remote optimization system performed very well.Fil: Rodriguez, Mariela Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Díaz, María Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentin

Indirect monitoring of energy efficiency in a simulated chemical process

Abstract. Energy efficiency is an important part of chemical process sustainability. Wasted energy contributes significantly to process costs and overall emissions. Therefore, contributions to improving energy efficiency in chemical processes are of value. The main objective of this thesis is the exploration of indirect energy efficiency monitoring methods and their compilation into a generalized framework. As part of the proposed framework, data-based modelling methods were explored and used to identify a model for estimating energy efficiency in a simulated process. The proposed framework can act as a potential tool in different practical applications with energy efficiency improvements as an objective. As a simulated test process for this thesis, the Tennessee Eastman process was utilized. This process is widely used in research, especially regarding fault diagnosis and control design. The process includes slow dynamics and nonlinearity, providing interesting challenges for research. Even though the process has been studied extensively, the energy efficiency aspect of the process has not been taken into account in research. The results of the thesis show that data-based models provide sufficient accuracy for real-time estimation of energy efficiency for the Tennessee Eastman process. Parts of the proposed framework were tested with the explored methods, but some areas were beyond the scope of this thesis. As such, further research, for example prediction of the energy efficiency horizon, fault diagnosis and advanced process control, could be beneficial.Energiatehokkuuden epäsuora monitorointi simuloidussa kemiallisessa prosessissa. Tiivistelmä. Energiatehokkuus on tärkeä osa kemiallisen teollisuuden kestävyyttä. Energian käytön tehottomuus näkyy merkittävästi kasvavina prosessikustannuksina ja kokonaispäästöinä. Toimet energiatehokkuuden nostamiseksi ovat siksi merkityksellisiä. Diplomityön päätavoitteena on erilaisten epäsuorien energiatehokkuuden seurantamenetelmien tutkiminen ja niiden kokoaminen yleistettävään menetelmäkehykseen. Datapohjaisia mallinnusmenetelmiä tutkitaan osana esitettyä kehystä, ja niitä hyödynnetään energiatehokkuutta arvioivan mallin muodostuksessa. Esitetty menetelmäkehys voi toimia mahdollisena työkaluna erilaisissa käyttökohteissa, joissa energiatehokkuuden parantaminen on päämääränä. Tutkittavana kohteena diplomityössä käytettiin simuloitua Tennessee Eastman prosessimallia. Vaikka prosessia on tutkittu laajasti, energiatehokkuuden tarkempi tarkastelu on jäänyt vajaaksi. Simuloitua prosessidataa hyödynnettiin tässä työssä prosessin energiatehokkuuden mallipohjaisen arvion muodostuksessa. Työssä analysoitiin myös mallinnuksen luotettavuuteen vaikuttavia tekijöitä, kuten opetusdatan rajallisuutta ja siitä seuraavaa mallin ekstrapolointia. Diplomityön tulokset osoittavat, että Tennessee Eastman prosessin energiatehokkuuden reaaliaikainen arviointi datapohjaisilla menetelmillä onnistuu riittävällä tarkkuudella. Esitetyn menetelmäkehyksen osia testattiin tutkituilla menetelmillä, mutta jotkin alueet jäivät työn ulkopuolelle. Tulevaisuuden mahdollisiin tutkimusalueisiin kuuluukin energiatehokkuuden ennustaminen, vikadiagnostiikka ja niitä yhdistävä kehittynyt prosessisäätö

Optimization of hybrid dynamic/steady-state processes using process integration

Much research in the area of process integration has focused on steady-state processes. However, there are many process units that are inherently unsteady-state or perform best when operated in an unsteady-state manner. Unsteady-state units are vital to chemical processes but are unable to be included in current process optimization methods. Previous methods to optimize processes containing unsteady-state units place restrictions or constraints on their use. This optimization still does not give the best system design because the solution found will only be the best out of the available options which likely excludes the true optimal design. To remedy this, a methodology was created to incorporate unsteady-state process units into process optimization analysis. This methodology is as general as possible. Unlike many existing unsteadystate optimization methods, it determines all three main components of process design: the network configuration, sizes of units, and operation schedule. This generality ensures that the truly optimal process design will be found. Three problems were solved to illustrate the solution methodology. First, a general mass exchange network was optimized. The optimization formulation resulted in a mixed-integer nonlinear program, and linearization techniques were used to find the global solution. A property interception network was also optimized, the first work done using property integration for systems with unsteady-state behavior. Finally, an industrial semi-batch water purification system was optimized. This problem showed how process integration could be used to optimize a hybrid system and gain insights into the process under many different operating conditions

Contribution à l'optimisation des systèmes dynamiques : application au génie des procédés

Ces travaux présentent une méthode d'optimisation dynamique pour le contrôle optimal en génie des procédés. Plus généralement, ils s'inscrivent dans une thématique plus vaste, concernant l'optimisation des systèmes dynamiques hybrides. La méthode développée repose sur une méthode hybride entre deux méthodes directes : approches séquentielles et simultanées. Cette méthode nommée Méthode directe a tirs multiples, se caractérise par un découpage de l'horizon de temps en sous intervalles d'intégration et par une réduction de l'espace de recherche des fonctions de contrôle par une paramétrisation de ces dernières. Contrairement aux méthodes séquentielles, la méthode présentée permet de prendre en compte facilement des contraintes sur les variables d'état, ce qui représente une classe importante de problème en Génie des Procédés. De plus, la taille du problème d'optimisation reste acceptable car seules les fonctions de contrôle sont paramétrisées. Toutefois, ce problème d'optimisation est plus complexe que celui des approches séquentielles, à cause de la mise en oeuvre de contraintes de continuité (sur les variables d'état et de contrôle) entre les différents sous intervalles issus de la subdivisons du temps. Cette méthode est intégrée dans le noyau numérique de la plate forme PrODHyS. Son utilisation est illustrée au travers de trois exemples en génie des procédés. ABSTRACT : These works present a method for dynamic optimization and more precisely for optimal control in Chemical Engineering. More generally, they are included in a vaster research which deals with optimization of hybrid dynamic systems. The developed method is a hybrid one between two direct methods: sequential and simultaneous approaches. In this method, called multiple shooting type techniques (direct method), the original time interval is divided into multiple shooting intervals with DAE solved numerically on each subintervals. In order to reduce the research space for control function, we use control parameterization. With this method we can take into account state path constraints, which include an important number of problems in chemical engineering (which is not feasible with sequential approach). Moreover, the optimization problem size is less important than in the simultaneous approach because only the control functions are parameterized. The optimization problem is more complex because of continuity constraints imposed across subintervals (on state and control variables). This method is integrated within the numerical kernel of the platform PrODHyS. It is illustrated with three chemical engineering example

Energy-efficient driving strategies for rail vehicles

The majority of railways internationally are striving to improve their financial performance while meeting competition from other modes of transport. A key factor in achieving this is by reducing energy consumption, which accounts for a significant proportion of all operating costs. The research undertaken for this Thesis addresses this challenge by applying optimization approaches to develop energy-efficient train operation strategies. It does this by developing a hybrid optimization approach, which combines global optimization techniques, for their "global" optimality properties, with local ones, for their faster convergence rate. Due to the number of control constraints and the number of decision stages involved for the control of a typical running train, a ruled-based quasi-global optimal control strategy is developed. This means that instead of first optimizing the control strategy for each particular running scenario, the Thesis shows how to develop optimized parameterized train operational control policies from empirical experience. The second step to develop the control sequence/strategy is using the control strategy generated from the optimized train operational control policies as initial searching point(s), then necessary optimality conditions are applied to locate the sub-optimal strategy for the vehicle in the particular running scenario. The proposed hybrid optimization method has been assessed and validated with the use of examples. The method shows good potential for significantly improving the fuel economy of running trains. The method has also shown significant numerical advantages over other conventional optimization methods in solving the optimization problem of the optimal/sub-optimal operation of a general running train with a long control horizon

plant-wide control of industrial processes using rigorous simulation and heuristics

Ph.DDOCTOR OF PHILOSOPH

Stochastics global optimization methods and their applications in Chemical Engineering

Ph.DDOCTOR OF PHILOSOPH

Proceedings. 22. Workshop Computational Intelligence, Dortmund, 6. - 7. Dezember 2012

Dieser Tagungsband enthält die Beiträge des 22. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) der vom 6. - 7. Dezember 2012 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für - Fuzzy-Systeme, - Künstliche Neuronale Netze, - Evolutionäre Algorithmen und - Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen

A study on modelling, data reconciliation and optimal operation of hydrogen networks in oil refineries

Se ha llevado a cabo un estudio sobre la gestión óptima en tiempo real de redes de hidrógeno en refinerías de petróleo, con referencia a la refinería de Petronor perteneciente al grupo Repsol y situada en Muskiz (Vizcaya). La tesis consiste en la aplicación de técnicas bien conocidas y establecidas como el modelado de procesos y la optimización a un tema interesante en la actualidad: las redes de hidrógeno en refinerías de petróleo. Los resultados obtenidos son coherentes y robustos, y las soluciones alcanzadas pueden ser directamente aplicadas en la práctica industrial. El problema abordado tiene gran relevancia industrial, con el propósito general de mejorar la operación en tiempo real, ahorrar recursos en este caso materiales relativos al hidrógeno, y aumentar el conocimiento del sistema en la medida de lo posible. Asimismo se han desarrollado librerías en el entorno de simulación EcosimPro. Los objetivos de la tesis son: i) la estimación correcta tanto de variables medidas como de variables desconocidas de la red de hidrógeno; ii) la determinación de las condiciones óptimas de operación así como de las producciones óptimas; iii) la exploración de otros enfoques dirigidos a la operación óptima como la técnica de control self-optimizing; iv) la evaluación del interés y potencial aplicabilidad de modelos simplificados de las plantas para la predicción del consumo de hidrógeno en función de la carga de hidrocarburo.Departamento de Ingeniería de Sistemas y Automátic