Diseño de una estrategia de reducción de variabilidad en procesos con controladores tipo PID frente a perturbaciones oscilatorias

La calidad y el costo de los productos de las plantas industriales se ven afectados por la variabilidad inevitable en los procesos, sumado a las distintas perturbaciones que se presenten en el proceso. Desde la perspectiva del control automático de procesos, las perturbaciones oscilatorias son perjudiciales tanto porque afecta componentes mecánicos del proceso como porque su propagación conlleva a un aumento en la varianza del proceso. De igual forma, el rendimiento financiero de un proceso industrial se ve influenciado de manera negativa por lazos de control con controladores pobremente sintonizados, y que no fueron concebidos para actuar de manera óptima ante las perturbaciones mencionadas. Usualmente los controladores se han sintonizado para que la variable controlada alcance un valor fijo deseado ante un cambio abrupto en el sistema. Por todo lo anterior, en el marco de esta investigación se desarrolló una técnica de reducción de variabilidad en lazos de control en procesos con controladores tipo PID frente a perturbaciones oscilatorias mediante la re-sintonización del controlador. La metodología contemplada abarcó dos rutas, primero se realizó un desarrollo analítico y posteriormente uno experimental desde un punto de vista computacional, en donde se utilizó la función de transferencia del controlador como mecanismo para predefinir el comportamiento de la función de transferencia en lazo cerrado. A partir de estos desarrollos se obtuvo mejores resultados que al utilizar sintonías tradicionalmente implementadas

An adaptive autopilot design for an uninhabited surface vehicle

An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council

Dynamics, Control and Extremum Seeking of the Rectisol Process

Pendant la dernière décennie, les bioraffineries basées sur la gazéification ont fait l’objet de nombreuses études dans le cadre des efforts mondiaux visant à remplacer les combustibles fossiles qui produisent de l’énergie et des produits chimiques à valeur ajoutée. Une partie importante de ces bioraffineries est l’unité de purification des gaz de synthèse issus de l’oxydation partielle, qui enlève le CO2 et l’H2S. Un des procédés de purification considéré dans ces études est le Rectisol. Ce procédé est utilisé car il est plus environnemental et requière moins de coûts d’investissement et d’opération par rapport à d’autres procédés similaires. Afin de faire l’étude dynamique de ce procédé, une simulation en régime permanent à d’abord, été menée à l’aide du logiciel Aspen plus R. ----------ABSTRACT Gasification based biorefineries have been studied in the past decade as part of a global e↵ort to replace fossil fuels to produce energy and added value chemicals. An important part of these biorefineries is the acid gas removal units, that remove CO2 and H2S from the produced synthesis gas. One of the acid gas removal processes associated in these studies is Rectisol. Rectisol has been chosen since it’s environmental friendly and requires a lower amount of operational and capital costs compared to its opponents. To carry out a dynamic study of the process, as a first step, a steady-state simulation was carried out in Aspen Plus

Practical on-line model validation for model predictive controllers (MPC)

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2010.A typical petro-chemical or oil-refining plant is known to operate with hundreds if not thousands of control loops. All critical loops are primarily required to operate at their respective optimal levels in order for the plant to run efficiently. With such a large number of vital loops, it is difficult for engineers to monitor and maintain these loops with the intention that they are operating under optimum conditions at all times. Parts of processes are interactive, more so nowadays with increasing integration, requiring the use of a more advanced protocol of control systems. The most widely applied advanced process control system is the Model Predictive Controller (MPC). The success of these controllers is noted in the large number of applications worldwide. These controllers rely on a process model in order to predict future plant responses. Naturally, the performance of model-based controllers is intimately linked to the quality of the process models. Industrial project experience has shown that the most difficult and time-consuming work in an MPC project is modeling and identification. With time, the performance of these controllers degrades due to changes in feed, working regime as well as plant configuration. One of the causes of controller degradation is this degradation of process models. If a discrepancy between the controller’s plant model and the plant itself exists, controller performance may be adversely affected. It is important to detect these changes and re-identify the plant model to maintain control performance over time. In order to avoid the time-consuming process of complete model identification, a model validation tool is developed which provides a model quality indication based on real-time plant data. The focus has been on developing a method that is simple to implement but still robust. The techniques and algorithms presented are developed as far as possible to resemble an on-line software environment and are capable of running parallel to the process in real time. These techniques are based on parametric (regression) and nonparametric (correlation) analyses which complement each other in identifying problems -iiwithin on-line models. These methods pinpoint the precise location of a mismatch. This implies that only a few inputs have to be perturbed in the re-identification process and only the degraded portion of the model is to be updated. This work is carried out for the benefit of SASOL, exclusively focused on the Secunda plant which has a large number of model predictive controllers that are required to be maintained for optimal economic benefit. The efficacy of the methodology developed is illustrated in several simulation studies with the key intention to mirror occurrences present in industrial processes. The methods were also tested on an industrial application. The key results and shortfalls of the methodology are documented