The control and optimization of chemical plants is a major area of research as it has the potential to improve both economic output and plant safety. It is often prudent to separate control and optimization tasks of varying complexities and time scales, creating a hierarchical control structure. Within this structure, it is beneficial for one control layer to be able to account for the effects of other layers. A clear example of this, and the basis of this work, is closed-loop dynamic real-time optimization (CL-DRTO), in which an economic optimization method considers both the plant behavior and the effects of an underlying model predictive controller (MPC). This technique can be expanded on to allow its use and methods to be employed in a greater diversity of applications, particularly unstable and uncertain plant environments. First, this work seeks to improve on existing robust MPC techniques, which incorporate plant uncertainty via direct multi-scenario modelling, by also including future MPC behavior through the use of the CL modelling technique of CL-DRTO. This allows the CL robust MPC to account for how future MPC executions will be affected by uncertain plant behavior. Second, Lyapunov MPC (LMPC) is a generally nonconvex technique which focuses on effective control of plants which exhibit open-loop unstable behavior. A new convex LMPC formulation is presented here which can be readily embedded into a CL-DRTO scheme. Next, uncertainty handling is incorporated directly into a CL-DRTO via a robust multi-scenario method to allow for the economic optimization to take uncertain plant behavior into account while also modelling MPC behavior under plant uncertainty. Finally, the robust CL-DRTO method is computationally expensive, so a decomposition method which separates the robust CL-DRTO into its respective scenario subproblems is developed to improve computation time, especially for large optimization problems. / Thesis / Doctor of Philosophy (PhD) / It is common for control and optimization of chemical plants to be performed in a multi-layered hierarchy. The ability to predict the behavior of other layers or the future behavior of the same layer can improve overall plant performance. This thesis presents optimization and control frameworks which use this concept to more effectively control and economically optimize chemical plants which are subject to uncertain behavior or instability. The strategy is shown, in a series of simulated case studies, to effectively control chemical plants with uncertain behavior, control and optimize unstable plant systems, and economically optimize uncertain chemical plants. One of the drawbacks of these strategies is the relatively large computation time required to solve the optimization problems. Therefore, for uncertain systems, the problem is separated into smaller pieces which are then coordinated towards a single solution. This results in reduced computation time.

<p>Process operability represents the ability of a process plant to operate satisfactorily away from the nominal operating or design condition, where flexibility and dynamic operability are two important attributes of operability considered in this thesis. Today's companies are facing numerous challenges, many as a result of volatile market conditions. Key to sustainable profitable operation is a robust process supply chain. Within a wider business context, flexibility and responsiveness, i.e. dynamic operability, are regarded as key qualifications of a robust process supply chain.</p> <p>The first part of this thesis develops methodologies to rigorously evaluate the dynamic operability and flexibility of a process supply chain. A model is developed which describes the response dynamics of a multi-product, multi-echelon supply chain system. Its incorporation within a dynamic operability analysis framework is shown, where a bi-criterion, two-stage stochastic programming approach is applied for the treatment of demand uncertainty, and for estimating the Pareto frontier between an economic and responsiveness criterion. Two case studies are presented to demonstrate the effect of supply chain design features on responsiveness. This thesis has also extended current paradigms for process flexibility analysis to supply chains. The flexibility analysis framework, where a steady-state supply chain model is considered, evaluates the ability to sustain feasible steady-state operation for a range of demand uncertainty.</p> <p>The second part of this thesis develops a decision-support tool for supply chain management (SCM), by means of a robust model predictive control (MPC) strategy. An effective decision-support tool can fully leverage the qualifications from the operability analysis. The MPC formulation proposed in this thesis: (i) captures uncertainty in model parameters and demand by stochastic programming, (ii) accommodates hybrid process systems with decisions governed by logical conditions/rulesets, (iii) addresses multiple supply chain performance metrics including customer service and economics, and (iv) considers both open-loop and closed-loop prediction of uncertainty propagation. The developed robust framework is applied for the control of a multi-echelon, multi-product supply chain, and provides a substantial reduction in the occurrence of back orders when compared with a nominal MPC framework.</p> / Master of Applied Science (MASc)

Made available in DSpace on 2014-12-17T14:55:47Z (GMT). No. of bitstreams: 1 JoseSBL_DISSERT.pdf: 1769944 bytes, checksum: 43863b3b32771c922314a0fa73be8bf8 (MD5) Previous issue date: 2011-02-14 / This work deals with an on-line control strategy based on Robust Model Predictive Control (RMPC) technique applied in a real coupled tanks system. This process consists of two coupled tanks and a pump to feed the liquid to the system. The control objective (regulator problem) is to keep the tanks levels in the considered operation point even in the presence of disturbance. The RMPC is a technique that allows explicit incorporation of the plant uncertainty in the problem formulation. The goal is to design, at each time step, a state-feedback control law that minimizes a 'worst-case' infinite horizon objective function, subject to constraint in the control. The existence of a feedback control law satisfying the input constraints is reduced to a convex optimization over linear matrix inequalities (LMIs) problem. It is shown in this work that for the plant uncertainty described by the polytope, the feasible receding horizon state feedback control design is robustly stabilizing. The software implementation of the RMPC is made using Scilab, and its communication with Coupled Tanks Systems is done through the OLE for Process Control (OPC) industrial protocol / Este trabalho tem como objetivo desenvolver uma estrat?gia de controle on-line baseado no Controlador Preditivo Robusto (RMPC, acr?nimo do ingl?s Robust Model Predictive Control) aplicado a um sistema real de tanques acoplados. Este processo consiste em sistema de dois tanques conectados, cujo liquido ? enviado aos mesmos por uma bomba. O objetivo do controle (problema regulat?rio) ? deixar os n?veis dos tanques no ponto de opera??o considerado, mesmo na presen?a de perturba??es. A s?ntese da t?cnica RMPC consiste em incorporar de forma explicita as incertezas da planta na formula??o do problema. O objetivo do projeto, a cada per?odo de amostragem, ? encontrar uma realimenta??o de estados que minimiza o pior caso de uma fun??o objetivo com horizonte infinito, sujeita a restri??es no sinal de controle. O problema original, do tipo Min-max, ? reduzido em a problema de otimiza??o convexa expresso em desigualdades matriciais lineares (LMI, Linear Matriz Inequalities). Mostram-se, neste trabalho, a descri??o da incerteza da planta na forma polit?pica e as condi??es de factibilidade do problema de otimiza??o. A implementa??o do algoritmo RMPC foi feita utilizando o software Scilab e a sua comunica??o com o sistema de tanques acoplados foi feita atrav?s do protocolo OPC (do ingl?s OLE for Process Control)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work presents the development of a novel digital acceleration controller for sinusoidal vibration tests using switching-mode AC power source (ACPS) fed electrodynamic vibration machines. The proposed scheme is based on the interaction of two control loops: one for the shaker's acceleration regulation and another for the ACPS output voltage control. A robust model reference adaptive algorithm (RMRAC) is used in the voltage control loop. To reduce the effects caused by the plant's parameters variations as well harmonic vibrations and resonances of the test specimen, the acceleration feedback controller is augmented with a feedforward and a robust controller. Experimental results show that the proposed system is capable to achieve excellent acceleration reference tracking performance and robustness in the closed loop control from 20 Hz to 200 Hz. Investigations shall be performed to evaluate the response of this scheme when controlling vibrations as fast as 2000 Hz. A specific instrumentation system has been developed to feedback the electrodynamic vibration machine's acceleration. The proposed solution employs piezoelectric accelerometers, voltage mode preamplifiers, circuit for signal conditioning, analog to digital conversion and filtering. A study concerning the commonly used vibration measurement techniques and the design procedure, simulation, implementation and experimental results are described in detail. / Esse trabalho apresenta o desenvolvimento de um novo tipo de controlador digital de aceleração para ensaios com vibrações senoidais em máquinas de vibra ção eletrodinâmicas supridas por fontes de potência CA com comutação. O esquema proposto é baseado na interação entre duas malhas de controle: uma para regulação de aceleração da máquina de vibração e outra para ajuste da tensão de saída da fonte de potência CA. Um algoritmo adaptativo robusto por modelo de referência (RMRAC) é utilizado na malha de controle de tensão. Os efeitos de variações paramétricas existentes na planta bem como de vibrações harmônicas e ressonâncias do objeto ensaiado são minimizados com o uso de um controlador feedforward e um controlador feedforward robusto na malha de aceleração. Resultados experimentais demonstram que o sistema proposto é capaz de garantir excelente rastreamento da aceleração de referência e robustez em malha fechada entre 20 Hz e 200 Hz, necessitando ainda de investigação para controle de vibrações tão rápidas quanto 2000 Hz. A realimentação da aceleração da máquina é realizada com o desenvolvimento de uma instrumentação adequada a esse fim. A solução proposta utiliza acelerômetros piezoelétricos, pré-amplificadores modo tensão, circuito para condicionamento de sinais, conversão analógico-digital e filtragem. Um estudo sobre as principais técnicas utilizadas em medições de vibrações bem como a metodologia de projeto, simulação, implementação e testes experimentais são descritos detalhadamente.