Controle de uma coluna de destilação fracionada projetada com solidworks®, utilizando a biblioteca simmechanics / Control of a fractional distillation column designed with solidworks®, using the simmechanics library

O objetivo deste trabalho é apresentar o comportamento de uma coluna de destilação fracionada, a partir do uso de controle fuzzy baseado nas regras de Mandani, sem a necessidade de calcularum modelo matemático. O projeto da coluna de destilação é baseado em um modelo existente no Departamento de Engenharia Química da Universidade de Leuven, Bélgica, o qual é realizado com o uso do software SolidWorks®, e através da biblioteca SimMechanics, presente no MATLAB®/Simulink, obtem-se um diagrama de blocos que representa o comportamento da coluna de destilação, sem o uso de técnicas de identificação ou baseado no uso de modelagem caixa branca. Dessa forma, o foco do projeto se estabelece no desenvolvimento das partes físicas de determinados processos, sem a preocupação da obtenção de um modelo matemático que possa representar o processo

Inferential active disturbance rejection control of distillation columns

PhD ThesisThe distillation column is an important processing unit in the chemical and oil refining industry. Distillation is the most widely employed separation method in the world’s oil plants, chemical and petrochemical industrial facilities. The main drawback of the technique is high energy consumption, which leads to high production costs. Therefore, distillation columns are required to be controlled close to the desired steady state conditions because of economic incentives. Most industrial distillation columns are currently controlled by conventional multi-loop controllers such as proportional-integral-derivative (PID) controllers, which have several shortcomings such as difficulty coping with sudden set-point jumps, complications due to the integral term (I), and performance degradation due to the effect of noise on the derivative term (D). The control of ill-conditioned and strongly non-linear plants such as high purity distillation needs advanced control schemes for high control performance. This thesis investigates the use of active disturbance rejection control (ADRC) for product composition control in distillation columns. To the author’s knowledge, there are few reported applications of ADRC in the chemical industry. Most ADRC applications are in electrical, robotics and others. Therefore, this research will be the first to apply the ADRC scheme in a common chemical processing unit, and can be considered as a first contribution of this research. Initially, both PI and ADRC schemes are developed and implemented on the Wood–Berry distillation column transfer function model, on a simulated binary distillation column based on a detailed mechanistic model, and on a simulated heat integrated distillation column (HIDiC) based on a detailed mechanistic model. Process reaction curve method and system identification tools are used to obtain the 2×2 multi-input multi-output (MIMO) transfer function of both binary and HIDiC for the purpose of PI tuning where the biggest log-modulus tuning (BLT) method is used. Then, the control performance of ADRC is compared to that of the traditional PI control in terms of set-point tracking and disturbance rejection. The simulation result clearly indicates that the ADRC gives better control performance than PI control in all three case studies. The long time delay associated with product composition analysers in distillation columns such as gas chromatography deteriorates the overall control performance of the ADRC scheme. v To overcome this issue an inferential ADRC scheme is proposed and can be considered as a second contribution of this research. The tray temperatures of distillation columns are used to estimate both the top and bottom product compositions that are difficult to measure on-line without a time delay. Due to the strong correlation that exists in the tray temperature data, principal component regression (PCR) and partial least square (PLS) are used to build the soft sensors, which are then integrated into the ADRC. In order to overcome control offsets caused by the discrepancy between soft sensor estimation and actual compositions measurement, an intermittent mean updating technique is used to correct both the PCR and PLS model predictions. Furthermore, no significant differences were observed from the simulation results in the prediction errors reported by both PCR and PLS. The proposed inferential ADRC scheme shows effective and promising results in dealing with non-linear systems with a large measurement delay, where the ADRC has the ability to accommodate both internal uncertainties and external disturbances by treating the impact from both factors as total disturbances that will then be estimated using the extended state observer (ESO) and cancelled out by the control law. The inferential ADRC control scheme provides tighter product composition control that will lead to reduced energy consumption and hence increase the distillation profitability. A binary distillation column for separating a methanol–water mixture and an HIDiC for separating a benzene–toluene mixture are used to verify the developed inferential ADRC control scheme.Petroleum Development of Oman (PDO) for their generous support and scholarshi

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering