216 research outputs found

    Inferential active disturbance rejection control of distillation columns

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    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

    Theory and Application of Nonlinear Wave Propagation Phenomena in Combined Reaction/Separation Processes

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    Reaction separation processes, reactive distillation, chromatographic reactor, equilibrium theory, nonlinear waves, process control, observer design, asymptoticaly exact input/output-linearizationMagdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2007von Stefan Grüne

    Fault tolerant control system design for distillation processes

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    PhD ThesisThe complexity and sophistication of modern control systems deployed in the re nery operation, particularly the crude distillation unit as a result of increasing demand for higher performance and improved safety, are on the increase. This growing complexity comes with some level of vulnerabilities, part of which is the potential failure in some of the components that make up the control system, such as actuators and sensors. The interplay between these components and the control system needs to have some built-in robustness in the face of actuator and sensor faults, to guarantee higher reliability and improved safety of the control system and the plant respectively, which is fundamental to the economy and operation of the system. This thesis focuses on the application of frugally designed fault tolerant control systems (FTCS) with automatic actuator and sensor faults containment capabilities on distillation processes, particularly atmospheric crude distillation unit. A simple active actuator FTCS that used backup feedback signal, switchable references and restructurable PID controllers was designed and implemented on three distillation processes with varying complexities { methanol-water separation column, the benchmark Shell heavy oil fractionator, and an interactive dynamic crude distillation unit (CDU) to accommodate actuator faults. The fault detection and diagnosis (FDD) component of the actuator FTCS used dynamic principal component analysis (DPCA), a data-based fault diagnostic technique, because of its simplicity and ability to handle large amount of correlated process measurements. The recon gurable structure of the PID controllers was achieved using relative gain array (RGA) and dynamic RGA system interaction analysis tools for possible inputs { outputs pairing with and without the occurrence of actuator faults. The interactive dynamic simulation of CDU was developed in HYSYS and integrated with MATLAB application through which the FDD and the actuator FTCS were implemented. The proposed actuator FTCS is proved being very e ective in accommodating actuator faults in cases where there are suitable inputs { outputs pairing after occurrence of an actuator fault. Fault tolerant inferential controller (FTIC) was also designed and implemented on a binary distillation column and an interactive atmospheric CDU to accommodate sensor faults related to the controlled variables. The FTIC used dynamic partial least squared (DPLS) and dynamic principal component regression (DPCR) based soft sensor techniques to provide redundant controlled variable estimates, which are then used in place of faulty sensor outputs in the feedback loops to accommodate sensor faults and maintain the integrity of the entire control system. Implementation issues arising from the e ects of a sensor fault on the secondary variables used for soft sensor estimation were addressed and the approach was shown to be very e ective in accommodating all the sensor faults investigated in the distillation units. The actuator FTCS and the FTIC were then integrated with the DPCA FDD scheme to form a complete FTCS capable of accommodating successive actuator and sensor faults in the distillation processes investigated. The simulation results demonstrated the e ectiveness of the proposed approach. Lastly, fault tolerant model predictive control (FTMPC) with restructurable inputs { outputs pairing in the presence of actuator faults based on preassessed recon gurable control structures was proposed, and implemented on an interactive dynamic CDU. The FTMPC system used a rst order plus dead time (FOPDT) model of the plant for output prediction and RGA and DRGA tools to analyse possible control structure recon guration. The strategy helped improve the availability and performance of control systems in the presence of actuator faults, and can ultimately help prevent avoidable potential disasters in the re nery operation with improved bottom line { Pro t. Overall, the proposed approaches are shown to be e ective in handling actuator and sensor faults, when there are suitable manipulated variables and redundant analytical signals that could be used to contain the e ects of the faults on the system.University of Lagos, Nigeria & Petroleum Technology Development Fund (PTDF) for the Scholarship award at the later stage of my research programme

    Nonlinear Model Predictive Control Of A Distillation Column Using Hammerstein Model And Nonlinear Autoregressive Model With Exogenous Input.

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    Turus penyulingan adalah unit proses penting dalam industri penapisan petroleum dan kimia. Ia perlu dikawal hampir dengan keadaan-keadaan pengendalian yang optima demi insentif- nsentif ekonomi. Distillation column is an important processing unit in petroleum refining and chemical industries, and needs to be controlled close to the optimum operating conditions because of economic incentives

    Methodologies for the optimisation, control and consideration of uncertainty of reactive distillation

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    The work presented in this thesis is motivated by the current obstacles hindering the implementation of reactive distillation in industry, mainly related to the complexities of its design and control, as well as the impact of uncertainties thereupon. This work presents a rigorous methodology for the optimal design and control under uncertainty of reactive distillation. The methodology can also be used to identify and investigate mitigation strategies for process failures arising due to design and/or operation deficiencies under changed processing conditions, based on the evaluation of different design and/or control alternatives. The first step of the methodology is the simultaneous (MINLP) optimisation of the design and operation of a reactive distillation process superstructure, used to explore the possible steady-state design alternatives available, including ancillary equipment such as pre- and side-reactors, side-strippers and additional distillation columns, based on product-related constraints and a detailed objective cost function. The next step is the investigation of the dynamic control performance of this optimal system, where conventional and advanced process control strategies are considered in order to investigate how robust the system is towards operational disturbances, or whether revising the optimal steady-state design is required. As the optimisation depends heavily on accurate data for reaction kinetics and separation performance, the final step of the methodology is the evaluation of the impact of parameter uncertainty on the performance of the optimal controlled system, including redesigning the controlled system if required. The methodology is demonstrated using a number of industrially relevant case studies with different reaction and separation characteristics in order to investigate how these determine the design and control of an economically attractive and rigorous reactive distillation process. It is demonstrated that the process characteristics have a significant impact on the design of the system, and that auxiliary equipment may be required to meet production specifications and/or to ensure robust controlled behaviour. It is also shown that, under parameter uncertainty, an optimal controlled system may nevertheless face performance issues, and revising the design and/or operation of the process may be required in order to mitigate such situations

    Nonlinear control of high purity distillation columns

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    Two simple models of distillation columns are studied to investigate their suitability for the practical use with exact I/O-linearization. An extension of exact I/O-linearization, the asymptotically exact I/O-linearization is applied to the control of a high purity distillation column, using one of these models to derive the static state feedback law. Simulation studies demonstrate the advantage of asymptotically exact I/O-linearization versus classical exact I/O-linearization techniques. Experimental results show the excellent performance of asymptotically exact I/O-linearization using a simple distillation model
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