216 research outputs found
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
Theory and Application of Nonlinear Wave Propagation Phenomena in Combined Reaction/Separation Processes
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
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.
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
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
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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