Reduced models and control of distillation columns with sharp temperature profiles

Distillation columns with sharp temperature profiles exhibit a characteristic dynamic behavior which is used in this correspondence to deduce a considerably simplified low-order dynamic model. Based on this model, linear state-space methods are employed in the design of the control of an extractive distillation column with a vapor side stream.     

A feedforward control strategy for distillation columns

The prime objective of this work is to demonstrate the potential of neural network modeling for advanced nonlinear control applications. In particular, for the case of a single composition distillation column, a model-based neural controller is developed to regulate the composition of the distillate stream. The neural controller relies on process inversion for the evaluation of the actuator action on the manipulated variable (reflux flowrate) to maintain the controlled variable (distillate composition) at the prescribed value.The performance of the neural controller is assessed and compared with that of a conventional temperature control loop and of a neural inferential control structure. The neural controller by far outperforms the other two in terms of the response speed by which the upsetting loads are compensated.     

A stable control structure for binary distillation columns

A Lyapunov-based controller for the composition control of binary distillation columns has been developed. It takes into account physical constraints on the inputs and ensures the global asymptotic stability of the closed-loop system with robustness to modelling errors and with the capability of performing set-point tracking and (approximate) disturbance rejection. This controller requires the knowledge of the internal state of the model and this leads to the design of an exponentially converging 'high-grain' observer. We arrive at the global asymptotic stability of the whole control structure (the controller+ the observer) by proving a somewhat general nonlinear separation principle.     

Using MPC to control middle-vessel continuous distillation columns

The use of model predictive control (MPC) in middle-vessel continuous distillation column (MVCC) is discussed. It is shown that using a 5 × 5 MPC implementation (where all levels are included in MPC as integral process variables) allows using a smaller middle-vessel, particularly when disturbances can be measured: a good performance is ensured without having the middle vessel drained or overfilled. Also, it is shown that MPC practically circumvents the issue of tuning the middle-vessel level controller. Furthermore, the MVCC design makes conventional decentralised control perform comparably to MPC.     

Elementary nonlinear decoupling control of composition in binary distillation columns

Elementary nonlinear decoupling (END) is a model based control algorithm intended to decouple and linearize a nonlinear multivariable process in order to achieve better control than can be obtained by conventional decentralized linear feedback control. The application of END to the composition control of a theoretical binary distillation column illustrates that the quality achievable is very high.     

Dynamics and control of totally refluxed reactive distillation columns

According to the mechanism of the reaction operation involved, reactive distillation columns are often designed to work in a totally refluxed operation mode. The totally refluxed operation mode makes the reflux drum interact solely with the reaction operation involved and retards considerably the dynamics of the latter. The resultant great difference in process dynamics between the reaction operation and the separation operation involved leads frequently to under-damped responses with the degree of under-dampness closely dependent on the inventory control of the reflux drum. With the tight inventory control of the reflux drum, the degree of under-dampness can be suppressed and this presents a favorable effect to process dynamics and controllability of the totally refluxed reactive distillation columns. Two hypothetical ideal reactive distillation columns with and without a side reaction, respectively, and a high-purity ethylene glycol reactive distillation column are employed to examine the unique dynamics and controllability of the totally refluxed reactive distillation columns. The results obtained are in good accordance with the above interpretation. The current work reveals the general behaviors of the totally refluxed reactive distillation columns and can be particularly useful in control system synthesis and design.     

Multivariable control of ill-conditioned distillation columns utilizing process knowledge

In distillation, a reliable model of the column is generally considered as a prerequisite for the design of efficient two-product control by multivariable methods. However, such models are often very hard to obtain. In fact, even very small identification errors may introduce features which are in conflict with physical knowledge, and which make the model useless. Instead of focusing on the development of consistent models, this work is concerned with the utilization of physical process knowledge directly for multivariable control, even if a reliable input-output model is lacking. Such knowledge is, for example, the sign of the RGA-values and an estimate of the input-directionality. It is shown that such structural information of the process can form an entity of control-relevant knowledge that is sufficiently rich for the design of a multivariable SVD controller.     

Optimal fuzzy control to reduce energy consumption in distillation columns

Distillation columns constitute a significant fraction of the capital invested in the refineries around the world; their control requires a major part of the total operating cost of chemical processes, if the used strategy is not adequate. This article presents the application of optimal fuzzy control to reduce the energy consumption of a Benzene-Toluene distillation column. This method is based on the determination of the specific values of the fuzzy controller parameters such that certain performance criterion is minimised. Results of a simulation study are presented showing the potential improvement offered by this method.Translated from Avtomatika i Telemekhanika, No. 2, 2005, pp. 36–45.Original Russian Text Copyright © 2005 by Bouyahiaoui, Grigoriev, Laaouad, Khelassi.     

Actuator fault monitoring and fault tolerant control in distillation columns

This paper presents, from a practical viewpoint, an investigation of real-time actuator fault detection, propagation and accommodation in distillation columns. Addressing faults in industrial processes, coupled with the growing demand for higher performance, improved safety and reliability necessitates implementation of less complex alternative control strategies in the events of malfunctions in actuators, sensors and or other system components. This work demonstrates frugality in the design and implementation of fault tolerant control system by integrating fault detection and diagnosis techniques with simple active restructurable feedback controllers and with backup feedback signals and switchable reference points to accommodate actuator fault in distillation columns based on a priori assessed control structures. A multivariate statistical process monitoring based fault detection and diagnosis technique through dynamic principal components analysis is integrated with one-point control or alternative control structure for prompt and effective fault detection, isolation and accommodation. The work also investigates effects of disturbances on fault propagation and detection. Specifically, the reflux and vapor boil-up control strategy used for a binary distillation column during normal operation is switched to one point control of the more valued product by utilizing the remaining healthy actuator. The proposed approach was implemented on two distillation processes: a simulated methanol-water separation column and the benchmark Shell standard heavy oil fractionation process to assess its effectiveness.     

Reparametrized ARX models for predictive control of staged and packed bed distillation columns

This work is aimed at the development of reparametrized ARX type models for high dimensional and distributed parameter systems. To keep data length small while identifying a model with ARX structure, the feasibility of reparametrizing ARX models using the fractional order differential operators and orthonormal basis filters is explored. The identified noise model is further used for developing a novel observer based MPC scheme, which explicitly uses the identified unmeasured disturbance model for the future trajectory predictions. The efficacy of the proposed modeling technique and the MPC scheme is demonstrated by conducting (a) simulation studies on a staged distillation column and (b) experimental evaluations on a laboratory scale packed bed distillation column.     

Robust control with uncertainty estimation for feedback linearizable systems: application to control of distillation columns

The goal of this paper is to describe a linearizing feedback adaptive control structure which leads to a high quality regulation of the output error in the presence of uncertainties and external disturbances. The controller consists of three elements: a nominal input–output linearizing compensator, a state observer and an uncertainty estimator, which provides the adaptive part of the control structure. In this way, the feedback controller, based on the disturbance observer, compensates for external disturbances and plant uncertainties. The effectiveness of the controller is demonstrated on a distillation column via numerical simulations. ©     

Reduced order bilinear models for distillation columns

The distillation columns are considered as compartmental systems. Because the inputs act linearly upon the transports matter flows, it is possible to include this type of system in the class of bilinear systems. By considering as output the distillate concentration of one of the products contained in the distilled mixture, the distillation column can be assimilated to a compartmental system with three compartments each of them replacing a set of distillation plates. These considerations finally permit a characterization of a distillation column by a minimal bilinear model having only three state variables and eight structural parameters. An identification algorithm permitting the identification of such a reduced order bilinear model is presented and is based on the minimization of the output error. Simulation results obtained by using as reference a complete non-linear model of a distillation column are presented. These results illustrate the validity of the approach proposed as well as the performance of the identification method. The reduced order bilinear model obtained is robust and valid for large variations of the inputs and of the working points. The use of the resulting bilinear model for control purposes is discussed.     

A wave propagation approach for reduced dynamic modeling of distillation columns: Optimization and control

Reduced models enable real-time optimization of large-scale processes. We propose a reduced model of distillation columns based on multicomponent nonlinear wave propagation (Kienle 2000). We use a nonlinear wave equation in dynamic mass and energy balances. We thus combine the ideas of compartment modeling and wave propagation. In contrast to existing reduced column models based on nonlinear wave propagation, our model deploys a hydraulic correlation. This enables the column holdup to change as load varies. The model parameters can be estimated solely based on steady-state data. The new transient wave propagation model can be used as a controller model for flexible process operation including load changes. To demonstrate this, we implement full-order and reduced dynamic models of an air separation process and multi-component distillation column in Modelica. We use the open-source framework DyOS for the dynamic optimizations and an Extended Kalman Filter for state estimation. We apply the reduced model in-silico in open-loop forward simulations as well as in several open- and closed-loop optimization and control case studies, and analyze the resulting computational speed-up compared to using full-order stage-by-stage column models. The first case study deals with tracking control of a single air separation distillation column, whereas the second one addresses economic model predictive control of an entire air separation process. The reduced model is able to adequately capture the transient column behavior. Compared to the full-order model, the reduced model achieves highly accurate profiles for the manipulated variables, while the optimizations with the reduced model are significantly faster, achieving more than 95% CPU time reduction in the closed-loop simulation and more than 96% in the open-loop optimizations. This enables the real-time capability of the reduced model in process optimization and control.     

Nonlinear parametric predictive temperature control of a distillation column

The integration of a nonlinear reduced process model with Parametric Predictive Control (PPC) is discussed for the bottom temperature control of a stabilizer distillation column. One of the main objectives is ensure the quality of the bottom product despite disturbances and complex dynamics. The purpose is to balance nonlinear control with simplicity, facilitating implementation in a DCS. The controllers developed were first tested in a simulated environment and then in the field, showing good performance under a wide range of operating conditions. The use of an estimator to compensate for modeling errors and unmeasured disturbances is also discussed.     

High-purity control of internal thermally coupled distillation columns based on nonlinear wave model

Internal thermally coupled distillation column (ITCDIC) is a frontier of energy saving distillation researches, which is a great improvement on conventional distillation column (CDIC). However its high degree thermal coupling makes the control design a bottleneck problem, where data-driven model leads to obvious mismatch with the real plant in the high-purity control processes, and a first-principle model which is comprised of complex mass balance relations and thermally coupled relations could not be directly used as control model for the bad online computing efficiency. In the present work, wave theory is extended to the control design of ITCDIC with variable molar flow rates, where a general nonlinear wave model of ITCDIC processes based on the profile trial function of the component concentration distribution is proposed firstly; combined with the thermally coupled relations, a novel wave model based generic model controller (WGMC) of ITCDIC processes is developed. The benzene-toluene system for ITCDIC is studied as illustration, where WGMC is compared with another generic model controller based on a data-driven model (TGMC) and an internal model controller (IMC). In the servo control and regulatory control, WGMC exhibits the greatest performances. Detailed research results confirm the efficiency of the proposed wave model and the advantage of the proposed WGMC control strategy.     

Soft sensors for product quality monitoring in debutanizer distillation columns

The paper deals with the design of neural based soft sensors to improve product quality monitoring and control in a refinery by estimating the stabilized gasoline concentration (C5) in the top flow and the butane (C4) concentration in the bottom flow of a debutanizer column, on the basis of a set of available measurements. Three-step predictive dynamic neural models were implemented in order to evaluate in real time the top and bottom product concentrations in the column. The soft sensors designed overcome the great time delay introduced by the corresponding gas chromatograph, giving on-line estimations that are suitable for monitoring and control purposes.     

改进的分子设计方法在萃取剂选择中的应用

为解决萃取精馏中的萃取剂选择问题,本文提出一种改进的萃取剂分子设计方法,其特点是分别考察萃取剂基团在待分离体系中的溶解性能和分离选择性,结合Gani的官能团预选经验规则,对基团做一个较全面的筛选.以乙酸-水体系的萃取剂选择为实例证明,该方法有效解决了分子设计过程中的组合爆炸问题和基团选择的纯经验性的局限,提高了萃取剂选择的效率和准确性,为试验和工业生产提供了理论指导.     

Robust control of distillation columns: μ- vs H∞-synthesis

High-purity distillation columns are known to be difficult to control due to their ill-conditioned and non-linear behaviour. Two approaches for the design of robust controllers yielding high performance are presented. For the first approach, first principles are used to develop an uncertainty model describing the nonlinear dynamics within the entire operating range of an industrial distillation column. This structured uncertainty model is used for μ-synthesis. In a second approach which is based on loop shaping ideas, an H_∞-controller is designed. This controller performs as well as the μ-controller. The H_∞-approach offers the advantage that the burden for uncertainty modelling and computation is greatly reduced. However, the GS/T augmentation scheme, which is developed in this paper, must be used for the design of the H_∞-controller to avoid the inversion of the plant in the controller. The paper concludes with a comparison of the H_∞- and μ-synthesis methods.     

Robust stabilization of binary distillation columns with input constraints

This paper examines the problem of robust stabilization of binary distillation columns with input constraints. We develop a controller that takes into account the physical constraints on the inputs and which ensures the global asymptotic stability of the closed loop system, with robustness to modelling errors, and the capability of performing set point tracking and disturbance rejection. We stress that this controller does not require all the internal states to be measurable.