基于自适应神经模糊推理系统的间歇反应精馏组分估算(英文)

Composition estimation plays very important role in plant operation and control. Extended Kalman filter (EKF) is one of the most common estimators, which has been used in composition estimation of reactive batch distillation, but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium, which is difficult to initialize and tune. In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system (ANFIS), which is a model base estimator, is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation. The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics. The mathematical model is verified by pilot plant data. The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation. The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.     

Online composition estimation and experiment validation of distillation processes with switching dynamics

This study explores both the theoretical and experimental investigations of applying a continuum and noncontinuum state estimator to composition estimation in a distillation process with switching dynamics. In a hybrid distillation modeling, the column compositions are considered as continuum states while the operating modes are modeled as noncontinuum states. A moving horizon estimator (MHE), which has the capability to handle process constraints is developed for composition estimation in a distillation process under known switching mode criteria using the available temperature measurements. The performance of a MHE is shown to be better than that of EKF in handling process and measurement noise under switching dynamics. For some situations where the system operating mode transition is unknown, a new approach to state estimation under unknown switching functions is investigated. The proposed method combines a MHE for composition estimation with a mode change detector to detect a change in the system operating mode and an operating mode estimator to identify the functioning mode. In the presence of both the measurement noise and plant-model mismatch, the developed estimator is shown to be effective in estimating both the column composition and the system operating mode accurately.     

Nonlinear adaptive control algorithm for a multicomponent batch distillation column

In this article, a nonlinear adaptive control strategy is proposed for a multicomponent batch distillation column. The hybrid control scheme consists of a generic model controller (GMC) and a nonlinear adaptive state estimator (ASE). In the first part of the study, an adaptive observer is designed aiming to estimate the partially known parameters based on the measured compositions in the presence of process/predictor mismatch. The open-loop dynamic behavior of the developed ASE estimator is investigated under initialization error, disturbance, and uncertain parameters. In the subsequent part, the adaptive GMC-ASE controller (GMC control structure in conjunction with ASE estimator) has been synthesized for the example distillation column. A simulation-based comparative study has been conducted between the derived nonlinear GMC-ASE control algorithm and a gain-scheduled proportional integral (GSPI) law in terms of constant composition control. The proposed adaptive control scheme is shown to be quite promising due to the exponential error convergence capability of the ASE estimator in addition to the high-quality performance of the GMC controller.     

Dynamic composition estimation for a ternary batch distillation

A novel Kalman estimator has been proposed to provide the estimates of dynamic composition in a ternary batch distillation process operated in an optimal-reflux policy. The estimator is formulated based on a sequence of reduced-order process models representing a whole batch behavior. Therefore, the full-order models are first developed around different pseudo-steady-state operating conditions along batch optimal profiles. Then they reduce their orders to achieve all state observability and controllability by a balanced truncation method. In the estimator scheme, the reduced models as well as relevant covariance matrices of process noise are pre-scheduled and switched according to any desired periods. Four important issues have been studied including selection of a sensor frequency, effects of an integrating step size, a state initialization and a measurement noise. The performances of the reduced estimator have been investigated and compared with those of a conventional nonlinear estimator. Simulation results have demonstrated that the performances of the novel linear estimator are reasonably good and almost identical to the nonlinear estimator in all cases, though the linear estimator performs rather sensitively to the effect of high measurement noise. Nevertheless, it has been found to be applicable to implement in real plants with much lower computation effort, easier state initialization and unrequired a priori knowledge of thermodynamics.     

Nonlinear state estimation and control of a batch reactive distillation

This paper deals with the advanced adaptive control of a batch reactive distillation (RD) column for the production of ethyl acetate. The nonlinear adaptive control law consists of the generic model controller (GMC) and an adaptive state estimator (ASE). In the first part of the present work, the design approach of the ASE scheme in two different forms, namely ASE1 and ASE2, has been addressed for a batch reactive rectifier. The predictor model of both the ASE estimators includes only a component mole balance equation around the condenser-reflux drum system and an extra state equation having no dynamics, and therefore, there is a large process/predictor mismatch. In presence of this structural discrepancy, the adaptive estimation schemes compute the imprecisely known parameters quite accurately based on the measured distillate composition under initialization error, disturbance and uncertainty. In the subsequent part, the adaptive GMC–ASE1 control structure has been formulated for the sample reactive column. This nonlinear control strategy shows comparatively better closed-loop performance than the gain-scheduled proportional integral (GSPI) controller due to the exponential error convergence capability of the estimation scheme and the high-quality control of the GMC law.     

An experimental application of the inferential control scheme to the binary distillation column control

The inferential control scheme based on a linear estimator was applied to control of top composition of a pilot scale packed distillation column. Experimental and simulation studies were used to evaluate the inferential control scheme and to compare its performance with a conventional single temperature feedback control with proportional plus integral actions. Both digital simulation and experimental verification showed that the top composition control achieved with the inferential control scheme was superior to that achieved with the conventional control scheme.     

Offset‐Free Inferential Feedback Control of Distillation Compositions Based on PCR and PLS Models

An offset‐free inferential feedback control strategy for distillation composition control using principal component regression (PCR) and partial least squares (PLS) models is presented in this paper. PCR and PLS model based software sensors are developed from process operational data so that the top and bottom product compositions can be estimated from multiple tray temperature measurements. The PCR and PLS software sensors are then used in the feedback control of the top and bottom product compositions. With this strategy the problem of substantial time delay in composition analyzer based control and of substantial bias in single tray temperature control can be overcome. A practically very important issue in software sensor based feedback control is that static control offsets often exist due to a static estimation bias, especially when the process operating condition changes. A technique for eliminating the static estimation bias and the resulting static control offsets through mean updating of process measurements is proposed in this paper. Applications to a simulated methanol‐water separation column demonstrate the effectiveness of this control strategy.     

二异丁烯反应精馏生产过程控制策略设计

二异丁烯是一种重要的化工中间体,近年来研究者提出了以催化裂化C₄为原料、采用反应精馏技术同时生产高纯二异丁烯和汽油添加剂的新工艺。由于反应精馏过程中非线性程度高,稳定控制困难,使得采用反应精馏技术生产二异丁烯过程的控制策略研究较少。采用Aspen dynamic软件进行动态模拟,针对反应精馏生产二异丁烯过程开发了温度控制方案、组分温度联合控制方案和组分温度串级控制方案。对3个控制系统进行（±10）%的进料流量扰动和（±5）%组成扰动测试并进行对比。结果表明：组分温度串级控制方案在添加扰动的情况下依然保证了二异丁烯质量分数99%,三异丁烯质量分数小于10%以及异丁烯转化率大于99%的要求,并且最终稳定时间约5 h,具有更好的抗干扰性能。研究结果能够为二异丁烯产品的生产工艺工业化应用提供设计依据。     

Nonlinear model based control of two-product reactive distillation column

Nonlinear feedback control scheme for reactive distillation column has been proposed. The proposed control scheme is derived in the framework of Nonlinear Internal Model Control. The product compositions and liquid and vapor flow rates in sections of the reactive distillation column are estimated from selected tray temperature measurements by an observer. The control scheme is applied to an example reactive distillation column in which two products are produced in a single column and the reversible reaction A+B=C+D occurs. The relative volatilities are favorable for reactive distillation so that the reactants are intermediate boilers between the light product C and the heavy product D. Ideal physical properties, kinetics, and vapor-liquid equilibrium are also assumed. It is shown that the proposed control scheme keeps tight product composition control.     

基于样条插值模型的间歇精馏模拟与预测控制

以Aspen Batch Distillation（ABD）中的间歇精馏仿真系统为过程原型,提出了利用过程的模拟测试数据来建立间歇精馏过程的样条插值简化模型（spline interpolation model, SIM）。结合变回流比下的动态修正函数,构造出了一种简单实用的动态模型。该模型可有效模拟不同组分浓度下回流比发生变化时馏出液浓度和流量的动态变化情况。以该模型作为预测模型,进一步提出了一种变回流比的预测控制（model predictive control, MPC）算法来使馏出液浓度按照期望的设定值变化。控制仿真结果表明该控制方案计算简单,同时具有较好的控制效果。     

Optimal control and on-line operation of reactive batch distillation

While both the dynamic modelling, and to a lesser extent, the optimisation of reactive batch distillation have been studied in the past, issues related to control and on-line operation of such a process have not been properly addressed. In this study, a priori optimal profiles of the operating variables are established for an industrial reactive distillation process. The optimal profiles are found either by maximising the profitability or by minimising the operating time subject to constraints on the reactor temperature and the loss of volatile reactant in the distillate. The control properties at optimal and non-optimal conditions are assessed. A one-point column control scheme with a PI-controller is designed to implement the optimal profiles. The controller performance is tested using a simulation model including disturbances in the reaction model and in the reboiler heat duty. The fully automatic operation of the process according to the optimal policies is demonstrated in conjunction with both a continuous controller and a real time control system. Good performance is achieved in both cases.     

多变量统计过程监控:进展及其在化学工业的应用

Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks an     

Property evaluation and control in a semibatch MMA/MA solution copolymerization reactor

This article deals with the property control of polymer product in a semibatch MMA/MA copolymerization reactor by applying the extended Kalman filter (EKF) based nonlinear model predictive control (MPC). In addition to the feeding of the more reactive monomer, the solvent is continuously supplied so as to maintain the viscosity of the reaction mixture within a reasonable range. This measure then provides favorable conditions not only for the on-line estimation with the EKF but also for the performance of the EKF based nonlinear MPC. Indeed, the improved performance of the state estimator is confirmed by experiment under isothermal and nonisothermal conditions over a prolonged reaction time. On the basis of the estimated state, the EKF based nonlinear MPC is implemented to the semibatch reactor to produce copolymers with desired properties. The experimental results clearly demonstrate the superiority of the present control strategy compared to the result of our previous work obtained without having additional feed of solvent.     

Estimator-based control of reactive distillation system: Application of an extended Kalman filtering

This paper demonstrates that a state estimator can be successfully designed and implemented in a feedback control system of reactive distillation. The work of the state estimator is to provide the state compositions that are required to be used in the controller for necessary action. The control performance of a system that relies on the state estimator is examined and compared to a system that takes direct measurement from the process assuming the availability of a perfect online analyzer. It is found that the estimator-based system is robust against a moderate measurement errors and erroneous initial conditions. If the state estimator is designed from a highly erroneous process model, noisy measurements and approximate initial conditions, the use of estimator together with an online analyzer (for easily measured states) is recommended to achieve an effective control of a reactive distillation system.     

Observer-based control algorithms for a distillation column

This paper studies the synthesis of nonlinear observer-based globally linearizing control (GLC) algorithms for a multivariable distillation column. Two closed-loop observers/estimators, namely extended Kalman filter (EKF) and adaptive state observer (ASO), have been designed within the GLC framework to estimate the state variables along with the poorly known parameters. Exactly same basic model structure was used for developing the observers. The model structure is so simple that the estimator design was performed based on only two component balance equations around the condenser-reflux drum and the reboiler-column base systems of the distillation column. To construct these observers, the poorly known parameters, namely component vapor flow rate leaving top tray, component liquid flow rate leaving bottom tray and distribution coefficient in the reboiler, were considered as extra states with no dynamics. The comparative study has been carried out between the proposed GLC in conjunction with ASO (GLC-ASO) and that coupled with EKF (GLC-EKF). The GLC-ASO control scheme showed comparatively better performance in terms of set point tracking and disturbance as well as noise rejections. The control performance of GLC-ASO and a dual-loop proportional integral derivative (PID) controller was also compared under set point step changes and modeling uncertainty. The proposed GLC-ASO structure provided better closed-loop response than the PID controller.     

DESIGN OF A COMPOSITION ESTIMATOR FOR INFERENTIAL CONTROL OF DISTILLATION COLUMNS

In distillation column control, secondary measurements such as temperatures and flows are widely used in order to infer product composition. This paper addresses the design of the linear static estimators using the secondary measurements for estimating product compositions of distillation columns. Based on the unified framework for the estimator design, the relationships among various static estimators are discussed in terms of the estimator structure. Il is shown that the projection estimator is equivalent to the regression estimators in the special cases. Since the projection estimator heavily depends on the measured inputs such as reflux flow and heat input to the reboiler due to its structural characteristic, the estimation performance is far more sensitive to measurement noise and nonlinearity of them, compared wiih the regression estimators based on the PCR or PLS method. It is also found that the use of the measured inputs leads to performance deterioration of both the projection and regression estimators because of their nonlinear effects on the product compositions especially in high-purity columns. Design guidelines for the PCR and PLS estimators are presented by analyzing the results of the simulation studies on a high-purity column example. The estimator based on the guidelines is robust to sensor noise and has a good predictive power     

Temperature control of an ideal heat-integrated distillation column (HIDiC)

In this work, a novel temperature control scheme is derived for an ideal heat-integrated distillation column (ideal HIDiC), in which a temperature difference between two stages is designated as the controlled variable to circumvent the side-effect of continuous pressure variations in the rectifying section. For making an appropriate compensation to the changes in operating conditions, an inferential signal extracted from the feed stage, n/2+1, is used to adjust the set-point of the temperature difference controller. Control of two ideal HIDiCs, separating, respectively, a binary mixture of benzene and toluene and an ideal ternary mixture of hypothetical components A-C is studied. It is demonstrated that the proposed temperature control scheme can retain a stable operation around the vicinity of the nominal steady state with improved dynamic performance and tolerable steady state discrepancies in comparison with the direct composition control scheme. Moreover, the proposed temperature control scheme is also found to be quite robust to the selection of temperature measurements.     

Input reconstruction for statistical‐based fault detection and isolation

In this work, an input reconstruction scheme for detecting and isolating sensor, actuator, and process faults is proposed. The scheme uses model‐based and statistical‐based FDI methods, which yields an improved analysis of abnormal operation conditions in chemical processes. The main advantage of the proposed approach over existing works lies in the reconstruction of system inputs and the subsequent estimation of fault signatures. This advantage is demonstrated through simulation examples and the analysis of recorded process data from a reactive batch distillation column. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

用夹紧区原理确定多元恒回流比分批精馏最小回流比

夹紧区是精馏塔内出现的浓度几乎不变的区域,对于三元物系来说,根据夹紧区在塔内出现的位置可分为：上、中和下加紧区.在无穷板数和任一瞬时釜浓下选择不同的回流比可使分批精馏处于不同夹紧区下操作.文中讨论了三元恒回流比分批精馏过程中夹紧区的演变,在Rayleigh方程的基础上建立了应用夹紧区原理确定多元恒回流比分批精馏最小回流比的方法.该方法并不要求相对挥发度为常数,并且能准确计算顶浓,克服了通常所采用的Underwood公式法的缺陷,为多元分批精馏的简捷设计奠定了基础.     

Optimal reflux rate policy determination for multicomponent batch distillation columns

This paper describes a procedure to obtain optimal reflux or optimal distillate rate policy for multicomponent batch distillation columns using the Pontryagin's continuous maximum principle. For application to multicomponent systems, a reduced order model is used using short-cut procedure developed specifically for analysis and design of batch columns.