多组分精馏过程推断控制及鲁棒性仿真

在化工生产实践中,基于被控变量检测的反馈控制系统占有绝对比重。不过,实际生产过程中却存在着这样一类情况,过程的被控制变量,甚至过程的扰动均无法测量或难以测量,如精馏塔塔顶、塔底产品的组成,因而难于实现反馈控制或前馈控制。精馏塔是化工等行业中广泛使用的分离设备,其控制方案在化工过程中具有十分关键的作用。为满足工艺要求和节能,需将塔顶和塔底产品流控制在设计值。理论上,将产品组成直接作为被控变量是最佳的,但仍有一些问题限制了其在实践中的推广使用。基于可测辅助变量推断难以直接测量或测量滞后太大的关键过程变量的推断控制能弥补这一缺陷。本文以多组分精馏过程为例,在MATLAB平台进行了多变量推断控制控制设计及仿真,并对其鲁棒性进行了分析,达到较好的控制仿真效果。     

Combined internal model and inferential control of a distillation column via closed-loop identification

A procedure for designing distillation control systems with spe cified nominal properties is presented. The desired behaviour of the control system for both setpoint changes and disturbances in the feed flow rate and the feed composition can be specified. Both types of specifications can be handled because the disturbances can be inferred from the behaviour of the inventory control system. The control system is realized as a combined internal model and inferential control (CIMIC) system. A disturbance rejecting and decoupling (DRD) control structure is obtained as a special case. The performance of the control system is demonstrated experimentally on a pilot-scale distillation column. For comparison, experiments with pure internal model control (IMC) are also illustrated. A preliminary model of the distillation column was determined from step tests carried out in open-loop operation, but the final model used in the control system designs was obtained via a control-relevant closed-loop identification.     

Two-point control of a reactive distillation column for composition and conversion

Reactive distillation is a hybrid process with dual process objectives: reactant conversion and product composition. Control schemes for reactive distillation frequently neglect the effect of the principal operating parameters on the reactant conversion, and this has a detrimental effect on the overall process profitability. An ETBE reactive distillation column has been used as a case study to show how a two-point control configuration, which recognises the importance of both composition and conversion, can be developed and implemented for a reactive distillation process. The combined composition and conversion control configuration was tested using SpeedUp dynamic simulations and proved to be effective in maintaining a high isobutylene conversion despite process disturbances. The two-point control scheme also had superior disturbance rejection capability, especially for feed rate changes, and composition set-point sensitivity compared with a one-point control scheme.     

Nonlinear model predictive control of a reactive distillation column

This paper considers the application of nonlinear model predictive control (NLMPC) to a highly nonlinear reactive distillation column. NLMPC was applied as a nonlinear programming problem using orthogonal collocation on finite elements to approximate the ODEs that constitute the model equations for the reactive distillation column. Diagonal PI controls were used to identify that the [L/D,V] and the [L/D,V/B] configurations performed best. NLMPC was applied using the [L/D,V] configuration and found to provide a factor of 2–3 better performance than the corresponding PI controller. The effect of process/model mismatch on the performance of the NLMPC controller was also evaluated.     

Control strategies for reactive batch distillation

A batch reactor may be combined directly with a distillation column by distilling off the light component product in order to increase the reactor temperature or to improve the product yield of an equilibrium reaction. The same amount of the light product should be removed as the amount being formed by the reaction at any time. A linearized model has been developed which describes the process behaviour satisfactorily for control analysis purposes. The controllability of a combined batch reactor/batch distillation column is found to depend strongly on the operating conditions and on the time during the run. In general, controlling only the reactor temperature (one-point bottom control) is difficult since the set-point has to be specified below a maximum value in order to avoid break-through of an intermediate component in the ditillate. This maximum value may be difficult to know a priori. For the example considered in this study, control of both reactor temperature and distillate composition (two-point control) is also found to be difficult due to large interactions in the column. As with one-point bottom control, the reactor temperature has to be specified below a maximum value. However, energy can be saved since the heat duty can be decreased with time. Controlling the temperature on a tray in the column (one-point column control) is found to give good performance for the given process with no loss of reactant and a high reactor temperature, although no direct control of the reactor temperature is obtained.     

Neural control of a batch distillation

The purpose of the work reported here was to investigate the application of neural control to a common industrial process. The chosen problem was the control of a batch distillation. In the first phase towards deployment, a complex software simulation of the process was controlled. Initially, the plant was modelled with a neural emulator. The neural emulator was used to train a neural controller using the back propagation through time algorithm. A high accuracy was achieved with the emulator after a large number of training epochs. The controller converged more rapidly, but its performance varied more widely over its operating range. However, the controlled system was relatively robust to changes in ambient conditions.     

High gain observer based extended generic model control with application to a reactive distillation column

This article aims at synthesizing an estimator based hybrid control scheme that consists of a high gain nonlinear observer and the extended generic model controller (EGMC) that is developed by the application of differential geometry theory. The model-based EGMC control system demands the knowledge of some physical state variables of the process and therefore, the development of a suitable algorithm to perform the state estimation has captured the attention. Here, we design a high gain observer so that it estimates a limited number of states which are solely required for the controller simulation. As a consequence, there exists a significant structural discrepancy. Despite this large mismatch, the state observer performs satisfactorily in converging the estimation error for the case of an example ethylene glycol reactive distillation system. With the same reduced-order predictor model, a comparison is also made between the high gain observer and the extended Kalman filter (EKF). Finally, the high gain observer based EGMC control structure shows promising performance in regulating the ethylene glycol column.     

Triangular decoupling for a binary distillation column under pressure control

The possibility of attaining complete decoupling of a distillation column under pressure control is studied using the state space model. It is shown that under the situation where the distillation column is under pressure control by the manipulation of the condenser duty, the complete decoupling using constant state feedback may not be attained, while the triangular decoupling can be attained with eight measurement variables.     

Identification and bilinear control of a binary distillation column

A reduced bilinear model of a binary distillation column is presented. This model has as control inputs the reflux rate and the heat flow of the reboiler, as perturbations the feed stream and its concentration, and as outputs the distillate and residual concentrations. A bilinear model is simpler than a non-linear one, and more exact than a linear model. Furthermore, its structure allows the design of a control structure via optimal control theory. In this paper, two optimal control schemes designed by the reduced bilinear model have been applied to a column simulated by an analytical model, obtaining a robust control of the process even in the presence of disturbances.     

Optimal selection of sensors for state estimation in a reactive distillation process

The success of state estimation in a high dimensional system like multicomponent reactive distillation depends on the rigorous evaluation of the observability and appropriate selection of measurements that adequately characterize the process behavior. In this work, the dynamic state sensitive measurement information extracted from the nonlinear reactive distillation process is employed to configure the gramian covariance matrices which are then subjected to various scalar quantification measures to find the degree of observability in order to select the temperature sensors for state estimation in the process. These optimally configured process measurements are then incorporated in a process model based composition estimation scheme. The validity of the sensors that are selected by the gramian quantification measures are further ascertained through the evaluation of the estimator performance for various nonoptimal measurement combinations. The results on application to a metathesis reactive distillation column exhibit the usefulness of the empirical observability gramian based sensor selection strategy for inferential state estimation of reactive distillation process.     

酯交换合成碳酸二苯酯反应精馏塔的模拟与优化

在Aspen Plus软件平台上开展了以碳酸二甲酯(DMC)和苯酚(Ph OH)为原料、反应精馏塔合成碳酸二苯酯(DPC)的模拟研究。利用实验测定的化学反应平衡常数和反应动力学数据,通过一种调优方法对反应精馏塔进行逐步优化,取得了优化的设计参数。结果表明,反应塔板上液体持液量大小对反应精馏过程有重要影响,当持液量小于0.2 m3时,塔板上的酯交换反应由反应动力学控制,当持液量大于0.5 m3时,塔板上的化学反应接近平衡,反应精馏过程由化学平衡控制。通过对设计参数进行优化,反应精馏塔中Ph OH和DMC的转化率分别达到29.22%和22.01%,DPC收率达到7.21%,而在相同条件下用釜式反应器合成DPC时Ph OH和DMC的转化率仅为7.8%和7.5%,表明了反应精馏技术的优越性。     

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.     

Application of predictive control techniques to a distillation column

Model-based predictive control techniques are widely recognized as having useful application to control problems characterized by complex dynamics and severe time delay. The establishment of a representative process model is the key step in the procedure and for anything other than trivially simple systems is a major hurdle. This paper describes the application of predictive control techniques to a distillation problem which embodies a pure time delay of 2–3 h and time constants of 3–4 h. A sampled-data process model is identified from monitored input/output data and from this a predictive control algorithm is designed. The application of the controller has resulted in very effective closed-loop control of the base composition of the distillation column, where previously only manual supervision was possible.     

Multivariable adaptive predictive control of a binary distillation column

This paper presents a tutorial review of an adaptive predictive control system (APCS). Special emphasis is given to the key issues involved in the practical application of APCS to real processes. These practical issues are illustrated by actual application of SISO and MIMO control of a pilot scale binary distillation column. The experimental evaluation of this method reveals the simplicity of the adaptive algorithm and its excellent performance in an industrial type environment. The experimental results easily outperformed well-tuned classical PID controllers. A brief review of other applications of adaptive control to chemical processes is also included in this paper.     

Multimodel identification for control of an ill-conditioned distillation column

Identification for control of an ill-conditioned system requires special techniques. The directionality of such a system should be taken into account in the design of identification experiments. In distillation, information about the directionality properties can be obtained from certain flow gains, which are easy to determine in practice. Based on such information, the high- and low-gain directions of the plant can be explicitly excited. In this paper, a pilot-scale distillation column is identified by this approach at two different operating points. At each operating point, a nominal second-order plus time-delay model with logarithmic outputs is determined. This model structure makes it possible to capture the dynamic directionality of the plant. In addition, models describing variations and uncertainties in the high- and low-gain directions are determined by a special technique. The models obtained are superior to models determined via traditional step tests. The former satisfy integral controllability requirements, while the latter tend to violate them.     

Column and batch reactive transport experiment parameter estimation using a genetic algorithm

This paper focuses on the application of a genetic algorithm (GA) in estimating the fate and transport parameters of a reacting solute from the column and batch experiments involving a saturated porous medium. A program is developed using C++ to model the column and batch data using kinetically controlled one- or two-site sorption models including linear and/or nonlinear forms. The objective of the algorithm is to minimize the sum of squared differences between the measured and modeled solute concentration data associated with column effluent (i.e., “breakthrough curves”). The GA is capable of estimating transport and reactions parameters such as forward and reverse reaction rates and parameters of the nonlinear reaction models, from a given set of measured data. Further simulations have been performed to estimate the appropriate configurations of the GA, which assist the method in estimating the fate and transport parameters more efficiently. It is shown that a wide range of the GA parameters can lead to convergence to appropriate estimations. The results obtained from this study show that the capability of GAs to fit the column and batch experiment data is promising.     

Robust model predictive control of a pilot plant distillation column

In this paper, the development and application of a robust MPC to a pilot plant ethanol–water distillation column is described. It is shown through experimental tests in the pilot plant, how the linear model can change depending on the operating point. The obtained model has time delays and repeated poles. For this kind of system, the development of an MPC, based on the step response model that is robust to multi-model uncertainty, is presented. The experimental results that confirm the good performance of the proposed controller are also shown.     

Constrained multivariable control of a distillation column using a simplified model predictive control algorithm

Distillation columns are important process units in petroleum refining and need to be maintained close to optimum operating conditions because of economic incentives. Model predictive control has been used for control of these units. However, the constrained optimization problem involved in the control has generally been solved in practice in a piece-meal fashion. To solve the problem without decomposition, the use of a linear programming (LP) formulation using a simplified model predictive control algorithm has been suggested in the literature. In this paper, the LP approach is applied for control of an industrial distillation column. The approach involved a very small size optimization problem and required very modest computational resources. The control algorithm eliminated the large cycling in the product composition that was present using SISO controllers. This resulted in a 2.5% increase in production rate, a 0.5% increase in product recovery, and a significant increase in profit.     

Nonlinear identification and control of a high-purity distillation column: a case study

Identification and control of ill-conditioned, interactive and highly nonlinear processes pose a challenging problem to the process industry. In the absence of a reasonably accurate model, these processes are fairly difficult to control. Using a high-purity distillation column as an example, model identification and control issues are addressed in this paper. The structure of the identified models is that of the polynomial type nonlinear autoregressive models with exogenous inputs (NARX). While most of the work in this area has concentrated on linear models (one-time scale and two-time scale models), this work is aimed at identifying the inherent nonlinearities. Comparisons are drawn between the identified models based on statistical criteria (AIC etc.) and other validation tests. Simulation results are provided to demonstrate the closed-loop performance of the nonlinear ARX models in the control of the distillation column. The controller employed is based on a nonlinear model predictive scheme with state and parameter estimation.