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.     

节能型甲基异丙基酮精馏过程的控制研究

在酸-醛法合成甲基异丙基酮(MIPK)的反应粗产物中,水与MIPK、二异丙基酮(DIPK)等均形成共沸物,使得MIPK的分离精制变得非常困难,文中采用剩余曲线图法对丙酮-MIPK-水三元物系进行分析,提出了通过利用中间分离器在一个精馏塔中实现丙酮和水同时脱除的节能型精馏过程,并运用奇异值分解(SVD)方法对提出的节能型精馏过程进行分析,选择了适宜的控制方案,建立了相应的动态模拟模型,对控制系统的动态特性进行了分析.结果表明,文中提出的控制方案具有较好的抗扰动能力.     

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.     

Dynamics and control of benzene hydrogenation via reactive distillation

This work develops a dynamic, first principles-based model of a reactive distillation column used for benzene hydrogenation of a reformate stream and investigates different control structures for this process. The model is used initially to develop and evaluate a feedback control strategy which provides good regulatory performance for small disturbances, however, it tends to be sluggish for significant disturbances in the feed composition. In order to address this point, adding a feedforward controller to the feedback structure has also been investigated. However, the feedforward controller can only be implemented if composition measurements of the feed are taken. As online composition measurements are expensive in practice, several different scenarios have been investigated where samples of the feed are taken and subsequently analyzed in a lab, as represented by measurement time delays. Simulation results show that adding feedforward control to the feedback scheme can be very beneficial for this process, however, this is only the case if the composition disturbance measurements do not involve a significant time delay.     

Multivariable process identification for mpc: the asymptotic method and its applications

In this work we will introduce the asymptotic method (ASYM) of identification and provide two case studies. The ASYM was developed for multivariable process identification for model based control. The method calculates time domain parametric models using frequency domain criterion. Fundamental problems, such as test signal design for control, model order/structure selection, parameter estimation and model error quantification, are solved in a systematic manner. The method can supply not only input/output model and unmeasured disturbance model which are asymptotic maximum likelihood estimates, but also the upper bound matrix for the model errors that can be used for model validation and robustness analysis. To demonstrate the use of the method for model predictive control (MPC), the identification of a Shell benchmark process (a simulated distillation column) and an industrial application to a crude unit atmospheric tower will be presented.     

Nolinear model predictive control using Hammerstein models

Nonlinear models that are composed of a linear dynamic element in series with a nonlinear static element prove to be very attractive in describing the behaviour of many chemical processes. In this paper, a model predictive control scheme is proposed using the Hammerstein model structure. Two simulation examples, a pH neutralization process and a binary distillation column, are used to demonstrate the effectiveness of the method.     

Modelling of uncertain systems with application to robust process control

A method for black-box identification of uncertain systems is presented. The method identifies a nominal model and an uncertainty model set, consisting of unfalsified uncertainty models. Minimisation of a Chebyshev criterion leads to computationally favourable linear programming problems and allows the possibility to include a priori information in the form of linear constraints without making the computations more complex. Using data compression via correlation computations solves the computation problem associated with identifying unfalsified uncertainty models. The application of set-valued uncertainty models to robust process control is illustrated in a simulation study of robust model predictive control of a distillation column.     

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.     

精馏过程动态仿真建模

精馏过程的模型化与仿真在化工操作和工艺设计中具有重要的意义。对精馏塔进行动态数学模型的建立与仿真,不仅可以研究精馏过程在不同工况下的变化情况,而且还可以用于精馏塔的优化控制,进而提高精馏过程的生产效益。本文针对精馏塔操作过程,建立了基于平衡级假设和非平衡级假设的精馏过程动态机理数学模型,并对平衡级假设的模型进行了动态模拟。该模型从机理分析入手,进行合理的简化,模型的计算时间大大的缩短,从而使模型具有比较广泛的实用性。该模型采用的动态数学模型为METSH(质量平衡方程、相平衡方程、塔板效率方程、摩尔分数归一化方程、能量平衡方程)方程,通过计算METSH方程,可以模拟出精馏塔内温度、汽相流量、液相流量、汽相组分以及液相组分的变化趋势。通过仿真结果可以看到,该模型比较准确的预测了精馏塔中各个操作参数的动态趋势,与实际情况基本一致,其稳态结果与实际情况也基本吻合。该模型对于仿真培训及精馏过程的控制分析具有较高的理论研究意义和实际应用价值。     

Identification of a simulated Shell distillation column using time series analysis methods

This paper presents details of a multivariate time series identification of the simulated Shell distillation column described in the introductory paper by Cott. The approach applied here involves the use of time series identification techniques and may be considered as representative of the system identification procedures adopted by Shell Canada for quadratic dynamic matrix control (QDMC) applications. Results indicate that techniques of time series analysis are very flexible and capable of producing satisfactory step response models under both low and high signal to noise conditions.     

精馏过程动态机理模型建立

为了实现精馏过程的动态优化和先进控制,必须解决精确的动态数学模型的建立问题.本文介绍了精馏过程动态机理建模的方法,该模型的特点是考虑每块塔板温度、压力、汽相流量、液相流量、汽相浓度、液相浓度和持液量的动态特性,并且,利用正交配置方法离散模型方程,利用牛顿一拉夫逊方法解模型方程,研究结果表明该模型精度高,误差小于3%,适合于精馏过程的动态优化和先进控制策略研究.     

Case studies on closed-loop identification for MPC

In this work we study multivariable and closed-loop identification of industrial processes for use in model predictive control. The advantages of closed-loop identification are discussed and related problems of identification are outlined. Subsequently, two case studies are used to demonstrate the advantages of closed-loop identification. The first process is a simulated high purity distillation column; the second process is a deethanizer of an industrial scale ethylene unit.     

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.     

精馏塔的机理-神经网络混合建模

酒精精馏过程是一个复杂的化工过程,动态响应缓慢,内在机理复杂,参数间相互关联.为了解决精馏塔机理模型精度低和神经网络模型外推能力差的缺点,同时也为了精馏塔的先进控制提供一种可靠的先进模型,针对试验室酒精精馏塔,充分发挥机理模型和神经网络模型的特点,建立一种基于机理模型和神经网络补偿模型的酒精精馏塔的混合模型.最后对混合模型进行了仿真试验,仿真结果显示有很好的性能,精馏塔的精馏精度和精馏效率都得到了很大的提高.而且下一步正准备以此模型为基础,设计精馏塔的先进控制算法.     

Comparison of pressure-swing distillation and extractive distillation with varied-diameter column in economics and dynamic control

Pressure-swing distillation and extractive distillation are two common methods for azeotrope separation. The economics and controllability are two crucial factors for evaluating the feasibility of a separation process. A varied-diameter column (VDC) was used in the process design to evaluate its economics and controllability. Five azeotropic systems were investigated in order to compare the economics of pressure-swing distillation and extractive distillation with a VDC. Results indicate that pressure-swing distillation with a VDC saves more money than extractive distillation. The dynamic control were evaluated in the acetone-methanol system for both processes with a VDC. The improved control structure for pressure-swing distillation with a VDC can handle ±20% disturbances effectively, while the improved control structure for extractive distillation with a VDC can only handle ±10% disturbances. A comparison of the two methods from the viewpoint of economics and controllability demonstrates that pressure-swing distillation is more suitable when using a VDC.     

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.     

多变量预测控制软件在加氢分馏装置控制中的应用

针对加氢分馏装置分馏塔工艺特点,重点分析加氢分馏塔所面临的控制问题,采用多变量预测控制软件APC—Adcon,通过实验测试、模型辨识、控制器和切换逻辑设计等工作实现该工业分馏塔系的多变量预测控制,显著提高两塔运行参数的平稳性,有效降低操作人员的操作难度。实际应用的结果表明该多变量预测控制系统可有效地克服干扰、确保工艺参数的平稳运行。通过该系统的应用使在相同的负荷下,燃料气减少5％,显著提高分馏塔的分离能力,组分切割更为合理,航空煤油收率提高0.4％,取得了显著的经济效益。     

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.     

The interactions of design and control: double-effect distillation

A general, rigorous dynamic model is described for studying the interactions of design and control in a double-effect distillation system. Two approaches are adopted. In the first, the steady-state process design and the control system are optimized sequentially; potential operability bottlenecks are identified and the economic advantage of double-effect systems over conventional single column systems is demonstrated. In the second approach, the process design and the control system are optimized simultaneously leading to a more economically beneficial system than that obtained using the sequential approach.