Adaptive generalised predictive control of high purity internal thermally coupled distillation column

Internal thermally coupled distillation column (ITCDIC) is a frontier in the energy‐saving distillation researches. One of the bottlenecks to prevent the column from being commercialised is the difficulty in control design due to the high sensitivity to disturbances, strong asymmetric nonlinearity and inverse response especially under high purity. An adaptive multivariable generalised predictive control (AM‐GPC) strategy of ITCDIC process is proposed to solve the difficulties in high purity. The simulation results of AM‐GPC are compared with single input and single output GPC (S‐GPC), multivariable GPC (M‐GPC), modified IMC (M‐IMC) and traditional PID control in detail. The performances confirm the accuracy and validity of AM‐GPC for the high‐purity ITCDIC process. © 2011 Canadian Society for Chemical Engineering     

基于一般模型控制的高纯内部热耦合精馏策略

内部热耦合精馏塔（ITCDIC）是精馏节能控制的一个前沿。本文提出了一种基于一般模型控制（GMC）的内部热耦合精馏塔的先控策略,以解决导致传统线性控制策略难以得到较好控制效果的高纯下内部热耦合精馏塔的非线性。以苯-甲苯物系作为研究实例,对所提出的高纯ITCDIC控制策略进行了详细研究。设定值改变和过程扰动下的控制品质表明了所提出的高纯ITCDIC的GMC控制策略的切实有效性。     

基于内部热耦合精馏塔非线性wave模型的高纯控制

首先建立内部热耦合精馏塔（ITCDIC）的非线性波动降阶模型,并将非线性波动理论（wave）应用到ITCDIC控制问题中,实现一般模型控制（GMC）方案.与传统控制方案相比,基于波动理论的一般模型控制（waveGMC）不再采用ITCDIC的近似线性模型,更好地解决了高纯控制过程中的非线性问题,通过对波形的速度和位置控制能够在短时间内使系统达到稳定.苯-甲苯物系的实例研究表明,ITCDIC波动模型在高纯控制过程中能够精确反映ITCDIC的动态特征,waveGMC控制方案较传统控制方案更加稳定可靠.     

Model based control of a high purity distillation column

In this paper, the problem of dual product composition control of an industrial high purity distillation column, a deisohexanizer (DIH), is addressed using a Generic Model Control framework. A dynamic simulation of the DIH was performed for preliminary studies of the performance of different controller strategies/algorithms. The performance of Generic Model Control incorporating different process models was studied. Process models are presented ranging from simple first order approximations to mechanistic short cut distillation models where a tradeoff between model complexity and model adaptivity is investigated. The different controllers were implemented and compared using a dynamic simulation of an industrial deisohexanizer (DIH) to select the best condidate controller. A controller using a nonlinear process model emerged as the best controller and was implemented on the actual process, resulting in improved performance over the original controller. Simulation results and industrial plant data are presented.     

基于集合算法的高纯度精馏塔分段建模方法

多级汽液平衡过程的严格模型变量维数高,用于构建优化命题时不易收敛且计算速度较慢。采用集合算法中基于吸收因子和理论塔板数计算全塔段组分回收率的组分分配特征方程代替逐板相平衡,加入物料衡算等基本方程与若干简化关系匹配模型自由度,建立可用于联立计算的逐段降维GM模型。同时根据高纯度精馏塔吸收因子沿塔分布的强非线性特征,提出分段线性拟合求解组分回收率的线性GM模型。以空分装置的粗氩塔为例进行高纯度精馏塔的严格模型、降维GM模型和线性GM模型的对比计算。结果表明,降维GM模型的计算变量由逐板模型的2377个减少到34个,同样平台下收敛时间由6136 s缩短至1 s左右,而关键组分纯度模拟与严格机理模型一致。     

内部热耦合反应精馏塔塔构型的研究

以乙酸甲酯水解为例,利用模拟软件Aspen Plus建立了内部热耦合反应精馏塔模型,研究了内部热耦合反应精馏塔各种可能塔构型的节能效果,并且在相同的产品要求和最小传热温差下对内部热耦合反应精馏塔各构型的能耗进行了对比。结果表明,塔构型对乙酸甲酯水解的内部热耦合反应精馏流程的操作性能有重要的影响,并且存在最佳构型。其最佳构型的特征为,反应段全部在内部热耦合反应精馏塔的精馏塔内且精馏塔与提馏塔中理论板均参与传热的塔构型。     

完全能量耦合精馏塔的设计、模拟与优化

本文针对完全能量耦合精馏塔,基于Fenske-Underwood-Gilliland方程建立了完整的简捷设计方法,得到了各塔的实际理论板数、适宜的进料和侧线液相采出位置以及各塔回流比等参数。并在此基础上通过严格模拟,对完全热耦合精馏塔的中间组分分配比β、气相分割比R_v和液相分割比R_l等参数进行了优化,得出了它们对再沸器热负荷的影响规律。     

Structural design of fully thermally coupled distillation column using approximate group methods

The thermally coupled distillation column systems can save energy and capital cost compared with traditional distillation columns. Since the thermally coupled column system was introduced, the design concerns have been peeped out due to more degrees of freedom. This paper introduces a new design method that can be used to determine the structure of thermally coupled distillation column systems, namely the number of stages in all sections of the column system. The design method employs the approximate group methods. To explore the design performance of the proposed design method, three feed systems are analyzed to investigate its usefulness. The design procedure gives the optimum structure for a given ternary separation; with given product specifications various design methods can yield approximately the same results. Like structure designs, the optimal internal flow distributions are examined. The results indicate that the method works well for a variety of process conditions.     

TCS-S热偶精馏过程的最优控制

根据严格逐板计算的结果，运用差分法求出灵敏度系数，对TCS-S热偶精馏进行灵敏度分析，确定灵敏板的位置即确定最优控制点，在此基础上选择最优控制方案，实现TCS-S热偶精馏塔的最优设计。     

Synthesis of complex thermally coupled distillation systems including divided wall columns

The design of thermally coupled distillation sequences explicitly including the possibility of divided wall columns (DWC) is described. A DWC with a single wall can be considered thermodynamically equivalent to a fully thermally coupled (FTC) subsystem formed by three separation tasks (a Petlyuk configuration in the case of three‐component mixtures). It is shown how to systematically identify all the sequences of separation tasks that can produce configurations that include at least a DWC. Feasible sequences that explicitly include DWCs are enforced through a set of logical relationships in terms of Boolean variables. These logical relationships include as feasible alternatives from conventional columns (each column must have a condenser and a reboiler) to FTC systems (only one reboiler and one condenser in the entire system). A comprehensive disjunctive programming formulation for finding the optimal solution is presented. The model is based on the Fenske, Underwood Gilliland equations. However, the disjunctive formulation allows easily the use of any other shortcut, aggregated or even rigorous model without modifying much the structure of the model. Two illustrative examples illustrate the procedure. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1139–1159, 2013     

Parametric and nonparametric model based control of a packed distillation column

Parametric and nonparametric model based control systems were applied to control the overhead temperature of a packed distillation column separating methanol–water mixture. Experimental and theoretical studies have been done to observe the efficiency and performance of both control systems. Generalized predictive control (GPC) system based on a parametric model has been tried to keep the overhead temperature at the desired set point. First, a parametric model which is controlled auto regressive integrated moving average (CARIMA) was developed and then the parameters of this model were identified by applying pseudo random binary sequence (PRBS) and using Bierman algorithm. After that this model was used to design the GPC system. Tuning parameters of the GPC system have been calculated using the simulation program of the packed distillation column. Using the predicted parameters, experimental and theoretical GPC systems were found very effective in controlling the overhead temperature. Dynamic matrix control (DMC) system based on a nonparametric model has been used to track the overhead temperature of the packed distillation column. For this purpose, a nonparametric model known as the dynamic matrix was determined using the reaction curve method. A step change in heat input to the reboiler was applied to the manipulated variable and the temperature of the overhead product was observed. After that, the dynamic matrix was used to design the DMC system. Several calculations have been done to define the DMC control parameters. The best values of the tuning parameter were used to realize the DMC system for controlling the overhead temperature experimentally and theoretically. In the presence of some disturbances, the DMC system gives oscillation and offset in experimental studies.     

Adaptive Robust Generic Model Control of High‐Purity Internal Thermally Coupled Distillation Column

The internal thermally coupled distillation column (ITCDIC) is a frontier in energy‐saving distillation research. The process inside a high‐purity ITCDIC is of great nonlinear dynamics, which trouble the conventional control schemes. A multivariable adaptive robust generic model control (ARGMC) is presented to solve the difficulties, where an ARX model is derived and a recursive least squares estimation (RLSE) method is introduced. The benzene/toluene system is studied as an illustrative example. The results of ARGMC are compared with the robust generic model control (RGMC) and traditional PID control in detail. The performances in both servo control and regulatory control confirm the accuracy and validity of ARGMC for the high‐purity ITCDIC process.     

Multi-loop nonlinear internal model controller design based on a dynamic fuzzy partial least squares model

In this paper, a dynamic fuzzy partial least squares (DFPLS) modeling method is proposed. Under such framework, the multiple input multiple output (MIMO) nonlinear system can be automatically decomposed into several univariate subsystems in PLS latent space. Within each latent space, a dynamic fuzzy method is introduced to model the inherent dynamic and nonlinear feature of the physical system. The new modeling method combines the decoupling characteristic of PLS framework and the ability of dynamic nonlinear modeling in the fuzzy method. Based on the DFPLS model, a multi-loop nonlinear internal model control (IMC) strategy is proposed. A pH neutralization process and a methylcyclohexane (MCH) distillation column from Aspen Dynamic Module are presented to demonstrate the effectiveness of the proposed modeling method and control strategy.     

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。     

Real‐time economic model predictive control of nonlinear process systems

Closed‐loop stability of nonlinear systems under real‐time Lyapunov‐based economic model predictive control (LEMPC) with potentially unknown and time‐varying computational delay is considered. To address guaranteed closed‐loop stability (in the sense of boundedness of the closed‐loop state in a compact state‐space set), an implementation strategy is proposed which features a triggered evaluation of the LEMPC optimization problem to compute an input trajectory over a finite‐time prediction horizon in advance. At each sampling period, stability conditions must be satisfied for the precomputed LEMPC control action to be applied to the closed‐loop system. If the stability conditions are not satisfied, a backup explicit stabilizing controller is applied over the sampling period. Closed‐loop stability under the real‐time LEMPC strategy is analyzed and specific stability conditions are derived. The real‐time LEMPC scheme is applied to a chemical process network example to demonstrate closed‐loop stability and closed‐loop economic performance improvement over that achieved for operation at the economically optimal steady state. © 2014 American Institute of Chemical Engineers AIChE J, 61: 555–571, 2015     

Economic model predictive control of nonlinear time‐delay systems: Closed‐loop stability and delay compensation

Closed‐loop stability of nonlinear time‐delay systems under Lyapunov‐based economic model predictive control (LEMPC) is considered. LEMPC is initially formulated with an ordinary differential equation model and is designed on the basis of an explicit stabilizing control law. To address closed‐loop stability under LEMPC, first, we consider the stability properties of the sampled‐data system resulting from the nonlinear continuous‐time delay system with state and input delay under a sample‐and‐hold implementation of the explicit controller. The steady‐state of this sampled‐data closed‐loop system is shown to be practically stable. Second, conditions such that closed‐loop stability, in the sense of boundedness of the closed‐loop state, under LEMPC are derived. A chemical process example is used to demonstrate that indeed closed‐loop stability is maintained under LEMPC for sufficiently small time‐delays. To cope with performance degradation owing to the effect of input delay, a predictor feedback LEMPC methodology is also proposed. The predictor feedback LEMPC design employs a predictor to compute a prediction of the state after the input delay period and an LEMPC scheme that is formulated with a differential difference equation (DDE) model, which describes the time‐delay system, initialized with the predicted state. The predictor feedback LEMPC is also applied to the chemical process example and yields improved closed‐loop stability and economic performance properties. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4152–4165, 2015     

On identification of well‐conditioned nonlinear systems: Application to economic model predictive control of nonlinear processes

The focus of this work is on economic model predictive control (EMPC) that utilizes well‐conditioned polynomial nonlinear state‐space (PNLSS) models for processes with nonlinear dynamics. Specifically, the article initially addresses the development of a nonlinear system identification technique for a broad class of nonlinear processes which leads to the construction of PNLSS dynamic models which are well‐conditioned over a broad region of process operation in the sense that they can be correctly integrated in real‐time using explicit numerical integration methods via time steps that are significantly larger than the ones required by nonlinear state‐space models identified via existing techniques. Working within the framework of PNLSS models, additional constraints are imposed in the identification procedure to ensure well‐conditioning of the identified nonlinear dynamic models. This development is key because it enables the design of Lyapunov‐based EMPC (LEMPC) systems for nonlinear processes using the well‐conditioned nonlinear models that can be readily implemented in real‐time as the computational burden required to compute the control actions within the process sampling period is reduced. A stability analysis for this LEMPC design is provided that guarantees closed‐loop stability of a process under certain conditions when an LEMPC based on a nonlinear empirical model is used. Finally, a classical chemical reactor example demonstrates both the system identification and LEMPC design techniques, and the significant advantages in terms of computation time reduction in LEMPC calculations when using the nonlinear empirical model. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3353–3373, 2015