Control of reactive distillation process for production of ethyl acetate

In this paper, plant-wide control for the production of ethyl acetate using reactive distillation is studied. Four important issues are considered in developing control schemes, including: (1) economics; (2) steady-state deviation of key product purities; (3) controllability in terms of degree of oscillation and settling time; and (4) feasible region of disturbances for effective control. Starting with two basic control schemes that have been studied before, new control schemes are developed to improve the operability of the process. These new control schemes have evolved from the basic schemes by making a trade-off between optimal design and control or by selecting sensor locations using closed-loop sensitivity analysis. It is found that, while being subjected to fluctuations in the composition of the acid feed or in the production flow-rate, sensor location based on traditional open-loop sensitivity causes a larger overshoot and steady-state deviation of key product purities. Sensor location on the basis of a closed-loop sensitivity analysis provides a better alternative for feedback control. The resulting scheme for control is found to be effective in reducing the steady-state deviation and in promoting good control performance.     

Role of multiplicity in reactive distillation control system design

The impact of steady-state multiplicities on the control of a simulated industrial scale methyl acetate reactive distillation (RD) column is studied. At a fixed reflux rate, output multiplicity, with multiple output values for the same reboiler duty, causes the column to drift to an undesirable steady-state under open loop operation. The same is avoided for a fixed reflux ratio policy. Input multiplicity, where multiple input values give the same output, leads to “wrong” control action under feedback control severely compromising control system robustness. A new metric, rangeability, is defined to quantify the severity of input multiplicity in a steady-state input–output (IO) relation. Rangeability is used in conjunction with conventional sensitivity analysis for the design of robust control structures for the RD column. Results for the two synthesized control structures show that controlling the most sensitive reactive tray temperature results in poor robustness due to low rangeability causing “wrong” control action for large disturbances. Controlling a reactive tray temperature with acceptable sensitivity but larger rangeability gives better robustness. It is also shown that controlling the difference in the temperature of two suitably chosen reactive trays further improves robustness of both the structures as input multiplicity is avoided. The article brings out the importance of IO relations for control system design and understanding the complex dynamic behavior of RD systems.     

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.     

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.     

模拟甲基叔丁基醚反应精馏的过程

为揭示反应精馏过程的耦合特性,从而为进一步开发新的反应精馏设计方法提供理论支持,采用Gibbs自由能平衡级模型,应用Aspen Plus软件模拟分析MTBE的反应精馏过程,研究了回流比与理论板数对反应和分离效果的影响。结果表明由于反应和精馏的相互作用,在固定回流比改变理论板数(或固定理论板数改变回流比)的情况下,存在最佳理论板数(或回流比)使产品组成最高,这种特性与普通精馏有所不同。     

Pattern-based predictive control for ETBE reactive distillation

Synthesis of ethyl tert-buty ether (ETBE), a high-performance fuel additive, through reactive distillation (RD) is an attractive route, while its operation and control are exceptionally difficult due to its functional combination and complex dynamics. Modern control technology greatly relies on good process models, while a reasonable RD model is too complex for control design. Moreover, RD contains considerable uncertainties that cannot be well described in process modelling. Alleviating the model requirement, this work aims to maintain the product purity in RD of ETBE through developing a pattern-based predictive control (PPC) scheme. Process dynamics, control structure, nonlinear transformation, feature pattern extraction, and pattern-based prediction and its incorporation with a conventional linear controller are discussed. Case studies show the effectiveness of the proposed method     

Effect of interaction multiplicity on control system design for a MTBE reactive distillation column

The control strategy of a reactive distillation for synthesis of MTBE is investigated. Although steady state multiplicities occur in the column, a linear control is still possible since a controlled and manipulated variable-pairing scheme that exhibits a sufficiently large range of near linear relations can be found, if we operate at constant reflux ratio. Reboiler duty is used to control the temperature of a stage just below the reaction section and near the top of the stripping section. Stoichiometric balance is controlled by a feed ratio plus internal composition control scheme, using a control valve installed on the C4 feed-line as the manipulated variable. Such a scheme is capable of maintaining the desirable steady state that achieves high product purity and reactant conversion. However, a similar scheme that uses a control valve installed on the methanol feed-line as the manipulated variable shows severe oscillation. It is caused by multiplicity in the interaction between the temperature and stoichiometric control loops.     

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.     

Model predictive control of reactive distillation for benzene hydrogenation

Benzene hydrogenation via reactive distillation is a process that has been widely adopted in the process industry. However, studies in the open literature on control of this process are rare and seem to indicate that conventional decentralized PI control results in sluggish responses when the reactive distillation column is subjected to disturbances in the feed concentration. In order to overcome this performance limitation, this work investigates model predictive control (MPC) strategies of a reactive distillation column model, which has been implemented in gPROMS. Several MPCs based upon different sets of manipulated and controlled variables are investigated where the remaining variables remain under regular feedback control. Further, MPC controllers with output disturbance correction and, separately, with input disturbance correction have been investigated. The results show that the settling time of the column can be reduced and the closed loop dynamics significantly improved for the system under MPC control compared to a decentralized PI control structure.     

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.     

基于Aspen Plus用户模型的MTBE反应精馏模拟

采用用户模型技术,运用Fortran语言编写反应动力学子程序,并将其嵌入Aspen Plus精馏过程中,从而在Aspen Plus平台实现了MTBE反应精馏过程的动力学模拟,结果表明,所建用户模型MrBE反应精馏塔内温度、液相组成分布模拟值较好地吻合了文献值。以MTBE收率及纯度作为目标函数,研究了精馏塔相关参数改变对目标函数的影响,得到的优化条件分别如下:操作压力为1100 kPa,回流比为6,甲醇进料位置为第10块板,反应段塔板数为8块,此时MTBE收率为95.53%,纯度为99.2%。     

Nonlinear model-based control of a batch reactive distillation column

The inherent trade off between model accuracy and computational tractability for model-based control applications is addressed in this article by the development of reduced order nonlinear models. Traveling wave phenomena is used to develop low order models for multicomponent reactive distillation columns. A motivational example of batch esterification column is used to demonstrate the synthesis procedure. Tight control of the column is obtained with the use of reduced model in a model predictive control algorithm.     

反应精馏生产丁酸酐的过程模拟

丁酸酐是一种重要的有机原料,其现有的生产工艺为间歇操作,收率较低。本研究将反应精馏技术应用到生产丁酸酐的工艺中,建立了连续生产工艺的数学模型,在实验数据的基础上,用数据分析软件MATLAB对实验数据分析处理,得到相应的模型参数。采用ASPEN PLUS进行工艺方案的模拟计算,结果表明,利用反应精馏连续生产丁酸酐的工艺是可行的,以分离促反应,产品收率到达了97%以上,同时选择工艺操作参数,最佳条件为:酸酐比2:1、反应压力0.2 atm、塔板数为12块,确定反应区主要为进料板附近。     

酯交换合成碳酸二乙酯反应精馏工艺的模拟

碳酸二乙酯是一种性能优良的绿色溶剂,在锂离子电池行业中应用广泛。以碳酸二甲酯和乙醇为原料,采用反应精馏工艺生产碳酸二乙酯是目前的主流工艺。由于该反应分为两步进行,因此如何调节工艺参数来提高反应物转化率和产品收率是工业生产中的重要问题。本文以实验得出的动力学方程、UNIQUAC热力学模型,应用Aspen Plus流程模拟软件实现了工业实际反应精馏工艺流程的模拟,详细讨论了进料位置、回流比、进料配比等因素对反应精馏塔性能的影响。结果表明,模拟值与实验值吻合良好,在回流比为4.5～5,混合进料第20块板,进料醇酯比2.5:1,反应段（2～31）块板,空速小于0.18 hr^-1时,反应精馏塔性能最佳。模拟结果对反应精馏合成碳酸二乙酯的工艺设计和过程优化具有重要指导意义。     

基于塔板组成线的反应精馏图解设计

图解设计法,可以相对较少的已知数据快速、直观地获得反应精馏塔的结构和操作参数。本文在转换组成变量的基础上,系统地推导了反应精馏塔板组成线方程及设计可行性判据,提出了基于塔板组成线进行反应精馏图解设计的策略,并对理想反应体系进行了实例设计计算。研究结果表明：利用塔板组成线进行反应精馏设计可同时获得多组可行设计方案,比现有精馏线和剩余曲线设计方法更为有效,并有利于设计的优化和经济效益评价。     

乙酸丁酯反应精馏过程模拟设计

反应精馏偶合了反应和精馏两种单元操作,通过精馏促进反应,可以提高反应转化率和收率,为可逆反应的化工过程生产提供了新的设计途径。基于严格热力学分析计算,利用计算机模拟和优化手段。提出了乙酸丁酯反应精馏、分离纯化的生产流程。采用UNIQUAC方程表征乙酸-正丁醇-乙酸丁酯-水四元非理想体系的汽液平衡,首先,根据实验数据回归了热力学模型中的交互作用参数,并预测了体系中5个共沸物组成,模型的计算结果与实际数据吻合。基于平衡级模型,提出了由平衡反应器、反应精馏塔、倾析器和纯化塔构成的可行流程,对提出的设计流程进行了模拟、优化,得到了操作工艺参数。模拟结果对工业过程的设计和改造具有一定的指导意义。     

反应精馏合成乙酸乙酯的实验研究与模拟

本文以Amberlyst-36Wet离子交换树脂为催化剂,采用间歇搅拌釜式反应器,在消除内外扩散影响的条件下,测得不同温度下反应速率常数.研究自制反应精馏塔中(直径25 mm,高2.2 m)乙酸乙酯的合成工艺,得到反应精馏的工艺参数.在实验基础上,建立改进工艺的Aspen Plus模拟流程图.实验结果与模拟计算值吻合良好,表明所建立的Aspen Plus模型能够很好地描述反应精馏合成乙酸乙酯过程.以乙醇转化率、产品乙酸乙酯的收率和塔顶油相乙酸乙酯的质量分率为考察目标,通过流程模拟和灵敏度分析,确定该工艺的最佳工艺参数:精馏段、反应段和提馏段的理论板数分别为9、7和7;醋酸和乙醇的最佳进料位置在第9块和第16块塔板上;回流比R为1.6.在此工艺条件下,产品乙酸乙酯的含量是95.2%(wt),乙醇转化率为96.1%.     

A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study

Model-based control strategies are widely used for optimal operation of chemical processes to respond to the increasing performance demands in the chemical industry. Yet, obtaining accurate models to describe the inherently nonlinear, time-varying dynamics of chemical processes remains a challenge in most model-based control applications. This paper reviews data-driven, Linear Parameter-Varying (LPV) modeling approaches for process systems by exploring and comparing various identification methods on a high-purity distillation column case study. Several LPV identification methods that utilize input–output and series expansion model structures are explored. Two LPV identification perspectives are adopted: (i) the local approach, which corresponds to the interpolation of Linear Time-Invariant (LTI) models identified at different steady-state operating points of the system and (ii) the global approach, where a parametrized LPV model structure is identified directly using a global data set with varying operating points. For the local approach, various model interpolation schemes are studied under an Output Error (OE) noise setting, whereas in the global case, a polynomial parametrization based OE prediction error minimization approach, an Orthonormal Basis Functions (OBFs) based model estimator and a Least-Square Support Vector Machine (LS-SVM) based non-parametric approach are investigated. Through extensive simulation studies, the aforementioned LPV identification approaches are analyzed in terms of the attainable model accuracy and local frequency response behavior of the obtained models. Recommendations are provided to achieve adequate choice between the methods for a particular process system at hand.     

Control structure selection for energy integrated distillation column

This paper treats a case study on control structure selection for an almost binary distillation column. The column is energy integrated with a heat pump in order to transfer heat from the condenser to the reboiler. This integrated plant configuration renders the possible control structures somewhat different from what is usual for binary distillation columns. Further the heat pump enables disturbances to propagate faster through the system. The plant has six possible actuators of which three must be used to stabilize the system. Hereby three actuators are left for product purity control. An MILP screening method based on a linear state space model is used to determine economically optimal sets of controlled and manipulated variables. The generated sets of inputs and outputs are analysed with frequency dependent relative gain array (RGA), relative disturbance gain (RDG) and condition number (CN) to determine the best structure in terms disturbance rejection and setpoint tracking. The pairing and controller design are implemented and evaluated through nonlinear simulation. The suggested control structure is also qualitatively compared to a control structure applied experimentally.