Dynamic parameter estimation and optimization for batch distillation

This work reviews a well-known methodology for batch distillation modeling, estimation, and optimization but adds a new case study with experimental validation. Use of nonlinear statistics and a sensitivity analysis provides valuable insight for model validation and optimization verification for batch columns. The application is a simple, batch column with a binary methanol–ethanol mixture. Dynamic parameter estimation with an ℓ₁-norm error, nonlinear confidence intervals, ranking of observable parameters, and efficient sensitivity analysis are used to refine the model and find the best parameter estimates for dynamic optimization implementation. The statistical and sensitivity analyses indicated there are only a subset of parameters that are observable. For the batch column, the optimized production rate increases by 14% while maintaining product purity requirements.     

ON-LINE OPTIMIZATION OF DISTILLATION COLUMNS IN SERIES

This paper presents an on-line optimization algorithm for distillation columns which consists of a steepest descent technique based on a simple model of product recovery with on-line estimation of the critical model parameter. The algorithm is developed for a single column and then for a two-column train. A dynamic programming approach is used to reduce the optimization problem to a single parameter search for each column in the train. The resulting algorithm is simple and computer resource requirements are small. The algorithm has been successfully used in two industrial applications, one consisting of two columns in series and the other of three columns in series.     

Composition estimation of multicomponent reactive batch distillation with optimal sensor configuration

An inferential state estimation scheme based on extended Kalman filter (EKF) with optimal selection of sensor locations using principal component analysis (PCA) is presented for composition estimation in multicomponent reactive batch distillation. The properties of PCA are exploited to provide the most sensitive dynamic temperature measurement information of the process to the estimator for accurate estimation of compositions. The state estimator is supported by a simplified dynamic model of reactive batch distillation that includes component balance equations together with thermodynamic relations and reaction kinetics. The performance of the proposed scheme is evaluated by applying it for composition estimation on all trays, reboiler, reflux drum and products of a reactive batch distillation column, in which ethyl acetate is produced through an esterification reaction between acetic acid and ethanol. This quaternary system with azeotropism is highly nonlinear and typically suited for implementation of the proposed scheme. The results demonstrate that the proposed EKF estimation scheme with optimal temperature sensor configuration is effective for inferential estimation of compositions in multicomponent reactive batch distillation.     

Design and control of butyl acrylate reactive distillation column system

In this study, the design and control of a reactive distillation column system for the production of butyl acrylate has been investigated. The proposed design is quite simple including only one reactive distillation column and an overhead decanter. The optimal design is selected based on the minimization of total annual cost (TAC) for the overall system. At this optimized flowsheet condition, output multiplicity was found with reboiler duty or feed ratio as the bifurcation parameter. The highest purity stable steady state was selected as the base case condition for the control study. The overall control of this system can be achieved with no on-line composition measurements. Simple single-point tray temperature control loop is designed to infer final product purity. From results of dynamic simulation, the proposed control strategy performs very well in rejecting various disturbances while maintaining butyl acrylate product at high purity. One of the important finding in this paper is that it is better to operate this reactive distillation column not at the exact feed stoichiometric balance point for better operability reason. The control performances of the proposed operating point and the operating condition right at the exact stoichiometric balance point will be compared.     

2-乙基丙烯醛精馏分离过程模拟与实验研究

基于Aspen Plus软件,利用软件的物性估算功能及UNIFAC-HOC热力学模块,完成了数据库中缺少组分2-乙基丙烯醛物性估算,获得了模拟所需基础数据,通过对软件的相平衡曲线、残余曲线的分析,设计了分离体系的4塔分离流程,采用简捷模拟、严格模拟和灵敏度分析模块完成精馏塔的设计和操作参数的优化,从而获得了纯度达到99%的2-乙基丙烯醛精馏分离流程和操作条件。间歇精馏实验表明:模拟共沸物组成符合实际过程,模拟结果可以指导实际过程的分析和设计。     

Nonlinear model-based feedforward control of distillation columns

An approach to feedforward distillation control based on inverse computation of nonlinear stage models is presented. The feedforward model calculates dynamic trajectories of manipulated variables from measured disturbances and product purity set points independently of the control configuration. Because the model includes the dominant dynamics and nonlinearities of the column, dynamic decoupling of the control loops is achieved. A superimposed linear controller only has to compensate model uncertainties and disturbances that cannot be measured. The proposed approach improves the control performance. Simulation studies show the applicability of the method to multicomponent distillation as well as to distillation trains. Experiments on a pilot plant scale binary distillation column verify the simulation results.     

Applications of linear estimation theory to chemical processes: a feasibility study

The feasibility of using linear recursive least-squares estimators for on-line state and parameter estimation is studied with reference to two examples. With the first example, a binary distillation column, it is shown that the estimators are insensitive to the statistical approximations involved and provide a powerful means of dealing with bias, drift, or scatter in instrument readings. In the second example, a reactor with decaying catalyst, it is shown that drastic model simplification is possible and model parameters can be successfully estimated from indirect measurements. Formulae for growing memory and limited memory filters for measurement noise are derived in the appendix.     

Parameter identification for state estimation—application to bioprocess software sensors

Together with some on-line measurements, a reliable process model is the key ingredient of a successful state observer design. In common practice, the model parameters are inferred from experimental data so as to minimize a model prediction error, e.g. so as to minimize an output least-squares criterion. In this procedure, no care is actually exercised to ensure that the unmeasured model states are sensitive to the measured states. In turn, if sensitivity is too low, the resulting state observer will probably generate poor estimates of the unmeasured states. To alleviate these problems, a new parameter identification procedure is proposed in this study, which is based on a cost function combining a conventional prediction error criterion with a state estimation sensitivity measure. Minimization of this combined cost function produces a model dedicated to state estimation purposes. A thorough analysis of the procedure is presented in the context of bioreactor modeling, including parameter identification, model validation and design of extended Kalman filters and full horizon observers.     

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.     

乳酸提纯新工艺动态建模仿真和控制系统设计

基于非平衡级和拟均相假设,建立了乳酸提纯反应精馏新工艺实验装置的动态机理模型.通过改进的数值计算方法提高了模型的求解效率,设计并实现了包含物性估算系统的模型仿真平台,以促进新工艺的工业化应用.利用仿真平台对新工艺装置进行了动态特性分析,在此基础上设计了两种单端质量控制方案:直接物料平衡和间接物料平衡方案.在不同类型和幅度的过程扰动下,分析比较了两种控制方案的调节性能.结果表明直接物料平衡方案控制品质优于间接物料平衡方案,可在不同扰动情况下满足过程的产品质量和转化率的联合控制要求.非平衡级动态机理模型能够反映反应精馏过程的动态特性,分析发现反应精馏过程有着独特的过程特性,基于机理模型的仿真平台是分析反应精馏特性的有效工具.     

甲醇-苯共沸体系变压精馏分离工艺的动态控制

基于甲醇和苯共沸体系的压敏性,利用Aspen Plus和Aspen Dynamics软件对变压精馏分离该体系的稳态工艺进行了模拟和优化,研究了该工艺的动态特性,提出了控制产品纯度的3种控制结构：基础控制结构、比例控制结构和双比例与温度?组分联合控制结构,通过对控制结构添加±20%的组分和流量干扰测试控制结构的稳定性. 结果表明,基础控制结构基本能实现稳健控制,但不能解决组分干扰引起的产品纯度偏差过大等问题;比例控制结构可实现相对稳健的控制,但改进效果不显著;双比例与温度?组分联合控制结构在受到20%进料和组分干扰后,产品纯度能较快恢复至设定值的99.90%,实现稳健控制.     

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。     

Distributed Heat Supply for Distillation Control to Reduce Feed Composition Disturbance Effects

The introduction of heat in stages combined with a reboiler as a distillation control strategy is proposed aiming to reduce the transition time when a feed composition disturbance occurs. This is the most severe type of perturbation and results in long transition times which are economically undesirable. Experiments were carried out in a pilot distillation column with ethanol and water for testing two different control strategies: conventional, i.e., using both last stage and reboiler temperature controllers, and distributed, i.e., conventional strategy plus stage temperature controllers. The results with this operational approach indicated a significant reduction in the time required for the column to stabilize when compared with the conventional approach. The proposed strategy proved to be a viable alternative, enabling faster dynamics and smaller volumes to be processed outside the predefined quality parameters.     

Simulation of a plant scale reactive distillation column for esterification of acetic acid

Most of the reactive distillation (RD) simulation studies rely upon very scarce laboratory scale experimental data available in literature. In the present work, an industrial scale RD finishing column for ethyl acetate production is simulated using rate based model of Aspen Plus™. Available activity coefficient data could not predict composition profiles of the industrial column. Therefore, a new set of NRTL model parameters is established for this system using the vapor–liquid equilibrium data available in literature. This new set of data was used in the non-equilibrium, rate based model of Aspen Plus™. Baring the variation in concentration profile due to fluctuations caused by pseudo-steady-state behavior of industrial unit, the predicted concentration profile closely matches the plant data. The simulation results for stage efficiencies, component generation rates, and the effect of the organic reflux rate on ethanol conversion and ethyl acetate purity are also presented and discussed.     

基于优化的反应精馏塔简捷设计

基于G ibbs自由能最小原理达到反应和相平衡的假设,提出了一种适用于初步设计的新的设计方法。以甲基叔丁基醚和碳酸二乙酯的反应精馏过程为例,应用Aspen软件模拟,经分析发现,通过改变理论级数和回流比,可得到合理的塔内温度分布,从而使塔底产品组成得到最大程度的提高,为此提出基于优化的设计法:以产品组成最大为目标函数,以回流比、精馏段和提馏段理论板数为决策变量,采用遗传算法进行优化计算。结果表明,2个设计实例均能在保证低能耗的同时得到较高的产品纯度,说明该设计方法可以设计出较好的反应精馏塔。     

Reduced Hessian based parameter selection and estimation with simultaneous collocation approach

Parameter estimation is a critical step in the building of process models. Given the nonlinear structure and limited measurements, it is often difficult to correctly estimate all the parameters involved in the model. Linear dependence and low correlation among the parameters are the main problems to be handled in parameter estimation. The common approach is to estimate a subset of the parameters by fixing the others at reasonable values. However, it is a challenge to determine which parameters can be properly estimated. In this work, the ratio between standard deviation and estimated parameter value is introduced for evaluating the estimability. A Gauss-Jordan elimination based approach is proposed for parameter estimability ranking. Combined with the proposed ranking approach and approximate ratio criterion, a reduced Hessian based approach is proposed for parameter selection and estimation under a simultaneous collocation framework. The proposed approach is at least as effective and more efficient than competing approaches based on multiple eigenvalue decompositions or orthogonalizations for larger problems. Three case studies with increasing complexity are presented to demonstrate the performance of the proposed approach.     

Parameter set selection for dynamic systems under uncertainty via dynamic optimization and hierarchical clustering

It is common that only a subset of the parameters of models can be accurately estimated. One approach for identifying a subset of parameters for estimation is to perform clustering of the parameters into groups based upon their sensitivity vectors. However, this has the drawback that uncertainty cannot be directly incorporated into the procedure as the sensitivity vectors are based upon the nominal values of the parameters. This article addresses this drawback by presenting a parameter set selection technique that can take uncertainty in the parameter space into account. This is achieved by defining sensitivity cones, where a sensitivity cone includes all sensitivity vectors of a parameter for different values, resulting from the uncertainty, in the parameter space. Parameter clustering can then be performed based upon the angles between the sensitivity cones, instead of the angle between sensitivity vectors. The presented technique is applied to two case studies. © 2013 American Institute of Chemical Engineers AIChE J, 60: 181–192, 2014     

Design,optimization, and control of reactive distillation column for the synthesis of tert-amyl ethyl ether

The temperature–composition cascade strategy was proposed to control the reactive distillation (RD) process for the synthesis of tert-amyl ethyl ether. Aspen Split tool was adopted to obtain the design parameters of the column from the viewpoint of separating tert-amyl alcohol, ethanol, and water. The proposed procedure which included optimization and sensitivity analysis was implemented to optimize the design parameters of the column and obtain the sensitive variables simultaneously, without requirement to run various simulations. In those optimized conditions, the proposed control strategy was introduced to manage the RD process by changing the sensitive variables. From results of the dynamic simulation, it can be known that the proposed strategy was able to handle disturbances while maintain the tert-amyl ethyl ether product purity and quickly reach the steady state. Therefore, the proposed control strategy was regarded as the effective control structure and could successfully control this RD system.     

基于能量目标的芳烃萃取精馏溶剂评价模型

以NRTL活度系数模型为基础，利用Aspen Plus对不同单组分萃取剂回收芳烃的萃取精馏装置进行了全流程模拟和工艺操作参数优化。综合考虑各操作变量及其关联，提出了基于局部耦合参数迭代优化的整体协同优化策略，在保证分离要求的条件下，以能耗为目标，对萃取精馏塔（EDC）、溶剂回收塔（ERC）的进料位置、ERC回流比等关键操作参数进行优化，建立过程能耗的物性关联模型。通过分析不同溶剂对芳烃萃取精馏过程能耗和分离效果的影响，提出了基于能耗目标的芳烃萃取精馏溶剂评价模型，结果表明影响芳烃萃取精馏过程能耗的关键物性为溶剂的分子量及常压沸点，所建立的过程能耗关联模型具有较高的关联性，其R ²均大于0.9，可有效指导萃取精馏溶剂选择，为计算机辅助分子设计提供简化的目标函数。     

基于工业色谱仪的精馏脱炔烃塔软测量建模

提出一种基于工业色谱仪的软测量建模方法,并针对碳五馏分分离过程中的精馏脱炔烃塔塔底成分估计问题,建立了合适的工业软测量模型。介绍了工业色谱仪在线质量检测原理和LM-BP神经网络模型的建立,并利用工业色谱仪在线检测的质量数据进行系统的在线和周期性模型更新,提高了软测量模型的在线估计精度。研究结果表明,基于工业色谱仪的LM-BP神经网络模型是一种有效的软测量建模方法。