连续流化催化精馏与间歇精馏制备乳酸乙酯的对比研究

乳酸乙酯具有无毒、溶解性好、不易挥发、有果香气味等特点,是极具开发价值和应用前景的"绿色溶剂"。由于乳酸乙酯的特性,国内外学者渐渐致力于开发更高效更环保的制备方法。连续流化催化精馏是利用乳酸和乙醇酯化、粉末状大孔强酸性交换树脂作为催化剂,随反应物流动的连续催化精馏方式,是一种更优的高效而环保的乳酸乙酯生产方式。首次利用连续流化催化精馏制备乳酸乙酯,并旨在研究连续流化催化精馏与间歇精馏制备乳酸乙酯的对比来突出前者的优势,对连续流化催化精馏的产物进行精馏提纯,得到更高纯度的乳酸乙酯,为连续流化催化精馏制备乳酸乙酯的优势提供了可靠的理论依据,并在食品工业等高纯度要求的工业中的运用起到了推进作用。     

间歇精馏过程模拟的发展与应用

介绍了间歇精馏模拟过程中常用的数学建模的经典方法以及近两年在数学建模及其算法领域的发展,讨论各模型的特点及其在精馏工业和试验中的应用实例。     

间歇精馏的计算

该文研究用正、反分段函数插值法及离散点下数值积分等数值方法进行间歇精馏的设计型计算和操作型计算;编写了通用的计算机程序;列举了应用实例.     

基于SIMULINK系统的间歇萃取精馏动态模拟

间歇萃取精馏属于非理想性极强的非稳态过程,很难找到一种快捷有效且易于执行的求解方法.基于此,分别建立了全回流开工、加溶剂全同流及产品采出3个阶段的平衡级动态模型,编写S函数将该模型求解过程嵌套于SIMULINK模块中,并在此基础上开发了间歇萃取精馏的动态模拟平台.通过将不同解法与实验值进行对比,确定了该研究条件下动态模型的求解策略:ODE45解法适于求解全回流开工和加溶剂全回流动态模型;ODE 15s解法和ODE 23s解法适于求解产品采出动态模型,其中ODE 23s的计算过程相对更快捷.     

间歇精馏模拟及操作方式的研究进展

综述了间歇精馏模拟研究常用的数学模型,主要介绍了严格模型和简捷模型,简要讨论了各个模型的特点.较详细地介绍了国内外间歇精馏操作方式的研究进展,讨论了动态累积间歇精馏塔、反向间歇精馏塔、中间罐间歇精馏塔、多罐间歇精馏塔等新塔型的操作.     

双再沸器间歇萃取精馏及模拟

本文提出了具有双再沸器的间歇萃取精馏新方法。实验选用乙二醇作为溶剂分离乙醇-水共沸物．结果表明,双再沸器间歇精馏具有再沸器温度稳定、操作简便、节省操作时间、收率提高等优点。为了更深入的研究间歇萃取精馏,本文建立了描述间歇萃取精馏的平衡级恒摩尔持液模型,并对此数学模型求解,通过模拟结果与实验结果的对比分析了塔顶与塔底浓度和温度的变化、溶剂流率等操作参数对实验的影响。     

间歇萃取精馏制备高纯甲醇模拟与实验研究

针对工业甲醇制备高纯甲醇的间歇萃取精馏过程,建立了准稳态模型,采用Newton迭代法进行求解。模拟分析了影响高纯甲醇收率的溶剂流率、溶剂加入位置、回流比等因素,并通过实验验证了模型的准确性和可靠性。在实验与模拟计算的基础上,确定了工业甲醇制备高纯甲醇的最佳工艺条件:总理论板数35,萃取剂二甲基亚砜在第10块理论板加入,萃取剂与原料甲醇的体积比0.3,前馏分段回流比8,产品段回流比5,此时,高纯甲醇单程收率可达60%。     

间歇反应精馏过程的模拟及其刚性问题

本文修正了Boston等人用于间歇精馏过程的数学模型,使之能适用于间歇反应精馏过程;提出了用解离法完成间歇反应精馏过程模拟的计算步骤;比较了SIRK,GEAR和EPISODE等软件在间歇精馏过程刚性微分方程数学模型积分中的有效性;并以醋酸丁酯用乙醇转移酯化反应精馏过程为例说明了方法的应用。     

带有中间储罐的间歇萃取精馏的模拟研究

本文提出了带有中间储罐塔间歇萃取精馏过程计算模型,将中间罐看成一块理论板,萃取剂看成第三组份进行建模,利用调节因子和泡点法相结合对整个塔进行迭代计算,经实例模拟表明,本模型只需较简单的赋初值,能以较快的速度收敛,本模型对带有中间储罐间歇萃取精馏操作及模拟具有一定的指导作用。     

Data‐driven model predictive quality control of batch processes

The problem of driving a batch process to a specified product quality using data‐driven model predictive control (MPC) is described. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behavior until batch termination. In this work, we handle this “missing data” problem by integrating a previously developed data‐driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a MPC framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via closed‐loop simulations of a nylon‐6,6 batch polymerization process with limited measurements. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2852–2861, 2013     

带有中间储罐的间歇精馏工艺动态控制模拟优化

甲酸甲酯-甲醇-水是化工生产过程中最常见的三元混合物之一。目前,间歇精馏工艺分离该三元混合物的研究较少,在动态控制方面也少有报道。本文研究了分离甲酸甲酯-甲醇-水的带有中间储罐的间歇精馏工艺动态控制模拟优化。利用Aspen Plus和Aspen Plus Dynamics软件,在稳态模拟的基础上,分别考察了液位控制结构和组分控制结构两种控制方案。结果表明,液位控制结构控制性能较差,达到稳定后甲醇和水的纯度较低。组分控制结构虽能提高产品纯度,但出现了较为严重的振荡现象。根据对组分控制结构的动态响应分析,本文提出了一种改进的组分控制结构,该控制结构能实现带有中间储罐的间歇精馏工艺的稳健控制,使各产品纯度得到提高。     

Robust model predictive control and fault handling of batch processes

This work considers the control of batch processes subject to input constraints and model uncertainty with the objective of achieving a desired product quality. First, a computationally efficient nonlinear robust Model Predictive Control (MPC) is designed. The robust MPC scheme uses robust reverse‐time reachability regions (RTRRs), which we define as the set of process states that can be driven to a desired neighborhood of the target end‐point subject to input constraints and model uncertainty. A multilevel optimization‐based algorithm to generate robust RTRRs for specified uncertainty bounds is presented. We then consider the problem of uncertain batch processes subject to finite duration faults in the control actuators. Using the robust RTRR‐based MPC as the main tool, a robust safe‐steering framework is developed to address the problem of how to operate the functioning inputs during the fault repair period to ensure that the desired end‐point neighborhood can be reached upon recovery of the full control effort. The applicability of the proposed robust RTRR‐based controller and safe‐steering framework subject to limited availability of measurements and sensor noise are illustrated using a fed‐batch reactor system. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

High-order iterative learning model predictive control for batch chemical processes

In order to address two-dimensional (2D) control issue for a class of batch chemical processes, we propose a novel high-order iterative learning model predictive control (HILMPC) method in this paper. A set of local state-space models are first constructed to represent the batch chemical processes by adopting the just-in-time learning (JITL) technique. Meanwhile, a pre-clustered strategy is used to lessen the computational burden of the modelling process and improve the modelling efficiency. Then, a two-stage 2D controller is designed to achieve integrated control by combining high-order iterative learning control (HILC) on the batch domain with model predictive control (MPC) on the time domain. The resulting HILMPC controller can not only guarantee the convergence of the system on the batch domain, but also guarantee the closed-loop stability of the system on the time domain. The convergence of the HILMPC method is ensured by rigorous analysis. Two examples are presented in the end to demonstrate that the developed method provides better control performance than its previous counterpart.     

间歇萃取精馏过程模拟计算

提出了一种可用于间歇萃取精馏过程模拟计算的快捷模型－准稳态模型，并对该方法的数学模型进行了推导。用此方法对间歇萃取精馏过程中塔顶、塔釜浓度，各塔板温度随时间的变化进行模拟，其结果与实验值吻合较好。它具有计算精度高、计算速度快等优点。     

拟稳态模型用于间歇萃取精馏的数值模拟

Batch extractive distillation (BED) is a special method used in the distillation process by adding a solvent into the batch distillation column to alter the relative volatility of the components and improve the separation. A comprehensive design and simulation method is required due to the complexity of BED. In this study, a quasi-steady-state model for BED is proposed, the derivation and solution of the model are presented. This shortcut model can be used to simulate the composition and temperature of the reboiler, the top and other plates of the column in a batch extractive distillation operation. The calculated values are in good agreement with the experi-mental data. The results show that the quasi-steady-state model is a practical method because of some advantages such as high precision and fast calculation.     

Optimal operation and tracking control of vapor-recompressed batch distillation

Vapor recompressed batch distillation (VRBD) is an energy-integrated configuration which works on the principle of a heat pump. Operation of such a column is challenging due to unsteady, nonlinear dynamics and strong interplay between separation and energy efficiency. In this paper, a two-step approach is proposed for optimal operation and control of such a column. Initially, an openloop optimal operation policy is generated for maximization of an overall performance index using offline optimization. To this end, three performance indices are proposed to capture interplay between separation and energy efficiency. Subsequently, a model-based output feedback controller is designed to track this optimal performance trajectory. The effectiveness of the proposed approach is demonstrated using a benzene-toluene separation case study wherein it is shown that the proposed approach helps to achieve optimal operation in the presence of operational disturbances.     

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.     

Integrated batch‐to‐batch and nonlinear model predictive control for polymorphic transformation in pharmaceutical crystallization

Polymorphism, a phenomenon in which a substance can have more than one crystal form, is a frequently encountered phenomenon in pharmaceutical compounds. Different polymorphs can have very different physical properties such as crystal shape, solubility, hardness, color, melting point, and chemical reactivity, so that it is important to ensure consistent production of the desired polymorph. In this study, an integrated batch‐to‐batch and nonlinear model predictive control (B2B‐NMPC) strategy based on a hybrid model is developed for the polymorphic transformation of L ‐glutamic acid from the metastable α‐form to the stable β‐form crystals. The hybrid model comprising of a nominal first‐principles model and a correction factor based on an updated PLS model is used to predict the process variables and final product quality. At each sampling instance during a batch, extended predictive self‐adaptive control (EPSAC) is employed as a NMPC technique to calculate the control action by using the current hybrid model as a predictor. At the end of the batch, the PLS model is updated by utilizing the measurements from the batch and the above procedure is repeated to obtain new control actions for the next batch. In a simulation study using a previously reported model for a polymorphic crystallization with experimentally determined parameters, the proposed B2B‐NMPC control strategy produces better performance, where it satisfies all the state constraints and produces faster and smoother convergence, than the standard batch‐to‐batch strategy. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Adaptive model predictive inventory controller for multiproduct batch plant

An inventory control system was developed for multiproduct batch plants with an arbitrary number of batch processes and storage units. Customer orders are received by the plant at order intervals and in order quantities that are subject to random fluctuations. The objective of the plant operation is to minimize the total cost while maintaining inventory levels within the storage or warehouse capacity by adjusting the startup times, the quantities of raw material orders, and production batch sizes. An adaptive model predictive control algorithm was developed that uses a periodic square wave model to represent the flows of the material between the processes and the storage units. The boundedness of the control output and the convergence of the estimated parameters in implementations of the proposed algorithm were mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of this approach was demonstrated by performing simulations. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1867–1880, 2015