基于Profibus-DP现场总线的多功能色谱分离装置控制系统设计

为了实现多功能色谱分离装置的精准化控制,加速模拟移动床色谱纯化分离速度,设计基于Profibus-DP现场总线的多功能色谱分离装置控制系统;以STM32F103微处理器作为底层控制设备,连接Profibus-DP协议芯片、VPC3与RS-485接口电路,转化从站地址电路与设定应用模块,利用模拟移动床控制回路,完成Profibus-DP现场总线的硬件电路设计;建立梯度SMB色谱,采用异步切换方式,控制变浓度进料的实际操作行为,分离处理模拟移动床多功能色谱,结合多功能色谱分离装置的控制原理,运用气相色谱法,分析色谱分离过程中的基质效应形式,实现多功能色谱分离装置控制系统软件设计;实验结果表明,基于Profibus-DP现场总线控制系统的模拟移动床色谱纯化分离速度较快,能够有效满足多功能色谱分离装置精准化控制的操作需求.     

模拟移动床吸附分离技术及其应用

详细地介绍了模拟移动床色谱吸附分离技术的发展历史及其工作原理,由于模拟移动床色谱吸附分离过程是一个具有非线性、非理想、多变量耦合、色谱连续动态分离过程与物料进出口位置周期性切换离散事件相互混合等特征的复杂混合系统,因此重点讨论了描述模拟移动床色谱分离行为的理论模型。在此基础上,综述了该技术国内外的研究进展以及在精细化工、石油化工、制药等方面的主要应用,对该技术在我国的研究现状以及存在的问题也作了介绍。     

模拟移动床色谱分离体系模式与关键因子的计算机模拟研究

采用浮点编码遗传算法对模拟移动床色谱的分离系统模式进行了设计,并研究了,分离过程中传质和扩散对分离效果的影响。结果表明模拟移动床色谱中区2和区3的色谱柱的数目对提高生产强度起决定性的作用,而区1和区4的影响较小。同时改善分离过程中的传质与扩散可提高生产强度,在分离过程中传质所起的影响比扩散大得多。     

模拟移动床分离过程的模型预测控制方法研究

模拟移动床是一种能够实现连续色谱分离的装置,目前已广泛应用于石油化工、生物制药等领域。模拟移动床色谱分离过程是连续变量和离散变量并存的混杂系统,对其模型研究以及控制方法的研究都存在很大难度。在对模拟移动床过程建模基础上,运用线上求解法对其进行数值求解,生成数据,应用MATLAB系统辨识工具箱进行辨识,得出两入两出的模拟移动床传递函数模型,并在此基础上设计模型预测控制器。仿真结果表明了所用方法的有效性,为模拟移动床过程控制方法的研究和推广奠定了基础。     

模拟移动床色谱计算机辅助系统的设计

介绍了采用Visual C 开发的模拟移动床色谱计算机辅助系统。该系统可根据实验参数，快速准确地对模拟移动床色谱的分离过程进行模拟，为操作条件选择提供可靠的依据。该系统的核心部分为模拟移动床色谱动态模型，该模型采用正交配置有限元法求解。该软件用户界面友好，操作简单方便，分层设计的思想利于软件的维护与升级。     

苯丙氨酸模拟移动床色谱分离的建模和优化

根据模拟移动床（SMB）的发展现状和操作流程要求,对模拟移动床色谱分离的工艺流程进行了重新设计,使得系统的整个操作流程更为简单实用。用BP神经网络对苯丙氨酸模拟移动床的色谱分离过程进行了建模,用实数编码的遗传算法对分离参数和操作条件进行了优化,并在实际生产中得到应用,使产品的纯度和回收率都得到了较大的提高。该工艺具有分离效果好等优点,现已在江阴某公司成功投入使用,苯丙氨酸的纯度和回收率都达到了98％以上。     

模拟移动床的控制系统设计和优化

为了满足现代工业色谱分离的要求,对模拟移动床（SMB）色谱分离的工艺流程进行了改进,设计了一套由工控机、西门子PLC、变频器和智能仪表构成的模拟移动床集散控制系统,提高了系统的自动化水平。用改进的BP神经网络对苯丙氨酸模拟移动床的色谱分离过程进行了建模,并根据所建的模型,用实数编码的遗传算法对分离参数和操作条件进行了优化。在开发过程中,上位机软件采用了国产“组态王”和VB相结合的方式,克服了“组态王”命令语言环境较弱的缺点。该方法在苯丙氨酸生产中得到应用,使得产品的纯度和回收率都得到了较大的提高。     

模拟移动床色谱过程的数值分析

用正交配置法对模拟移动床色谱过程进行了数值分析，并讨论了切换时间对该系统分离结果的影响。     

非线性模拟移动床色谱的浮点编码遗传算法优化策略

开发了一种基于浮点编码遗传算法的模拟移动床色谱自动优化系统。该优化方法中将归一化的生产强度取最大值作为目标函数，同时将纯度的n次方作为罚函数。采用模拟移动床色谱的稳态模型计算浮点编码遗传算法的适度值。采用该优化算法对手性化合物binapththol的模拟移动床分离操作条件进行了优化，结果表明生产强度提高了40％以上。     

模拟移动床过程的软测量建模仿真研究

模拟移动床色谱作为主要的现代吸附分离技术,近年来在石油化工、精细化工、生物制药、食品加工等领域的复杂混合物分离过程中得到越来越多的应用。模拟移动床系统是一种具有强非线性、强耦合、混杂性、分布参数等特征的复杂工程系统,其建模问题一直受到广泛关注。本文提出用神经网络与遗传算法结合的混合建模方法建立SMB色谱分离过程4区流量与组份纯度的软测量模型。为了解决RBF神经网络训练时隐含层节点数选取无依据,只能依靠反复仿真尝试的问题,提出将混合递阶遗传算法与RBF神经网络结合,建立SMB色谱分离过程软测量模型,以较高的精度实现了SMB组份纯度软测量,通过仿真验证了方法的可行性。     

基于Vericol工艺的模拟移动床的动态仿真

针对传统模拟移动床(SMB)的工艺是各个进出料口切换周期固定且同步的问题,采用了模拟移动床零附加成本的Varicol工艺设计改造。因为SMB运行方式中各个进出料口在每个固定周期末是同步切换,而Varicol运行方式通过将每个切换周期细化,在细化后的小周期内实现进出料口的异步切换,使得模拟移动床各功能区的柱数配置可随时间变化而进行周期性的变换。本文设计了多种Vericol模式,并对不同模式下的模型进行仿真获得了其对应分离指标。仿真结果表明,Varicol工艺比传统SMB工艺具有更高自由度和更好的表现性。     

SMB色谱分离过程的动态矩阵控制

针对SMB色谱分离过程的工作原理,分析了现有系统开环不加控制时过程运行的缺陷,在机理模型的基础上通过辨识得到了对象局部简化模型。根据此模型设计了多变量动态矩阵控制器,实现了过程的预测控制。通过仿真实验分别比较了系统在不加控制和DMC控制时在轨迹跟踪和扰动抑制方面的性能,结果表明采用DMc控制算法的SMB过程,比不加控制时的系统有良好的性能和优越性,从而为系统的实际运行提供理论基础。     

Model-based control of a simulated moving bed chromatographic process for the separation of fructose and glucose

Chromatographic separations are an expanding technology for the separation of high value products, particularly in the area of pharmaceutics, food, and fine chemicals. The simulated moving bed (SMB) process as a continuous chromatographic separation process is an interesting alternative to conventional batch chromatography, and gained more and more impact recently. The SMB process is realized by connecting several single chromatographic columns in series. A countercurrent movement of the bed is approximated by a cyclic switching of the inlet and outlet ports in the direction of the fluid stream. Because of its complex dynamics, the optimal operation and automatic control of SMB processes is a challenging task. This paper presents the design of a model-based optimization and control scheme for SMB chromatographic separation processes and its application to the separation of fructose and glucose. We propose a two-layer control architecture where the optimal operating trajectory is calculated off-line by dynamic optimization based on a rigorous process model. The parameters of the model are adapted based on online measurements. The low-level control task is to keep the process on the optimal trajectory despite disturbances and plant/model mismatch. Here identification models based on simulation data of the rigorous process model along the optimal trajectory are combined with a suitable local controller. The efficiency of the trajectory control algorithm is shown in a simulation study for the separation of fructose and glucose on an 8-column SMB plant.     

Automatic control of the simulated moving bed process for C8 aromatics separation using asymptotically exact input/output-linearization

An approach to automatic control of the simulated moving bed process (SMB) applied to the separation of C₈ aromatics is presented. The principle of asymptotically exact input/output-linearization is used. The controller is based on a nonlinear state estimator using the true moving bed model (TMB). The estimator receives measurement data from four spectroscopic measurement cells. The problem of moving measurement positions with respect to the TMB model is addressed. An exactly linearizing feedback of the estimated states is designed using the nonlinear TMB model equations. The performance of the controller is shown in simulations using a detailed SMB model as a representative of the real process.     

Multi-rate optimizing control of simulated moving beds

This paper presents an optimizing control scheme for simulated moving beds (SMB) that enables to incorporate multi-rate (MR) sampled measurements into the control and estimation problem in a clear and transparent manner. This is particularly relevant for chiral separations where online monitoring requires the combination of various analytical techniques that may operate on widely varying time scales. An MR periodic linear time-varying (PLTV) model is derived for the SMB process. The cyclic nature of the process is exploited by formulating the MR-PLTV model within a repetitive model predictive control framework. Simulation results for the chiral separation of the guaifenesin enantiomers are presented. The proposed multi-rate controller is able to deliver increased productivity while respecting the process and product specifications.     

Neural network-based identification of SMB chromatographic processes

In this contribution, the identification problem for the control of nonlinear simulated moving bed (SMB) chromatographic processes is addressed. For process control the flow rates of extract, desorbent, and recycle of the SMB process, and the switching time are the manipulated variables. But these variables influence the process in a strongly coupled manner. Therefore, a new set of input variables is introduced by a nonlinear transformation of the physical inputs, such that the couplings are reduced considerably. The front positions of the axial concentration profile are taken as model outputs. Multilayer feedforward neural networks (NN) are utilized as approximating models of the nonlinear input–output behavior. The gradient distribution of the model outputs with respect to the inputs is used to determine their structural parameters and the network size is chosen by the SVD method. To illustrate the effectiveness of the identification method, a laboratory scale SMB process is used as an example. The simulation results of the identified model confirm a very good approximation of the first principles models and exhibit a satisfactory long-range prediction performance.     

Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives

A new bi-objective genetic programming (BioGP) technique has been developed for meta-modeling and applied in a chromatographic separation process using a simulated moving bed (SMB) process. The BioGP technique initially minimizes training error through a single objective optimization procedure and then a trade-off between complexity and accuracy is worked out through a genetic algorithm based bi-objective optimization strategy. A benefit of the BioGP approach is that an expert user or a decision maker (DM) can flexibly select the mathematical operations involved to construct a meta-model of desired complexity or accuracy. It is also designed to combat bloat – a perennial problem in genetic programming along with over fitting and under fitting problems. In this study the meta-models constructed for SMB reactors were compared with those obtained from an evolutionary neural network (EvoNN) developed earlier and also with a polynomial regression model. Both BioGP and EvoNN were compared for subsequent constrained bi-objective optimization studies for the SMB reactor involving four objectives. The results were also compared with the previous work in the literature. The BioGP technique produced acceptable results and is now ready for data-driven modeling and optimization studies at large.