间歇过程动态优化方法综述

间歇过程的动态优化近年来引起了广泛关注.针对近期主要的研究成果,综述了间歇过程动态优化中的数学模型、求解方法及控制架构等问题,介绍了间歇过程目前主要的操作优化方法,具体分析了含不确定性间歇过程的动态优化策略,总结了间歇过程常用的优化模拟计算工具.最后探讨了这一领域中值得进一步研究的问题和可能的发展方向.     

间歇结晶过程建模和动态模拟

间歇结晶过程广泛应用于精细化工、医药、生物等过程,该过程的模拟将对产品质量和过程控制具有重要作用。本文建立了间歇结晶过程数学模型。该模型是一个包含双曲型偏微分方程(粒数衡算方程)及微分-积分方程的复杂方程组,其较强的非线性使得难以求得解析解。本文用数值方法计算了粒数密度分布函数、过饱和度、成核与生长速率等重要的过程参数,定义并计算了过程的产品质量评价指数。定量模拟了晶种对结晶过程的影响,以及结晶过程中成核和生长的竞争关系,同时,对上述现象和规律进行了理论分析。     

迭代粒子群算法及其在间歇过程鲁棒优化中的应用

针对无状态独立约束和终端约束的间歇过程鲁棒优化问题,将迭代方法与粒子群优化算法相结合,提出了迭代粒子群算法．对于该算法,首先将控制变量离散化,用标准粒子群优化算法搜索离散控制变量的最优解．然后在随后的迭代过程中将基准移到刚解得的最优值处,同时收缩控制变量的搜索域,使优化性能指标和控制轨线在迭代过程中不断趋于最优解．算法简洁、可行、高效,避免了求解大规模微分方程组的问题．对一个间歇过程的仿真结果证明了迭代粒子群算法可以有效地解决无状态独立约束和终端约束的间歇过程鲁棒优化问题．     

化工间歇过程的时间Petri网模型和优化控制

针对化工间歇生产过程的特点及优化问题,本文提出用时间Ｐｅｔｒｉ网对过程建模,用动态规化方法对过程实行优化控制,并研究了该优化策略下的算法和控制设计问题。     

甘油间歇生物歧化过程的动态优化

研究了甘油间歇生物歧化过程的动态优化。针对甘油间歇生物歧化过程的非线性过量动力学系统,首先在终端时刻是固定和可变两种情况下,分别以终端时刻产物1,3-丙二醇的浓度、终端时刻产物1,3-丙二醇的产率为目标函数,提出了三个动态优化模型;然后基于有限元配置法将甘油间歇生物歧化过程的状态方程转化为代数方程,从而得到动态优化模型的非线性规划形式。最后对提出的三个动态优化模型进行求解,并对所得优化结果进行了分析与讨论。与已有结果相比,获得了更高的1,3-丙二醇产率。     

间歇过程的批内自优化控制

针对间歇过程的实时优化问题, 提出了一种基于自优化控制的批内优化方法. 以测量变量的线性组合为被控变量, 在单批次内跟踪控制被控变量实现间歇过程的实时优化. 根据是否在间歇过程的不同阶段切换被控变量, 给出了两种自优化控制策略, 对每种策略又分别提出两种设定轨线选取方案. 为求解这些情形下的最优被控变量(组合矩阵), 以最小化平均经济损失为目标, 推导了组合矩阵和经济损失之间的函数关系, 分别将其描述为相应的非线性规划问题. 在此基础上, 进一步引入了扩张组合矩阵, 将这些非线性规划问题归纳为求解扩张组合矩阵的一致形式(扩张组合矩阵具有不同的结构约束), 并推导得到了其中一种方案的解析解计算方法. 以一个间歇反应器为研究对象, 验证了方法的有效性.     

MATLAB在氟硅酸钠干法间歇结晶过程中优化模拟的应用

氟硅酸钠是磷复肥行业的一种重要的化工副产品,干法制取时是将氯化钠(或硫酸钠)固体盐加入氟硅酸(8～10%)中进行反应,氟硅酸钠呈结晶型沉淀而析出;在此过程中,加盐速度是影响粒度大小的关键因素,以前主要凭经验操作、可靠性差,结晶粒度细小,造成离心脱水时浪费大、产率低.本文根据结晶原理,对加盐过程进行计算机模拟,运用MATLAB编程、循环计算并解出了135个一元三次线性方程,得到了结晶过程中保证氟硅酸钠过饱和度处于介稳区的动态加盐曲线.按此曲线控制加盐速度,同等条件下,使得氟硅酸钠结晶粒度较大,产品收率提高.计算机的模拟运用和便捷计算,可为此类反应型结晶物质的制取过程提供科学的指导,为自动化精确操作打下了基础,是科研和生产中强有力的手段和工具.     

间歇生产过程优化调度的新方法

本文提出了间歇生产过程在线生产调度算法的评价指标，该指标较好地综合了间歇过程等待时间和操作单元闲置时间对经济效益的影响；并提出基于预测的短期生产调度在线校正算法ＰＯＭＡ，改进了以往算法的“近视“效应。仿真结果表明，ＰＯＭＡ算法具有很好的效果和很强的鲁棒性。该算法原理简单，对间歇工业生产过程优化调度具有较大的实用价值。     

煮糖结晶过程的多模型动态矩阵控制研究

由于工业结晶过程存在非线性和滞后现象,因此采用常规的控制策略已经达不到工业生产的要求。针对这种情况,通过对煮糖结晶过程的机理分析建立了反映实际煮糖过程的动态模型,在此基础上采用一种先进的预测控制算法即多模型非线性动态矩阵控制(DMC)算法进行控制。仿真和对比研究有力地证明了所建立的模型是有效的,同时也证明该算法可以应用到工业结晶过程弗能够取得较好的控制品质。     

Optimal control of batch reactors by iterative dynamic programming

Four batch reactor systems are chosen to examine the viability of using iterative dynamic programming (IDP) for highly nonlinear systems encountered by chemical engineers. The first system is mildly nonlinear and rapid convergence resulted with the use of only a single state grid point. The use of piecewise linear continuous control with 40 stages yielded better results that the use of 80 stages with piecewise constant control. The need for more than a single grid point for the other three systems led to a systematic study of the effects of the number of grid points, of the number of allowable values for control and of the region contraction factor on the convergence of IDP. In every case the global optimum could be obtained with reasonable computational effort, and no difficulties were encountered even with systems exhibiting several local optima. The use of stages of different length allowed a refined solution to be obtained with a reasonably small number of stages in the last example.     

Real-time dynamic optimization of batch systems

In this paper a methodology for designing and implementing a real-time optimizing controller for batch processes is proposed. The controller is used to optimize a user-defined cost function subject to a parameterization of the input trajectories, a nominal model of the process and general state and input constraints. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov based extremum seeking approach is used to compute the trajectory parameters. The technique is applicable to general nonlinear systems. A precise statement of the numerical implementation of the optimization routine is provided. It is shown how one can take into account the effect of sampling and discretization of the parameter update law in practical situations. A simulation example demonstrates the applicability of the technique.     

一种间歇反应过程迭代动态规划方法

随着工业过程对降低产品成本、改进产品质量、满足安全要求和环境规范,间歇反应过程的优化变得越来越重要.本文因此给出了一种有效的基于随机选点的间歇反应过程迭代动态规划算法,并给出了算法实现的详细步骤,能够有效实现间歇反应过程中温度、浓度等变量的动态优化问题.所述的迭代动态规划算法通过调节分段数P和离散点数(N和M)可以有效的避免计算量激增的问题,具有稳定可靠、易寻找到全局最优解的优点.以典型的间歇反应动态优化问题作为实例进行了研究,并与国际上公开报道结果进行了详细的比较研究,结果表明了所述方法的可靠有效性.     

Optimal operation of batch reactors—a personal view

This paper presents a personal, thus necessarily subjective, view of the operation of batch and semi-batch reactors. The emphasis is on safety, product quality and scale-up. Key characteristics of discontinuous reaction systems are discussed, along with the resulting implications for monitoring, control and optimization. The industrial needs are compared with the research solutions proposed by academia. It is argued that, in industry, measurement and modeling issues are often more important than the algorithmic aspects related to the computation of control and optimization strategies. Major challenges and selected research opportunities are discussed.     

基于Cape-Open标准的间歇精馏过程模拟系统的开发

在合理假设的前提下建立间歇精馏过程的严格动态模型,此模型按精馏塔的结构分解为:再沸器、塔板、冷凝器、回流釜、分配器及产品罐模型等6个子模型,塔板子模型考虑了液相持液量。基于Cape-Open标准采用面向对象程序设计方法,实现各个子模型程序,将各个子模型的参数及解算方法封装在各自的类中,利用四阶龙格库塔积分方程,采用逐板计算法由塔釜至产品罐自下而上地求解该模型。最后引入一3组份混合物间歇精馏实例,模拟结果较好地反映了该物系间歇精馏的动态特征,表明该模型准确可靠,所开发的系统稳定。     

Interval optimization of dynamic response for structures with interval parameters

This paper presents an interval optimization method for the dynamic response of structures with interval parameters. The matrices of structures with interval parameters are given. Combining the interval extension of function with the perturbation theory of dynamic response, the method for interval dynamic response analysis is derived. The interval optimization problem is transformed into a corresponding deterministic one. Because the mean values and the uncertainties of the interval parameters can be elected as the design variables, more information of the optimization results can be obtained by the present method than that obtained by the deterministic one. The present method is implemented for a truss structure and a frame structure. The numerical results show that the method is effective.     

A comparison of nonlinear observers for output feedback model-based control of seeded batch crystallization processes

This paper presents a comparative analysis of various nonlinear estimation techniques when applied for output feedback model-based control of batch crystallization processes. Several nonlinear observers, namely an extended Luenberger observer, an extended Kalman filter, an unscented Kalman filter, an ensemble Kalman filer and a moving horizon estimator are used for closed-loop control of a semi-industrial fed-batch crystallizer. The performance of the nonlinear observers is evaluated in terms of their closed-loop behavior as well as their ability to cope with model imperfections and process uncertainties such as measurement errors and uncertain initial conditions. The simulation results suggest that the extended Kalman filter and the unscented Kalman filter provide accurate state estimates that ensure adequate fulfillment of the control objective. The results also confirm that adopting a time-varying process noise covariance matrix further enhances the estimation accuracy of the latter observers at the expense of a slight increase in their computational burden. This tuning method is particularly suited for batch processes as the state variables often vary significantly along the batch run. It is observed that model imperfections and process uncertainties are largely detrimental to the accuracy of state estimates. The degradation in the closed-loop control performance arisen from inadequate state estimation is effectively suppressed by the inclusion of a disturbance model into the observers.     

考虑功率预测的储能电站动态优化控制方法

提出了基于超短期功率预测的储能电站平抑风电功率波动的动态优化控制方法,系统考虑影响储能电站的多种因素,构建基于风功率超前信息的充放电策略;以荷电状态偏移量最小为目标构建优化控制模型,同时考虑储能介质的性能约束,得到下一次控制步长内的优化控制模式,由此形成递进式的动态控制策略。实际风电场运行数据算例分析表明,所提方法可有效实现储能电站的高效控制,具有一定实际应用价值。     

A stochastic dual dynamic programming method for two-stage distributionally robust optimization problems

This paper studies a class of two-stage distributionally robust optimization (TDRO) problems which comes from many practical application fields. In order to set up some implementable solution method, we first transfer the TDRO problem to its equivalent robust counterpart (RC) by the duality theorem of optimization. The RC reformulation of TDRO is a semi-infinite stochastic programming. Then we construct a conditional value-at-risk-based sample average approximation model for the RC problem. Furthermore, we analyse the error bound of the approximation model and obtain the convergent results with respect to optimal value and optimal solution set. Finally, a so-called stochastic dual dynamic programming approach is proposed to solve the approximate model. Numerical results validate the solution approach of this paper.