含时滞测量值下间歇过程的双维状态估计

基于粒子滤波研究了间歇过程的状态估计问题。根据间歇过程双维动态特性,针对关键参数在线检测精度低、离线分析时滞大等问题,分别建立一种双维状态转移模型和时滞测量模型,并利用贝叶斯方法及前/后向平滑,提出一种含时滞测量值下的双维状态估计算法。该算法通过融合先前批次和时滞测量值的信息提高估计精度,并且克服了离线采样周期和时滞时间不确定的问题。在数字仿真和啤酒发酵过程中的仿真应用验证了该算法的有效性。     

一种融合无时滞测量值和含时滞测量值的状态估计方法

在很多工业过程中,常常可获得两种测量数据,无时滞测量值和含时滞测量值,其中,无时滞测量值直接由传感器在线测得,即时却精度较低,含时滞测量值通过人工实验分析离线得到,精度高却有时滞。引入状态增广卡尔曼滤波法对上述两种数据进行融合以估计当前状态值。考虑到无时滞测量值建立的在线软测量模型存在不可避免的模型不匹配问题,引入模型偏差作为待估计状态,通过离线测量值对其进行估计,从而实现对在线软测量模型的校正。最后将所提方法运用到线性化的非线性二元蒸馏塔模型中估计填料压板各成分浓度,取得了良好效果。     

基于粒子滤波的高浓度染料色泽软测量模型辨识方法

为了解决高浓度混合染料间歇染色过程织物色泽的在线测量问题,即非线性色泽软测量,提出了基于粒子滤波的软测量方法,该方法通过测定间歇染机内染液的吸光度,采用粒子滤波算法来估计该染液中各染料浓度,再根据染料浓度与织物色泽的软测量模型计算出染机内织物色泽。以3种活性染料拼染纯棉为例,对该方法的可行性进行验证。实例证明,基于粒子滤波的软测量方法的色泽估计值与人工离线实测值之间的色差值在1.0 CIELAB之内,模型的估计值能够满足工艺要求。因此,该方法是有效可行的。     

基于核偏鲁棒M-回归的间歇反应过程混合模型辨识

提出了一种混合模型两步辨识策略,用以解决间歇反应过程的建模问题,并能够有效融合先验知识及过程数据信息。该策略将混合模型的同步辨识分解成为两个独立的步骤,首先确定混合模型的结构,并利用Tikhonov正则化方法实现间歇反应过程反应速率的精确估计;接下来采用核偏鲁棒M-回归(kernel partial robust M-regression,KPRM)算法建立过程变量与反应速率间的经验模型,从而有效抑制过程数据中离群点的影响。利用半间歇过程仿真实验对所提出的策略进行验证,获得了相比于传统方法更高的估计及预测精度。     

选择性集成LTDGPR模型的自适应软测量建模方法

随着时间的增加,传统时间差（TD）模型会出现性能显著下降的问题。为了提高TD模型的可靠性和预测精度,同时考虑过程的时滞特征,基于一种选择性集成策略,提出一种局部时间差高斯过程回归（LTDGPR）模型的自适应软测量建模方法。首先,提取出数据库中的时滞动态信息,对建模数据进行重构;然后,采取局部化策略对差分后的重构样本进行统计划分,得到LTDGPR模型集。对于新来的输入样本,选择部分泛化能力强的LTDGPR模型进行集成,估计出含一定时间差的主导变量动态偏移值;最后,基于TD模型思想对当前时刻主导变量值进行在线预测。通过脱丁烷塔过程的数据建模仿真研究,验证了所提方法的有效性和精度。     

选择性集成LTDGPR模型的自适应软测量建模方法

随着时间的增加,传统时间差(TD)模型会出现性能显著下降的问题。为了提高TD模型的可靠性和预测精度,同时考虑过程的时滞特征,基于一种选择性集成策略,提出一种局部时间差高斯过程回归(LTDGPR)模型的自适应软测量建模方法。首先,提取出数据库中的时滞动态信息,对建模数据进行重构;然后,采取局部化策略对差分后的重构样本进行统计划分,得到LTDGPR模型集。对于新来的输入样本,选择部分泛化能力强的LTDGPR模型进行集成,估计出含一定时间差的主导变量动态偏移值;最后,基于TD模型思想对当前时刻主导变量值进行在线预测。通过脱丁烷塔过程的数据建模仿真研究,验证了所提方法的有效性和精度。     

基于高斯过程和贝叶斯决策的组合模型软测量

为了提高化工生产过程中软测量建模的估计精度,提出了一种基于高斯过程和贝叶斯决策的组合模型建模方法。该方法在对原始数据进行分类的基础上,利用高斯过程对每个子类建立软测量子模型,通过贝叶斯决策方法实现模型的联合估计输出。将该建模方法应用于某双酚A装置的软测量建模中,仿真结果表明,相比于传统的开关切换或加权组合多模型,该组合模型能在实际生产中充分利用样本信息,使得具有更高的估计精度和更强的泛化性能。     

模型有界失配下基于MAP准则的扩展集员粒子滤波方法

在非线性非高斯系统中,当状态转移模型存在有界失配时,采用粒子滤波往往无法获得理想的状态估计值。考虑有界失配对粒子的约束条件,提出一种基于MAP准则的扩展集员粒子滤波算法（MAP-ESMPF）。该算法采用扩展集员求取真实状态的可信域,并基于MAP密度函数的准则,定义优化方程,从而将可信域外的粒子映射到可信域内,保证了状态估计的精度。在数值仿真和连续搅拌反应釜（CSTR）过程中的仿真应用,验证了该算法的有效性。     

异构化机理软测量模型在工业装置中的在线应用

讨论芳烃异构化机理模型的性能及其在线应用。基于已开发的动力学模型,针对大量工业数据,进行离线模拟,结果发现模型的＂老化＂问题。为了兼顾模型的估计精度和计算效率,提出将软测量模型拆分成在线模拟计算模块和离线参数估计模块,实时更新模型参数。然后将模型应用于工业异构化装置,在线估计系统组分的浓度,结果表明,该软测量模型具有良好的性能。     

基于STA-K均值聚类的电化学废水处理过程离子浓度软测量

针对电化学废水处理过程出口离子浓度无法在线检测的问题,提出了一种基于状态转移的K均值聚类算法的软测量建模方法。在分析内部反应机理的基础上,结合物料平衡和吸附动力学定理建立电化学过程的机理模型;由于单一的软测量模型难以满足实际的精度要求,提出一种基于状态转移的K均值聚类算法将原始数据集进行聚类,应用状态转移算法对K均值算法的初始聚类中心进行优化,同时,引入离群值矩阵动态迭代同时实现数据聚类和异常值检测;最后,对聚类后的不同训练子集分别建立子模型,综合各子模型得到基于多模型切换方法的软测量模型。通过某废水处理厂的现场数据进行实例验证,结果证明了所建立的电化学废水处理过程离子浓度软测量模型合理有效。     

Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements☆

State estimation of biological process variables directly influences the performance of on-line monitoring and op-timal control for fermentation process. A novel nonlinear state estimation method for fermentation process is proposed using cubature Kalman filter (CKF) to incorporate delayed measurements. The square-root version of CKF (SCKF) algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithm is provided and the implementation of the proposed algorithm is constructed. Then a nonlinear state space model for fermentation process is established and the SCKF algorithm incorporating delayed measurements based on fermentation process model is presented to implement the nonlinear state estimation. Finally, the proposed nonlinear state estimation methodology is applied to the state estimation for penicillin and industrial yeast fermentation processes. The simulation results show that the on-line state estimation for fermentation process can be achieved by the proposed method with higher esti-mation accuracy and better stability.     

Data reconciliation strategy with time registration for the evaporation process in alumina production

In real industrial production, many mass and heat transfer processes are influenced by high temperature, high pressure, and even strong acid or alkali conditions. In addition, some important variables cannot be measured and chemical compositions are analyzed offline with a long time delay, which leads to inaccurate measurements of the process data. In this paper, a layered data reconciliation (LDR) method based on time registration is proposed to improve the measurement accuracy and estimate unmeasured variables. Considering that the material cannot be tagged and tracked in process manufacturing, a temporal and spatial matching strategy for the process data is designed based on a time‐correlation analysis matrix which is determined to describe the correlation of each time sequence in the data matrix. Then, a layered data reconciliation model with time registration is developed by reconciling the mass balance layer and the heat balance layer separately and stepwise, and the model is solved by the state transition algorithm. Meanwhile, regular terms and engineer's knowledge are introduced into the data reconciliation model to solve the problem of insufficient redundancy. The industrial verification results from the actual industrial evaporation process indicate that the accuracy of measured values is improved by using the proposed reconciliation strategy.     

基于混合差分进化算法的软测量时延参数估计

时延参数估计是系统控制与信号处理的关键问题。通过构造一个适当的适应度函数,将软测量系统的时延参数估计问题转化为一个多维非线性优化问题,然后利用混合差分进化算法的全局搜索能力求解该优化问题。对两个典型问题进行了仿真实验,仿真结果表明了混合算法的有效性和鲁棒性。以石油炼制工业中典型装置常压塔为例,对其一线航空煤油的闪点软测量进行了应用验证,结果表明,时延参数估计的引入大大提高了软测量模型的精度,证实了混合差分进化算法的有效性。     

A real-time estimation approach to time-varying time delay and parameters of NARX processes

This paper presents a solution to the joint time-varying time delay and parameter estimation of NARX (nonlinear autoregressive with exogenous inputs) processes, where only pure time delay in input signal is considered. A modified strong tracking filter (MSTF) is proposed, and is adopted as an adaptive estimation algorithm. Three kinds of specific NARX processes are considered. The first is also the simplest, the output signal is the input with time delay plus disturbance; The second one is a simple NARX process plus disturbance; The third NARX process even has unknown time-varying parameters. For each of the NARX processes, we set up a specific estimation model, with these models the proposed MSTF algorithm can be applied to the real-time time delay and parameter estimation of the above three NARX processes. Computer simulation results demonstrate the effectiveness of the proposed approach. Moreover the robustness of the proposed algorithm against some model/process parameter mismatches is also tested via computer simulations.     

An Intelligent Tray Efficiency Estimation Method for the Fractionation System of Industrial Hydrocracking Processes

An engineering‐oriented tray efficiency estimation method for the hydrocracking fractionation system is proposed. As key parameter of the fractionation system, estimation of the tray efficiency is important and challenging due to the large computational burden given by the complexity of the fractionation system. Here, the high‐dimensional tray efficiencies are screened first and divided into groups according to the spatial position. Then, an improved artificial bee colony algorithm is proposed to estimate the tray efficiencies of each group, in which two taboo lists are introduced to avoid repetitive search. The proposed method is validated using real industry data. The results demonstrate that the proposed approach is efficient in terms of estimation accuracy.     

离线辨识和在线辨识相结合的广义预测控制算法在固定床反应器温度控制中的应用

An improved generalized predictive control algorithm is presented in this paper by incorporating offline identification into onlie identification.Unlike the existing generalized predictive control algorithms.the proposed approach divides parameters of a predictive model into the time invariant and time-varying ones,which are treated respectively by offline and onlie identification algorithms.Therefore,both the reliability and accuracy of the predictive model are improved,Two simulation examples of control of a fixed bed reactor show that this new algorithm is not only reliable and stable in the case of uncertainties and abnormal distrubances,but also adaptable to slow time varying processes.     

Iterative identification of output error model for industrial processes with time delay subject to colored noise

To dealwith colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares (ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method.     

基于相似度网格模型的在线经济性能分级评估方法

针对经济性能评估方法中目标函数难以在线计算问题提出一种基于过程数据的在线经济性能分级评估方法。采用自回归潜结构映射（AR-PLS）算法对输入数据矩阵进行分解,在与输出潜变量相关的子空间上建立不同性能等级的离线模型,从而排除无关变化的干扰。然后采用“先标定分区,再对比邻级相似度”的策略设计一个相似度网格模型,将过程性能分为稳定性能级状态和过渡状态,并对离线模型中未出现过的因素造成的性能变化进行识别,以进一步丰富离线数据库。对于不属于最优性能级的过程数据,能够根据变量贡献度诊断造成性能变差的原因。乙烯裂解过程的现场数据测试实验表明本方法可以及时、准确地检测到经济性能的偏移。     

Estimation of catalytic activity using an unscented Kalman filtering in condensation reaction

The catalytic activity of cation exchange resins will be continuously reduced with its use time in a condensation reaction for bisphenol A (BPA). For online estimation of the catalytic activity, a catalytic deactivation model is studied for a production plant of BPA, state equation and observation equation are proposed based on the axial temperature distribution of the reactor and the acetone concentration at reactor entrance. A hybrid model of state equation is constructed for improving estimation precision. The unknown parameters in observation equation are calculated with sample data. The unscented Kalman filtering algorithm is then used for on-line estimation of the catalytic activity. The simulation results show that this hybrid model has higher estimation accuracy than the mechanism model and the model is effective for production process of BPA.     

Distributed state estimation from infrequent and delayed measurements in large-scale processes

Effective control and monitoring of a process usually require frequent and delay-free measurements of important process output variables. However, these measurements are often either not available or available infrequently with significant time delays. This article presents a method that allows for improving the performance of distributed state estimators implemented on large-scale manufacturing processes. The method uses a sample state augmentation approach that permits using delayed measurements in distributed state estimation. The method can be used with any state estimator, including unscented Kalman filters, extended Kalman filters, and moving horizon state estimators. The method optimally handles the tradeoff between computational time and estimation accuracy in distributed state estimation implemented using a computer with parallel processors. Its implementation and performance are shown using a few simulated examples.