基于有界误差分布假设的常压塔稳态过程数据校正方法

可靠的过程数据是实施操作优化等的关键.现有的稳态数据校正方法都是假设测量误差服从零均值和已知方差的正态分布,而实际的测量误差都是有界的.在假设测量误差服从有界正态分布前提下,提出一种新的数据协调和过失误差检测同步算法,并在一个工业常压塔上的应用表明了该算法的有效性.     

化工过程操作状态分析及测量方差估计

对化工过程测量数据进行校正的算法中通常假定测量数据是在过程处于稳态操作条件下测得的,且其方差已知。本文提出了由过程测量数据判断过程操作状态的组合统计检验方法并分析和讨论了估计测量数据方差的诸方法,从而可以有效地指导如何正确选取测量数据应用于校正、模拟、优化和控制过程。     

基于改进投影矩阵法的甲醇合成系统数据校正

以甲醇合成系统为背景建立一套数据校正方法,以流程模拟数据为基础来识别测量数据的显著误差,改进投影矩阵法来校正测量数据的随机误差;调整精馏塔的部分结构参数,用校正数据进行稳态过程精馏塔模拟优化。结果表明,本文的数据校正方法能够有效识别出测量数据的显著误差,同时校正后的数据组分摩尔流量的方差普遍下降,说明校正效果较好;调整设备参数后的稳态模拟数据与校正数据对比,验证了模拟方法的可靠性,基于此方法提出的节能措施能更加有效地减少能量的损耗。因此本文校正方法可应用于甲醇合成系统的数据校正。     

过程系统变负荷下的数据校正与参数估计方法

过程系统的数据校正与参数估计是进行实时操作优化与过程控制的基础。过程系统变负荷下由于模型参数变化的非线性及显著误差的影响,导致数据校正与参数估计的结果不准确,从而影响实时操作优化与过程控制的效率。针对此问题,本文提出了一种用于变负荷下的数据校正与参数估计方法。此方法主要包括过程的稳态检测与数据采样,多工况下的数据聚类和基于多组测量的数据校正与参数估计。首先选择有效和可靠的过程测量数据,根据变负荷下工况的波动性与系统的非线性特征进行数据聚类,最后基于聚类结果调整模型参数使得模型输出与过程测量数据偏差最小。此方法可有效地减小模型参数变化的非线性及显著误差对数据校正与参数估计结果的影响。基于现场的测量数据,将此方法应用于空气分离流程系统中,结果显示了基于此方法的数据校正与参数估计结果更准确。     

基于模糊自适应遗传算法的数据校正方法

针对数据校正中的非线性数据分类还比较困难的问题,提出表上作业法,结合遗传算法同时进行数据协调与过失误差侦破.并将模糊数据协调模型中的三角形约束引入遗传算法界定其变异上下限,还对交叉算子进行自适应改进,从而形成了基于模糊自适应的数据校正方法并用该方法对一个稳态多组分精馏过程进行大量仿真试验,结果表明了算法的有效性.     

数据校正在精馏塔系中的应用

针对某芳烃联合装置中精馏塔系的双线性数据校正问题，通过引入测度因子函数，将双线性问题转化为线性数据校正问题，进行了稳态数据校正的研究，得出了该类问题的一种解法。仿真实验表明该方法迅速、准确     

化工过程稳态检验方法的应用研究

稳态检验在化工过程质量控制、数据校正、过程优化及模拟应用等方面起着重要的作用。研究了置信度法和滤波法的原理及步骤,并在探讨滤波系数、区间长度影响的基础上,将其应用于实例过程稳态检验。结果表明,滤波法比MTE法计算量小,且检验能力较高,但两者均可用于过程数据的稳态检验。     

数据样正在精馏塔系中的应用

针对某芳烃联合装置中精馏塔系的双线性数据校正问题，通过此入测度因子函数，将双一问题转化为线性数据校正问题，进行了稳态数据校正的研究，得出了该类问题的一种解法，仿真实验表明该方法迅速、准确。     

软测量在橡胶混炼过程建模中的应用

介绍软测量技术在橡胶混炼过程中的应用。软测量技术的关键是建模，橡胶混炼过程建模主要有基于机理和基于数据驱动两种方法。由于混炼过程的复杂性，基于机理的建模方法在实际应用中受到限制，仅用于估算或定性分析；而基于数据驱动的建模方法对过程知识要求较低且具有自学习功能，因而得到快速发展。混炼过程模型还受辅助变量、数据预处理的影响，且模型建立后还需进行校正，目前对数据预处理和模型校正的研究还较少。     

化工过程模拟及相关高新技术(Ⅰ)化工过程稳态模拟

化工过程稳态模拟又称静态模拟或离线模拟[1～16](Steady State Simulation,off-line Simulation).通常所说的化工过程模拟或流程模拟多指稳态模拟.它是根据化工过程的稳态数据,诸如物料的压力、温度、流量、组成和有关的工艺操作条件、工艺规定、产品规格以及一定的设备参数,如蒸馏塔的板数、进料位置等,采用适当的模拟软件,用计算机模拟实际的稳态生产过程,得出详细的物料平衡和热量平衡.其中包括人们最为关心的原材料消耗、公用工程消耗和产品、副产品的产量和质量等重要数据.简言之,化工过程模拟就是在计算机上"再现"实际的生产过程.由于这一"再现"过程并不涉及到实际装置的任何管线、设备以及能源的变动,因而给了化工模拟人员最大的自由度,可以在计算机上"为所欲为"地进行不同方案和工艺条件的探讨、分析.并且化工过程模拟所需的成本以及完成一定研究任务所需的时间也是任何实验研究所无法比拟的,因而化工过程稳态模拟已成为研究、开发、设计、挖潜改造、节能增效、生产指导以至于企业管理等工作必不可少的工具,并且在科研和实际生产中发挥着愈来愈大的作用.     

Enhancing dynamic data reconciliation performance through time delays identification

In order to improve the performance of data reconciliation methods, an efficient Genetic algorithm (GA) for determining time delays has been developed. Delays are identified by searching the maximum correlation among the process variables. The delay vector (DV) is integrated within a dynamic data reconciliation (DDR) procedure based on Kalman filter through the measurements error model. The proposed approach can be satisfactorily applied not only off-line but also on-line. It was firstly validated in a dynamic process with recycles and feedback control loops. Then, the methodology was successfully applied to a highly non-linear and complex challenging control case study, the Tenessee Eastman benchmark process, demonstrating its robustness in complex industrial problems. This case study required to implement an extended Kalman filter to deal with the existing non-linearities.     

基于粒子群优化的磨矿分级过程多层数据协调

准确稳定的过程数据是选矿厂进行过程优化控制和决策管理的依据,今针对磨矿分级过程数据特点,建立了多层数据协调模型,包括总物料平衡层、粒度分布/品位层和不同粒度下的成分分析层(金属分布率层);针对模型维数较高的问题,引入粒子群优化(PSO)算法进行求解。根据不同的测量信息,可选择相应的层次进行协调,并采用从低层向高层逐层协调的方法,实现了部分非线性约束到线性约束的转化,提高了数据协调效率。将该多层模型和PSO算法用于某选矿厂磨矿分级过程实际生产数据的协调,结果表明协调后的数据更准确、更稳定,包含的信息更丰富完整。     

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.     

一种混杂系统数据校正新方法

对于既包含连续生产过程又包含离散事件的混杂系统,尤其是对于带有生产方案切换的实际生产过程,通过在物料平衡模型中引入随机调度方程,从而构造出包含随机调度方程参数变量θ的新型协调模型,然后利用一种不确定模型的协调算法对此模型进行求解,最后,通过仿真研究证实了该方法的有效性和鲁棒性.     

A new framework for data reconciliation and measurement bias identification in generalized linear dynamic systems

This article describes a new framework for data reconciliation in generalized linear dynamic systems, in which the well‐known Kalman filter (KF) is inadequate for filtering. In contrast to the classical formulation, the proposed framework is in a more concise form but still remains the same filtering accuracy. This comes from the properties of linear dynamic systems and the features of the linear equality constrained least squares solution. Meanwhile, the statistical properties of the framework offer new potentials for dynamic measurement bias detection and identification techniques. On the basis of this new framework, a filtering formula is rederived directly and the generalized likelihood ratio method is modified for generalized linear dynamic systems. Simulation studies of a material network present the effects of both the techniques and emphatically demonstrate the characteristics of the identification approach. Moreover, the new framework provides some insights about the connections between linear dynamic data reconciliation, linear steady state data reconciliation, and KF. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

不敏变换鲁棒Kalman滤波器在发酵过程中的应用(英文)

State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However, there are many difficulties in dealing with a non-linear system, such as the instability of process, un-modeled dynamics, parameter sensitivity, etc. This paper discusses the principles and characteristics of three different approaches, extended Kalman filters, strong tracking filters and unscented transformation based Kalman filters. By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance, an improved Kalman filter, unscented transformation based robust Kalman filter, is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process. The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.     

Simultaneous Measurement Bias Correction and Dynamic Data Reconciliation

The presence of measurement bias and random noise significantly deteriorates the information quality of plant data. Data reconciliation techniques for steady-state processes have been widely applied to processing industries to improve the accuracy and precision of the raw measurements. This paper develops an algorithm for simultaneous bias correction and data reconciliation for dynamic processes. The algorithm considers process model error as an important contributing factor in the estimation of the measurement bias and process state variables. It employs black-box models for the process as would be done when phenomenological models are difficult or impractical to obtain. Simulation results of a distillation column demonstrated that this algorithm effectively compensates constant and non-constant measurement biases yielding much improved reconciled values of process variables. It has computational advantages over previously proposed algorithms based on non-linear dynamic data reconciliation because an analytical solution is available when using linear process models to approximate the process.     

基于Kalman滤波的换热器非线性状态参数校正

炼化工艺系统中换热网络数据的准确提取将直接影响到集成优化方案和优化控制的性能。针对换热器非线性状态参数的数据校正,构建了换热器分段线性集总参数传热过程模型,有效地解决了换热器流股物性非线性变化所引起的非线性状态空间方程求解的问题;提出了分段线性的Kalman滤波状态空间方程建立和换热器状态参数校正方法,并通过蜡油加氢装置反应流出物高压换热器工业实例阐述了所提出方法的实现过程和效果。研究表明：换热器分段线性集总参数模型中分段数对Kalman滤波的计算收敛性具有重要影响,随着分段数的增大,换热器状态参数收敛于固定值,分段数需根据计算精度通过试差确定。本文方法可对换热器非线性状态参数实施有效的数据校正,对流股物性进行分段线性化处理具有较高的计算精度,可用于大温差或物性变化较剧烈情况下换热器非线性状态参数的数据校正。     

混杂系统基于参数规划的状态反馈预测控制

In this article, state feedback predictive controller for hybrid system via parametric programming is proposed. First, mixed logic dynamic （MLD） modeling mechanism for hybrid system is analyzed, which has a distinguished advantage to deal with the logic rules and constraints of a plant. Model predictive control algorithm with moving horizon state estimator （MHE） is presented. The estimator is adopted to estimate the current state of the plant with process disturbance and measurement noise, and the state estimated are utilized in the predictive controller for both regulation and tracking problems of the hybrid system based on MLD model. Off-line parametric programming is adopted and then on-line mixed integer programming problem can be treated as the parameter programming with estimated state as the parameters. A three tank system is used for computer simulation, results show that the proposed MHE based predictive control via parametric programming is effective for hybrid system with model/olant mismatch, and has a potential for the engineering applications.     

含反应节点过程数据校正的扩展独立物流法

Among the techniques developed for bilinear data reconciliation problems, the method based on independent flows is well known in terms of both accuracy and efficiency. However, the independent flow method is not effective when reactor units are present in the process. In this paper, an extended independent flow method is proposed for the data reconciliation of the process with chemical reaction. By the new method, the independent flows finding algorithm is adjusted to avoid the difficulties caused by the reactors in the process, and the reaction constraints are introduced into the objective function of data reconciliation. As a result, the new method can be applied to the process with chemical reaction while retaining high solution accuracy. To test the performance, the new method and the most typical Crowe‘s projection method are used in the data reconciliation of a typical industrial process. The results show that the new method can effectively accomplish the data reconciliation of the muhicomponent process with chemical reaction and gives more accurate estimates than the Crowe‘s method.