金氰化浸出过程建模及实时优化自适应策略

以金氰化浸出过程为背景,基于物料守恒方程建立动态机理模型,用Tikhonov正则化方法估计动力学反应速度,进而辨识模型未知参数,有效降低了测量噪声对估计及辨识结果的影响;采用实时优化约束自适应方法减小模型参数失配对优化结果的影响.仿真结果表明,在模型参数失配时,所提出的方法仍能收敛到实际过程的最优设定点,不必求实际数据梯度,且受噪声影响小,便于实际应用,为湿法冶金全流程优化控制的顺利实施奠定了基础.     

模块化模型相容性分析方法研究

对多领域建模与协同模型的相容性分析是系统仿真设计中的一个重要部分.针对陈述式物理建模语言模型的检验方法,都是从整个模型出发,研究模型内所有方程和变量之间的关系.但是当系统所包含的方程和变量比较多时,方法在分析过程中容易出错.现提出了一种以模块为研究对象的模型检验的新方法.它将系统划分为多个子模块,将各个模块内部和模块之间的约束关系表示为二部图的形式,以DM分解为基本算法,来分析模型的约束性,从而确定系统模型的正确性.由于每个分析过程只涉及一小部分方程和变量,因此,大大减小了出错的几率.通过一个分析实例,描述了模块化模型检验方法的具体实现.上述分析方法结合了Modelica建模语言的特性,层次清晰.     

金氰化浸出过程实时优化

本文以某湿法冶炼厂金氰化浸出过程为背景,建立了动态机理模型,为提高模型参数辨识精度,提出了基于Tikhonov正则化思想利用含噪声的浓度测量数据估计动力学反应速度的策略.为减小模型与实际过程不匹配对实时优化结果的影响,提出了基于对数–线性闸–罚函数和输出反馈的直接输入自适应方法,加入闸–罚函数后,该方法可以解决含有不等式约束的优化问题,将其应用到金氰化浸出过程实时优化中,仿真结果表明在输出测量值无噪声时,该方法能很快地局部收敛到实际过程的最优设定点;而当测量噪声较小或对目标函数影响较小时,该方法也显示出了优越的性能,而且只需要过程标称模型和实际输出,不需要求实际过程数据梯度,受测量噪声影响较小,更易于实际实施,这为湿法冶金全流程优化控制的顺利实施奠定了重要基础.     

基于VDE-PSO的汽油调合设计公式挖掘

提出一种将多变量非线性问题线性化的VDE-PSO-MLR的建模方法.该方法基于变量扩维、微粒群优化等手段选择扩维变量,在此基础上再建立拟线性的多元回归方程;并通过对所建立的各回归方程及其回归系数的显著性检验结果确定最佳回归模型.将该方法用于某炼油厂的汽油调合设计公式的挖掘,研究表明,与直接用自变量建立的线性回归方程以及二次回归方程相比,只有该方法建立的最佳模型方程和方程变量同时通过显著性检验.最后将最佳模型用于生产数据预测,计算调合汽油辛烷值测定值与预测值误差绝对值AE最大为0.185,符合AE≤0.3的要求.     

分布式状态机的代数模型及其模型检验算法

分布式状态机（DSM）是一个分布式计算模型，特别适用于反应系统，有广泛的用途，但一般其正确性证明与模型检验的复杂性却很高，不易实用。作者曾提出了一个DSM的代数模型及其模型检验算法，复杂较低，但模型推导过程有点问题。该文加强了对DSM模型检验问题的提法，研究了解的结构特征，证明了可将DSM模型检验问题归结为不动点计算问题，并将Cleaveland和Steffen求不动点的方法用于同步模型不动点计算过程，然后将Bertsekas关于欧氏空间上的异步收敛定理推广到完备格上，从而将求解异步DSM方程不动点问题转化为求解同步方程不动点问题，证明了在适当的条件下，有vgEA＝vgES,vgES与vgEA分别是DSM同步方程与异步方程的大不同动点，该同步算法的复杂性为O（|S|log|TRAN|),S为状态集，TRAN为转移关系式集，利用本文结果，可以有效地克服了DSM进程之间的并发干扰所带来的各种困难，对分布式反应系统的可靠设计有很大的帮助。     

基于平均场退火的二值纹理图象恢复

本文根据平均场退火技术，提出了一种二值纹理图象的估计和恢复算法，纹理图象描述为一个马尔可夫随机场模型和噪声过程的综合结果，算法递归地进行模型参数估计和图象恢复，其核心是一个统计松驰搜索算法，平均场方法将统计松驰方程转化为一组确定性方程，从而有效地提高了计算效率，对二值噪声纹理图象的实验结果说明了算法的有效性。     

模态转移系统的三值逻辑模型检验

分析了现有的模型检验技术应用于模态转移系统的三值逻辑公式的模型检验中存在的问题.提出了把模态转移系统转换成Kripke结构的算法以及三值逻辑公式转换成2个二值逻辑的算法,经过转换后可用现有的模型检验技术进行模型检验.用该算法转换后,状态数、转移数和原子命题数目与原模型呈线性关系,没有增加模型检验的复杂度.     

一种基于扩展不完全Kripke结构的三值逻辑模型检测方法

多值模型检测是解决形式化验证中状态爆炸问题的一种重要方法,三值模型检测是多值模型检测的基础,其中如何检验不确定状态的真值是一难点。针对不确定状态检验,提出了一种模型检测方法,首先对不完全Kripke结构PKS进行了扩展,然后在扩展后的模型上给出了检测不确定状态真值的方法,最后给出了基于扩展不完全Kripke结构的三值逻辑模型检测算法。与已有的三值逻辑模型检测算法相比,该算法降低了算法复杂度,完善了对于不确定或不一致信息的处理,从而增强了三值逻辑模型检测的实用性。     

OOCP∶面向对象的化工过程计算机辅助开发环境（Ⅰ）－面向对象方法建立过程系统模型

从过程系统的角度说明了面向对象概念，完成了对化工过程的多层次抽象，建立了近２０个化工类，并提出了用面向对象方法建立化工单元和过程系统面向方程模型的思想。文中介绍了化工类库的分类方法，类库定义、类库组织形式和面向对象建模机制等，可以看出，面向对象方法能快速、方便地建立新的化工单元和系统的面向方程模型，克服了传统设计方法中面向方程法模拟流程难以建模和利用已有模型产生新单元模型的缺点，为迅速、有效地建立化工模型探索了一条新途径。这也是作者提出的基于Ｗｉｎｄｏｗｓ的面向对象化工过程计算机辅助开发环境─ＯＯＣＰ的核心内容。     

基于区间数的黄金湿法冶炼过程建模与优化研究

针对黄金湿法冶炼生产过程中某些关键变量不能准确在线测量，导致局部工序无法定量建模、难以基于定量模型实现过程优化控制的问题，提出一种基于区间数的过程分层优化方法.在对黄金湿法冶炼生产过程特点进行分析的基础上，提出了基于区间数的过程分层优化框架.基于专家知识和现场操作人员经验等信息，建立了调浆过程的模糊定性模型.结合氰化浸出和置换等工序的定量模型及调浆过程的定性模型，建立了以综合经济效益最大为优化目标的黄金湿法冶炼生产过程优化模型.针对模糊定性模型的每一输出模态，利用区间数代替无法检测关键变量，提出了基于区间优化和分层优化思想相结合的优化方法，实现了黄金湿法冶炼过程的优化.与传统全流程优化方法的仿真对比实验表明，所提方法在具有不确定性的流程工业生产过程优化中具有一定的应用价值.     

Estimating parameters of a synthetic deformable human head model using a set of photographs

In this paper a new method for estimating the parameters of a synthetic deformable model of human head using a set of photographs is proposed. As opposed to statistical models, synthetic models have a well defined correspondence between the parameters and morphological features of the human head, though this bounds variability and complicates the task of fitting the model to photographs. A novel heuristic coarse-to-fine algorithm based on the psychophysical process of face perception and recognition by humans is proposed. A user study for evaluating the perceived quality of the generated models is also designed and conducted. Applicability of the proposed method for human-head modeling from photographs is corroborated by results of experiments on real world data.     

On-line model identification for the machining process based on multirate process data

Accurate identification of the model parameters of the machining process based on on-line process data is a crucial prerequisite for its model-based control and diagnostics. A typical machining process generates multi-output and multirate data streams. Whereas various sensors provide in-process information about the process, many important process outcomes including product qualities can be only measured in postprocess manner. This paper proposes to improve the identification by using both in-process and postprocess data and by analyzing the identifiability of model parameters. The identification of the model parameters based on multirate output is formulated using the maximum-likelihood estimation and the Fisher information matrix for a multirate-sampled system is derived to study the identifiability of model parameters. A strategy is developed to improve accuracy and robustness of the model identification considering the identifiability. The proposed method is tested on two batches of multirate process data from the cylindrical grinding process. The test results demonstrate using both in-process and postprocess data improves the identifiability and the proposed identification strategy results in improved prediction performance.     

An expert system for setting parameters in machining processes

In this paper, an hybrid system is proposed for setting machining parameters from experimental data. A symbolic regression alpha–beta is used to build mathematical models. Every model is validated using statistical analysis then evolutionary computation is used to minimize or maximize the generated model. Symbolic regression α–β is used to build mathematical models by estimation of distribution algorithms. A practical case considering measured data of two machining process on three materials are used to illustrate the utility of the expert system because generates a set of parameters that improve the machining process.     

An extended EM algorithm for joint feature extraction and classification in brain-computer interfaces

For many electroencephalogram (EEG)-based brain-computer interfaces (BCIs), a tedious and time-consuming training process is needed to set parameters. In BCI Competition 2005, reducing the training process was explicitly proposed as a task. Furthermore, an effective BCI system needs to be adaptive to dynamic variations of brain signals; that is, its parameters need to be adjusted online. In this article, we introduce an extended expectation maximization (EM) algorithm, where the extraction and classification of common spatial pattern (CSP) features are performed jointly and iteratively. In each iteration, the training data set is updated using all or part of the test data and the labels predicted in the previous iteration. Based on the updated training data set, the CSP features are reextracted and classified using a standard EM algorithm. Since the training data set is updated frequently, the initial training data set can be small (semi-supervised case) or null (unsupervised case). During the above iterations, the parameters of the Bayes classifier and the CSP transformation matrix are also updated concurrently. In online situations, we can still run the training process to adjust the system parameters using unlabeled data while a subject is using the BCI system. The effectiveness of the algorithm depends on the robustness of CSP feature to noise and iteration convergence, which are discussed in this article. Our proposed approach has been applied to data set IVa of BCI Competition 2005. The data analysis results show that we can obtain satisfying prediction accuracy using our algorithm in the semisupervised and unsupervised cases. The convergence of the algorithm and robustness of CSP feature are also demonstrated in our data analysis.     

Mathematical modelling and parameter identification of a stainless steel annealing furnace

A new, comprehensive mathematical model of continuous annealing furnaces is developed, under consideration of both the radiative and convective heat transfer of the furnace components. Based on measured normal operating data from an industrial stainless steel plant, parameter identification is basically carried out using a nonlinear least-squares optimization algorithm for the whole annealing furnace, to estimate optimal values of uncertain parameters, such as emissivities. Due to the complexity of the model, a sequential approach for parameter identification is proposed and implemented, i.e. the parameter set is divided into different subsets, and the parameter estimation is carried out sequentially in several steps and iterations. The performance of the model with the estimated parameters is then evaluated on a different test data set. It is shown that the obtained model can predict temperature evolutions along the furnace in good agreement to measured data, under both steady-state and transient conditions. The presented model is suitable for controller design and process optimization.     

A Measurement‐Based Approach for Designing Fixed‐Order Controllers for Unknown Closed‐Loop Architecture

This paper presents a new technique to design fixed‐structure controllers for linear unknown systems using a set of measurements. In model‐based approaches, the measured data are used to identify a model of the plant for which a suitable controller can be designed. Due to the fact that real processes cannot be described perfectly by mathematical models, designing controllers using such models to guarantee some desired closed‐loop performance is a challenging task. Hence, a possible alternative to model‐based methods is to directly utilize the measured data in the design process. We propose an approach to designing structured controllers using a set of closed‐loop frequency‐domain data. The principle of such an approach is based on computing the parameters of a fixed‐order controller for which the closed‐loop frequency response fits a desired frequency response that describes some desired performance indices. This problem is formulated as an error minimization problem, which can be solved to find suitable values of the controller parameters. The main feature of the proposed control methodology is that it can be applied to stable and unstable plants. Additionally, the design process depends on a pre‐selected controller structure, which allows for the selection of low‐order controllers. An application of the proposed method to a DC servomotor system is presented to experimentally validate and demonstrate its efficacy.     

多变量时滞系统的结构辨识

本文利用投影技术提出一种辨识结构的新方法.它能在多变量系统模型参数被估计之 前,直接从输入输出数据中确定该模型的时滞因子集.可观指数集及最小估计参数集.     

金氰化浸出过程的自优化控制

针对湿法冶金金氰化浸出过程的实时优化问题,提出一个自优化控制方案.基于系统的参数不确定模型,选择并求解输出变量的线性组合为被控变量,在反馈控制器作用下跟踪其恒定设定值,实现浸出过程在不确定性扰动下的自优化控制.仿真结果验证了该控制方案的有效性:扰动产生时,系统的经济指标在常规控制作用下得到改善,并最终运行于真实的最优点附近,提高了浸出过程的生产效益.