污水生化处理过程建模与控制

污水生化处理是利用微生物清除污水中有机污染物的一种有效方法, 是当前处理工业有机污水和城市生活污水的主要途径. 本文综述了污水生化处理过程, 尤其是活性污泥法处理过程的建模与控制. 从控制理论的角度, 生化污水处理过程是典型的非线性、时变、不确定性、时滞的复杂系统, 其建模与控制给控制界提出了严峻的挑战.     

Modeling continuous-time processes via input-to-state filters

A direct algorithm to estimate continuous-time ARMA (CARMA) models is proposed in this paper. In this approach, we first pass the observed data through an input-to-state filter and compute the state covariance matrix. The properties of the state covariance matrix are then exploited to estimate the half-spectrum of the observed data at a set of user-defined points on the right-half plane. Finally, the continuous-time parameters are obtained from the half-spectrum estimates by solving an analytic interpolation problem with a positive real constraint. As shown by simulations, the proposed algorithm delivers much more reliable estimates than indirect modeling approaches, which rely on estimating an intermediate discrete-time model.     

Statistical analysis and online monitoring for handling multiphase batch processes with varying durations

In the present work, statistical analysis and online monitoring is presented for handling uneven-length multiphase batch processes. Firstly, the irregular batches are classified into different uneven-length groups according to the changes of underlying characteristics. Then multi-source measurement data can be dealt with, each corresponding to one operation mode. The basic principle is that over different uneven-length groups, despite the uneven-length operation patterns, there are both similarity and dissimilarity to a certain extent among their underlying correlations. By an adequate decomposition, two different subspaces are separated, modeling the group-common and specific information respectively. Their corresponding confidence regions are constructed by searching similar patterns respectively. Accordingly, the online monitoring system is set up, which can track different types of variations closely. This analysis adds a detailed insight into the inherent nature of uneven-length multiphase batch processes. Its feasibility and performance are illustrated by a typical practical case with uneven cycles.     

活性污泥脱氮过程的建模与控制

有效处理废水的各种方法包括过程的最佳设计、安装更多的仪表和自动控制系统等日益受到关注.综述了污水生化处理过程,主要是脱氮工艺的建模与控制方面的最新研究成果.讨论了活性污泥、生物膜和厌氧消化过程的模型化以及最新的Benchmark仿真问题.在控制策略方面,强调了曝气、污泥停留时间和外碳源补加等有效的控制手段.最后指出了一些值得关注的研究问题:过程与控制并行设计、集成化的城市污水处理系统以及现代微生物分析方法的利用.     

Unsupervised slow subspace-learning from stationary processes

We propose a method of unsupervised learning from stationary, vector-valued processes. A projection to a low-dimensional subspace is selected on the basis of an objective function which rewards data-variance and penalizes the variance of the velocity vector, thus exploiting the short-time dependencies of the process. We prove bounds on the estimation error of the objective in terms of the β$\beta$-mixing coefficients of the process. It is also shown that maximizing the objective minimizes an error bound for simple classification algorithms on a generic class of learning tasks. Experiments with image recognition demonstrate the algorithms ability to learn geometrically invariant feature maps.     

一种基于工况分解的热工过程非线性控制模型建立方法及应用

充分考虑大多数复杂热工控制对象非线性特性与运行工况密切相关的实际特点,采用基于工况分解的多模型建模思路,提出一种面向控制的非线性过程建模方法.将该方法应用于某电厂300MW机组锅炉过热汽温对象,实际考核结果表明采用该方法建立的模型,即使在运行工况大范围变化时也具有满意的动态预测效果,验证了提出的方法的有效性.     

多模态过程中新模态过程建模方法

多模态过程中新出现的模态过程短期内无法获得充足的建模数据,且传统统计控制方法无法有效地估计过程特性.鉴于此,提出一种基于历史模型数据相关特性建立初步模型的方法,充分利用已有多模态历史数据的相关特性,从历史数据中寻找与当前数据特征相似的数据进行补充,建立初始模型,并利用新积累的数据迭代初步模型,逐步实现准确描述过程特性的算法.通过在田纳西-伊斯曼过程中的大量仿真,表明了所提出方法的可行性和有效性.     

Dissipativity and stabilization of nonlinear repetitive processes

Repetitive processes are characterized by repeated executions of a task defined over a finite duration with resetting after each execution is complete. Also the output from any execution directly influences the output produced on the next execution. The repetitive process model structure arises in the modeling of physical processes and can also be used to effect in the control of other systems, the design of iterative learning control laws where experimental verification of designs has been reported. The existing systems theory for them is, in the main, linear model based. This paper considers nonlinear repetitive processes using a dissipative setting and develops a stabilizing control law with the required conditions expressed in terms of vector storage functions. This design is then extended to stabilization plus disturbance attenuation.     

多模导航模拟器时间同步控制系统设计与实现

在多模导航模拟器的设计过程中,由于需要模拟的导航信号频点较多,使得如何修正由于多个上位机程序启动时间差异导致的数据包不同步,以及如何控制不同中频板卡同步启动以产生时间同步的中频信号成为了关键问题。针对这两个问题,设计并实现了一种应用于基于软件无线电体系架构多模导航信号模拟器的时间同步控制系统。该系统通过由系统计时板对外设板卡同步触发启动控制,驱动软件与外设板卡1s时隙同步控制,以及驱动软件向外设板卡传输数据包的同步控制,实现对各系统频点的时间同步控制,能够支持多模导航信号模拟器长时间持续稳定的工作,具有各系统频点时间同步精度高和系统复杂度低等优点。通过联合定位测试结果表明,采用本控制系统的多模导航模拟器的各系统频点的输出信号是时间同步的。     

Modeling and monitoring of E-commerce workflows

Electronic commerce (E-commerce) is an important Internet application that is significantly changing the way of commercial transaction. Due to the complexity of E-commerce systems and lack of well-designed formal models, providing a suitable method for their modeling and monitoring turns out to be a challenging job. This paper introduces labeled workflow nets (LWN) and their semantics on the basis of labeled Petri nets. Then an inter-organizational labeled workflow net (ILWN) - which can graphically model the dynamic behavior of the systems - is presented. Furthermore, the nonblocking property of ILWNs is defined to achieve a common goal of E-commerce participants. This nonblocking property helps to analyze the soundness of ILWNs and the undeniable property of interactive actions. The proposed framework for E-commerce workflows (ECW) can record a history of interactive events and monitor the execution of interactive activities to achieve a common goal. Therefore, it can elegantly model the execution of interorganizational workflows and analyze the accountability of cooperative activities. The main contribution of the paper is to present a new formal mechanism - ILWN. It can efficiently support the modeling and monitoring of ECWs. Finally, this paper illustrates how to use the proposed method by modeling and analyzing a Customer-Producer-Supplier example.     

Design of input shapers using modal cost for multi-mode systems

This paper presents two techniques for the determination of modal weights for the design of robust input shapers for multi-mode systems. The weights for the first technique are derived based on the scaling factor for the potential energy in each mode, such that the modal displacement is the same. The second technique exploits the modal cost analysis which uses the angle between the right eigenvectors and the row vectors of the output matrix to determine the contribution of each mode to the output. Two examples are used to illustrate the improvement in performance of the modal weighted minimax shapers compared to the traditional minimax shaper which equally weights all the modes.     

Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes

Since most real-world processes exhibit both nonlinear and time-varying characteristics, there exists a need for accurate and efficient models that can adapt in nonstationary environments. Also for adaptive control purpose, it is vital that an adaptive model has a fixed small model size. In this paper, we propose an adaptive tunable gradient radial basis function (GRBF) network for online modeling of nonlinear dynamic processes, which meets these practical requirements. Specifically, a compact GRBF model is constructed by the orthogonal least squares algorithm in training, which is capable of modeling variations of local mean and trend in the data well. During online operation, the adaptive GRBF model tacks the time-varying process’s dynamics by replacing a worst performing node with a new node which encodes the current new data. By exploiting the local predictor property of the GRBF node, the new node optimization can be done extremely efficiently. The proposed approach combining the advantages of both the GRBF network structure and fast tunable node mechanism is capable of tracking the time-varying nonlinear dynamics accurately and effectively. Extensive simulation results demonstrate that the proposed fast tunable GRBF network significantly outperforms the existing state-of-the-art methods, in terms of both adaptive modeling accuracy and online computational complexity.     

子空间辨识技术和MATLAB/xPCTarget在智能结构系统建模中的应用

本文采用子空间辨识方法和基于MATLAB/xPCTarget构建的实验系统对一表面粘贴有压电传感器和执行器的复合材料薄壳结构进行了实验研究,并与传统方法进行了比较。实验结果表明了所述方法的有效性。     

Approximation problems with the divergence criterion for Gaussian variables and Gaussian processes

System identification for stationary Gaussian processes includes an approximation problem. Currently, the subspace algorithm for this problem enjoys much attention. This algorithm is based on a transformation of a finite time series to canonical variable form followed by a truncation. There is no proof that this algorithm is the optimal solution to an approximation problem with a specific criterion. In this paper it is shown that the optimal solution to an approximation problem for Gaussian random variables with the divergence criterion is identical to the main step of the subspace algorithm. An approximation problem for stationary Gaussian processes with the divergence criterion is formulated.     

时段划分的多向主元分析间歇过程监测及故障变量追溯

针对间歇过程的多时段特性,提出一种生产过程操作时段划分方法.该方法利用反映过程特性变化的负载矩阵以及主成份矩阵的变化实现了间歇过程子时段的两步划分.提出了基于加权负载向量夹角余弦的负载矩阵相似性度量以及基于加权奇异值变化的奇异值矩阵相似性度量方法,以更客观的反映负载矩阵以及奇异值矩阵的相似性,进而更准确的判断过程特性的变化.根据同一操作子时段的过程特性,其负载矩阵和奇异值矩阵相似性较大的特点,实现了生产过程的子时段划分.将基于子时段划分的多向主元分析(MPCA)建模应用于三水箱系统的在线监测和故障变量追溯,实验结果验证了该方法的有效性.     

计算机建模实现路口交通流量实时监测技术探析

目前我国城市的交通控制多采用信号灯方式,人工设置时间,定时自动放行,这种方法,无法根据对各个路口实际交通流量变化进行实时监测与控制,因此,需要一种新型化的模式来对路口交通流量实时监测,计算机建模的应用,有利于设计优化管理模型,使得信号灯自动优化,实时监测,根据实际情况自动疏导拥堵,从而最大限度的改善交通拥挤等状况。     

Process pattern construction and multi-mode monitoring

A novel framework for process pattern construction and multi-mode monitoring is proposed. To identify process patterns, the framework utilizes a clustering method that consists of an ensemble moving window strategy along with an ensemble clustering solutions strategy. A new k-independent component analysis-principal component analysis (k-ICA-PCA) modeling method captures the relevant process patterns in corresponding clusters and facilitates the validation of ensemble solutions. Following pattern construction, the proposed framework offers an adjoined multi-ICA-PCA model for detection of faults under multiple operating modes. The Tennessee Eastman (TE) benchmark process is used as a case study to demonstrate the salient features of the method. Specifically, the proposed method is shown to have superior performance compared to the previously reported k-PCA models clustering approach.     

Input-output linearizing control of constrained nonlinear processes

An input-output linearization strategy for constrained nonlinear processes is proposed. The system may have constraints on both the manipulated input and the controlled output. The nonlinear control system is comprised of: (i) an input-output linearizing controller that compensates for processes nonlinearities; (ii) a constraint mapping algorithm that transforms the original input constraints into constraints on the manipulated input of the feedback linearized system; (iii) a linear model predictive controller that regulates the resulting constrained linear system; and (iv) a disturbance model that ensures offset-free setpoint tracking. As a result of these features, the approach combines the computational simplicity of input output linearization and the constraint handling capability of model predictive control. Simulation results for a continuous stirred tank reactor demonstrate the superior performance of the proposed strategy as compared to conventional input-output linearizing control and model predictive control techniques.     

Design patterns for monitoring and evaluating CSCL processes

Both quantitative and qualitative methods are being increasingly used to investigate the learning dynamics that take place within CSCL environments. Since such practices are a crucial aspect of the CSCL field, Design Patterns (DPs) can be used for capitalizing on experience and sharing know-how among practitioners. This paper describes three DP instances that have been developed and fine tuned by a community of practice consisting of researchers, instructional designers and tutors with the aim of supporting monitoring and evaluation of CSCL interactions. The DP solutions are based on a set of indicators, some of quantitative and some of qualitative nature, as well as on the methods to gauge these indicators, starting from data tracked by the e-learning system. The process of development of these DPs is described and examples of use are reported and discussed in order to advance the instructional design field and inform the development of CSCL systems. The proposed DPs belong to a Language, aimed at describing tracking problems in different types of e-learning systems and at capturing in their solutions the know-how developed by communities of experts in the different fields.     

序批式反应器污水处理工艺建模与控制研究

近年来采用序批式反应器工艺进行生化污水处理的研究日益受到关注.在工艺流程设计、模型建立、操作优化、控制器设计等都取得大量的成果.􀁱本文综述了这个领域一些最新的!与未来应用有关的研究进展.􀁱