Observability and redundancy in process data estimation

By analogy to the development for dynamic systems, concepts of observability and redundancy may be developed with respect to a steady state system. These concepts differ from their counterparts for dynamic systems in that they can be used to characterize individual variables and local behavior as well as system and global behavior. Relations between local observability, global observability, calculability and redundancy are established and explored in this paper. It is shown that these concepts are useful in characterizing the performance of process data estimators with regard to bias and uniqueness of an estimate, convergence of estimation procedures and the feasibility and implications of problem decomposition.     

Design of instrumentation in process plants using Groëbner bases

Reliability and optimal sensor design are of great interest for control engineers and the researcher community. The severity of faults is addressed through redundancy analysis. A structural analysis is introduced which uses symbolic calculus to determine which redundancy exists in the system and provides the means to find the particular sensor defaults.The paper is organized as follows. The first part gives an overview on Groëbner bases and their programming. Definitions are included to specify explicitly what should be understood by the terms “symbolic calculus software” and more specifically lexicographic programming and Groëbner base algorithms. The second part develops the concept of observability bases and in the third part we show how to use these bases to make optimal and reliable design. The biggest benefit of this method is the ability to extend the mathematical process to the n-linear case.     

Sensor location for nonlinear dynamic systems via observability analysis and MAX-DET optimization

This paper presents an approach for determining the optimal placement of multiple sensors for processes described by a class of nonlinear dynamic systems. This approach is based upon maximizing a criterion, i.e., the determinant, applied to the empirical observability Gramian in order to optimize certain properties of the process state estimates. The determinant directly accounts for redundancy of information for placing multiple sensors via the covariance terms in the observability matrix. However, the resulting optimization problem is nontrivial to solve as it is a mixed integer nonlinear programming problem. In order to address this point, this paper also presents a decomposition of the optimization problem such that the formulated sensor placement problem can be solved quickly and accurately on a desktop PC. Properties of the presented technique are demonstrated and discussed in two case studies, one involving a binary distillation column and the other a packed bed reactor.     

CONTROLLABILITY AND OBSERVABILITY OF A TIME-VARYING LINEAR SYSTEM USING THE BLOCK-PULSE FUNCTIONS

A linear time-varying system has been analyzed using the operational properties of block-pulse functions. This method permits the calculation of the controllability and observability matrices at a series of sampling times. Digital algorithms are formulated and the method is illustrated for a chemical reaction system.     

New sensor network design and retrofit method based on value of information

Traditionally, the sensor network design procedure was based on positioning sensors so that certain network monitoring capabilities (e.g., observability, redundancy, and error detectability) of key variables are assured at minimum sensors cost. We present a new approach that is based on maximizing economic value of information minus cost instead of the traditional approach that requires the satisfaction of performance targets. This article presents the conceptual aspect and computation issues of this new approach: the connection between the new approach and the traditional minimum‐cost approaches is explored and the computational methods to solve the proposed problem are presented. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

化工过程基于广义非线性状态观测器的不可测输入和不确定模型参数软仪表(英文)

Chemical processes are usually nonlinear singular systems. In this study, a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes, which are augmented as state variables. Based on the observability of the singular system, this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters. When the observability is satisfied, the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer. The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation. With the catalyst circulation rate as the only unknown input without model error, one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst cir- culation rate. However, when uncertain model parameters also exist, additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.     

化工过程监测系统的传感器网络设置问题研究

化工过程监测系统的性能与其传感器网络的设置方式有直接联系。通过建立化工过程有向图模型,结合故障传播模式分析和传感器网络搜索算法,从过程监测的角度研究了传感器设置问题。给出了考虑故障可观性与分辨率下的传感器设置方法。并以CSTR-heat exchanger过程为例,说明了统计过程监测方法与传感器网络设置问题相结合的重要性。     

AlPOs Synthetic Factor Analysis Based on Maximum Weight and Minimum Redundancy Feature Selection

The relationship between synthetic factors and the resulting structures is critical for rational synthesis of zeolites and related microporous materials. In this paper, we develop a new feature selection method for synthetic factor analysis of (6,12)-ring-containing microporous aluminophosphates (AlPOs). The proposed method is based on a maximum weight and minimum redundancy criterion. With the proposed method, we can select the feature subset in which the features are most relevant to the synthetic structure while the redundancy among these selected features is minimal. Based on the database of AlPO synthesis, we use (6,12)-ring-containing AlPOs as the target class and incorporate 21 synthetic factors including gel composition, solvent and organic template to predict the formation of (6,12)-ring-containing microporous aluminophosphates (AlPOs). From these 21 features, 12 selected features are deemed as the optimized features to distinguish (6,12)-ring-containing AlPOs from other AlPOs without such rings. The prediction model achieves a classification accuracy rate of 91.12% using the optimal feature subset. Comprehensive experiments demonstrate the effectiveness of the proposed algorithm, and deep analysis is given for the synthetic factors selected by the proposed method.     

Constrained regression in chemical engineering practice

Absolute equality constraints in multiple regression are shown to introduce only minor modifications in the normal equations by a subset of the regression parameters. It is essential that these are ordered such that the subset is observable through the constraints. The constraints are shown to give greater residuals about the regression, but the precision in the parameter estimation is improved.In general power function curve fitting, a quadratically convergent iterative computation of the optimal exponents uses the normal equations as equality constraints. The correlations between the coefficients and the exponents in a sum of power functions become very high, when the number of terms increases such that their individual observability is being greatly reduced. The effect is shown to be a very poor precision in the parameter estimation and a heavy oscillation in the coefficients.The algorithms suggested are illustrated by curve fitting of some binary equilibrium data, where the relative volatility model gives a superior fit to the data.     

Subsystem decomposition and configuration for distributed state estimation

Distributed state estimation plays a very important role in process control. Improper subsystem decomposition for distributed state estimation may increase the computational burdens, degrade the estimation performance, or even deteriorate the observability of the entire system. The subsystem decomposition problem for distributed state estimation of nonlinear systems is investigated. A systematic procedure for subsystem decomposition for distributed state estimation is proposed. Key steps in the procedure include observability test of the entire system, observable states identification for each output measurement, relative degree analysis and sensitivity analysis between measured outputs and states. Considerations with respect to time‐scale multiplicity and direct graph are discussed. A few examples are used to illustrate the applicability of the methods used in different steps. The effectiveness of the entire distributed state estimation configuration procedure is also demonstrated via an application to a chemical process example used in coal handling and preparation plants. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1995–2003, 2016     

Synthesis of distillation trains by thermodynamic analysis

Thermodynamic principles, involving mainly: (1) minimum reversible work of separation and (2) second-law analysis that accounts for irreversibility, are used to synthesize separation trains. These principles lead to a development and classification into two groups of many of the heuristics appearing in the literature. An efficient algorithm based on reversible thermodynamics leads to optimal structures for simple acyclic trains. Complex structures are efficiently synthesized by algorithms using reversible thermodynamics and second-law analysis. The procedures presented are depth-first algorithms. The optimal structures developed are shown to be strongly influenced by feed and product compositions.     

基于PC104的DCS冗余软件设计与实现

介绍DCS中冗余组态软件和冗余运行软件的设计与实现,包括冗余系统硬件结构、冗余系统软件结构、冗余参数组态、组态信息的存储、组态信息的下传通信、冗余运行软件切换控制逻辑、错误表管理、冗余运行软件与I/O模块的数据交换。这一软件实现能有效提高DCS在工业应用中的可靠性和安全性。     

基于近红外光谱的汽油牌号快速识别

提出基于近红外(NIR)光谱的汽油牌号快速识别算法,主要包括预处理、特征提取和分类建模几部分,比较了各种分类方法的识别能力。实验结果表明:采用主元分析(PCA)提取特征进行模式识别的性能普遍优于直接在光谱波长域的方法,通过选择合适的PCA主元可以获得满意的分类效果,可用于汽油产品的牌号快速识别。     

State observer scheme for joint kinetic parameter and state estimation

The development and acceptance of model-based monitoring tools in the bioprocess industry is made difficult by the usually large uncertainty associated with the process model. A natural approach to handle this issue is the design of adaptive state observers for the joint estimation of the process state and some of the uncertain model parameters. However, the state extension is often restricted to a few parameters only, for which observability conditions are satisfied with the available measurement information. In this study, this latter issue is circumvented by the combination of two observers: (a) a receding-horizon observer is designed for the joint estimation of the state and uncertain model parameters, and (b) an asymptotic observer, which provides state estimates independently of the kinetic model, is used to provide the missing additional information to the receding-observer, thus avoiding observability loss. This paper derives the properties of this combined state estimation scheme and demonstrates its performance with a realistic simulation case study of animal cell cultures.     

动态核聚类算法在乙烯生产中的优化模式识别

针对传统的C means聚类在小样本和类别交叉空间存在样本错分问题,研究分析一种基于核函数的动态聚类方法,以广义欧式距离作为高维特征空间的相似性度量,提高聚类的准确性,仿真实例验证了算法的有效性。提出利用动态核聚类方法来识别乙烯裂解炉生产过程最优操作模式,动态调整乙烯裂解炉的操作条件以及选择油品和预测收率,实际的工业应用说明动态核聚类方法的可行性。     

Construction of validated, non-redundant composite protein sequence databases

A strategy has been developed for the construction of a validated,comprehensive composite protein sequence database. Entries areamalgamated from primary source data bases by a largely automatedset of processes in which redundant and trivially differententries are eliminated. A modular approach has been adoptedto allow scientific judgement to be used at each stage of databaseprocessing and amalgamation. Source databases are assigned apriority depending on the quality of sequence validation andcommenting. Rejection of entries from the lower priority database,in each pairwise comparison of databases, is carried out accordingto optionally defined redundancy criteria based on sequencesegment mismatches. Efficient algorithms for this methodologyare embodied in the COMPO software system. COMPO has been appliedfor over 2 years in construction and regular updating of theOWL composite protein sequence database from the source databasesNBRF-PIR, SWISS-PROT, a GenBank translation retrieved from thefeature tables, NBRF-NEW, NEWAT86, PSD-KYOTO and the sequencescontained in the Brookhaven protein structure databank. OWLis part of the ISIS integrated data resource of protein sequenceand structure [Akrigg et al. (1988) Nature, 335, 745–746].The modular nature of the integration process greatly facilitatesthe frequent updating of OWL following releases of the sourcedatabases. The extent of redundancy in these sources is revealedby the comparison process. The advantages of a robust compositedatabase for sequence similarity searching and information retrievalare discussed.     

随机出、入水双容水箱液位控制实验及被控对象的数学模型

首先介绍双容水箱液位控制实验仪的实物系统装置,随后,设计了随机出入水的双容水箱供排液系统液位控制实验,并用机理分析法建立被控对象的数学模型,最后分析上述系统的能控性和能观性.     

Handling sensor faults in economic model predictive control of batch processes

The problem of sensor fault detection and isolation (FDI) and fault-tolerant economic model predictive control (FT-EMPC) for batch processes is addressed. To this end, we first model batch processes using subspace-based system identification techniques. The analytical redundancy within the identified model is subsequently exploited to detect, isolate, and handle the faulty measurements. The reconciled fault-free measurements are then incorporated in an economic model predictive controller formulation. Simulation case studies involving the application of the proposed data-driven FDI and FT-EMPC algorithms to the energy intensive electric arc furnace process illustrate the improvement in economic performance under various fault scenarios. © 2018 American Institute of Chemical Engineers AIChE J, 65: 617–628, 2019     

Normal Mode Dynamics Comparison of Proteins

Sequence and structure comparisons are fundamental techniques that enable exploration of the sequence and structural spaces of proteins. Homology detection, function prediction, and protein classification rely on these techniques. However, protein structures are dynamic, rather than static, and such protein dynamics play a key role in a wide range of biological activities. Therefore, protein dynamics comparison algorithms may shed light on the relationship between proteins′ dynamics and function. Here, we review different strategies for comparing dynamics of proteins. Special emphasis is given to newly developed algorithms that compare dynamics of proteins with no apparent sequence or structural similarity and to the qualitative differences between these algorithms.     

线性测量网的传感器配置与多目标优化

引　言数据是现代企业运作的基础 ,应该尽可能完备而准确 .其中流量数据主要是通过测量得到 .但是由于技术难度和经济约束 ,只能测量一部分物流 ,其他的通过数据协调技术估计获得 .由此引出测量网传感器配置问题 .早期的研究主要考虑数据的完备性 ,通过分析网络拓扑结构来配置[1～ 4 ] .2 0世纪 90年代末以来 ,研究集中在具有对偶关系[5] 的两类单目标优化问题 ,分别以费用最低[6,7] 或协调精度最高[8,9] 为目标 .配置费用低和协调精度高 ,这两个目标相互制约 ,本文将它们同时作为优化目标 ,定义多目标优化问题 ,采用遗传算法求解 .同时定义…