Data mining and control loop performance assessment: The multivariate case

Control loop performance assessment (CLPA) techniques assume that the data being analyzed is generated during steady state operation with fixed plant dynamics and controller parameters. However, in industrial settings one often encounters environmental and feedstock variations which can induce significant changes in the plant dynamics. Availability of data sets corresponding to fixed configurations is therefore questionable in industrial scenarios, in which case it becomes imperative to extract the same from routine plant operating data. This article proposes a technique for segmenting multivariate control loop data into portions corresponding to fixed steady state operation of the system. The proposed technique exploits the fact that changes in the operating region of the system lead to changes in variance‐covariance matrix of multivariate control loop data. The univariate interval halving technique is fused with Mahalanobis distance to develop a multivariate tool that accounts for interactions between variables. The resulting data segments can be used for reliable CLPA and/or for user defined benchmarking of control loops. A multivariate control loop performance index is also proposed that requires significantly less data as compared to one of the previously proposed techniques. The proposed technique requires only routine operating data from the plant, and is tested on benchmark systems in the literature with simulations. Experimental validation on a model predictive control system aimed at maintaining the temperature profile of a metal plate demonstrates applicability of the technique to industrial systems. The proposed technique acts as a tool for preprocessing data relevant to CLPA and can be applied to large scale interacting multivariate systems. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3311–3328, 2017     

Assessment of control loop performance

For processes described by linear transfer functions with additive disturbances, the best possible control in the mean square sense is realized when a minimum variance controller is implemented. It is shown that an estimate of the best possible control can be obtained by fitting a univariate time series to process data collected under routine control. No ‘identifiabüity’ constraints need be imposed. The use of this technique is demonstrated with pilot plant and production data.     

基于广义最小变差基准的多变量控制性能评估方法

This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark. An explicit expression for the feedback controller-invariant (the generalized minimum variance) term of the multi-variable control system is obtained, which is used as a standard benchmark for the assessment of the control performance for multi input multi output (MIMO) process. The proposed approach is based on the multivariable minimum variance benchmark. In com-parison with the minimum variance benchmark, the developed method is more reasonable and practical for the control performance assessment of multivariable systems. The approach is illustrated by a simulation example and an industrial application.     

A Bayesian approach for control loop diagnosis with missing data

This article is concerned with determination of the underlying source of problematic control performance through a data‐driven Bayesian approach. This approach synthesizes information from different monitoring algorithms to isolate possible problem sources. A main issue encountered in the application of the data‐driven approach is the problem of missing data or missing monitor reading. By introducing the concept of missing pattern, data missing problems are classified into single and multi missing patterns. A novel method based on marginalization over underlying complete evidence matrix is proposed to circumvent missing data problems. Performance of the proposed Bayesian approach is examined through simulations as well as an industrial application example to verify its ability of information synthesis. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Performance assessment of multivariate control loops on a paper-machine headbox

A large number of control loops in the pulp and paper industry are multivariate in nature. The main control objective of these loops is to reduce process variation. The satisfactory performance of these loops is therefore important for maintaining product quality. This paper introduces a multivariate performance measure of such control loops. The multivariate minimum variance control performance which provides an absolute lower bound on process variance can be estimated from routine operating data, and serves as a good benchmark to assess control loop performance. The proposed measure of performance is evaluated by application to the multiloop control of a headbox machine.     

Performance assessment measures for univariate feedforward/feedback control

An analysis of variance for univariate feedforward/feedback systems has been derived which allows the variance contributions due to both disturbances and controllers to be established. A performance index which assesses the performance of the overall control scheme has been developed. Estimates of the analysis of variance and performance index can be obtained using ordinary closed loop data and least squares regression. These quantities can be used on-line or off-line for either the design of new controllers or as a means of assessing the performance of existing feedforward/feedback control schemes. The practicality of the results in the paper are demonstrated using simulated and industrial data.     

DBFact: A better approach to calculate the minimum variance control law for nonminimum phase MIMO systems

This paper presents the diagonal with Blaschke products factorization (DBFact) approach to factor out multivariable time delay and nonminimum phase zeros from multi-input multi-output (MIMO) systems. Based on that, a new output-order independent minimum variance (MV) control law for MIMO systems is proposed. The DBFact method is a two-step factorization procedure, relying on the diagonal and Blaschke factorization methods. This method has the advantage of being a direct and non-iterative procedure. This new factorization approach allows the calculation of an MV control law considering the multivariable time delay as a limiting-performance factor and the nonminimum phase zeros and their corresponding directions. Based on the proposed MV control law, a performance benchmark is introduced, which can be calculated by the DBFact filters and routine operating data. The DBFact methodology was applied to two control structures of the linear plant model of Linde's heat integrated air separation, in which the MV control law output-order dependency property and the suitability of the performance benchmark were evaluated. Some results were compared with those obtained by admitting the generalized interactor matrix instead of the DBFact filters. The results show the capability of the DBFact methodology to factor the nonminimum phase terms to provide a reliable MIMO controller performance benchmark and illustrate the importance of considering the nonminimum phase zeros and their actual directionality in the MV control law.     

Performance assessment of processes with abrupt changes of disturbances

This paper is concerned with feedback controller performance assessment of time-variant processes due to abrupt changes of disturbances. It is shown that in addition to minimum variance control, alternative performance benchmarks that are more suitable for time-variant processes can be found from routine operating data. Three types of performance assessment problems and their solutions are discussed. The proposed methods are illustrated through a simulated example as well as a case study on Shell distillation column data.     

Estimating performance enhancement with alternate control strategies for multiloop control systems

A filter based methodology, studied earlier for SISO systems, is extended to MIMO systems. The presented approach facilitates the calculation of best achievable performance for proportional-integral (PI) controller and the optimal multiloop (ML) PI settings for stochastic disturbance rejection in ML control systems. The filter based approach is further extended to answer some of the key questions for ML control systems such as: (a) performance enhancement possible with the alternate pairing scheme, (b) benefits that will accrue through the employment of decouplers and (c) the performance achievable with the use of multivariable controller (as opposed to an ML controller). Further, the trade-off curve between output variance and control effort is generated for the various control configurations within PI controller domain.     

A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era

Owing to wide applications of automatic control systems in the process industries, the impacts of controller performance on industrial processes are becoming increasingly significant. Consequently, controller maintenance is critical to guarantee routine operations of industrial processes. The workflow of controller maintenance generally involves the following steps:monitor operating controller performance and detect performance degradation, diagnose probable root causes of control system malfunctions, and take specific actions to resolve associated problems. In this article, a comprehensive overview of the mainstream of control loop monitoring and diagnosis is provided, and some existing problems are also analyzed and discussed. From the viewpoint of synthesizing abundant information in the context of big data, some prospective ideas and promising methods are outlined to potentially solve problems in industrial applications.     

一种基于优先级的控制系统性能评估和安全监控系统

控制系统性能监控和评估的研究对保障生产过程安全高效运行具有重要意义。对于一个控制系统,各种监控评估指标的重要性不同却有内在冲突性,例如控制系统的性能"卡边"操作与安全生产有时是矛盾的。因此,提出了优先级性能评估与监控的思想,系统依照优先级序列对各指标进行性能评估,并对事关安全的关键过程变量重点监控;开发了针对MPC多变量控制系统的优先级性能评估与监控系统,并应用于催化裂化反应-再生系统。     

Attainment of PI Achievable Performance for Linear SISO Processes with Deadtime by Iterative Tuning

This work presents a practical scheme to attain PI achievable performance (the performance achievable with a proportional integral (PI) controller) for linear SISO processes with dead time driven by stochastic disturbances. It uses a combination of a quantitative measure called Control Loop Performance Index (CLPI) and signature plots of the closed loop disturbance impulse response (IR) to arrive at PI achievable performance in an iterative manner. The proposed methodology requires only routine operating data and knowledge of the process delay, and its efficacy is illustrated through several case studies.     

Deterministic vs. stochastic performance assessment of iterative learning control for batch processes

The performance assessment of linear time‐invariant batch processes when iterative learning control (ILC) is implemented has been discussed. Previous literatures show that conventional performance assessment cannot be directly applied to batch processes due to the nature of batch operations. Chen and Kong have suggested a new method to assess the control performance of batch processes using optimal ILC as the benchmark. In their work, ILC controllers are assumed to affect either stochastic or deterministic performance but without considering their interaction. This work elaborates the controllers effects on both stochastic and deterministic control performance of batch processes. It is shown that the optimal solution based on the minimum variance control law has a trade‐off between deterministic and stochastic performance, which can be shown by a trade‐off curve. Furthermore, a method is proposed to estimate this curve from routine operating data, against which the performance of ILC controllers can be assessed. Simulation studies are conducted to verify the proposed method. © 2012 American Institute of Chemical Engineers AIChE J, 59: 457–464, 2013     

基于动态多属性决策方法的工业过程控制性能评价

工业控制系统由于设备老化而导致控制性能发生变化,而传统的控制性能评价方法对于这种慢时变特性具有局限性。提出了一种基于变权动态多属性决策的控制性能评价方法。首先在对系统性能进行判断时,利用系统故障率与运行时间相结合的计算方法对系统的运行状态进行划分,获得不同运行阶段的决策信息;采用四个评价指标分别为超调量、非线性指标、输出方差、阀黏滞指标构造多属性决策判断矩阵,对老化慢时变系统进行评价,并运用色谱分析法计算决策过程中属性权重的变化,最终确定当前性能运行状态;将该方法应用于某工业DMF回收装置中,对比实验结果验证了该方法的有效性。     

Enhanced data envelopment analysis for sustainability assessment: A novel methodology and application to electricity technologies

Quantifying the level of sustainability attained by a system is a challenging task due to the need to consider a wide range of economic, environmental and social aspects simultaneously. This work explores the application of data envelopment analysis (DEA) to evaluate the sustainability ‘efficiency’ of a system. We propose an enhanced DEA methodology that uses the concept of ‘order of efficiency’ to compare and rank alternatives according to the extent to which they adhere to sustainability principles. The capabilities of the proposed approach are illustrated through a sustainability assessment of different technologies for electricity generation in United Kingdom. In addition to screening the alternatives based on sustainability principles, enhanced DEA provides improvement targets for the least sustainable alternatives that, if achieved, would make them more sustainable. The enhanced DEA shows clearly the ultimate distance to sustainability, helping industry and policy makers to improve the efficiency of technologies, products and policies.     

一种对预测控制系统经济性能评估的实用方法及其工业应用

In this article, an approach for economic performance assessment of model predictive control (MPC) system is presented. The method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian (LQG) benchmark other than conventional minimum variance control (MVC) to estimate the potential of reduction in variance. The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance, and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction. Combining the LQG benchmark directly with benefit potential of MPC control system, both the economic benefit and the op-timal operation condition can be obtained by solving the economic optimization problem. The proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.