Routine Operating Data Research Articles

Feedforward and Feedback Controller Performance Assessment of Linear Time-Variant Processes

This paper is concerned with the feedforward and feedback control performance assessment of linear time-variant processes. The developed algorithms provide a way to calculate the time-varying minimum-variance feedforward and feedback control benchmark from routine operating data. The proposed techniques are illustrated through a simulated stirred-tank heater example and an industrial application.

Performance assessment using a model predictive control benchmark

Abstract Model predictive control (MPC) technology has been widely implemented throughout the petroleum, chemical, metallurgical and pulp and paper industries over the past three decades. The focus of this paper is the assessment of single-input, single-output MPC schemes against a new performance standard. The proposed MPC benchmark is shown to be useful both as a model diagnostic and as a tuning guide during commissioning. A formal assessment procedure is presented which emphasizes the use of routine operating data plus knowledge of the deadtime to determine when it becomes worthwhile to invest in re-identification of the plant dynamics and re-installation of the MPC application.

A relative performance monitor for process controllers

AbstractA monitor is developed to automatically detect poor control performance. It provides a measure (relative performance index—RPI) of a control‐loop performance relative to a reference model of acceptable control. The reference model simulates the controlled variable output of a user‐defined, acceptably tuned control loop. The inputs to the reference model are the setpoints (same as the true plant) and the disturbances (estimated from the measurements). The monitor uses routine plant operation data only. Pending ability to obtain temporally accurate process models, and the validity of process measurements, simulations and experiments show that the monitor can detect the poor control performance caused by improper controller parameter values or changes in plant characteristics, and can distinguish it from poor performance caused by external disturbances. Copyright © 2003 John Wiley & Sons, Ltd.

Coal mill modeling by machine learning based on onsite measurements

This paper presents a novel coal mill modeling technique using genetic algorithms (GA) based on routine operation data measured on-site at a National Power (NP) power station, in England, U.K. The work focuses on the modeling of an E-type vertical spindle coal mill. The model performances for two different mills are evaluated, covering a whole range of operating conditions. The simulation results show a satisfactory agreement between the model responses and measured data. The appropriate data can be obtained without recourse to extensive mill tests and the model can be constructed without difficulty in computation. Thus the work is of general applicability.

Coal Mill Modeling by Machine Learning Based on on-Site Measurements

This paper presents a novel coal mill modeling technique using genetic algorithms (GA) based on routine operation data measured on-site at a National Power (NP) power station, in England, U.K. The work focuses on the modeling of an E-type vertical spindle coal mill. The model performances for two different mills are evaluated, covering a whole range of operating conditions. The simulation results show a satisfactory agreement between the model responses and measured data. The appropriate data can be obtained without recourse to extensive mill tests and the model can be constructed without difficulty in computation. Thus the work is of general applicability.

Minimum variance control and performance assessment of time-variant processes

Abstract This paper is concerned with (1) an explicit solution of a minimum variance control law for linear time-variant (LTV) processes in the transfer function form, and (2) performance assessment of LTV processes using minimum variance control as the benchmark. It is shown that there exists a time-variant, absolute lower bound of process variance that is achievable under LTV minimum variance control and can be estimated from routine operating data. This lower bound can subsequently be used to assess the benefit of implementing LTV control such as adaptive control. The proposed methods are illustrated through simulated examples and an industrial case study.

Minimum Variance Control and Performance Assessment of Time-Variant Processes

This paper is concerned with 1) an explicit solution of the minimum variance control law for linear time variant (LTV) processes in the transfer function form, and 2) performance assessment of LTV processes using minimum variance control as the benchmark. It is shown that there exists a time-variant, absolute lower bound of process variance that is achievable under LTV minimum variance control and can be estimated from routine operating data. This lower bound can subsequently be used as incentive analysis of implementing LTV or adaptive control. The proposed methods are illustrated through simulated examples and a case study on industrial process data.

An investigation into the poor performance of a model predictive control system on an industrial CGO coker

In this paper, the successful trouble shooting of an industrial model predictive control system is reported. The approach is completely data driven. Routine closed-loop operating data is the only information required for applying such a diagnosis. The source of the problem has been attributed to inappropriate selection of the disturbance variables for the MPC controller. The problem is not unusual in industrial model predictive control systems. It is therefore recommended that such an analysis is carried out for other industrial MPC control systems as well if similar problems are identified.

Feedforward plus feedback controller performance assessment of MIMO systems

This paper is concerned with performance assessment of multiinput-multioutput (MIMO) feedforward plus feedback controllers. It is shown that the minimum feedforward plus feedback control variance can be estimated from routine operating data, and can then be used as a benchmark for performance assessment of feedforward and/or feedback controllers. The proposed method is illustrated by a numerical example and an industrial application.

Performance assessment of processes with abrupt changes of disturbances

AbstractThis 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.

Molecular and modeling analyses of the structure and function of nitrifying activated sludge

Activated sludge community structure was assessed directly by oligonucleotide probes for seven treatment plants in France and two treatment plants in The Netherlands. Routine operating data also were obtained for these plants. Mathematical modeling was employed to translate the operating data into parameters that reflect community function and structure. These model-translated measures were compared to the direct probing measures of community structure. Extraction of RNA and subsequent slot-blot hybridization with probes Nsol225 (for active ammonium oxidizers) and Eub338 (for all active bacteria) confirmed that the ratio of active nitrifiers to active heterotrophs was mainly explained by the ratio of TKN:BODL removed and that probe Nsol225 provided a quantitative measure of ammonium oxidizers easily distinguishable from background. Fluorescent in situ hybridization (FISH) followed similar trends. The ratio of active nitrifiers to total VSS depended on the TKN/BODL removal ratio and the sludge age. Finally, the ng rRNA/mg VSS hybridized by probe Eub338 was strongly and inversely correlated to the sludge age and suggests that specific rRNA contents of about 11,000 ng rRNA/mg VSSactive for heterotrophic bacteria and 14,000 ng rRNA/mg VSSactive for ammonium oxidizers can be used to estimate active VSS from probing results targeted to rRNA.

Feedback and Feedforward Performance Analysis of the Shell Industrial Closed Loop Data Set

Control loop Performance analysis has been carried out on three distillation column data sets from Shell. The objective in all three cases was to control single tray temperature at some desired set point using a single manipulated variable for control. All the controllers are Quadratic Dynamic Matrix type Controllers (QDMC). This paper summarizes the procedure involved in feedback and feedforward performance analysis using routine operating data. Spectral analysis on routine operating data can be used to complement the performance assesment techniques in time domain. The above techniques are illustrated by performance evaluation of the Shell Industrial data set as made available on their ftp site.

Good, bad or optimal? Performance assessment of multivariable processes

This paper is concerned with the estimation of a suitable explicit expression for the feedback controller-invariant term of the closed-loop MIMO process from routine operating data. The feedback controller-invariant or the minimum-variance term is subsequently used as a benchmark standard for the evaluation of control performance of MIMO processes. This new approach, towards control-loop performance analysis of closed-loop MIMO processes, is based on filtering and correlation (FCOR) analysis of the process output and filtered data. The FCOR algorithm is intuitively simple and computationally efficient. However, it does require a priori knowledge or estimation of the time-delay or interactor matrix of the MIMO process. The proposed algorithm is evaluated by application to simulated and industrial processes.

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

AbstractA 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.

Modelling, Control and On-Line Estimation of Activated Sludge Bulking

Sludge bulking is a problem widespread in the operation of activated sludge processes. Over the past fifteen years much research has been applied to identifying the nature of the microbial species responsible for bulking and to developing an understanding of their population dynamics. While some isolated attempts have been made at developing a unit process model capable of simulating the behaviour of bulking, none have been extensively evaluated against field data from full scale plants. This paper describes the development of a multiple-species model of the activated sludge process (for both the reactor and settler), its application to the assessment of various operational strategies for the control of bulking, and its simplification for incorporation into an on-line estimation scheme using a Kalman filter. Routine operating data from the Davyhulme Wastewater Treatment Works in Manchester are used for identification (calibration) of the model. Simulation studies of control strategies are based on the same Works. By using the Kalman filter to reconstruct real-time estimates of the “unmeasurable” states of the process model, the paper also explores the extent to which this filter-based control can bring improvements over similar control based entirely upon conventionally measured operating variables.

Statistical evaluation and design of an evaporator control system

AbstractThe utility of a statistical procedure suggested by Box and Jenkins is assessed for the evaluation and modification of an industrial evaporator control system. Results, obtained from the analysis of routine unperturbed operating data, indicate that this approach is (i) readily adaptable to the analysis of the dynamic behaviour of a full scale chemical unit process, (ii) characterized by effective model form identification, and (iii) extremely useful in deriving conventional and more novel control functions to supplement existing control schemes.