Plant-wide Disturbance Research Articles

Plant-wide root cause identification using plant key performance indicators (KPIs) with application to a paper machine

Previously, plant-wide disturbance analysis has looked into the propagation of faults through an industrial production process by investigating process measurements. However, the extent of the analysis has mostly been limited to a section of a plant. In this work, we propose a top-down approach which investigates measurements of the complete plant and identifies a section where the disturbance originates. Root cause analysis is carried out thereafter to pinpoint the faulty asset. The proposed approach has three novel elements: Using key performance indicators (KPI) as reference and starting point of the analysis, restricting measurements to a measurement type (e.g. flow) thus focusing on a section and applying the novel method of contribution plots of spectral PCA T2 statistic to find the contribution of each measurement towards the disturbance observed in the KPI. The approach is described and carried out on a paper machine where a quality KPI showed an established oscillation.

A General Method for Handling Disturbances on Utilities in the Process Industry

AbstractUtilities, such as steam or cooling water, have shown to play an important role within the process industry, since a malfunctioning utility is a plant-wide disturbance that can lead to large revenue losses due to reduced production quantities. This work focuses on identifying disturbances on utilities that give economical consequences. Measures of utility availability and area availability are introduced and used for estimating the ratio of disturbances on utilities. A generic method for handling disturbances on utilities is presented, which could be applied using site models of different level of detail. Some modeling approaches for modeling a site are described and the framework of the general method is demonstrated with a case study example at Perstorp AB, Sweden.

Process Control Performance Assessment: From Theory to Implementation (Ordys, A.W. et al.; 2007) [Book reviews

This book is timely as a state-of-the-art summary on some of the most recent developments in control loop performance assessment. In reality, the book provides a broader context to control performance assessment other than merely evaluating the controller performance. What is also discussed includes business performance benchmarking, economic auditing of control systems, plantwide disturbance diagnosis, oscillation detection, and valve stiction monitoring, As such, this book covers topics beyond traditional control loop performance assessment. Overall, the book is well written. An excellent feature is that detailed step-by-step procedures for algorithm implementation are given, which can be a good user's guide. Extensive simulation and industrial examples are cited to illustrate the algorithms. The book provides a food balance between academic and engineering aspects. Academic researchers, graduate students, and industrial practitioners who are interested in the general areas of control performance monitoring, business performance benchmarking, control system or instrument problem diagnosis will find this book a useful reference.

A practical method for identifying the propagation path of plant-wide disturbances

Disturbances in a continuous process often travel along the product stream and affect the performance of the process. To isolate the root cause and find the path along which the disturbance propagates requires an understanding of the cause-and-effect relationships between multiple variables. One way of identifying these relationships is through the time delays between process variables. A practical and robust approach is proposed that uses the cross-correlation function to estimate the time delay between process measurements and derives a qualitative model of the propagation path in the form of a causal map. The approach was applied to an industrial case study of a process affected by a plant-wide disturbance and was able to decide between two alternative root cause explanations. It was also applied successfully to a process with a recycle. The advantages of the proposed method are its ease of implementation and automation as well as simple interpretation of the resulting causal map.

Advances and new directions in plant-wide disturbance detection and diagnosis

This article reviews advances in detection and diagnosis of plant-wide control system disturbances in chemical processes and discusses new directions that look promising for the future. Causes of plant-wide disturbances include non-linear limit cycles in control loops, controller interactions and tuning problems. The diagnosis of non-linearity, especially when due to valve stiction, has been an active area. Detection of controller interactions and disturbances due to plant structure remain open issues, however, and will need new approaches. For the future, the linkage of data-driven analysis with a qualitative model of the process is an exciting prospect. Finally, the paper offers some brief comments about emerging applications.

Using process topology in plant-wide control loop performance assessment

This contribution describes how disturbances in a control system can be isolated and diagnosed automatically based on plant topology. In order to demonstrate this, a prototype software has been designed and implemented which, when given an electronic process schematic of a plant and results from a data-driven analysis, allows the user to pose queries about the plant and to find root causes of plant-wide disturbances. This hybrid system puts together two new technologies: the plant topology information written in XML according to the computer aided engineering exchange (CAEX) schema and the results of a signal analysis tool called plant-wide disturbance analysis (PDA). The isolation and diagnosis of the root causes of plant-wide disturbances is enhanced when process connectivity is considered alongside the results of data-driven analysis.

Diagnosis of plant-wide oscillation through data-driven analysis and process understanding

Disturbances that propagate throughout a plant due to recycle streams, heat integration or other means can have an especially large impact on product quality and running costs. There is thus a motivation for automated detection of a plant-wide disturbance and for determination of the root cause so that the disturbance may be removed. In this article, data-driven techniques are used to diagnose a plant-wide oscillation in an Eastman Chemical Company plant. A numerical non-linearity index derived from routine measurements was able to suggest the root cause. Process understanding possessed by the plant control engineers then enhanced the data-driven analysis, for instance by identifying a proxy measurement for an unmeasured flow through the valve suspected of being the root cause. In situ tests of just one valve confirmed the suspected root cause and the plant-wide oscillation disappeared after repairing the valve. The diagnosis was right first time and the maintenance effort was thus minimized. The success of the study suggests there exists a fruitful direction for future research in the automated linkage of data-driven analysis with information about the structure and connectivity of the process.

A hierarchical physical model-based approach to predictive control of a thermal power plant for ef”cient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat ‘ow on the boiler-side, while condenser vacuum was ‘uctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range. These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

ANALYSIS OF PLANT-WIDE DISTURBANCES THROUGH DATA-DRIVEN TECHNIQUES AND PROCESS UNDERSTANDING

Plant-wide disturbances can have an impact on product quality and running costs. Thus there is a motivation for automated detection of a plant-wide disturbance and for diagnosis of the root cause. In this article, data-driven techniques are used to analyze plant-wide disturbances caused, for instance, by limit cycle oscillation in a control loop. The control loops participating in the disturbance are detected and displayed on a process schematic. Other numerical signatures derived from the data trends are utilized for the diagnosis of the root cause. The outcome is a visual display that integrates process understanding and data-driven analysis.

Establishing Goals and Functions for a Plant-Wide Disturbance Analysis and Surveillance System (DASS)

This paper discusses part of the work performed for the Electric Power Research Institute under contract RP891-3 for the development of a plant-wide Disturbance Analysis and Surveillance System (DASS) for nuclear power plants. Specifically, a process is developed that allows the DASS design team to maintain control over the scope (number of) and depth (detail) of the plant systems and equipment which must be modelled in order to produce a diagnostic tool which reactor operators should find helpful in recognizing the nature of and in coping with unexplained plant transients.