A commonly encountered issue in process industry is concerned with the detection of plant-wide oscillations. In this paper, a new visualization tool termed as the power spectral correlation map (PSCMAP) is proposed for this purpose. The proposed colour map is based on a new measure defined as the power spectral correlation index (PSCI). A simple clustering algorithm is developed to group blocks of variables with similar spectral shapes. The combined visualization tool is shown to be simple, effective and powerful in collecting variables with common frequency-domain behaviour in a multivariate process. The potential of the combined technique is illustrated by an application to two industrial processes, (i) a simulated pulp and paper process and (ii) a SE Asia refinery. © 2005 Elsevier Ltd. All rights reserved.

Arun K. Tangirala

Department Of Chemical Engineering

Accident prevention

Oscillations

Principal component analysis

Spectrum Analysis

Stochastic control systems

Tuning

Waveform analysis

PLANT-WIDE OSCILLATION DETECTION

POWER SPECTRAL CORRELATION INDEX (PSCI)

POWER SPECTRAL CORRELATION MAPS (PSCMAP)

TIME-DOMAIN METHODS

Process control

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Journal of Process Control

Wavelets have been on the forefront for more than three decades now. Wavelet transforms have had tremendous impact on the fields of signal processing, signal coding, estimation, pattern recognition, applied sciences, process systems engineering, econometrics, and medicine. Built on these transforms are powerful frameworks and novel techniques for solving a large class of theoretical and industrial problems. Wavelet transforms facilitate a multiscale framework for signal and system analysis. In a multiscale framework, the analyst can decompose signals into components at different resolutions followed by the application of the standard single-scale techniques to each of these components. In the area of process systems engineering, wavelets have become the de facto tool for signal compression, estimation, filtering, and identification. The field of wavelets is ever-growing with invaluable and innovative contributions from researchers worldwide. The purpose of this chapter is threefold: (i) to provide a semi-formal introduction to wavelet transforms for engineers; (ii) to present an overview of their applications in process systems engineering, with specific attention to controller loop performance monitoring and empirical modeling; and (iii) to introduce the ideas of consistent prediction-based multiscale identification. Case studies and examples are used to demonstrate the concepts and developments in this work. © 2013 Elsevier Inc.

Advances in Chemical Engineering

Wavelets Applications in Modeling and Control

Model-plant mismatch is a crucial factor that influences the performance of a closed-loop system. A new approach is proposed for the diagnosis of poor control loop performance due to model-plant mismatch (MPM) in the internal model control framework. In particular, the objective is to identify the mismatch in specific components of a transfer function model, namely, the gain, time-constant, and delay from closed-loop operating data. A new quantity G p/Gm, called the plant model ratio (PMR) in the frequency domain is introduced as a measure of model-plant mismatch. It is shown that there exists a unique signature in PMR for each combination of mismatch in model parameters, which is the key step in the proposed method. A method to estimate the PMR from closed-loop operating data is provided. Theoretical and practical aspects of the mapping between the type of MPM and the proposed PMR are presented. Simulation studies demonstrate the effectiveness of the proposed method. © 2010 American Chemical Society.

Industrial and Engineering Chemistry Research

Diagnosis of poor control loop performance due to model-plant mismatch

Non-negative Matrix Factorization (NMF) has been used for source separation in various fields. However, the existing methods have ignored the presence of interactions among sources/measurements which leads to incorrect results. Interactions are common in a multivariate process where the variables are physically related/correlated with one another (for example: pressure-temperature dependency in an industrial process). In this work, conventional methods are extended to take into account the interactions. The contributions of this work are as follows: (i) an augmented NMF method to correctly determine the number of sources in the presence of multiple interactions; (ii) an algorithm to identify the correct signatures of the physical sources. The conventional method of NMF is shown to be a special case of the proposed method. Simulation studies are presented to demonstrate the practicality and utility of the proposed method. © 2009 Elsevier Inc. All rights reserved.

Digital Signal Processing: A Review Journal

Source separation in systems with correlated sources using NMF

In this paper, we propose the use of non-negative matrix factorization (NMF) of multivariate spectra for plantwide oscillation detection. One of the key features of NMF is that it provides a parts-based representation that allows us to retain the causal basis spectral shapes or parts that constitute the spectra of measurements, unlike the popular principal component analysis (PCA)-based methods. The contributions of this paper are as follows: (i) a novel measure known as the pseudo-singular value (PSV) to assess the order of the basis space (the PSV is also useful in determining the most dominant features of a data set); (ii) a power decomposition plot that contains the total power (defined in this work) and its decomposition by NMF (the power plot is a useful and compact visual tool that provides overall spectral characteristics of the plant and shows the decomposition of these characteristics into well-localized frequency components); and (iii) a novel measure defined as the strength factor (SF) to assess the strength of the localized features in the variables (it can be also used in isolating the root cause). Finally, it is shown that the proposed implementation of NMF is powerful and sensitive enough to capture small oscillations in the measurements. As a result, it largely eliminates the need to filter the data. Industrial case studies are presented to illustrate the applications of NMF and to demonstrate the utility and practicality of the proposed measures. © 2007 American Chemical Society.

Non-negative matrix factorization for detection and diagnosis of plantwide oscillations

Who's Who

Jarvis, Dr John Herbert, (born 16 May 1947), Senior Vice President Advisor, John Wiley & Sons Inc., 2007–09 (Senior Vice President, John Wiley and Sons - Europe, 1997–2007)

Journal	Journal of Process Control
ISSN	09591524
Open Access	No