1st Edition

Chemical Process Performance Evaluation

    344 Pages 128 B/W Illustrations
    by CRC Press

    The latest advances in process monitoring, data analysis, and control systems are increasingly useful for maintaining the safety, flexibility, and environmental compliance of industrial manufacturing operations.
    Focusing on continuous, multivariate processes, Chemical Process Performance Evaluation introduces statistical methods and modeling techniques for process monitoring, performance evaluation, and fault diagnosis.

    This book introduces practical multivariate statistical methods and empirical modeling development techniques, such as principal components regression, partial least squares regression, input-output modeling, state-space modeling, and modeling process signals for trend analysis. Then the authors examine fault diagnosis techniques based on episodes, hidden Markov models, contribution plots, discriminant analysis, and support vector machines. They address controller process evaluation and sensor failure detection, including methods for differentiating between sensor failures and process upset. The book concludes with an extensive discussion on the use of data analysis techniques for the special case of web and sheet processes. Case studies illustrate the implementation of methods presented throughout the book.

    Emphasizing the balance between practice and theory, Chemical Process Performance Evaluation is an excellent tool for comparing alternative techniques for process monitoring, signal modeling, and process diagnosis. The unique integration of process and controller monitoring and fault diagnosis facilitates the practical implementation of unified and automated monitoring and diagnosis technologies.

    Preface
    Nomenclature

    INTRODUCTION
    Motivation and Historical Perspective
    Outline

    UNIVARIATE SPM
    Statistics Concepts
    Univariate SPM Techniques
    Monitoring Tools for Autocorrelated Data
    Limitations of Univariate SPM Methods

    STATISTICAL METHODS FOR PERFORMANCE EVALUATION
    Principal Components Analysis
    Canonical Variates Analysis
    Independent Component Analysis
    Contribution Plots
    Linear Methods for Diagnosis
    Nonlinear Methods for Diagnosis

    EMPIRICAL MODEL DEVELOPMENT
    Regression Models
    PCA Models
    PLS Regression Models
    Input-Output Models of Dynamic Processes
    State-Space Models

    MONITORING OF MULTIVARIATE PROCESSES
    SPM Methods Based on PCA
    SPM Methods Based on PLS
    SPM Using Dynamic Process Models
    Other MSPM Techniques

    CHARACTERIZATION OF PROCESS SIGNALS
    Wavelets
    Filtering and Outlier Detection
    Signal Representation by Fuzzy Triangular Episodes
    Development of Markovian Models
    Wavelet-Domain Hidden Markov Models

    PROCESS FAULT DIAGNOSIS
    Fault Diagnosis Using Triangular Episodes and HMMs
    Fault Diagnosis Using Wavelet-Domain HMMs
    Fault Diagnosis Using HMMs
    Fault Diagnosis Using Contribution Plots
    Fault Diagnosis with Statistical Methods
    Fault Diagnosis Using SVM
    Fault Diagnosis with Robust Techniques

    SENSOR FAILURE DETECTION AND DIAGNOSIS
    Sensor FDD Using PLS and CVSS Models
    Real-Time Sensor FDD Using PCA-Based Techniques

    CONTROLLER PERFORMANCE MONITORING
    Single-Loop CPM
    Multivariable Controller Performance Monitoring
    CPM for MPC

    WEB AND SHEET PROCESSES
    Traditional Data Analysis
    Orthogonal Decomposition of Profile Data
    Controller Performance

    Bibliography
    Index

    *Each Chapter Contains a Summary Section

    Biography

    Ahmet Palazoglu, Ali Cinar, Ferhan Kayihan

    "Most texts that attempt to combine SPC or SPM (statistical process monitoring) with automated control methods fail to incorporate multivariate methods as well. This text does an excellent job of covering all the bases in that regard . . . I highly recommend this text for chemical engineers and statisticians interested in learning how statistical methods can be integrated with process control methods."

    – Dean V. Neubauer, Corning Inc., in Technometrics, February 2008, Vol. 50, No. 1