Cinar / Palazoglu / Kayihan Chemical Process Performance Evaluation

Erscheinungsjahr 2010
ISBN: 978-1-4200-2010-6
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 344 Seiten

Reihe: Chemical Industries

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.

Zielgruppe

Graduate students, advanced undergraduates, and industrial practitioners in process monitoring.

Autoren/Hrsg.

Cinar, Ali

Palazoglu, Ahmet

Kayihan, Ferhan

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen

Inhaltsverzeichnis

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