Data-Based Modeling: Application in Process Identification, Monitoring and Fault Detection

Abstract

Present thesis explores the application of different data based modeling techniques in identification, product quality monitoring and fault detection of a process. Biodegradation of an organic pollutant phenol has been considered for the identification and fault detection purpose. A wine data set has been used for demonstrating the application of data based models in product quality monitoring. A comprehensive discussion was done on theoretical and mathematical background of different data based models, multivariate statistical models and statistical models used in the present thesis.The identification of phenol biodegradation was done by using Artificial Neural Networks (namely Multi Layer Percetprons) and Auto Regression models with eXogenious inputs (ARX)
considering the draw backs and complications associated with the first principle model. Both the models have shown a good efficiency in identifying the dynamics of the phenol biodegradation process. ANN has proved its worth over ARX models when trained with sufficient data with an efficiency of almost 99.99%. A Partial Least Squares (PLS) based model has been developed which can predict the process outcome at any level of the process variables (within the range
considered for the development of the model) at steady state. Three continuous process variables namely temperature, pH and RPM were monitored using statistical process monitoring. Both univariate and multivariate statistical process monitoring techniques were used for the fault detection purpose. X-bar charts along with Range charts were used for univariate SPM and Principal Component Analysis (PCA) has been used for multivariate SPM. The advantage of multivariate statistical process monitoring over univariate statistical process monitoring has been
demonstrated.