Monitoring, diagnostics and improvement of process performance

Abstract

The data generated in a chemical industry is a reflection of the process. With the modern computer control systems and data logging facilities, there is an increasing ability to collect large amounts of data. As there are many underlying aspects of the process in that data, with its proper utilization, it is possible to obtain useful information for process monitoring and fault diagnosis in addition to many other decision making activities. The purpose of this research is to utilize the data driven multivariate techniques of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for the estimation of process parameters. This research also includes analysis and comparison of these techniques for fault detection and diagnosis along with introduction, explanation and results from a new methodology developed in this research work namely Hybrid Independent Component Analysis (HICA).The first part of this research is the utilization of models of PCA and ICA for estimation of process parameters. The individual techniques of PCA and ICA are applied separately to the original data set of a waste water treatment plant (WWTP) and the process parameters for the unknown conditions of the process are calculated. For each of the techniques (PCA and ICA), the validation of the calculated parameters is carried out by construction of Decision Trees on WWTP dataset using inductive data mining and See 5.0. Both individual techniques were able to estimate all parameters successfully. The minor limitation in the validation of all results may be due to the strict application of these techniques to Gaussian and non-Gaussian data sets respectively. Using statistical analysis it was shown that the data set used in this work exhibits Gaussian and non-Gaussian behaviour.In the second part of this work multivariate techniques of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used for fault detection and diagnosis of a process along with introduction of the new technique, Hybrid Independent Component Analysis (HICA). The techniques are applied to two case studies, the waste water treatment plant (WWTP) and an Air pollution data set. As reported in literature, PCA and ICA proved to be useful tools for process monitoring on both data set, but a comparison of PCA and ICA along with the newly developed technique (HICA) illustrated the superiority of HICA over PCA and ICA. It is evident from the fact that PCA detected 74% and 67% of the faults in the WWTP data and Air pollution data set respectively. ICA successfully detected 61.3% and 62% of the faults from these datasets. Finally HICA showed improved results by the detection of 90% and 81% of the faults in both case studies. This showed that the new developed algorithm is more effective than the individual techniques of PCA and ICA. For fault diagnosis using PCA, ICA and HICA, contribution plots are constructed leading to the identification of responsible variable/s for a particular fault. This part also includes the work done for the estimation of process parameters using HICA technique as was done with PCA and ICA in the first part of the research. As expected HICA technique was more successful in estimation of parameters than PCA and ICA in line with its working for process monitoring.