Development of Soft Sensor Model Using Moving Window Approach

• Main Author: Rajan, Lavaniya
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi PETRONAS 2012
• Subjects: TP Chemical technology
• Online Access: http://utpedia.utp.edu.my/9664/1/2012%20-%20Development%20of%20Soft%20Sensor%20Model%20using%20Moving%20Window%20Approach.pdf; http://utpedia.utp.edu.my/9664/

Soft sensors are used broadly in the industries to predict the process variables which are not measurable by sensors. The objective of this project is to develop a datadriven soft sensor using Moving Window approach with the selective regression techniques and to evaluate and validate the advantages and performances of Moving Window approach over the traditional soft sensor models. Time invariant and stationary process conditions are those assumptions made in developing soft sensors, and these assumptions causes degradations and limitations to the soft sensors in estimating process variables. Degradations of soft sensors are caused by process shift, catalyst performance lost and et cetera. Besides that, the restrictions of sensors in estimating difficult-to-measure variables and the delays during the laboratory tests have becomeone of the factors in developing soft sensor. This paper presents a study regarding the multivariate statistical process control techniques that can be used in developing soft sensors such as Least Square Regression method, Partial Least Square Regression method and Principle Component Analysis. The scope of study for the project includes understanding the concept andwhat are the adaptive schemes available to construct the soft sensors. Besides that further research on Moving Window approach together with MSPC techniques will be carried out which can be adapted into the adaptive models to develop the soft sensors. Systematic approach will be presented through this project in using Moving Window approach to construct the soft sensors and this includes an analysis of an appropriate case study where the approach can be implemented. Keywords: Multivariate Statistical Process Control techniques, Least Square Regression method, Partial Least Square Regression method and Principle Component Analysis