A Survey of Data Treatment Techniques for Soft Sensor Design

Abstract

Soft sensors have proved themselves as valuable alternatives to the traditional means for the acquisition of critical process variables, process monitoring and other tasks relating to process control. Most of the present day soft sensors for complex chemical processes are designed from actual industrial data because of the various difficulties associated with developing first principle models such as poor process understanding, impossible or difficult to determine model parameters and mathematical complexity of the models. This paper discusses characteristics of the process industry data which are critical for the design and development of data driven soft sensors. The focus of this paper is on the different shortcomings of the raw process data collected from the historical database and a review of different techniques available for processing of the raw data so as to make the data suitable for design of data driven soft sensors.