Support Vector Regression for soft sensor design of nonlinear processes

The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input-output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. In this work we demonstrate the application of SVR as an efficient and easy-to-use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady state Melt Index soft sensor for an industrial scale Ethylene Vinyl Acetate (EVA) polymer extrusion process using SVR. The SVR based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least squares based soft sensor in terms of lower prediction errors. Through a second case study, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for a laboratory scale twin screw polymer extrusion process.