Novel soft sensor modeling and process optimization technique for commercial petrochemical plant

Soft sensors have been widely used in industrial process control to improve the quality of product and assure safety in production in real-time basis. The core of a soft sensor is to construct a soft sensing model. This paper proposes a new soft sensing modeling method based on a recent advanced computational technique called support vector regression (SVR). The major advantage of the strategies is that soft sensor modeling can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Ultraviolet (UV) transmittance is one of the most important quality variables of monoethylene glycol (MEG) product that has impact on the polyester product quality. UV transmittance measures the presence of undesirable compounds in MEG that absorb light in the UV region of the spectrum and indirectly measures the impurity of MEG product. Off-line laboratory method for MEG UV measurement is common practice among the manufacturer, where a sample is withdrawn several times a day from the product stream and analyzed by time-consuming laboratory analysis. In the event of a process malfunction or operating under suboptimal condition, the plant continues to produce off-specification (off-spec) product until laboratory results become available. It results in enormous financial losses for a large-scale commercial plant. In this paper, a soft sensor was developed to predict the UV transmittance on real-time basis and an online hybrid SVR-differential evolution (DE) technique was used to optimize the process parameters so that UV is maximized. This paper describes a systematic approach for the development of inferential measurements of UV transmittance using SVR analysis. After predicting the UV accurately, model inputs are optimized using DEs to maximize the UV. The optimized solutions when verified in actual commercial plant resulted in a significant improvement in the MEG quality. (C) 2009 Curtin University of Technology and John Wiley & Sons, Ltd.