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Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR), a new powerful machine learning method based on a statistical learning theory (SLT) into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression - genetic algorithm (SVR-GA) for modeling and optimization of mono ethylene glycol (MEG) quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics etc.) is not required. Using SVR-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.

Soft sensor development and optimization of the commercial petrochemical plant integrating support vector regression and genetic algorithm