Abstract:

The petrochemical process is highly nonlinear and the observation data of the petrochemical process are non-continuous and have classified characteristics.A novel process modeling method, which combined adaptive resonance theory (ART) with support vector regression (SVR), was proposed.Firstly, ART was used to separate the input pattern space into several sub-spaces based on a modeling sample.Then, SVR was used to build up each sub-model for each sub-space.The results of simulation experiment and an application in dry point soft measurement of naphtha showed that ART-SVR could reduce the nonlinear degree of the sub-models and its fitting accuracy and prediction accuracy were both better than those of a single SVR model.