Aiming at problems appearing in model switching of multi-model predictive control

a multi-model switching method based on recognition of operating conditions was presented

and the variations of dynamic characteristics in the process reflected by measurable variables were adopted.Firstly

Gaussian mixture model(GMM)was used to classify historical data into several operating conditions. Then the load vector matrix and the predictive models were built based on the data of different operating conditions. Lastly

the predictive model was chosen according to squared prediction error(SPE)of the principal component analysis(PCA)model.This method was implemented in controlling the coil outlet temperature(COT)in an ethylene pyrolysis furnace.The simulation results showed that the stable and balanced control of multiple COTs were realized by the presented method

and the matched operating condition could be easily found by comparing SPE of different PCA models when the operating condition of the system varied.Meanwhile

the corresponding predictive model was selected

with which the problem of switching lag among predictive models when the dynamic characteristics of the system changed was resolved.