Multivariate Model Identification and Stochastic Control of a Chemical Reactor

Multivariate time series and process identification methods are used to develop a dynamicstochastic model for a packed bed tubular reactor carrying out highly exothermic hydrogenolysis reactions. A canonical analysis procedure is used on the data collected from the reactor to first reduce the dimensionality of the identification and control problems. The identified transfer function-ARIMA model is transformed into a state space model form and used to develop a multivariable optimal stochastic controller for the reactor. The controlled variables are inferred production rates reconstructed from temperature and flow measurements. The parameters of the inferential equation are updated recursively using measurements of actual concentrations available periodically. The controller is implemented using a process minicomputer, and is shown to perform very well.