Subspace identification based inferential control applied to a continuous pulp digester

The idea of constructing a data-driven stochastic system model through subspace identi®cation for the purpose of inferential control is investigated. Various available methods for designing an inferential controller are discussed and their limitations are brought out, particularly in applications involving multi-variable processes. Practical issues that arise in identifying a system model geared toward inferential control using a subspace method are discussed. They include: handling of nonstationary disturbances, handling of multi-rate measurements/missing data, and secondary measurement selection. With the identi®ed stochastic system model, a multi-rate Kalman ®lter can be designed and coupled with a model predictive controller. The method is applied to a continuous pulp digester, which is a complex distributed parameter system involving heterogeneous reactions. The application study indicates much potential for the data-based approach.