Iterative learning control of polyhydroxybutyrate production in a sequencing batch reactor

This study investigates the iterative learning control of a sequencing batch reactor for the production of polyhydroxybutyrate (PHB). In order to overcome the effect of model plant mismatches and unknown disturbances, a batch to batch iterative learning control strategy with incrementally updated models is developed. The reference batch is taken as the immediate previous batch in order to cope with nonlinearities and process variations. After each batch, the newly obtained process operation data is added to the historical process data base and an updated linearised model is re-identified. To cope with colinearity in the modeling data, principal component regression and partial least squares regression are used in identifying batch-wise linearised models. The proposed technique has been successfully applied to a sequencing batch reactor for the production of PHB.