LAUSR

Abstract Fluidised bed granulation is a widely used wet granulation technique. The operation of a fluidised bed granulator (FBG) as well as the quality of the product is strongly influenced by multiple process variables and disturbances. Controlling this process is difficult due to long lag times between sample analysis. Inference sensors are therefore an effective control solution for this complex process. A continuous industrial FBG was used to develop multiple linear regression (MLR) models that included two-way interaction effects. Elementary artificial neural network (ANN) models were developed to qualitatively assess the MLR models. The influences of the fluidizing air, the spray liquid and the seed particle size on the product quality were investigated and modelled. The spray liquid was found to have the largest correlation with the quality variables. Both modelling techniques produced accurate models, however undertraining of some ANN models resulted in a larger deviation between the model and validation data.