(371b) Optimal Control of a Fluid Bed Dryer Granulator Using Near Infrared Sensing with Advanced Process Control

In this work, the design of a model predictive control algorithm using Near Infrared (NIR) spectroscopy as moisture sensor for the optimization of a batch fluid bed dryer granulator (FBDG) is studied. For this purpose, activities were arranged in three simultaneous stages as follows: First, dynamic response of FBDG key variables is determined using step response or system identification algorithms in order to tune the local control loops to be managed by the model predictive controller. Secondly, a NIR calibration model is developed to predict lactose moisture content using loss on drying (LOD) and in-line NIR measurements of several test runs. Lastly, the integration of NIR sensor to the control loop is achieved by interfacing NIR real time measurements to the DCS using OLE for process control (OPC) communication. Preliminary results show good agreement of key process variables to models and fast response time of the drying air temperature controller to changes in input variables and disturbances. Activities are currently being implemented in each of the three stages explained above. The algorithm design will be undertaken following completion of the three stages.