Monitoring of a Hot Melt Coating Process via a Novel Multipoint Near-Infrared Spectrometer.

The aim of the present work was to develop a PAT strategy for the supervision of hot melt coating processes. Optical fibers were placed at various positions in the process chamber of a fluid bed device. Experiments were performed to determine the most suitable position for in-line process monitoring, taking into account such requirements as a good signal to noise ratio, the mitigation of dead zones, the ability to monitor the product over the entire process, and reproducibility. The experimental evidence suggested that the position at medium fluid bed height, looking towards the center, i.e., normal to particle movement, proved to be the most reliable position. In this study, the advantages of multipoint monitoring are shown, and an in-line-implementation was created. This enabled the real-time supervision of the process, including the fast detection of inhomogeneities and disturbances in the process chamber, and the compensation of sensor malfunction. In addition, a model for estimating the particle size distribution via NIR was successfully created. This ensures that the quality of the product and the endpoint of the coating process can be determined correctly.

Validation of fluid bed granulation utilizing artificial neural network.

Three innovative components (an annular gap spray system, a booster bottom and an outlet filter) have been developed by Innojet Technologies to improve fluid bed technology and to reduce the common interference factors (clogging of nozzles and outlet filters, spray loss, spray drying and fluidized bed heterogeneity). In a fluid bed granulator, three conventional components have been replaced with these innovative components. Validation of the modified fluid bed granulator has been conducted using a generalized regression neural network (GRNN). Under different operating conditions (by variation of inlet air temperature, liquid-binder spray rate, atomizing air pressure, air velocity, amount and concentration of binder solution and batch size), sucrose was granulated and the properties of size, size distribution, flow rate, repose angle and bulk and tapped volumes of granules were measured. To confirm the method's validity, the trained network has been used to predict new granulation parameters as well as granule properties. These forecasts were then compared with the corresponding experimental results. Good correlation has been obtained between the predicted and the experimental data. From these findings, we conclude that the GRNN may serve as a reliable method to validate the modified fluid bed apparatus.