Mengke Lu

This study evaluates a simulation model for drying hybrid rice seeds in deep-bed set-up. This model is based on a heterogeneous diffusion model for predicting grain moisture at different levels on a deep-bed drying set-up was developed. To evaluate this, hybrid rice seeds were dried in a laboratory dryer with a 50-cm depth test cell. Three drying setups (45°C, 55°C, and stepwise) were used. During drying, moisture content was measured at different levels of the cell and at specified time intervals. Around four months after storage, germination percentage of samples from different drying setting and grain locations were obtained. The drying experiment revealed that the fastest drying time is achieved with the 55°C drying temperature. Parameter estimation and model validation of the simulation have revealed that the model used can well predict the moisture content of the seeds across the deep-cell drying bin at any given time. This can be further supported by the mean relative error of the model which is below its acceptable values. Germination results have also shown that the drying procedures used have no negative effect on the seed quality. Thus, using the simulation model could be an accurate way to predict the moisture content with no effect on its seed quality.

The aim of this work is to evaluate thermal behaviors and develop a soft sensor for online prediction of LOD (loss-on-drying) in the segmented fluidized bed dryer (Seg-FBD) in the ConsiGma25 line, which is regarded as the intermediate critical quality attribute for the final drug product. Preheating and drying experiments are performed and heat transfers and conductions among the Seg-FBD are evaluated based on the temperature measurements from sensors and an infrared thermal camera. A temperature distribution in dryer cells and high heat conductions in walls are found. Considerable heat transfers between the neighboring dryer cells are determined, which equal approximately 7% of the energy provided from the heated air. The cell-to-cell heat transfers are implemented into the heat transfer and drying models of the Seg-FBD. The models are calibrated successively in gPROMS Formulated Products (gFP) and the temperature and LOD errors are less than 2 °C and 0.5 wt.%, respectively. Subsequently, a soft sensor is established by combining data sources, a real-time data communication method, and the developed drying model, and it shows the capability of predicting real-time LOD, where the error of end-point LOD is within 0.5 wt.%. The work provides detailed steps and applicable tools for developing a soft sensor, and the online deployment of the soft sensor could support continuous production in the Seg-FBD by enabling visualization of process status and determination of process end point.