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Wet granulation is a unit operation often necessary in solid particulate materials processing and employed in several industries such as food processing, pharmaceutical manufacturing, and detergent production. It involves the aggregation of smaller primary solid particles into larger particulates induced by some compatible liquid. Pharmaceutical industry, like many other chemical processing industries has begun to adopt continuous operations for the downstream manufacturing of oral solid dosage products. The implementation of continuous wet granulation comes with its challenges along with advantages. One example is the apparent discrepancy in the drug product or ���active pharmaceutical ingredient��� (API) content uniformity across granules size for low drug-load (~4\% target) formulations. Another issue industry practitioners face is determining the time taken for a product stream to reach an off-spec quality and the degree of discrepancy from the standard specifications in the event of a sudden unexpected change in the raw materials input stream. This dissertation study proposes a mathematical approach to looking at these diverse problems faced in continuous wet granulation. The approach looks at implementing a two-step solution to the problem. The first part includes building a predictive Residence Time Distribution (RTD) model that incorporates the effects of processing and equipment configuration parameters such as material flow rates, processing speed, length scales, and the shapes of the equipment. The proposed RTD model predicts intermediate factors, which predict the mean residence time (MRT) and the variance (Var) of the system at different input parameters and configurations. The second part implements these moments of the RTD into population balance equations (PBEs), which describe the flow and spatio-temporal evolution of the particles in the unit operation. Lastly, we will look at the applications of the proposed model to address issues in continuous wet granulation. |
