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(162c) Feeder Characterization and Model Development Accounting for Incoming Material Properties

Escotet-Espinoza, M. S., Rutgers, The State University of New Jersey
Cathy Pereira, G., Rutgers, The State University of New Jersey
D. Román-Ospino, A., Rutgers, The State University of New Jersey
Muzzio, F., Rutgers, The State University of New Jersey
Ierapetritou, M., Rutgers, The State University of New Jersey
In previous years, research focus has been placed in characterizing feeder performance and tooling selection using a formulation based approach [1-3]. This approach consists on studying feeder performance for a specific ingredient (e.g., excipient, lubricant, or API) based on its percentage in a formulation and a desired process flow rate [4, 5]. Although efficient for the early stages of continuous manufacturing, this approach does not provide process developers with sufficient information to ensure process performance on instances where process throughput is changed and/or material properties change from the ones measured during characterization. The formulation based approach also does not cut down significantly the amount of work needed to design new processes for new formulations. To address these issues a novel approach based on the characterization of feeders for a broad range of materials and flow rates (i.e., independent of formulation requirements) was performed by our group. The goal of this work is to develop models and tools that predict flow rate variability and feeding capacity over a broad range of ingredient material properties, which serve as inputs to the model.

In this presentation, we describe a characterization procedure applied to a GEA compact feeder using a series of materials relevant to the pharmaceutical industry. Our experimental plan involved flow rate characterization and Residence Time Distribution (RTD) characterization. Feeder flow rate characterization is performed by measuring the accuracy and variability of material dispensing as a function of volumetric throughput and feeder hopper fill level. Twenty (20) different materials were run in a GEA compact feeder. The materials span a broad range of material properties, as shown by study of our internal material property database using Principal Component Analysis (PCA). Eight (8) feeder configurations based on the screw size and gear box configuration were studied. At each feeder configuration, flow rate experiments were carried out in both volumetric and gravimetric flow. The objective was to understand the effect of gear box, screw size, and material properties on the outgoing flow of the feeder (i.e., feed factor deviation, mean flow rate, and standard deviation) to develop a model capable of predicting the behavior. The flow rate model, the flow rate data analysis, the procedure for calibration, and the overall flow rate model will be introduced in this presentation.

The RTD of the feeder was characterized for a variety of materials. Feeder RTD is important for two main reasons: (1) traceability of raw materials and (2) characterization of the dynamic response time to changes in incoming material properties. In both cases, the goal is to understand how long it takes for a new material (e.g., lot added due to refill or lots with new material properties) introduced into the feeder to “wash out” the material that was already in the feeder. The RTD for the GEA feeder was characterized using four (4) different materials (i.e., tracer-base combinations) and two (2) different flow rates. The aim was to understand if material properties had an impact on the RTD profile (i.e., mixing behavior) inside of the feeder. The results from the RTD profile were used to model the mixing behavior inside of the feeder using a tanks-in-series model with side compartments. Regressed coefficients were used to compare the mixing behavior between the materials and then correlated to those materials’ properties.



1. Boukouvala, F., et al., An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process. Computers & Chemical Engineering, 2012. 42: p. 30-47.

2. Toebermann, J., .-C., Rosenkranz, J., Werther, J., Gruhn, G., Block-oriented process simulation of solids processes. Computers & Chemical Engineering, 2000. 23: p. 1773-1782.

3. Hartage, E.U., Pogodda, M., Reimers, C., Schwier, D., Gruhn, G., Werther J., Flowsheet simulation of solids processes. KONA, 2006. 24: p. 146-156.

4. Engisch, W.E. and F.J. Muzzio, Method for characterization of loss-in-weight feeder equipment. Powder Technology, 2012. 228: p. 395-403.

5. Yang, S. and J.R.G. Evans, Metering and dispensing of powder; the quest for new solid freeforming techniques. Powder Technology, 2007. 178: p. 56-72.