(714f) Predicting Feeder Performance Based on Material Flow Properties

Purpose: Gravimetric powder feeders have been recognized as an important unit operation for many powder-based processes, including food, detergents, pharmaceuticals, minerals, and many others. Accurate and consistent delivery of materials by well-designed feeders ensures overall process stability. Importantly, feeder performance is strongly dependent on powder flow properties. The purpose of this study is to develop a methodology that identifies a predictive correlation between material flow properties and feeder performance.

Method: The proposed methodology includes techniques to characterize material flow properties, methods to quantify feeder performance of a loss-in-weight feeder, and predictive multivariate analysis. Materials with varying flow properties were firstly characterized. The flow properties of each material was represented by 30 flow indices. Two approaches to correlate feeding performance and material flow properties were examined in the study: principal component analysis followed by similarity scoring (PCA-SS), and partial least squares regression (PLSR).

Results: Experimental results showed that selection of the optimal feeder screw to achieve optimal feeder performance is heavily dependent on material flow properties. Both approaches were validated by testing an additional material. The predicted feeder results were generally in good agreement with the experimental results. In addition, a strong linear relation was observed between the initial feed factor of each material and its scores for the first principal component.

Conclusion: The work presented here has shown an efficient approach to correlate material properties with process performance using multivariate analysis. This approach is especially powerful in the early phase of process and product development, when the amount of a material is limited.