In powder handling industries, screw feeders are routinely used to control the ﬂow of material from a hopper into the subsequent stage of a process. The accurate and consistent feeding of materials into reaction systems, mixers and other processing equipment is necessary to maintain optimal conditions, and to generate products of the required quality at the required rate.
However, predicting feed rates has historically relied on engineering estimation based on experience and the extrapolation of and pre-existing information on performance – either from pilot scale trials, or from data collected at earlier installations. This can lead to equipment that does not meet the target design requirements, and result in sub-standard operation or, in extreme cases, complete process failure.
In an attempt to more accurately predict the expected feed rate of powder in a given feeding process, one approach is to use statistical, multivariate models to evaluate the relationships between powder behaviour and process performance. However, many studies are hampered by the use of overly simplistic characteristics of the bulk powder, such as angle of repose, or ﬂow through an oriﬁce, which do not necessarily represent the conditions that the powder is subjected to in the feeder. A method of accurately quantifying a powder’s response to process-relevant conditions is required for the development of a robust statistical model.
The FT4 Powder Rheometer® is a universal powder tester that provides automated, reliable and comprehensive measurement of bulk material characteristics. This information can be correlated with process experience to improve processing efﬁciency and aid quality control. Specialising in the measurement of Dynamic Flow properties, the FT4 also incorporates a Shear Cell, and the ability to measure Bulk properties such as density, compressibility and permeability.
This study demonstrates how to generate a design and operating model based around robust measurements of rheological properties of powders and standard Multiple Linear Regression (MLR) analysis.