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A method for estimating effective coalescence rates during emulsification from oil transfer experiments
Andreas Håkansson1, Christian Trägårdh, Björn Bergenståhl
1Lund University, Department of Food Technology, Engineering and Nutrition, P.O. Box 124, SE-221 00 Lund, Sweden. andreas.hakansson@food.lth.se
Journal of Colloid and Interface Science
|February 29, 2012
Summary
This study introduces a new method to quantitatively estimate droplet coalescence rates using the Oil Transfer Technique (OTT). By comparing experimental data with simulations, it enables better understanding of emulsification parameters.
Area of Science:
- Colloid and Surface Science
- Emulsion Technology
- Chemical Engineering
Background:
- The Oil Transfer Technique (OTT) is widely used for studying droplet coalescence during emulsification.
- Current limitations of OTT include its qualitative nature, hindering quantitative analysis of coalescence rates.
- Understanding coalescence is crucial for optimizing emulsion stability and formulation.
Purpose of the Study:
- To develop a novel evaluation method for OTT experimental results.
- To enable quantitative estimation of coalescence rates from OTT data.
- To investigate the scaling of coalescence with emulsification parameters like pressure and concentration.
Main Methods:
- Comparison of experimental OTT data with simulated OTT experiments.
- Utilizing bivariate Population Balance Equation (PBE) models for simulations.
- Investigating the influence of coalescence and fragmentation kernels on simulations.
Main Results:
- The proposed method allows for quantitative estimation of coalescence rates from OTT data.
- Accurate determination of coalescence rate scaling is achievable when the scaling of residence time is known.
- The method was successfully applied to analyze data from two prior studies.
Conclusions:
- The new evaluation method enhances the utility of the Oil Transfer Technique for quantitative coalescence studies.
- This approach facilitates a deeper understanding of how emulsification parameters influence coalescence.
- The findings support the accurate prediction of emulsion behavior based on key process variables.
