Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring.

This paper was accepted for publication in the journal Colloids and Surfaces A: Physicochemical and Engineering Aspects and the definitive published version is available at http://dx.doi.org/10.1016/j.colsurfa.2016.07.097© 2016 Elsevier B.V.A mathematical model of free drainage of foam built up by a power-law non-Newtonian liquid is developed. The theory predictions are compared with the experimental data on the drainage of foams formed using commercially available Aculyn™22 and Aculyn™33 polymeric solutions. The rheological parameters of the polymeric solutions were independently measured and used in the calculations. The deduced dimensionless equations were solved using finite element method with appropriate boundary conditions. The numerical simulations show that the decrease in the foam height and liquid content is very fast in the very beginning of the drainage; however, it reaches a steady state at longer time. The predicted values of the time evolution of the foam height and liquid content are in good agreement with the measured experimental data

Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x₅₀/x₁₀) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques.