The use of a microfluidic device in determining the extraction kinetics of Co II ions by di-(2-ethylhexyl) phosphoric acid (DEHPA) was demonstrated. Experimental data obtained using a Y-Y-shaped microchannel were modelled using a finite volume method.The contributions of diffusion and reaction transport resistances to the overall rate of mass transfer were obtained. A diffusion-controlled transfer assumption could not account for the experimental data, confirming that transport occurs under a mixed reaction-diffusion resistance regime. The reaction rate constant was determined to be (2.4 ± 0.6) × 10 −10 m/s, in good agreement with corresponding Lewis cell measurements from the literature.
An investigation of molecular diffusion of solutes across water/oil interfaces in a Y-Y-shaped microchannel with an integrated guide structure is presented. Finite volume numerical simulations were compared with experimental literature data. Analytical approaches including an infinite composite medium model, phase-specific mass transfer coefficient models and a static transfer cell model were also assessed. An increase in accuracy for the mass transfer coefficient models was achieved by using local coefficients as opposed to lengthaveraged expressions. The static transfer cell model was shown to improve when based on the interfacial contact time, as opposed to the organic phase residence time. The results presented in this work have immediate application to the determination of kinetic rate constants in reactive mass transfer systems, as considered in Part II of this study (Ciceri D, Mason LR, Harvie DJE, Perera JM, Stevens GW (2012) Modelling of interfacial mass transfer in
Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.
We introduce a novel rule-based approach for handling regression problems. The new methodology carries elements from two frameworks: (i) it provides information about the uncertainty of the parameters of interest using Bayesian inference, and (ii) it allows the incorporation of expert knowledge through rule-based systems. The blending of those two different frameworks can be particularly beneficial for various domains (e.g. engineering), where, even though the significance of uncertainty quantification motivates a Bayesian approach, there is no simple way to incorporate researcher intuition into the model. We validate our models by applying them to synthetic applications: a simple linear regression problem and two more complex structures based on partial differential equations. Finally, we review the advantages of our methodology, which include the simplicity of the implementation, the uncertainty reduction due to the added information and, in some occasions, the derivation of better point predictions, and we address limitations, mainly from the computational complexity perspective, such as the difficulty in choosing an appropriate algorithm and the added computational burden.
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