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The flow and mixing behavior of two miscible liquids has been studied in an innovative static mixer by using CFD, with Reynolds numbers ranging from 20 to 160. The performance of the new mixer is compared with those of Kenics, SMX, and Komax static mixers. The pressure drop ratio (Z-factor), coefficient of variation (CoV), and extensional efficiency (α) features have been used to evaluate power consumption, distributive mixing, and dispersive mixing performances, respectively, in all mixers. The model is firstly validated based on experimental data measured for the pressure drop ratio and the coefficient of variation. CFD results are consistent with measured data and those obtained by available correlations in the literature. The new mixer shows a superior mixing performance compared to the other mixers.

The present study is concerned with the computational fluid dynamics (CFD) simulation of turbulent dispersion of immiscible liquids, namely, water–silicone oil and water–benzene through Kenics static mixers using the Eulerian–Eulerian and Eulerian–Lagrangian approaches of the ANSYS Fluent 16.0 software. To study the droplet size distribution (DSD), the Eulerian formulation incorporating a population balance model (PBM) was employed. For the Eulerian–Lagrangian approach, a discrete phase model (DPM) in conjunction with the Eulerian approach for continuous phase simulation was used to predict the residence time distribution (RTD) of droplets. In both approaches, a shear stress transport (SST) k − ωturbulence model was used. For validation purposes, the simulated results were compared with the experimental data and theoretical values for the Fanning friction factor, Sauter mean diameter and the mean residence time. The reliability of the computational model was further assessed by comparing the results with the available empirical correlations for Fanning friction factor and Sauter mean diameter. In addition, the influence of important geometrical and operational parameters, including the number of mixing elements and Weber number, was studied. It was found that the proposed models are capable of predicting the performance of the Kenics static mixer reasonably well.

The optimization of chemical syntheses based on superstructure modeling is a perfect way for achieving the optimal plant design. However, the combinatorial optimization problem arising from this method is very difficult to solve, particularly for the entire plant. Relevant literature has focused on the use of mathematical programming approaches. Some research has also been conducted based on meta‐heuristic algorithms. In this paper, two approaches are presented to optimize process synthesis superstructure. Firstly, mathematical formulation of a superstructure model is presented. Then, an ant colony algorithm is proposed for solving this nonlinear combinatorial problem. In order to ensure that all the constraints are satisfied, an adaptive, feasible bound for each variable is defined to limit the search space. Adaptation of these bounds is executed by the suggested bound updating rule. Finally, the capability of the proposed algorithm is compared with the conventional Branch and Bound method by a case study.

Capillary pressure curves, which have been employed for a long period of time by researchers interested in pore size distribution, are commonly obtained from experimental measurements. The dynamic capillary pressure that influences the flow is affected by many factors including the pore size characteristics and pore scale dynamics. Hence, it is important to investigate the variation of the estimated pore size distribution with capillary number. In this study, a glass type micromodel is considered as the porous media sample. A parametric probability density function is proposed to express the pore size distribution of the porous model, which is also measured using an image analysis technique. The capillary pressure saturation mathematical model is developed by integrating the pore size distribution function. Model parameters with a physical significance are estimated by fitting the model to the measured capillary pressure data at different capillary numbers. The results of capillary pressure obtained are well matched to the measured values. The results show that the trends of the extracted pore size distribution curves have similar trends, but they are not exactly the same. Therefore, the dynamic capillary pressure data alone are not sufficient for estimation of the pore size distribution. As a related development, the prediction of the capillary pressure curves based on measured pore size distributions is also presented. The proposed probability distribution function has the flexibility of representing a wide variety of pore size distributions.