The results of the CFD analysis were used in the preliminary optimisation of the static mixer’s geometry since it has significant effect on the energy requirement of this advanced membrane technology. The self-developed static mixers were tested “in vitro” from the aspect of separation quality and process productivity as well to validate CFD results and to develop a cost effective, green method to recover unmanageable oily wastewaters for sustainable development. In this work the results of computational simulation of the fluid velocity and membrane separation experiments are discussed.

Oil-in-water emulsion is common by-product in chemical-, food-, mechanical- and other type of industries. Nowadays these waste waters cannot be discharged directly into the natural environment to avoid creating a significant ecological problem. With treatment of such waste oil emulsions, fluid can be separated to its components: oil and water. This separation can be done by evaporation too, but this method is very energy-consuming. Furthermore, in case of stable nano- or micro-emulsions the traditional water cleaning technologies are not always enough efficient to ensure the limited oil values in the released water ^[1]. With ultrafiltration, concentration of the oil in permeate can be reduced below 50 mg L-1 (limiting value for discharge to the public sewer in Hungary ^[2]). After filtration, the concentrated wastewater may contain less than 1/5 of original volume, so final evaporation might need approximately one-fifth of the original energy requirement to separate the water from the oil.

Using Kenics® static mixer inside a tubular membrane during ultrafiltration of an oil-in-water emulsion has a positive effect to permeate flux, retention of the oil and fouling delay ^[3]. Originally the aim of Kenics® type turbulence promoter’s geometry was the cost effective mixing two or more fluids. For this reason this kind of mixer causes big pressure drop along the membrane; due to this phenomena Kenics® mixer can be used only at lower recirculation flow rates for membrane separation to ensure cost-effectiveness ^[4]. The main aim of this work was to find new turbulence promoter geometry which will result in similar flux and retention improvement, raising tangential velocity, but keep pressure drop along membrane as low as possible.

In the first step, the new shapes were tested with “in silico” simulation experiments. Computational fluid dynamics (CFD) is a state-of-the-art numerical technique for solving fluid problems ^[5]. CFD calculations use a computational grid to solve the governing equations describing fluid flow, e.g., the continuity equation and the set of Navier-Stokes equations, and any additional conservation equations, such as energy balance, across each grid cell by means of an iterative procedure in order to predict and visualize the profiles of velocity, pressure, temperature, etc. Early users of CFD are found in the automotive, aerospace and nuclear industries. With the enhancement of computing power and efficiency and the availability of affordable CFD packages applications of CFD have extended into the food industry for modeling industrial processes, thereby generating comprehensive analyses leading to designing more efficient systems ^[6].

Lattice Boltzmann methods which were used for CFD analysis in this work are numerical techniques for the simulation of fluid flows ^[7]. Their strength lies however in the ability to easily represent complex physical phenomena, ranging from multiphase flows to chemical interactions between the fluid and the borders. Indeed, the methods find their origin in a molecular description of a fluid and can directly incorporate physical terms stemming from knowledge of the interaction between molecules. The methods are often regarded as particular discrete representations of the Boltzmann equation.

The Boltzmann equation is analogue of the Navier–Stokes equation at a molecular level, where it describes the evolution of the probability distribution function for a molecule to be present at a given point in the space of positions and velocities, the 6-dimensional phase space. The amount of physical phenomena contained in the model at this molecular level of description is larger than at the hydrodynamic level of the Navier–Stokes equation. This is because the Boltzmann equation is not subject to a separation of time scales and has the ability to describe fluids in non-hydrodynamic regimes with large molecular mean free paths. Furthermore, the molecular model is able to capture transport phenomena such as friction, diffusion and temperature transport and derive the corresponding transport coefficients. Boltzmann equation acts on real-valued quantities, but it describes some dynamics in a discrete phase space which can be called lattice ^[8]. As result of this simulation, 5 new geometries have been found which should be interesting for real time experiments. After producing 5 new turbulence promoters their effect on initial flux and retention has been tested.

For computational fluid dynamics (CFD) open source software based on lattice Boltzmann algorithm was used ^[8]. Input for this method was textual file with 3D matrix in it where rectangular space around membrane was divided into 0.1 mm cubes. Values in this matrix can be 1 (solid material) or 0 (means in that particle is fluid) (Eq. (1)): ai,j,k, where i = 1,2,...,72, j = 1,2,...,72, k = 1,2,...,500, a = 0 or 1 (1) Initial average flow velocity was also given as input parameter. After calculation vorticity and velocity norms are calculated for each particle and the output of this computation is also a matrix in textual (*.vti) file. This file can be visually represented with Paraview open source scientific visualization software [9]. The laboratory experiments were carried out in cross-flow mode, using a conventional ultrafiltration set-up with tubular single-channel module containing a ceramic zirconia (ZrO2) membrane (Exekia, Pall, USA) (Fig. 1). The ceramic membrane had a nominal pore size of 50 nm, inner diameter of 6.8 mm and the effective membrane area of 50 cm2. Inside the tube, membrane static mixers were installed as shown in Fig. 2. Turbulence promoter used as origin for comparison was Kenics® static mixer (Omega, USA) made from polyacetate. Based on CFD simulation, for laboratory testing, 5 new static mixers were produced from stainless steel (Fig. 2). A stable oil-in-water emulsion was prepared from a commercial cutting lubricant oil additive (Unisol, Mol, Hungary). The oil concentration in the emulsion was 5 mass%. The feed was pumped from a tank to the membrane module and then recirculated (Fig. 1). The recirculated flow rate (RFR) and transmembrane pressure (TMP) were controlled by means of regulation valves. The recirculated flow rate was 100 L h-1 and transmembrane pressure was 2 bar.

This entry is adapted from: https://doi.org/10.2298/hemind140312001g