Influence of Spacer Design and Module Geometry on the Filtration Performance during Skim Milk Microfiltration with Flat Sheet and Spiral-Wound Membranes

Increasing Performance of Spiral-Wound Modules (SWMs) by Improving Stability against Axial Pressure Drop and Utilising Pulsed Flow

Spacer-induced flow shadows and limited mechanical stability due to module construction and geometry are the main obstacles to improving the filtration performance and cleanability of microfiltration spiral-wound membranes (SWMs), applied to milk protein fractionation in this study. The goal of this study was first to improve filtration performance and cleanability by utilising pulsed flow in a modified pilot-scale filtration plant. The second goal was to enhance membrane stability against module deformation by flow-induced friction in the axial direction ("membrane telescoping"). This was accomplished by stabilising membrane layers, including spacers, at the membrane inlet by glue connections. Pulsed flow characteristics similar to those reported in previous lab-scale studies could be achieved by establishing an on/off bypass around the membrane module, thus enabling a high-frequency flow variation. Pulsed flow significantly increased filtration performance (target protein mass flow into the permeate increased by 26%) and cleaning success (protein removal increased by 28%). Furthermore, adding feed-side glue connections increased the mechanical membrane stability in terms of allowed volume throughput by ≥100% compared to unmodified modules, thus allowing operation with higher axial pressure drops, flow velocities and pulsation amplitudes.

Dynamic Modeling Using Artificial Neural Network of Bacillus Velezensis Broth Cross-Flow Microfiltration Enhanced by Air-Sparging and Turbulence Promoter

Cross-flow microfiltration is a broadly accepted technique for separation of microbial biomass after the cultivation process. However, membrane fouling emerges as the main problem affecting permeate flux decline and separation process efficiency. Hydrodynamic methods, such as turbulence promoters and air sparging, were tested to improve permeate flux during microfiltration. In this study, a non-recurrent feed-forward artificial neural network (ANN) with one hidden layer was examined as a tool for microfiltration modeling using Bacillus velezensis cultivation broth as the feed mixture, while the Kenics static mixer and two-phase flow, as well as their combination, were used to improve permeate flux in microfiltration experiments. The results of this study have confirmed successful application of the ANN model for prediction of permeate flux during microfiltration of Bacillus velezensis cultivation broth with a coefficient of determination of 99.23% and absolute relative error less than 20% for over 95% of the predicted data. The optimal ANN topology was 5-13-1, trained by the Levenberg-Marquardt training algorithm and with hyperbolic sigmoid transfer function between the input and the hidden layer.