The optimisation of ultrasonic cleaning procedures for dairy fouled ultrafiltration membranes

Ultrafiltration (UF) of whey is a major membrane based process in the dairy industry. However, commercialization of this application has been limited by membrane fouling, which has a detrimental influence on the permeation rate. There are a number of different chemical and physical cleaning methods currently used for cleaning a fouled membrane. It has been suggested that the cleaning frequency and the severity of such cleaning procedures control the membrane lifetime. The development of an optimal cleaning strategy should therefore have a direct implication on the process economics. Recently, the use of ultrasound has attracted considerable interest as an alternative approach to the conventional methods. In the present study, we have studied the ultrasonic cleaning of polysulfone ultrafiltration membranes fouled with dairy whey solutions. The effects of a number of cleaning process parameters have been examined in the presence of ultrasound and results compared with the conventional operation. Experiments were conducted using a small single sheet membrane unit that was immersed totally within an ultrasonic bath. Results show that ultrasonic cleaning improves the cleaning efficiency under all experimental conditions. The ultrasonic effect is more significant in the absence of surfactant, but is less influenced by temperature and transmembrane pressure. Our results suggest that the ultrasonic energy acts primarily by increasing the turbulence within the cleaning solution.     

Proton transfer between organic acids and bases at the acoustic bubble-aqueous solution interface

The multibubble sonoluminescence (MBSL) emission intensity from aqueous solutions containing simple aliphatic organic acids (RCOOH) and bases (RNH2) and mixtures of the two types of solutes has been examined as a function of pH. In solutions containing either an organic acid or base, under pH conditions where the solutes are predominately in their ionized form (i.e., RCOO- and RNH3+), the MBSL intensity is identical with that obtained in pure water. Alternatively, under pH conditions where the solutes are in their un-ionized form the MBSL intensity is suppressed. However, in solute mixtures of RCOO- and RNH3+ in the pH range of 7 to 9, the MBSL intensity was significantly suppressed relative to that from water. To explain the results of the mixed solute system it has been postulated that when the bubble/solution interface experiences the extreme temperature conditions that accompany bubble collapse, proton transfer occurs between acid-base ion-pair complexes, [RCOO-...RNH3+], adsorbed at the bubble/solution interface. The neutral forms of the solutes then evaporate into the bubble during its expansion phase and through a complex series of events, over a number of bubble oscillations, reduce the core temperature of the collapsing bubble and hence the SL intensity.     

Predicting thermal displacements in modular tool systems

In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated first from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and again get promising results. The thermally induced errors can be estimated with 1-2 microm accuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems.     

Probing molecular free energy landscapes by periodic loading

Single molecule pulling experiments provide information about interactions in biomolecules that cannot be obtained by any other method. However, the reconstruction of the molecule's free energy profile from the experimental data is still a challenge, in particular, for the unstable barrier regions. We propose a new method for obtaining the full profile by introducing a periodic ramp and using Jarzynski's relation for obtaining equilibrium quantities from nonequilibrium data. Our simulated experiments show that this method delivers significant more accurate data than previous methods, under the constraint of equal experimental effort.     

Use of an ionic liquid in a two-phase system to improve an alcohol dehydrogenase catalysed reduction

Due to favourable partition coefficients the highly enantioselective reduction of 2-octanone, catalysed by an alcohol dehydrogenase from Lactobacillus brevis, is faster in a biphasic system containing buffer and the ionic liquid [BMIM][(CF(3)SO(2))(2)N] compared to the reduction in a biphasic system containing buffer and methyl tert-butyl ether.