Preparation and Characterization of Porous Composite Filter Medium by Polytetrafluoroethylene Foam Coating
Abstract The high costs of ceramic and Teflon filter media for hot gas cleaning has limited their industrial applications. This paper presents a foam coating technology that can be used to produce an inexpensive and highly efficient filter for industrial applications. A new apparatus was designed and built that coats porous glass mats with liquid-phase polytetrafluoroethylene (PTFE). The machine generates bubbles, enables the formation of uniform micropores less than 45 μm in diameter, and produces a product with air permeability greater than 5.5 cm3/cm2/sec. The resulting filter was found to be thermally stable up to 270 °C without any visible distortion and was comparable in dust collection efficiency to other commercial filter media. In addition, its de-dusting efficiency was greater than 85%, which is similar to that of other test filter media.
Effect of formulation and processing factors on the properties of liquid food foams
Abstract The aim of this work was to determine the phenomena affecting bubble size, according to process conditions (pressure, flow rates, whipping rotation speed) and formulation properties. Model fluids were formulated in order to get simple and well-defined rheology. An instrumented foaming pilot-scale line was built and allowed us to monitor the process and to characterise bubble size under pressure, at the exit of the mixer. Viscosity and rotation speed of the whipping head are the most influent parameters on foam morphology: interaction between these factors have been highlighted. Experimental measurements obtained were consistent with the critical Weber number since a corrected fluid viscosity is used. It seems indeed that the fluid viscosity is reduced in the whipping head, due to the presence of local heating. These temperature variations consequent to the shear of viscous fluids in a narrow-gap geometry were quantified and modelled as a function of shear rate and fluid viscosity.
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.