Investigating droplet separation efficiency in wire-mesh mist eliminators in bubble column

AbstractEffects of design parameters on performance of wire-mesh mist eliminators were experimentally investigated in 15cm bubble column. The demisters performances were evaluated by droplet collection efficiency as a function of wide ranges of operating and design parameters. These parameters include: droplet size exiting the demister (250–380μm), specific surface area (236–868m2/m3), void fraction (97–98.3%), wire diameter (0.14–0.28mm), packing density (130–240kg/m3), and superficial gas velocity (0.109–0.118m/s. All demisters were 15cm in diameter with 10cm pad thickness, made from 316L stainless steel layered type demister pad wires. Experiments were carried out using air–water system at ambient temperature and atmospheric pressure. The experimental data on the droplet removal efficiency were obtained using Malvern Laser Droplet Sizer. The removal efficiency was found to increase with the increasing the demister specific surface area, packing density, and superficial gas velocity. In contrast, the removal efficiency was found to increase with decreasing the demister void fraction and wire diameter. The separation efficiency is correlated empirically as a function of the design parameters. A good agreement was obtained between the measured values and the correlation predictions with ±5% accuracy

Cocaine by-product detection with metal oxide semiconductor sensor arrays

A range of n-type and p-type metal oxide semiconductor gas sensors based on SnO2 and Cr2O3 materials have been modified with zeolites H-ZSM-5, Na-A and H–Y to create a gas sensor array able to successfully detect a cocaine by-product, methyl benzoate, which is commonly targeted by detection dogs. Exposure to vapours was carried out with eleven sensors. Upon data analysis, four of these that offered promising qualities for detection were subsequently selected to understand whether machine learning methods would enable successful and accurate classification of gases. The capability of discrimination of the four sensor array was assessed against nine different vapours of interest; methyl benzoate, ethane, ethanol, nitrogen dioxide, ammonia, acetone, propane, butane, and toluene. When using the polykernel function (C = 200) in the Weka software – and just five seconds into the gas injection – the model was 94.1% accurate in successfully classifying the data. Although further work is necessary to bring the sensors to a standard of detection that is competitive with that of dogs, these results are very encouraging because they show the potential of metal oxide semiconductor sensors to rapidly detect a cocaine by-product in an inexpensive way