Model sensitivity and uncertainty analysis using roadside air quality measurements

Most of the air quality modelling work has been so far oriented towards deterministic simulations of ambient pollutant concentrations. This traditional approach, which is based on the use of one selected model and one data set of discrete input values, does not reflect the uncertainties due to errors in model formulation and input data. Given the complexities of urban environments and the inherent limitations of mathematical modelling, it is unlikely that a single model based on routinely available meteorological and emission data will give satisfactory short-term predictions. In this study, different methods involving the use of more than one dispersion model, in association with different emission simulation methodologies and meteorological data sets, were explored for predicting best CO and benzene estimates, and related confidence bounds. The different approaches were tested using experimental data obtained during intensive monitoring campaigns in busy street canyons in Paris, France. Three relative simple dispersion models (STREET, OSPM and AEOLIUS) that are likely to be used for regulatory purposes were selected for this application. A sensitivity analysis was conducted in order to identify internal model parameters that might significantly affect results. Finally, a probabilistic methodology for assessing urban air quality was proposed.

Multicomponent gas-mixture measurements using an array of gas sensors and an artificial neural-network

An Electronic Nose is being jointly developed between the University of Greenwich and the Institute of Intelligent Machines to detect the gases given off from an oil filled transformer when it begins to break down. The gas sensors being used are very simple, consisting of a layer of Tin Oxide (SnO2) which is heated to approximately 640 K and the conductivity varies with the gas concentrations. Some of the shortcomings introduced by the commercial gas sensors available are being overcome by the use of an integrated array of gas sensors and the use of artificial neural networks which can be 'taught' to recognize when the gas contains several components. At present simulated results have achieved up to a 94% success rate of recognizing two component gases and future work will investigate alternative neural network configurations to maintain this success rate with practical measurements.

Effect of machine dependence on wall failure property measurements, Jenike versus an annular shear tester

Annular, ring or torsional shear testers are commonly used in bulk solids handling research for the purpose of powder characterisation or equipment design. This paper reports from a DEFRA sponsored project which aims to develop an industrial powder flow-ability tester, (based on the annular shear tester) that is economic to buy and quick and easy to use in trained but unskilled hands. This paper compares the wall failure loci measured with an annular shear cell with measurements obtained using the accepted standard wall friction tester, the Jenike shear cell. These wall failure loci have been measured for several bulk solids which range from fine cohesive powders to free-flowing granular materials, on a stainless steel 304 2B wall surface.

Solid phase velocity measurements utilising electrostatic sensors and cross correlation signal processing

Gas-solids two phase systems are widely employed within process plant in the form of pneumatic conveyors, dust extraction systems and solid fuel injection systems. The measurement of solids phase velocity therefore has wide potential application in flow monitoring and, in conjunction with density measurement instrumentation, solids mass flow rate measurement. Historically, a number of authors have detailed possible measurement techniques, and some have published limited test results. It is, however, apparent that none of these technologies have found wide application in industry. Solids phase velocity measurements were undertaken using real time cross correlation of signals from two electrostatic sensors spaced axially along a pipeline conveying pulverised coal (PF). Details of the measurement equipment, the pilot scale test rig and the test results are presented.

Laser Doppler velocimetry measurements of particle velocity profiles in gas-solid two-phase flows

The measurement of particle velocities in two-phase gas-solid systems has a wide application in flow monitoring in process plant, where two-phase gas-solids systems are frequently employed in the form of pneumatic conveyors and solid fuel injection systems. Such measurements have proved to be difficult to make reliably in industrial environments. This paper details particle velocity measurements made in a two phase gas-solid now utilising a laser Doppler velocimetry system. Tests were carried out using both wheat flour and pulverised coal as the solids phase, with air being used as the gaseous phase throughout. A pipeline of circular section, having a diameter of 53 mm was used for the test work, with air velocities ranging from 25 to 45 m/s and suspension densities ranging from 0.001 kg to 1 kg of solids per cubic meter of air. Details of both the test equipment used, and the results of the measurements are presented.