No abstract

The requirement to monitor and control industrial processes has increased over recent years, therefore innovative techniques are required to meet the demand for alternative methods of particulate measurement. Resonant mass sensors are now strong candidates for accurate mass measurement and are frequently used in many diverse fields of science and engineering. This paper presents the design, modelling, and optimal geometry selection for sensitivity improvement of a U-shaped glass tube as a resonant mass sensing cantilever with a view to becoming a component of particulate measurement equipment. Finite Element Analysis (FEA) was used to develop the system which was validated experimentally using a physical model. This paper focuses on both the proof of concept and the geometry selection of the sensor using analysis of the system sensitivity for best selection. Modal and harmonic analysis were undertaken across a range of commercially available glass tube sizes from 6 mm to 10 mm diameter, to determine the optimal geometry selection, validated with practical experimental data. Results show a consistent difference of 3–5% between the simulation and experimental results, showing strong correlation. This research provides a methodology on the development of using a U-shaped glass tube for accurate mass measurement with a view to exploring the design as a component of particulate emissions equipment. The experimental and simulation results confirm that the highest sensitivity is achieved when the geometry dimensions, and therefore the vacant mass of the tube, is reduced. The 6 mm diameter tube with the smallest bend radius was the most suitable design to meet the design criteria. The calibration curve was plotted to allow an unknown mass to be calculated, which gave an R2 value of 0.9984. All experimental work was repeated three times with results giving an average of 0.44% between the minimum and maximum showing strong linearity and suggesting the potential for implementation of the methodology in its intended application. The design provides possible solutions to some of the issues currently seen with particulate measurement from stationary sources.

The requirement to monitor and control particulate emissions from industrial processes using continuous emission monitoring systems (CEMS) has significantly increased over recent years. Under current legislation, CEMS equipment requires calibration against the standard reference method (SRM) using isokinetic sampling and gravimetric analysis under controlled conditions as detailed through BS EN 13284-1 “Stationary source emissions–Determination of low range mass concentration of dust. Manual gravimetric method”. This process includes pumping a known volume of gas through a filter, which is weighed before and after sampling, and the total mass of dust per m3 can then be calculated to output results in mg/m3. As tougher legislation is introduced and stringent emission limit values (ELVs) are imposed on emissions processes in the United Kingdom (UK), the calibration of CEMS is increasingly more difficult due to the reliability of the SRM at low concentrations. The accuracy of results from the SRM and therefore CEMS equipment must be questioned when the uncertainty of measurement is higher than process ELVs. This research analyses data taken from an industrial survey and 21 UK processes where the standard reference method, in accordance with the procedure in BS EN 13284-1 has been used for particulate measurement. Investigating the reliability of isokinetic sampling when used as a method to extract a representative sample from a stack process when used in conjunction with innovative, alternative methods of sample analysis. In processes with particulate emissions <5 mg/m3, 80.7% of the total sample was collected in the rinse, and for processes >5 mg/m3, 56.4% of the sample was collected in the rinse. The data does not suggest any correlation between any of the measured parameters and the percentage of particulate in the rinse, including the stack velocity, isokinetic percentage, sample volume, and total mass concentration.