Filter resistance and cleaning efficiency are the two critical parameters that define its performance. A prototype made of aluminum was constructed to validate the computer models. Spacers measuring 3.0, 2.0, and 3.0 mm were used to separate the successive flat filter surfaces.
A test set up as shown in Fig. 6 was constructed. The experimental set-up consisted of a system of ducts with a centrifugal fan attached to it. The fan was operated using a variable frequency drive (VFD). The duct measured 0.30 m × 0.45 m in cross-section and was 2.40 m in length to allow a well-developed flow profile. This was followed by a Dwyer pressure measurement station used to obtain total and static pressures in the duct. This also had honeycomb-like structures that served as the flow straighteners. Vane and rail arrangements were built around the corners to encourage a good airflow profile and to minimize the shock losses.
The filter was installed downstream of the bend in a duct with an identical cross-section area and length. A 1.20 m long duct was built with transparent polycarbonate sheets to install the filter at 45º to the airflow direction like a flooded-bed dust scrubber set up. All lines of contact were sealed to prevent any leakages. A full cone water spray was installed upstream of the filter to inject water normal to the filter plane as shown in Fig. 7. A flow control valve and an inline digital flowmeter were installed in series to control the water flow rate precisely.
Steady-state testing in the dry state
Tests were first run to determine the filter resistance. VFD frequency was set at 10.0 Hz and increased in steps of 5.0 Hz. Total and static pressure at different airflows were measured. Velocity pressure was calculated to determine the airflow through the filter. These steps were repeated three times to obtain a good representative average. A curve of best fit through these points was drawn as shown in Fig. 8 that represents the flow-pressure drop curve showing a quadratic dependence of pressure drop on volumetric flow rate for the filter. Filter resistance using this curve was obtained to be 1.675 kN s2/m8. An adjusted R2 value of 0.9998, a standard error value of 0.0034, and a t-value of 496.82 indicate an excellent fit.
Once the system curve was constructed, the duct discharge was connected to the dust exhausting system of the laboratory to minimize exposure of the researchers to coal dust particles while running cleaning efficiency experiments. Velocity in the duct was computed again with the water spray running using the pressure measurement station to determine the airflow through the filter.
Isokinetic sampling for aerosols
Two identical TSI optical particle sizers (OPS 3330) were used for airflow sampling. Air was sampled iso-kinetically upstream and downstream of the filter. This is a technique in which aerosol particles are extracted from the airstream without altering the airflow speed in the vicinity of the sampling nozzle (Wilcox 1956). If not done properly, this could lead to under-sampling or over-sampling of the aerosol particles leading to inaccurate results. Sampling nozzles were designed, and 3D printed for airflows of 0.47, 0.71, and 0.94 m3/s in the duct.
Keystone Mineral Black 325 A and limestone particles were used to study the cleaning efficiency of the dust filter. This coal dust specimen has a known particle size distribution and has been used to investigate scrubber performance earlier. An Arduino-controlled stepper motor-assisted dust injection system was built to inject dust upstream of the filter. TSI optical particle sizer (OPS) 3330 device was used to count and size the aerosol particles. Densities of coal (1220 kg/m3) and limestone dust (2200 kg/m3) were programmed in the OPS. The complex refractive index of coal and limestone were programmed to run experiments with suitable particles. The dead time correction parameter was enabled to minimize errors due to coincidence due to many particles in the sample. The presence of millions of dust particles were injected into the pressurized duct using compressed air. A sensitive digital weighing balance was used to precisely measure 3.2 gm of coal dust and 3.5 gm of limestone dust for each run of the tests. Dust particles were introduced into the duct for 5.0 min. The controlled injection of dust particles into the duct ensured that none of the OPSs was overwhelmed with dust particles. This effectively eliminated the coincidence error which would have resulted in an incorrect particle count and size. Iso-kinetic sampling nozzles were designed with dimensions computed using airflow velocity magnitudes, 3D printed, and attached to the tube. The duct was traversed precisely to locate points where the average airflow speed equaled the speed reported by the pressure measurement station.
Cleaning efficiency results
Particle concentration by count data obtained from the two OPSs installed upstream and downstream of the filter were converted into gravimetric concentrations using the aerosol instrument manager software. Figure 9 shows one such plot of the gravimetric concentration of coal dust particles upstream and downstream of the filter for an airflow of 0.94 m3/s. The difference in gravimetric concentrations is the cleaning efficiency under the operating conditions. These were computed for particle sizes 2.00 µm and above for the three airflows. Figure 10 shows the cleaning efficiency of the impingement filter for coal dust particles. The plot shows that the efficiency of the removal of dust particles from the airstream increases with an increase in airflow through the filter.
Bigger and heavier particles are removed significantly more efficiently compared to the smaller particles that tend to follow the airstreams and might escape the filter. Figure 11 shows the cleaning efficiency using limestone dust. Water flow was kept steady at 7.57 L/min (2.0 gpm) for these tests. The impingement type filter captured bigger particles more efficiently. The cleaning efficiency also improved with an increase in airflow through the filter.
Figure 12 shows that the particle removal efficiency from the airstream obtained from transient-state CFD models and laboratory experiments agree well. Figure 13 shows the cleaning efficiency of the filter for coal and limestone. The filter traps more limestone particles compared to coal under identical operating conditions. Limestone particles have a high density. This makes it difficult for them to follow the airstream and is more likely to impact the filter surface compared to the coal particles. This also affirms that particle impaction onto the filter surface is the primary particle capturing phenomenon.