A New Catalytic Stripper for Removal of Volatile Particles
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1 A New Catalytic Stripper for Removal of Volatile Particles David Kittelson University of Minnesota Martin Stenitzer Technische Universität, Wien 7th ETH Conference on Combustion Generated Particles Zurich, 18th - 20th August 2003 We gratefully acknowledge the support of Johnson-Matthey in developing and providing the catalyst section
2 Typical Composition and Structure of Diesel Particulate Matter Solid particles are typically carbonaceous chain agglomerates and ash and usually comprise most of the particle mass Volatile or semi-volatile matter (sulfur compounds and organics (SOF)) typically constitutes 30% (5-90%) of the particle mass, 90% (30-99%) of the particle number Carbon and sulfur compounds derive mainly from fuel SOF and ash derive mainly from oil Most of the volatile and semi-volatile materials undergo gas-to-particle conversion as exhaust cools and dilutes The smallest particles (nuclei mode) are usually volatile but may be solid in some cases We need to distinguish between solid and volatile particles across the size range Sulfuric Acid Particles 0.3 µm 10% 20% 5% Solid Carbonaceous / Ash Particles with Adsorbed Hydrocarbon / Sulfate Layer Hydrocarbon / Sulfate Particles 5% Carbon 60% Ash Sulfate and Water Lube Oil SOF Fuel SOF
3 Formation of a volatile nuclei mode downstream of a catalyzed Diesel particulate filter (DPF) dn/dlogdp, part/cm 3 dn/dlogdp, part/cm 3 1.0E E E E E E E E E E E E ppm S fuel Corrected for DR Not corrected for particle losses 1 ppm S fuel Dp (nm) No CDPF, No TD CDPF No TD CDPF With TD No CDPF, No TD CDPF No TD CDPF With TD The results shown are for tests of a catalyzed DPF on a modern Cummins engine DPF very efficient for solid particles May form a very large nuclei mode downstream of DPF at high load conditions Size of mode increases with sulfur content of fuel, but still observed with near zero sulfur fuel Sulfuric acid likely major component 1.0E+05 Corrected for DR Not corrected for particle losses 1.0E Dp (nm)
4 A volatile nuclei mode is all that survives downstream of a catalyzed (DPF) ISO 6 ISO 6 with CDPF ISO 6 with CDPF and TD dv/dlogdp (µm 3 /cm 3 ) This volume weighted distribution is roughly proportional to particle mass ppm S fuel, ultralow S lube oil Diameter (nm)
5 Thermal Denuder Measurements Show the Nuclei Mode is Usually Volatile but Reveal Nonvolatile Core at Light Load U of M Caterpillar C12, EPA Fuel, Idle U of M Caterpillar C12, EPA Fuel 1530 RPM, 704 N-m (Highway Cruise) 1.0E+10 Non-Volatile Nuclei Mode Residue 1.0E+10 Nuclei Mode Nearly All Volatile, A More Typical Situation 1.0E E+09 Increasing Thermal Denuder Temperature from Ambient to 300 C Increasing Thermal Denuder Temperature from Ambient to 300 C dn/dlog Dp (part./cm 3 ) 1.0E E+07 Nuclei Mode dn/dlog Dp (part./cm 3 ) 1.0E E+07 Nuclei Mode Accumulation Mode 1.0E E+06 Accumulation Mode 1.0E Dp (nm) 1.0E Dp (nm)
6 Means of identifying volatile and solid particles in near real time Some particle mass spectrometers Hot primary dilution Thermal denuder a heating section followed by an activated charcoal bed Needs to be characterized for losses Recondensation of volatiles on accumulation mode a problem Charcoal must be replaced frequently Catalytic stripper catalytic oxidation of hydrocarbons, trapping of sulfates, followed by a cooling or dilution section Original design 1 did not have sulfur trap and was not characterized for nanoparticle loss Needs to be characterized for losses Sulfur trap maintenance (yearly of more?) 1- Abdul-Khalek, I.S. and D.B. Kittelson "Real Time Measurement of Volatile and Solid Exhaust Particles Using a Mini-Catalyst," SAE Paper No
7 Stripper layout The stripper consists of a 2 substrate catalyst followed by a cooling cool The first substrate removes sulfur compounds The second substrate is an oxidizing catalyst The catalysts were provided by Johnson-Matthey
8 Properties of the catalysts Catalyst Properties Oxicat: S-Trap: Length: 110 mm 110 mm Diameter: 32 mm 32 mm Channel Dimensions: 1,116 x 1,116 x 110 mm 1,037 x 1,037 x 110 mm Channel Density: 350 cpsi 400 cpsi Wall Thickness: 5,5 mil 6,0 mil Washcoat Loading: 1,223 g/cm 3 1,267 g/cm 3 Washcoat Density: 1,500 g/cm 3 1,630 g/cm 3
9 Physical layout of the stripper
10 Heat and mass transfer performance 600 Heat Transfer Oxicat 550 Temperature [K] T 1 ( L 1 ) T 3 ( L 3 ) T 10 ( L 10 ) l/min 3 l/min 10 l/min L 1, L 3, L 10 Length [m]
11 Stripper performance tests: experimental setup
12 Sulfate particle ((NH 4 ) 2 SO 4 ) removal, 10 lpm Penetration [%] AT - 10l/min Particle Diameter [nm] C - 10l/min 240 C - 10l/min Temperature [ C]
13 Engine oil particle removal, 10 lpm Penetration [%] AT - 10l/min 80 C - 10l/min 160 C - 10l/min 240 C - 10l/min Temperature [ C] Particle Diameter [nm] C - 10l/min
14 Solid particle penetration (NaCl) at 10 lpm Penetration [%] AT 200 C C Temperature [ C] Particle Diameter [nm] C
15 Solid particle penetration at 10 lpm, 300 C Penetration [%] C 300 C - 10l/min - KK 300 C - 10l/min - KJ 300 C - 10l/min - JK 300 C - 10l/min - JJ 300 C - 10l/min - HK 300 C - 10l/min - HJ Particle Diameter [nm]
16 Solid particle penetration at 3 lpm, 300 C Penetration [%] C 300 C - 3l/min - KK 300 C - 3l/min - KJ 300 C - 3l/min - JK 300 C - 3l/min - JJ 300 C - 3l/min - HK 300 C - 3l/min - HJ Particle Diameter [nm]
17 Performance with a solid nuclei mode formed from lube oil metal compounds (Ca, Zn) E E+09 dn/dlogd p [#/cm 3 ] E E E E D p [nm] with ou t S tripp er with Stripper with St rip pe r-co rre cted fo r Lo sses
18 Conclusions Near 100% removal of volatile particles across size range of interest demonstrated With loss correction, correct measurement of of solid particles across size range of interest demonstrated Diffusion and thermophoretic losses of solid particles fully characterized Thermophoretic losses are particle size and flowrate independent, predictable and modest (~20% at design temperature) Diffusion losses mainly effect nuclei mode particles» Losses are strongly size and flowrate dependent» Losses of 10 nm particles about 60% at 10 lpm, 90% at 3 lpm» Losses are predictable and further optimization possible
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