Claims:1. A machine learning algorithm (10) to predict composition and quantity of urea
10 crystals in an exhaust gas mixing section, the machine learning algorithm (10)
comprising the steps of:
inputting a plurality of parameters including exhaust mass flow rate (12),
upstream and downstream NOx emissions (14), selective catalytic reduction
NOx conversion efficiency (16), and exhaust temperature and dosing quantity
15 (18) into an algorithm; and
estimating (50) the composition and quantity of urea crystals deposited in a
mixing section based on discrete time intervals that the urea crystals in the
mixing section is subjected to high temperature ranges over finite intervals of
time.
2. The machine learning algorithm (10) to predict quantity and composition of
urea crystals in an exhaust gas mixing section in accordance with Claim 1
wherein inputting exhaust mass flow rate (12), upstream and downstream NOx
emissions (14), selective catalytic reduction NOx conversion efficiency (16),
25 and exhaust temperature and dosing quantity (18) into an algorithm further
comprises calculating (20) a theoretical stoichiometric dosing quantity of urea
into the mixing section by the algorithm from the input in combination with
the actual quantity of NOx that flows into the mixing section from an engine,
the theoretical stoichiometric dosing quantity of urea adapted to determine the
30 dosing quantity of urea to be dosed for creating no or minimal urea crystal
deposits on a wall of said mixing section.
3. The machine learning algorithm (10) to predict quantity 5 and composition of
urea crystals in an exhaust gas mixing section in accordance with Claim 2
further comprising determining (22) if the actual quantity of urea that is dosed
into the mixing section is greater than the theoretical stoichiometric quantity
of urea dosed into the mixing section.
10
4. The machine learning algorithm (10) to predict quantity and composition of
urea crystals in an exhaust gas mixing section in accordance with Claim 3
further comprising estimating (24) by means of a first machine learning
algorithm as to the quantity of ammonia that is currently loaded in the selective
15 catalytic reduction system by means of the temperature of exhaust gases, if the
actual quantity of urea that is dosed into the mixing section is greater than the
theoretical stoichiometric quantity of urea dosed into said mixing section.
5. The machine learning algorithm (10) to predict quantity and composition of
20 urea crystals in an exhaust gas mixing section in accordance with Claim 4
further comprising computing (26) by means of a second machine learning
algorithm as to the quantity of ammonia that is loaded or unloaded in the
selective catalytic reduction system based on the quantity of ammonia that
exists in the selective catalytic reduction system in the span of two consecutive
25 time intervals.
6. The machine learning algorithm (10) to predict quantity and composition of
urea crystals in an exhaust gas mixing section in accordance with Claim 5
further comprising calculating (28) the quantity of urea that is deposited in said
30 mixing section by subtracting the quantity of ammonia that is loaded in the
selective catalytic reduction system from the actual quantity of urea that is
dosed into said mixing section and multiplying the difference with (1 – NOx
conversion efficiency) if ammonia is loaded in the selective catalytic 5 reduction
system.
7. The machine learning algorithm (10) to predict quantity and composition of
urea crystals in an exhaust gas mixing section in accordance with Claim 6
10 further comprising calculating (30) a quantity of ammonia that is deposited in
said mixing section by multiplying the actual quantity of urea that is dosed into
said mixing section with (1 – NOx conversion efficiency) if ammonia is being
consumed from the selective catalytic reduction system.
15 8. The machine learning algorithm (10) to predict quantity and composition of
urea crystals in an exhaust gas mixing section in accordance with Claim 7
further comprising (32) a quantity of unreacted urea that exists in said mixing
section by subtracting a decomposed quantity of solid urea due to exhaust gas
that flows over urea that is deposited into said mixing section at a known
20 temperature, and that occurs over a predetermined time interval from the
quantity of urea that is deposited in the mixing section.
9. The machine learning algorithm (10) to predict quantity and composition of
urea crystals in an exhaust gas mixing section in accordance with Claim 8
25 further comprising (50) determining the composition of urea deposits in said
mixing section based on discrete temperature ranges that the urea deposits in
said the mixing section is subjected to over corresponding discrete time
intervals as measured over a finite interval of time. , Description:A machine learning algorithm 10 to predict composition and quantity of urea crystals
in an exhaust gas mixing section is described. The machine learning algorithm 10
comprises the steps of inputting a plurality of parameters including exhaust mass flow
10 rate12, upstream and downstream NOx emissions 14, selective catalytic reduction
NOx conversion efficiency 16, and exhaust temperature 18 into an algorithm. The
method further comprises computing a composition of urea crystals deposited in the
mixing section based on discrete time intervals that a quantity of urea crystals that
exists in the mixing section is subjected to over corresponding discrete temperature
15 ranges over a finite interval of time.