CN114534466A - Method for controlling nitrogen oxide emission by control system - Google Patents
Method for controlling nitrogen oxide emission by control system Download PDFInfo
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- CN114534466A CN114534466A CN202111331998.XA CN202111331998A CN114534466A CN 114534466 A CN114534466 A CN 114534466A CN 202111331998 A CN202111331998 A CN 202111331998A CN 114534466 A CN114534466 A CN 114534466A
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- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 title claims abstract description 102
- 238000000034 method Methods 0.000 title claims abstract description 28
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims abstract description 68
- VHUUQVKOLVNVRT-UHFFFAOYSA-N Ammonium hydroxide Chemical compound [NH4+].[OH-] VHUUQVKOLVNVRT-UHFFFAOYSA-N 0.000 claims abstract description 32
- 229910021529 ammonia Inorganic materials 0.000 claims abstract description 32
- 235000011114 ammonium hydroxide Nutrition 0.000 claims abstract description 32
- 238000005507 spraying Methods 0.000 claims abstract description 23
- 238000002347 injection Methods 0.000 claims description 24
- 239000007924 injection Substances 0.000 claims description 24
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 22
- 239000001301 oxygen Substances 0.000 claims description 22
- 229910052760 oxygen Inorganic materials 0.000 claims description 22
- 239000003245 coal Substances 0.000 claims description 8
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 4
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 4
- 239000000779 smoke Substances 0.000 claims description 3
- 239000004568 cement Substances 0.000 abstract description 23
- 238000004519 manufacturing process Methods 0.000 abstract description 8
- 238000005260 corrosion Methods 0.000 abstract description 7
- 230000007797 corrosion Effects 0.000 abstract description 7
- 230000026676 system process Effects 0.000 abstract 1
- 238000007781 pre-processing Methods 0.000 description 7
- 238000009499 grossing Methods 0.000 description 5
- 239000002994 raw material Substances 0.000 description 4
- 238000000611 regression analysis Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 229910000069 nitrogen hydride Inorganic materials 0.000 description 2
- 238000001354 calcination Methods 0.000 description 1
- 238000010531 catalytic reduction reaction Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
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- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B7/00—Hydraulic cements
- C04B7/36—Manufacture of hydraulic cements in general
- C04B7/43—Heat treatment, e.g. precalcining, burning, melting; Cooling
- C04B7/44—Burning; Melting
- C04B7/4407—Treatment or selection of the fuel therefor, e.g. use of hazardous waste as secondary fuel ; Use of particular energy sources, e.g. waste hot gases from other processes
-
- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B7/00—Hydraulic cements
- C04B7/36—Manufacture of hydraulic cements in general
- C04B7/43—Heat treatment, e.g. precalcining, burning, melting; Cooling
- C04B7/44—Burning; Melting
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
- Y02P90/845—Inventory and reporting systems for greenhouse gases [GHG]
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- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Ceramic Engineering (AREA)
- Structural Engineering (AREA)
- Thermal Sciences (AREA)
- Materials Engineering (AREA)
- Physics & Mathematics (AREA)
- Organic Chemistry (AREA)
- Feedback Control In General (AREA)
- Treating Waste Gases (AREA)
- Waste-Gas Treatment And Other Accessory Devices For Furnaces (AREA)
- Exhaust Gas Treatment By Means Of Catalyst (AREA)
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Abstract
The invention provides a method for controlling nitrogen oxide emission by a control system, which comprises the steps that a processor in the control system processes a plurality of groups of data sets according to a preset range to generate a plurality of groups of updating data sets, each group of updating data set comprises ammonia water spraying amount and a plurality of updating variable values, a group of regression coefficients are generated according to a plurality of average nitrogen oxide concentrations and the plurality of groups of updating data sets, and a target ammonia water spraying amount is generated by the processor according to the target average nitrogen oxide concentration, the group of regression coefficients and the group of updating data sets, and the target ammonia water spraying amount is the current optimum ammonia water spraying amount. The invention can accurately generate the lowest ammonia water spraying amount under the given emission standard of the nitrogen oxide, thereby controlling the emission of the nitrogen oxide and simultaneously reducing the ammonia escape of a cement plant and the corrosion of cement production line equipment.
Description
Technical Field
The invention relates to cement production, in particular to a method for controlling nitrogen oxide emission by a control system.
Background
In order to avoid the impact of nitrogen oxides on the environment and to meet government regulations, selective non-catalytic reduction (SNCR) systems are currently used to suppress nitrogen oxides by spraying ammonia water. However, the excessive ammonia water can cause the unreacted ammonia water to be discharged and become ammonia to escape, thereby causing secondary pollution and accelerating the corrosion speed of the machine equipment.
Disclosure of Invention
The embodiment of the invention provides a control system, a method for controlling nitrogen oxide emission by calculating the optimal ammonia water spraying amount, the control system comprises a processor, the processor processes a plurality of groups of data sets to generate a plurality of groups of updating data sets, each group of updating data set comprises an ammonia water spraying amount and a plurality of updating variable values, then a group of regression coefficients are generated by a plurality of average nitrogen oxide concentrations and a plurality of groups of data sets, and the processor generates a target ammonia water spraying amount according to the target average nitrogen oxide concentration, the group of regression coefficients and the plurality of updating variable values, namely the current optimal ammonia water spraying amount.
The invention can accurately generate the lowest ammonia water spraying amount under the given emission standard of the nitrogen oxide, thereby controlling the emission of the nitrogen oxide, reducing the ammonia escape of a cement plant and reducing the corrosion of cement production line equipment.
Drawings
Fig. 1 is a block diagram of a control system according to an embodiment of the present invention.
FIG. 2 is a flow chart of a method of controlling NOx emissions by the control system of FIG. 1.
FIG. 3 illustrates the effect of the control system of FIG. 1 for controlling NOx emissions.
Symbol saying:
1 control system
101 to 10N sensor
12, processor
200 method
S202 to S206
40 predicted value of target ammonia water spraying amount
42 actual value of ammonia water injection amount
Actual value of NOx concentration 44
Detailed Description
Fig. 1 is a block diagram of a control system 1 according to an embodiment of the present invention. The control system 1 can be used in cement plants to accurately generate the lowest ammonia water spraying amount under the given emission standard of nitrogen oxides, thereby controlling the emission of the nitrogen oxides and simultaneously reducing the ammonia escape of the cement plants and the corrosion of cement production line equipment.
The control system 1 comprises sensors 101 to 10N and a processor 12, N being a positive integer greater than 1. The sensors 101-10N may be located on multiple in-line facilities in a cement plant and coupled to the processor 12 via wired or wireless connections. At least one sensor can be arranged on each production line device. The processor 12 may be disposed in a central control system, which may be disposed in a cement plant or a remote computer room. The multiple production line devices can comprise a raw material scale, a preheating tower, a calcining furnace, a smoke chamber, a chimney, a decomposing furnace, a rotary kiln, a grate cooler, a high-temperature fan, a cooling fan, a kiln head cover, a kiln head exhaust fan and other production line devices. The sensors 101 to 10N may be temperature sensors, pressure sensors, concentration sensors, flow meters or other kinds of sensors. In some embodiments, the location and type of the sensors 101-10N may be obtained by expert opinion. The sensors 101 to 10N may obtain the ammonia water spraying amount and the variable values of the plurality of variables in a timed or non-timed manner, and transmit the ammonia water spraying amount and the variable values of the plurality of variables to the processor 12 to be stored in the database as a plurality of sets of data sets according to a time sequence, wherein each set of data set corresponds to a time point and includes the ammonia water spraying amount and the variable values. Table 1 shows the variables obtainable from sensors 101 to 10N, and the units, maximum values and minimum values for each variable.
Table 1
The ammonia injection amount may be the ammonia injection amount in table 1, and the plurality of variables may include, but are not limited to, the calciner temperature, the chamber oxygen concentration, the chimney oxygen concentration, the head coal flow rate, the kiln head pressure, the kiln head temperature, the raw material feeding amount, the tail coal flow rate, the kiln tail nitrogen oxide concentration, the clinker temperature, the chamber temperature, the tertiary air temperature, the chimney nitrogen oxide concentration, and the preheater outlet carbon monoxide concentration in table 1.
Since the concentration of nitrogen oxides may be influenced by the conditions of the cement kiln and the amount of sprayed ammonia, the processor 12 may perform a regression analysis using the average concentration of nitrogen oxides as a variable and the amount of sprayed ammonia and the set of paired variables as corresponding variables to create a regression model, and then calculate the recommended amount of sprayed ammonia according to the regression model to achieve a target average concentration of nitrogen oxides, for example, the target average concentration of nitrogen oxides in the next 5 minutes may be 280mg/m3。
Fig. 2 is a flow chart of a method 200 of controlling nox emissions by the control system 1. The method 200 includes steps S202 to S206 for performing a regression analysis to predict the average nox concentration. Any reasonable variation of techniques or steps is within the scope of the present disclosure. The details of steps S202 to S206 are as follows:
s202, acquiring ammonia water spraying amount and a plurality of variable values by sensors 101 to 10N;
step S203, the processor 12 processes the plurality of sets of data sets to generate a plurality of sets of updated data sets, each set of updated data sets including the ammonia water injection amount and a plurality of updated variable values;
step S204, the processor 12 generates a set of regression coefficients according to the plurality of average NOx concentrations and the plurality of sets of updated data;
in step S206, the processor 12 generates a target ammonia injection amount according to the target average NOx concentration, the set of regression coefficients, and the set of updated data sets.
In step S202, sensors 101 to 10N of each cement plant periodically acquire ammonia injection amount and variable values of a plurality of variables. The processor 12 converts the ammonia injection amount and the plurality of variable values into the same time unit (for example, the time unit is per minute), and/or performs data processing (for example, calculating an average nox concentration of the stack nox concentration), and then transmits the converted ammonia injection amount, the plurality of converted variable values and the data processed variable values to the database to store the plurality of average nox concentrations and the plurality of sets of data. The average nox concentration may be an average nox concentration over a predetermined time, such as an average nox concentration of 5 minutes. Each group of data set comprises ammonia water spraying amount at specific time and a plurality of variable values.
In step S203, the processor 12 obtains the sets of data sets within a predetermined time period, performs extreme value preprocessing and/or data smoothing preprocessing on a plurality of corresponding variable values of the sets of data sets, and filters variables according to the processed sets of data sets to generate a plurality of sets of updated data sets. The predetermined time period may be set according to the requirement, for example, the predetermined time period may be 10 minutes, and each group of data set may include 10 variable values. Extreme preprocessing detects outliers of the corresponding variable values in the sets of data sets and updates the outliers to values within a predetermined range of corresponding variables, such as extreme values of the predetermined range of corresponding variables. The pre-data smoothing process may replace the plurality of strain values that suddenly increase or decrease by an average of the plurality of strain values. In some embodiments, the processor 12 may first perform extreme preprocessing and then perform data smoothing preprocessing to generate the updated variable values. Each set of update data sets includes an ammonia injection amount and a plurality of update variable values.
In some embodiments, when performing extreme preprocessing, for each variable in the sets of data sets, the processor 12 replaces a corresponding variable value out of a predetermined range of the corresponding variable values in the sets of data sets with an extreme value of the predetermined range. The processor 12 generates an updated variable value according to the latest value of the plurality of corresponding variable values. For example, the predetermined range of the stack NOx concentration is 0mg/m3To 397mg/m3. If the chimneyThe variable value of the nitrogen oxide concentration is less than 0, and the processor 12 updates the variable value of the nitrogen oxide concentration of the chimney to 0; if the variable value of the stack nox concentration is greater than 397, the processor 12 updates the variable value of the stack nox concentration to 397. Table 2 shows a plurality of corresponding variable values of the stack nox concentration in the examples, each corresponding variable value being spaced 1 second apart.
Table 2
| Time | NOx | Renewed NOx |
| 2020-4-14 00:50 | -20 | 0 |
| 2020-4-14 00:51 | -10 | 0 |
| 2020-4-14 00:52 | 0 | 0 |
| 2020-4-14 00:53 | 5 | 5 |
| 2020-4-14 00:54 | 10 | 10 |
| 2020-4-14 00:55 | 18 | 18 |
| 2020-4-14 00:56 | 58 | 58 |
| 2020-4-14 00:57 | 143 | 143 |
| 2020-4-14 00:58 | 181 | 181 |
| 2020-4-14 00:59 | 82 | 82 |
As shown in Table 2, at times 2020-04-1400: 50 and 2020-04-1400: 51, the processor 12 updates the corresponding strain value for stack NOx concentration to 0 since the corresponding strain value for stack NOx concentration is-20 and-10, respectively. Between time 2020-04-1400: 51 and time 2020-04-1400: 59, processor 12 does not need to update the variable value of the stack nox concentration since the variable value of the stack nox concentration is within the predetermined range.
In other embodiments, when performing extreme value preprocessing, for each variable in the multiple sets of data sets, if a difference between two consecutive corresponding variable values in the multiple corresponding variable values of the multiple sets of data sets is greater than a predetermined value, the processor 12 replaces a subsequent value of the two consecutive corresponding variable values with a previous value of the two consecutive corresponding variable values; if the difference between two consecutive corresponding variable values in the sets of data is less than the predetermined value, the processor 12 will not update the value. The processor 12 generates an updated variable value according to the latest value of the plurality of corresponding variable values. If the difference between two consecutive values of the corresponding variables is too large, the value of the corresponding variable measured by the sensor may be abnormal, so that the newer posterior value is replaced by the older anterior value to ensure the correctness of the regression coefficient. Table 3 shows a plurality of values of the corresponding variations of the stack oxygen concentration in the examples, each of which is spaced 1 second apart. The predetermined value may be 2%. When the difference between the two continuous corresponding variable values of the chimney oxygen concentration is greater than or equal to 2%, the chimney oxygen concentration is greater than 17%, or the chimney oxygen concentration is less than 4%, the chimney oxygen concentration may be regarded as abnormal, the processor 12 may replace the rear value of the two continuous corresponding variable values with the front value of the two continuous corresponding variable values, otherwise, the chimney oxygen concentration may be regarded as normal, and the processor 12 may not update the rear value.
Table 3
As shown in Table 3, between time 2020-05-2023: 40 and time 2020-05-2023: 47, the processor 12 does not update the post-value because the difference (0%) between two consecutive variables (5%, 5%) of the stack oxygen concentration is less than 2% and the variable (5%) of the stack oxygen concentration is between 2% and 17%; at time 2020-05-2023: 48, since the difference (8%) between the two continuously variable values of the stack oxygen concentration (5%, 13%) is greater than 2%, processor 12 updates the latter value of the two continuously variable values of the stack oxygen concentration to the former value (5%); at time 2020-05-2023: 49, since the variable value of the stack oxygen concentration (19%) is greater than 17%, processor 12 updates the back value of the two consecutive variable values of the stack oxygen concentration to the front value (5%); at time 2020-05-2023: 50, since the variable value of the stack oxygen concentration (17%) equals 17%, processor 12 updates the back value of the two consecutive variable values of the stack oxygen concentration to the front value (5%); between time 2020-05-2023: 51 and time 2020-05-2023: 59, processor 12 updates the post value of the two continuously variable values of the stack oxygen concentration to the pre value (5%) because the difference between the two continuously variable values of the stack oxygen concentration is greater than or equal to 2%.
In some embodiments, when performing pre-smoothing processing, for each variable in the plurality of sets of data, if a most recent value of the plurality of corresponding variable values of the plurality of sets of data is substantially equal to a predetermined value, the processor 12 replaces the most recent value with an average of the remaining values of the plurality of corresponding variable values; if the latest value in the sets of data is greater than the predetermined value, the processor 12 does not change the latest value. The processor 12 generates an update variable value according to the latest values of the sets of data. For example, the plurality of corresponding variable values of the plurality of sets of data may include 6 variable values (V (t-5), V (t-4), V (t-3), V (t-2), V (t-1), V (t)), (V (t)) arranged in time series, each variable value being spaced 1 minute apart, and the predetermined value may be 0. At time t, if the latest value is 0, processor 12 replaces the latest value V (t) by the average of the remaining values (V (t-1) to V (t-5)) of the plurality of corresponding variable values, as shown in equation (2):
v (t) ═ V (t-1) + V (t-2) + … + V (t-5))/5 equation (2)
In other embodiments, when performing pre-smoothing processing, for each variable in the plurality of sets of data, processor 12 replaces a most recent value in the plurality of corresponding variable values in the plurality of sets of data with an average value of the plurality of corresponding variable values, which may be a 5-point moving average, as shown in equation (3):
v (t) ═ V (t) + V (t-2) + … + V (t-4))/5 equation (3)
Where V (t) is the latest value and V (t-4)) is a variable value of 4 minutes earlier. The processor 12 generates an updated variable value according to the latest value of the plurality of corresponding variable values. Table 4 shows a plurality of strain values of the ammonia water spouting amount in the examples, each of which is spaced 1 minute apart.
Table 4
| Time (t) | NH3 | NH3_M5 | Updated NH3 |
| 2020-16-18 23:50 | 988.203 | - | 988.203 |
| 2020-16-18 23:51 | 994.297 | - | 994.297 |
| 2020-16-18 23:52 | 1021.953 | - | 1021.953 |
| 2020-16-18 23:53 | 1061.953 | - | 1061.953 |
| 2020-16-18 23:54 | 1100.234 | 1033.3280 | 1033.3280 |
| 2020-16-18 23:55 | 1137.422 | 1063.1718 | 1063.1718 |
| 2020-16-18 23:56 | 1178.047 | 1099.9218 | 1099.9218 |
| 2020-16-18 23:57 | 1215.938 | 1138.7188 | 1138.7188 |
| 2020-16-18 23:58 | 1253.281 | 1176.9844 | 1176.9844 |
| 2020-16-18 23:59 | 1293.672 | 1215.6720 | 1215.6720 |
As shown in table 4, at times 2020-16-1823: 54, processor 12 calculated a 5-point moving average NH3_ M5 of 1033.328 tons/h (═ 988.203+994.297+1021.953+1061.953+1100.234)/5) from times 2020-16-1823: 50 to times 2020-16-1823: 54, and at times 2020-16-1823: 55 to times 2020-16-1823: 59, processor 12 calculated its 5-point moving average NH3_ M5, respectively. In the time range from 2020-16-1823: 50 to the time range from 2020-16-1823: 53, the processor 12 takes the ammonia injection amount as the updated ammonia injection amount since there is no 5-point moving average NH3_ M5; at times 2020-16-1823: 54 to 2020-16-1823: 59, processor 12 uses the moving average NH3_ M5 as the updated ammonia injection.
The processor 12 may select a corresponding set of variables from the plurality of variables for a specific cement plant using forward selection (forward selection), backward selection (backward selection) or forward and backward step selection (stepwise selection) to generate the plurality of updated data sets according to the plurality of processed data sets. For example, the processor 12 may use a backward selection to cull the statistically insignificant corresponding variables from the plurality of variables one by one until a statistically significant set of corresponding variables remains. The regression model established by using the pair of strain numbers improves the accuracy. Using the regression model for this set of variables increased the R-square (R-square) from 0.94 to 0.95, the Mean Absolute Percentage Error (MAPE) from 3.46% to 3.22%, and the Mean Absolute Error (MAE) from 62 to 61.8, both indications indicating increased accuracy of the regression model, compared to the regression model using all corresponding variables.
In step S204, the processor 12 obtains the plurality of mean nox concentrations and the plurality of sets of updated data for the specific cement plant from the database to perform a regression analysis for the specific cement plant to generate a set of regression coefficients. The set of regression coefficients is used to build a regression model of the average NOx concentration. For example, equation (1) shows a regression model of the mean nox concentration:
average
NOx ═ β 0+ β 1 × 1+ β 2 × 2+ β 3 × 3+ β 4 × 4+ β 5 × 5+ β 6 × 6+ β 7 × X7+ β 8X 8+ β 9 × 9+ β 10X 10 formula (1)
Wherein the average NOx is the average NOx concentration;
x1 is the head coal flow;
beta 1 is the coefficient of head coal flow;
x2 represents the amount of raw material fed;
beta 2 is the coefficient of raw material feeding amount;
x3 is the tailing coal flow rate;
beta 3 is the coefficient of the tail coal flow;
x4 is the stack oxygen concentration;
beta 4 is the coefficient of the chimney oxygen concentration;
x5 is the concentration of carbon monoxide at the outlet of the preheating tower;
beta 5 is the coefficient of the concentration of carbon monoxide at the outlet of the preheating tower;
x6 is the kiln head temperature;
beta 6 is the coefficient of the kiln head temperature;
x7 is the temperature of tertiary air;
beta 7 is the coefficient of the tertiary air temperature;
x8 is the amount of ammonia sprayed one minute ago;
beta 8 is the coefficient of ammonia water spraying amount one minute before;
x9 is the stack NOx concentration;
beta 9 is the coefficient of the chimney nitrogen oxide concentration;
x10 is the amount of ammonia sprayed; and
beta 10 is a coefficient of the amount of sprayed ammonia.
After the regression model is established, in step S206, the processor 12 substitutes the updated variables (X1 to X9) of the updated data set of the regression model and the target average nox concentration into formula (1), so as to estimate the target ammonia injection amount X10. In some embodiments, the set of update data sets may be the most recent set of update data sets. In other embodiments, the set of update data sets may be a set of update data sets at regular intervals, such as every minute. Table 5 shows the updated variable values of the corresponding variables (X1-X9) in the example.
Table 5
In one example, the target average nox concentration for the next 5 minutes is 280, the target ammonia injection rate coefficient β 10 is 1, the offset value β 0 is 308.4710, and the processor 12 calculates the predicted value of the target ammonia injection rate to be 980.9118(280 ═ 308.4710+ β 1 × 1+ β 2 × 2+ β 3 × 3+ β 4 × 4+ β 5 × 5+ β 6 × 6+ β 7 × 7+ β 8 × 8+ β 9 × 9+ β 10 × 980.9118) based on the values in equation (1) and table 5. In step S206, the ammonia spraying device sprays ammonia water according to the predicted value (980.9118ton/h) of the target ammonia water spraying amount, thereby controlling the emission of nitrogen oxides. Each cement plant can monitor the concentration of nitrogen oxides, and the central control system can present the concentration of the nitrogen oxides in real time in each cement plant and mark government standards and internal control standards for management. Similarly, each cement plant may monitor the ammonia slip concentration, and the central control system may present the immediate ammonia slip concentration for each cement plant. Meanwhile, each cement plant can measure the thickness of the equipment according to the positions of a kiln tail chimney, a kiln head chimney, an air pipe inlet and outlet of the electric bag composite dust collection equipment and the like so as to monitor the corrosion of the equipment, and if the corrosion degree exceeds a set standard of the system, the central control system can warn.
Fig. 3 shows the effect of the control system 1 for controlling nox emissions, wherein the horizontal axis represents time and the vertical axis represents nox concentration and ammonia injection. The simulated curve 40 represents the predicted value of the target ammonia water injection amount, the simulated curve 42 represents the actual value of the ammonia water injection amount, and the simulated curve 44 represents the actual value of the nitrogen oxide concentration. FIG. 3 shows that the actual value of the ammonia water injection amount is substantially equal to the predicted value of the target ammonia water injection amount, and the concentration of nitrogen oxides is controlled to 280mg/m3Within.
The control system 1 and the method 200 are suitable for cement plants, and the lowest ammonia water spraying amount is accurately generated under the given emission standard of nitrogen oxides, so that the emission of the nitrogen oxides is controlled, and meanwhile, the ammonia escape of the cement plants and the corrosion of cement production line equipment are reduced.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (10)
1. A method for a control system to control nox emissions, the control system comprising a processor, the method comprising:
the processor processes the plurality of sets of data to generate a plurality of sets of updated data sets, each set of updated data set comprising an ammonia injection amount and a plurality of updated variable values;
the processor generating a set of regression coefficients based on the plurality of mean NOx concentrations and the plurality of sets of updated data; and
the processor generates a target ammonia injection amount based on a target average NOx concentration, the set of regression coefficients, and a set of updated data sets.
2. The method of claim 1, wherein the control system further comprises a plurality of sensors disposed on the plurality of in-line equipment, wherein the method further comprises:
the sensors acquire the ammonia water spraying amount and the variable values.
3. The method of claim 1 or 2, wherein the plurality of variable values comprise a calciner temperature, a smoke chamber oxygen concentration, a chimney oxygen concentration, a head coal flow, a head pressure, a head temperature, a raw feed rate, a tail coal flow, a tail nitrogen oxide concentration, a clinker temperature, a smoke chamber temperature, a tertiary air temperature, and a carbon monoxide concentration at an outlet of a preheater tower.
4. The method of claim 1, wherein the processor processing the plurality of data sets to generate the plurality of updated data sets comprises:
the processor obtains the sets of data; and
the processor processes the plurality of corresponding variable values of the plurality of sets of data to produce a processed plurality of sets of data.
5. The method of claim 4, wherein the processor processing the plurality of corresponding variable values for the plurality of sets of data comprises:
and replacing the corresponding variable value which exceeds a preset range in the plurality of corresponding variable values with the extreme value of the preset range.
6. The method of claim 4, wherein the processor processing the plurality of corresponding variable values for the plurality of sets of data comprises:
when the difference value between two continuous corresponding variable values in the corresponding variable values is larger than a preset value, the processor replaces a rear value of the two continuous corresponding variable values with a front value of the two continuous corresponding variable values.
7. The method of claim 4, wherein the processor processing the plurality of corresponding variable values for the plurality of sets of data comprises:
if a latest value of the plurality of corresponding variable values is substantially equal to a predetermined value, the processor replaces the latest value with an average of the remaining values of the plurality of corresponding variable values.
8. The method of claim 4, wherein the processor processing the plurality of corresponding variable values for the plurality of sets of data comprises:
the processor replaces a most recent value of the plurality of corresponding variable values with an average of the plurality of corresponding variable values.
9. The method of claim 4, further comprising: selecting a corresponding variable from the processed data sets to generate the updated data sets.
10. The method of claim 1, further comprising the processor generating an average ammonia injection based on a plurality of ammonia injections of the plurality of sets of data.
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| TWI824333B (en) | 2023-12-01 |
| TW202219853A (en) | 2022-05-16 |
| CN114534466B (en) | 2024-03-22 |
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| CN114538810A (en) | 2022-05-27 |
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