CN114791102A - Combustion optimization control method based on dynamic operation data analysis - Google Patents
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Abstract
The invention discloses a combustion optimization control method based on dynamic operation data analysis, which comprises the following steps: acquiring relevant historical operating data stored by a DCS (distributed control System) of the unit; grouping data packets of different loads and air temperature intervals into different data subsets, calculating the mean value of each operating parameter of each load and air temperature interval, and performing gap filling and smooth filtering to obtain steady-state components of the operating data for analysis; after the steady state components are obtained, subtracting the corresponding steady state components from all the operation data sets, and obtaining a result which is a fluctuation operation data set; calculating correlation coefficients between each parameter and total coal quantity and/or flue gas NOx concentration; and giving optimization suggestions according to the obtained correlation coefficients. According to the method, the dynamic operation data under the unsteady state working condition is analyzed, the feasibility of revealing the unit operation characteristics and extracting the optimized operation control rule is realized, and the accuracy of operation control is improved.
Description
Technical Field
The invention relates to the technical field of correlation analysis and combustion optimization control, in particular to a combustion optimization control method based on dynamic operation data analysis.
Background
Since an intelligent power plant becomes a chasing hot spot in the power generation industry, the research and development of the intelligent power generation technology in the traditional thermal power industry are gradually expanded from the application of the advanced information technology to the research and development level of the technology with the core difficulty at the bottom layer. The operation data stored in the power plant DCS system is an important basis and condition for developing an intelligent power generation technology. An intelligent power generation technology center can be built in a technical research institute of a power generation group with a certain scale, and the research and development of intelligent technologies can be intensively carried out; each power plant and generator set transmits real-time data and information to the center, and obtains technical support in intelligent control, intelligent operation and maintenance, intelligent management and the like from the center. However, the theory and technology of intelligent application of the historical data of the unit operation are not sufficient so far. In recent years, researchers also provide that the working condition of the whole boiler is divided into different subareas, and the multi-objective combustion optimization problem is solved through a data-driven mixing strategy; an operation optimization target value determination method based on fuzzy association rules is provided, and a data mining technology is introduced into an optimization process of a thermal power plant; an integrated combustion optimization system ThermalNet based on a Deep Q Network (DQN) and a long-short time memory (LSTM) module; using Computational Fluid Dynamics (CFD) simulation to generate data serving as training samples for modeling of an artificial neural network, and adding operation historical operation data to establish an Artificial Neural Network (ANN) model for predicting operation and emission characteristics of a boiler; and the dynamic data mining model is used for adjusting the gate flow characteristic calculation and other methods to improve the operation efficiency and the intelligent degree of the unit. Because the unit is always in the process of dynamic change in operation, the average value of the system parameters in the same time period is considered to be capable of truly reflecting the unit operation state only when the unit is in a relatively stable working condition, so that the stable working condition of the unit needs to be defined, and a sample data set needs to be calculated and selected.
A thermal power generating unit NOx combustion optimization method and system are disclosed in China (application number: 201310574516.2, and authorization notice number: CN 103574581B), and boiler operation parameters are collected; calculating a correlation coefficient between the boiler operation parameter and the boiler efficiency and NOx emission, and selecting the boiler operation parameter meeting the correlation coefficient condition; and calculating the boiler efficiency and the NOx emission according to the selected boiler operation parameters meeting the correlation coefficient conditions, and adjusting the operation parameters of the boiler according to the boiler operation parameters meeting the boiler efficiency and the NOx emission conditions.
Aiming at the NOx combustion optimization method and system of the thermal power generating unit, the following defects are found: the more severe the change of the unit operation parameters is, the larger the deviation of the unit economic index calculated by the change parameters is. Therefore, when analyzing the operating economy of a unit, most documents consider that only data under stable working conditions are valid and have reference value. All the operating conditions of the unit are recorded by the data in the unit historical database, so that the effective conditions of the historical data need to be detected and identified.
Disclosure of Invention
The invention provides a combustion optimization control method based on dynamic operation data analysis, which combines steady-state component and fluctuation data for correlation analysis.
The invention is realized in the following way: a combustion optimization control method based on dynamic operation data analysis is characterized in that: the method comprises the following steps:
step 1: acquiring relevant historical operating data stored by DCS in a set in a past period of time;
and 2, step: all operational data sets are set as: a (M, k), where M is a sample number, M is 1,2,3... M, and M is a total data sample number; k represents an operating parameter, K being 1,2,3.. K; grouping data packets of different load and temperature intervals into different data subsets, and expressing the operation data as follows: x (I, j, k, l) I represents the number of divided load sections, I being 1,2,3.. times.i; j represents the number of the divided air temperature intervals, and J is 1,2,3.. J; k represents an operating parameter, K being 1,2,3.. K; l represents the number of data packets in the subset, i.e. the number of operating points, where L is 1,2,3. Calculating the average value of all the operation parameters in the operation data subsets of different loads and different air temperature intervals by the formula (1):
and step 3: for calculated
Performing gap filling and smooth filtering to obtain steady state component of operation data for further analysis
And 4, step 4: after obtaining the steady state components of the operational data, the corresponding steady state components are subtracted from the total operational data set A (m, k)
The result is a fluctuating operational data set;
and 5: calculating correlation coefficients between each parameter and total coal quantity and/or flue gas NOx concentration;
and 6: and analyzing the influence of the operating parameters with positive correlation coefficient more than 0.1 and negative correlation coefficient less than-0.1 on the combustion economy and pollutant generation according to the obtained correlation coefficient, and giving optimization suggestions.
Preferably, in step 1, the relevant historical operating data stored in the DCS within a past period of time of the unit is obtained, and the whole load and temperature interval of the unit may be divided into 20 to 30 intervals.
Preferably, the operation parameters selected in step 2 cover main monitoring and control parameters of boiler operation, including actual load, flow rate in main steam parameters, pressure, temperature, water supply flow rate, total fuel amount, coal supply amount of burners at each layer (corner), total air volume, air supply amount, air induction amount, opening degree of all secondary air doors, flue gas oxygen content, exhaust gas temperature, NOx concentration, and air temperature at an inlet of an air feeder.
Preferably, in step 4, the relative fluctuation component Δ a (m, k) is further calculated according to the difference between the values of the different operation parameters:
and (i, j) is the serial number of the load and air temperature interval where the unit is located at the m sampling time.
Preferably, in the step 5, a linear correlation coefficient method is adopted to calculate a correlation coefficient between each parameter and the total coal amount and/or the flue gas NOx concentration:
wherein: the correlation coefficient r (k, k ') is for all k sampling parameters, when r (k, k ') equals r (k ', k) and r (k, k) equals 1; and analyzing the change of the correlation coefficient between each sampling parameter and all other sampling parameters.
Preferably, in the step 6, the DCS fuel quantity is directly selected as a boiler efficiency and unit economy evaluation reference index, and the NOx emission value is added and combined to be used as an optimized working condition screening basis;
where L ' (i ', j ') is a subset of L (i, j), k Fuel Is the magnitude of the DCS fuel quantity, k NO Is a NOx emission value.
And (3) calculating the mean value of each section by using the formula (1) after the obtained L ' (i ', j ') is used as the basis for optimizing the control parameters.
The invention has the beneficial effects that:
1. the average value of the operation data of different load and temperature intervals is calculated on the basis that the dynamic operation data of all the units in a period of time are divided into two-dimensional intervals according to the load and the temperature, the correlation coefficient among the dynamic components of all the operation parameters is calculated, fluctuation data are obtained, the characteristic of the relevant change rule among the operation parameters is revealed through the fluctuation data, reference is provided for an operator to control the operation data of the boiler, the stability of the boiler and the operation is improved, and the alarm frequency of the boiler is reduced.
2. The operating data of the unit containing dynamic working conditions are selected, the working conditions are divided more finely according to loads and air temperatures, the boiler operating results of an optimization rule are used for displaying that the boiler efficiency is obviously improved, the NOx emission is obviously reduced, the obtained steady-state component and fluctuation component are more accurate, the calculated correlation coefficient is more accurate, and the obtained optimization method is more applicable.
Drawings
FIG. 1 is a flow chart of the operation of the present invention.
FIG. 2 is a schematic of the main steam flow over time.
Fig. 3 is a comparison graph of the total coal amount and the total air amount in the actual operation value, the average operation value, and the relative pulsation value of the parameters.
Fig. 4 is a comparison graph of the flue gas NOx concentration and the flue gas oxygen amount in the actual operation value, the average operation value, and the relative pulsation value of the parameters.
Fig. 5 is a comparison diagram of the main steam temperature and the position of the B rear wall overfire air outlet electric adjusting door 1 in the actual operation value, the average operation value and the relative pulsation value of the parameters.
Detailed Description
The invention will be further described below with reference to the accompanying drawings.
A combustion optimization control method based on dynamic operation data analysis,
step 1: acquiring relevant historical operating data stored by DCS of a unit in the past year;
selecting historical data stored by DCS in the past one-year time period of the unit, taking 1-3min as a period, dividing the whole load and temperature interval of the unit into 20-30 intervals respectively, basically dividing the unit into 10MW load sections for a 600 MW-level unit, and considering that the load is basically unchanged because the load in one load section is not changed greatly;
and 2, step: all operational data sets are set as: a (M, k), wherein M is a sample number, M is 1,2,3.... M, and M is a total number of data samples; k represents an operating parameter, and K is 1,2,3.. K; grouping data packets of different load and temperature intervals into different data subsets, and expressing the operation data as follows: x (I, j, k, l) I represents the number of divided load sections, I being 1,2,3.. I.; j represents the number of the divided air temperature intervals, and J is 1,2,3.. J; k represents an operating parameter, K being 1,2,3.. K; l represents the number of data packets in the subset, i.e. the number of operating points, where L is 1,2,3. The selected operation parameters cover main monitoring and control parameters of boiler operation, including actual load, flow, pressure, temperature, water supply flow, total fuel quantity, coal supply quantity of burners at each layer (corner), total air quantity, air supply quantity, air guide quantity, opening degree of all secondary air doors, oxygen content of flue gas, smoke exhaust temperature, NOx concentration and air temperature at an inlet of an air feeder;
calculating the average value of all the operation parameters in the operation data subsets of different loads and different air temperature intervals by the formula (1):
and 3, step 3: for calculated
Making up and smoothing filtering to obtain steady component of operation data for further analysis
And 4, step 4: after obtaining the steady state components of the operational data, the corresponding steady state components are subtracted from the total operational data set A (m, k)
The result is a fluctuating operational data set;
according to the fact that the numerical values of different operation parameters are different greatly, the relative fluctuation component delta A (m, k) is further calculated as follows:
wherein, (i, j) is the serial number of the load and the air temperature interval where the unit is located at the sampling moment m;
and 5: calculating correlation coefficients between each parameter and total coal quantity and/or flue gas NOx concentration;
and (3) calculating the correlation coefficient between each parameter and the total coal quantity and/or the flue gas NOx concentration by adopting a linear correlation coefficient method:
wherein: the correlation coefficient r (k, k ') is for all k sampling parameters, when r (k, k ') equals r (k ', k) and r (k, k) equals 1; analyzing the change condition of the correlation coefficient between each sampling parameter and all other sampling parameters;
and 6: analyzing the influence of the operating parameters with positive correlation coefficient more than 0.1 and negative correlation coefficient less than-0.1 on the combustion economy and the pollutant generation according to the obtained correlation coefficient; giving an optimization suggestion; the DCS fuel quantity is directly used as a reference index for evaluating the boiler efficiency and the unit economy; adding NOx emission values, and jointly using the NOx emission values as a screening basis for optimizing working conditions;
where L ' (i ', j ') is a subset of L (i, j), k Fuel Is the size of DCS fuel quantity, k NO Is a NOx generation or emission value;
after obtaining L ' (i ', j '), a mean value of each section is calculated using formula (1) as a basis for optimizing the control parameter.
A350 MW coal-fired steam turbine generator unit of a certain power plant is adopted to carry out data operation analysis and optimization operation tests, a unit boiler is selected as a supercritical parameter variable-pressure operation direct-current boiler, and the unit boiler is designed and manufactured by Oriental boiler (group) GmbH, and the model of the boiler is DG1150/25.4-n 2. The type is a single-hearth and front-rear wall opposed firing mode, the tail part is of a double-flue structure, and the baffle is adopted to adjust the temperature of a reheater, reheat once, balance ventilation, solid slag discharge, an all-steel framework, a full-suspension structure and door type open-air arrangement. A membrane water-cooling wall is arranged on a boiler hearth, a screen superheater is arranged at the outlet of the hearth, a high-temperature superheater and a low-temperature superheater are arranged in a horizontal flue, a two-stage economizer and a two-stage air preheater are arranged in a tail vertical shaft in a staggered mode, and the horizontal flue and a steering chamber are membrane wall ceiling tube wall-wrapped tubes. The width of the boiler is 15101.2mm, and the depth of the boiler is 13678.8 mm. A medium-speed coal mill, a cold primary fan, a positive-pressure direct-blowing negative-pressure hearth and a balanced ventilation pulverizing combustion system are adopted. 5 medium-speed coal mills are allocated, 4 of the medium-speed coal mills are operated, and 1 medium-speed coal mill is reserved. The fineness Rg of the coal powder is 21 percent. With a sampling period of 1 minute, the number of run data acquisitions was 24000, which was approximately 16 days of run data.
The main steam flow is selected as a marking parameter of the output of the boiler and the unit, and fig. 2 is a data graph of the main steam flow changing along with time. The actual operation values, the average operation values and the relative pulsation values of typical parameters are shown in fig. 3, fig. 4 and fig. 5, which include the total coal amount (a), the total air amount (B), the flue gas NOx concentration (c), the flue gas oxygen amount (d), the main steam temperature (e) and the position (f) of the B rear wall overfire air outlet electric adjusting door 1. The pulsation values are shown below the respective figures. The total coal quantity, the total air quantity and the position of the B rear wall over-fire air outlet electric adjusting door 1 obviously change along with the load; the oxygen content of the flue gas changes with the load to a certain extent; the concentration of the NOx in the flue gas and the temperature of the main steam do not obviously change along with the load. Their pulse values are all close to 0 mean random number.
Table 1 shows all analysis parameters and total coal amount on the left side; the middle side is the concentration of NOx in the inlet flue gas of the reactor at the side A of the #2 furnace; the right side is the correlation coefficient for total coal amount + #2 furnace A side reactor inlet flue gas NOx concentration.
The data in table 1 are analyzed, and the positive correlation coefficient between the parameters such as the total air volume, the flue gas oxygen volume, the control instruction of the movable vane regulating gate of the air blower A, B, the air induction volume, the flue gas NOx concentration at the inlet of the reactor at the side B of the furnace #2, the ammonia flow volume before the ammonia mixer at the side #2A and the side #2B, the flue gas oxygen volume, the coal volume or the flue gas NOx concentration and the parameters such as the positions of the secondary air main regulating gates at the side A, B, C and A of the group of burners and the group of burners is larger, and the proper reduction of the parameters is helpful for reducing the total coal consumption, reducing the pollutant emission, improving the economy of unit operation and the like.
A threshold value of positive correlation coefficient greater than 0.1 in table 1 indicates that there is a significant homodromous change between the two parameters; the coal feeder A, B, E feed rate, #1, #2SCR reactor ammoniated dilution air flow, meaning that fuel consumption will be reduced in response; as shown in table 1, the NOx concentration of the flue gas at the reactor inlet on the a side of furnace #2 and the NOx concentration of the flue gas at the reactor inlet on the B side of furnace #2, and the steam pressure of the high-temperature superheater header on the a side and the B side indicate that the parameter values are in negative correlation with the total coal amount, and these parameters should be increased appropriately during operation in order to reduce the total fuel consumption.
In actual operation, the operation economy or the emission of nitrogen oxides is not simply pursued, and the economy and the generation of pollutants are generally comprehensively optimized. According to the running parameter optimization control rule obtained by dynamic data analysis, subtracting the running average value of each parameter to obtain the positive and negative of the result, namely the direction of optimization adjustment; the adjustment direction of the main control parameters with the positive correlation coefficient larger than 0.1 obtained by comprehensively considering the coal quantity and the NOx on the right side in the table 1 is consistent, and the results obtained by the correlation analysis of the dynamic component of the dynamic operation data are proved to have internal relation with the combustion optimization.
The working principle is as follows: the method comprises the steps of dividing dynamic operation data of all units in a period of time into two-dimensional intervals according to load and air temperature, calculating the average value of the operation data of different load and air temperature intervals, then subtracting the average value from the actual operation data to obtain the dynamic component of the operation data, and calculating the correlation coefficient among the dynamic components of all operation parameters. And (3) calculating values of correlation coefficients between the total coal quantity, the NOx generation quantity and the comprehensive parameters of the total coal quantity and the NOx generation quantity and dynamic components of all the operating parameters are given, and analyzing the influence of the operating parameters with positive correlation coefficients larger than 0.1 and negative correlation coefficients smaller than-0.1 on combustion economy and pollutant generation and suggestions for optimizing and adjusting the operation.
The above description is only an embodiment of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (7)
1. A combustion optimization control method based on dynamic operation data analysis is characterized in that: the method comprises the following steps:
step 1: acquiring relevant historical operating data stored by DCS in a period of time in the past by a unit;
and 2, step: all operational data sets are set as: a (M, k), where M is a sample number, M is 1,2,3... M, and M is a total data sample number; k represents an operating parameter, K being 1,2,3.. K; grouping data packets of different load and temperature intervals into different data subsets, and expressing the operation data as follows: x (I, j, k, l) I represents the number of divided load sections, I being 1,2,3.. I.; j represents the number of the divided air temperature intervals, and J is 1,2,3.. J; k represents an operating parameter, and K is 1,2,3.. K; l represents the number of packets in the subset, i.e., the number of operating points, where L is 1,2,3.. linear. Calculating the average value of all the operation parameters in the operation data subsets of different loads and different air temperature intervals by the formula (1):
and 3, step 3: for calculated
Performing gap filling and smoothing filtering to obtain steady state component of operation data for further analysis
And 4, step 4: after obtaining the steady state components of the operational data, the corresponding steady state components are subtracted from the total operational data set A (m, k)
The result is a fluctuating operational data set;
and 5: calculating correlation coefficients between each parameter and total coal quantity and/or flue gas NOx concentration;
step 6: and analyzing the influence of the operating parameters with positive correlation coefficient more than 0.1 and negative correlation coefficient less than-0.1 on the combustion economy and pollutant generation according to the obtained correlation coefficient, and giving optimization suggestions.
2. The combustion optimization control method based on dynamic operation data analysis as claimed in claim 1, wherein: in the step 1, relevant historical operating data stored by the DCS in a past period of time of the unit is obtained, and the whole load and temperature interval of the unit is divided into 20-30 intervals respectively.
3. The combustion optimization control method based on dynamic operation data analysis according to claim 1, characterized in that: the operation parameters selected in the step 2 cover main monitoring and control parameters of boiler operation, including actual load, medium flow, pressure, temperature, water supply flow, total fuel quantity, coal supply quantity of each layer of combustor, total air quantity, air supply quantity, air guide quantity and all two parametersOpening degree of secondary air door, oxygen content of flue gas, exhaust gas temperature and NO x Concentration and blower inlet air temperature parameters.
4. The combustion optimization control method based on dynamic operation data analysis according to claim 1, characterized in that: in step 4, according to the fact that the numerical values of different operation parameters have large differences, the relative fluctuation component Δ a (m, k) is further calculated as follows:
and (i, j) is the serial number of the load and air temperature interval where the unit is located at the m sampling time.
5. The combustion optimization control method based on dynamic operation data analysis according to claim 1, characterized in that: in the step 5, a linear correlation coefficient method is adopted to calculate the correlation coefficient between each parameter and the total coal quantity and/or the flue gas NOx concentration:
wherein: the correlation coefficient r (k, k ') is for all k sampling parameters, with r (k, k ') ═ r (k ', k) and r (k, k) ═ 1; and analyzing the change of the correlation coefficient between each sampling parameter and all other sampling parameters.
6. The combustion optimization control method based on dynamic operation data analysis according to claim 1, characterized in that: in the step 6, the fuel quantity recorded by the DCS is directly used as a reference index for evaluating the boiler efficiency and the unit economy, and the NOx emission value is added and combined to be used as a screening basis for optimizing the working condition;
where L ' (i ', j ') is a subset of L (i, j), k Fuel Is the magnitude of the DCS fuel quantity, k NO Is the NOx emission.
7. The combustion optimization control method based on dynamic operation data analysis as claimed in claim 6, wherein: and calculating the mean value of each section by using the formula (1) after the obtained L ' (i ', j ') is used as the basis for optimizing the control parameters.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008003304A1 (en) * | 2006-07-07 | 2008-01-10 | Alstom Technology Ltd. | Method for controlling the combustion air supply in a steam generator that is fueled with fossil fuels |
| CN103256719A (en) * | 2013-04-27 | 2013-08-21 | 深圳市佳运通电子有限公司 | Furnace condition optimizing and monitoring device and method using same for optimizing |
| CN103574581A (en) * | 2013-11-15 | 2014-02-12 | 神华集团有限责任公司 | Thermal power generating unit NOx combustion optimization method and system |
| CN103759290A (en) * | 2014-01-16 | 2014-04-30 | 广东电网公司电力科学研究院 | Large coal-fired unit online monitoring and optimal control system and implementation method thereof |
| EP3088803A1 (en) * | 2015-04-30 | 2016-11-02 | General Electric Company | Combustion optimization system and method |
| WO2020088485A1 (en) * | 2018-11-02 | 2020-05-07 | 浙江大学 | Intelligent multi-pollutant ultra-low emission system and global optimization method |
| US20200175435A1 (en) * | 2018-11-30 | 2020-06-04 | Doosan Heavy Industries & Construction Co., Ltd. | System and method for optimizing combustion of boiler |
| WO2020181679A1 (en) * | 2019-03-13 | 2020-09-17 | 西安交通大学 | Control method for transient varying load coal supply quantity considering exergy storage modification of coal-fired boiler |
-
2022
- 2022-04-21 CN CN202210419853.3A patent/CN114791102B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008003304A1 (en) * | 2006-07-07 | 2008-01-10 | Alstom Technology Ltd. | Method for controlling the combustion air supply in a steam generator that is fueled with fossil fuels |
| CN101490476A (en) * | 2006-07-07 | 2009-07-22 | 阿尔斯通技术有限公司 | Method for controlling the combustion air supply in a steam generator that is fueled with fossil fuels |
| CN103256719A (en) * | 2013-04-27 | 2013-08-21 | 深圳市佳运通电子有限公司 | Furnace condition optimizing and monitoring device and method using same for optimizing |
| CN103574581A (en) * | 2013-11-15 | 2014-02-12 | 神华集团有限责任公司 | Thermal power generating unit NOx combustion optimization method and system |
| CN103759290A (en) * | 2014-01-16 | 2014-04-30 | 广东电网公司电力科学研究院 | Large coal-fired unit online monitoring and optimal control system and implementation method thereof |
| EP3088803A1 (en) * | 2015-04-30 | 2016-11-02 | General Electric Company | Combustion optimization system and method |
| WO2020088485A1 (en) * | 2018-11-02 | 2020-05-07 | 浙江大学 | Intelligent multi-pollutant ultra-low emission system and global optimization method |
| US20200175435A1 (en) * | 2018-11-30 | 2020-06-04 | Doosan Heavy Industries & Construction Co., Ltd. | System and method for optimizing combustion of boiler |
| WO2020181679A1 (en) * | 2019-03-13 | 2020-09-17 | 西安交通大学 | Control method for transient varying load coal supply quantity considering exergy storage modification of coal-fired boiler |
Non-Patent Citations (4)
| Title |
|---|
| 唐振浩;张海洋;曹生现;: "变负荷工况下NO_x排放量预测控制", 化工进展, no. 01 * |
| 张尚志;谭鹏;何彪;张成;方庆艳;陈刚;: "基于滑动判别算法的低NO_x燃烧优化分析", 热力发电, no. 05 * |
| 徐廖斌等: "基于工况划分的机组优化运行寻优方法", 《节能技术》, vol. 36, no. 2 * |
| 王国栋等: "运行数据动态分量的相关性分析机器在燃烧优化中的应用", 《广东电力》, vol. 35, no. 7 * |
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