CN115935784A - Method and system for analyzing fuel data in combustion system of architectural ceramic kiln - Google Patents
Method and system for analyzing fuel data in combustion system of architectural ceramic kiln Download PDFInfo
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- 238000002485 combustion reaction Methods 0.000 title claims abstract description 117
- 238000000034 method Methods 0.000 title claims abstract description 35
- 239000000919 ceramic Substances 0.000 title claims abstract description 26
- 239000000446 fuel Substances 0.000 title claims abstract description 22
- 238000012360 testing method Methods 0.000 claims abstract description 42
- 238000012549 training Methods 0.000 claims abstract description 25
- 238000012545 processing Methods 0.000 claims abstract description 23
- 238000013459 approach Methods 0.000 claims abstract description 12
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 126
- 239000003345 natural gas Substances 0.000 claims description 60
- 239000007789 gas Substances 0.000 claims description 20
- 238000009533 lab test Methods 0.000 claims description 15
- 238000010276 construction Methods 0.000 claims description 8
- 238000011156 evaluation Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000012986 modification Methods 0.000 claims description 7
- 230000004048 modification Effects 0.000 claims description 7
- 239000002912 waste gas Substances 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 239000002699 waste material Substances 0.000 claims description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 229910052760 oxygen Inorganic materials 0.000 claims description 4
- 239000001301 oxygen Substances 0.000 claims description 4
- 230000006870 function Effects 0.000 description 9
- 238000004519 manufacturing process Methods 0.000 description 6
- 238000004134 energy conservation Methods 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
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- 240000000220 Panda oleosa Species 0.000 description 1
- 235000016496 Panda oleosa Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
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- Y02P10/143—Reduction of greenhouse gas [GHG] emissions of methane [CH4]
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Abstract
The invention relates to the technical field of combustion of architectural ceramic kilns, in particular to a method and a system for analyzing fuel data in a combustion system of an architectural ceramic kiln, wherein the method comprises the following steps: s1, obtaining an initial data set of a combustion system, and performing initial processing on the initial data set to obtain a modeling data set suitable for modeling; s2, optimizing the XGboost model by adopting a GridSearchCV algorithm, and training modeling data in the optimized XGboost model to obtain an opening difference prediction model; s3, acquiring an online data set of the combustion system, and performing online processing on the online data set to obtain a test data set suitable for testing; putting the test data set into an opening difference prediction model to obtain an opening difference prediction value; s4, acquiring opening analog quantity of a control valve of the combustion system in a gradual approach mode according to the opening difference predicted value and a real-time opening difference numerical value of the combustion system; and S5, issuing a flow valve control instruction to the combustion system according to the opening degree analog quantity of the control valve of the combustion system.
Description
Technical Field
The invention relates to the technical field of combustion of building ceramic kilns, in particular to a method and a system for analyzing fuel data in a combustion system of a building ceramic kiln.
Background
At present, natural gas is basically adopted as fuel in kilns in the building ceramic industry of China, and with the continuous rising of the price of the natural gas and the national double-carbon requirement, energy conservation and emission reduction become main targets for manufacturers of building ceramics to reduce the cost, but the natural gas combustion system of the traditional kiln has the following problems: the utilization rate of natural gas is low, so that the heat efficiency is low and the energy utilization rate is low; and the air input is too high, even if the natural gas burns fully, the heat can be taken away by the redundant air, thereby leading to the energy waste.
Disclosure of Invention
The invention aims to provide a method and a system for analyzing fuel data in a combustion system of a building ceramic kiln, which integrate an automatic control principle of a gas control valve and a neural network learning model so as to comprehensively control the combustion system, and can achieve the effects of energy conservation and emission reduction, reduce the manufacturing cost and improve the production efficiency.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for analyzing fuel data in a combustion system of a building ceramic kiln comprises the following steps:
s1, obtaining an initial data set of a combustion system, and performing initial processing on the initial data set to obtain a modeling data set suitable for modeling; wherein the initial data set includes historical device data and laboratory test data;
s2, optimizing the XGboost model by adopting a GridSearchCV algorithm, and training modeling data in the optimized XGboost model to obtain an opening difference prediction model;
s3, acquiring an online data set of the combustion system, and performing online processing on the online data set to obtain a test data set suitable for testing; putting the test data set into an opening difference prediction model to obtain an opening difference prediction value; the opening difference prediction value represents the difference value of the opening M1 of the natural gas pipe flow and the opening M2 of the air pipe flow;
s4, acquiring opening analog quantity of a control valve of the combustion system in a gradual approach mode according to the opening difference predicted value and a real-time opening difference numerical value of the combustion system; the analog quantity of the control valve of the combustion system comprises an opening analog quantity of a natural gas pipe flow intelligent control valve and an opening analog quantity of an air pipe flow intelligent control valve;
and S5, issuing a flow valve control instruction to the combustion system according to the opening degree analog quantity of the control valve of the combustion system.
Preferably, the combustion system comprises:
the natural gas phase analyzer is used for measuring the CH4 content;
the natural gas intelligent meter is used for measuring the natural gas flow X1;
the online oxygen content detection monitor is used for measuring the O2 content and the relative humidity;
the air-gas intelligent meter is used for measuring the air gas flow X2;
an exhaust gas detector for measuring the CO content;
the waste gas-gas intelligent meter is used for measuring the waste gas flow X3;
the intelligent natural gas pipe flow control valve is used for controlling the opening M1 of the natural gas pipe flow;
the intelligent control valve of the air pipe flow is used for controlling the opening M2 of the air pipe flow;
the initial data set and the online data set respectively comprise CH4 content, natural gas flow X1, O2 content, relative humidity, air gas flow X2, CO content, waste gas flow X3, opening M1 of natural gas pipe flow and opening M2 of air pipe flow.
Preferably, in S1, the initial processing is performed on the initial data set to obtain a modeling data set suitable for modeling; the method specifically comprises the following steps:
s21, respectively exploring and analyzing the historical equipment data and the laboratory test data, and removing missing values and abnormal values after analysis to obtain correct historical equipment data and laboratory test data;
s22, carrying out correlation and combination processing on the correct historical equipment data and the laboratory test data through correlation conditions to obtain a combined data set; the association conditions comprise time, equipment numbers and pipeline numbers;
s23, calculating data characteristics of the merged data set through a difference value formula to obtain difference value parameters;
s24, distributing the difference parameters and the corresponding combined data sets into a group of modeling data, and taking the plurality of groups of modeling data as modeling data sets;
and S25, randomly distributing the modeling data sets into a 70% training set and a 30% testing set.
Preferably, the difference formula is:
difference parameter = data at current time-data at last time.
Preferably, in the step S3, the GridSearchCV algorithm is adopted to optimize the XGBoost model, and the modeling data is put into the optimized XGBoost model for training to generate the opening difference prediction model; the method specifically comprises the following steps:
s31, carrying out parameter setting on iteration times n _ estimators of a tree model of the XGboost model, the depth max _ depth of a tree of the tree model and a loss function threshold gamma during node splitting to obtain multiple groups of parameters and generate a corresponding prediction model;
s32, training the plurality of initial prediction models by sequentially adopting a training set, and sequentially obtaining corresponding prediction data accuracy rates by sequentially adopting an evaluation function;
s33, selecting a prediction model corresponding to the highest prediction data accuracy, predicting the prediction model by adopting a test set, and obtaining the corresponding prediction data accuracy through an evaluation function;
s34, if the numerical value of the accuracy rate of the predicted data is more than 0.75 and less than 1, outputting the prediction model; if the value of the accuracy of the predicted data is less than 0.75 or greater than 1, the prediction model is discarded, and S31 to S33 are repeated until the prediction model meeting S34 is obtained.
Preferably, the evaluation function is:
where R represents the predicted data accuracy rate,
represents the opening degree difference prediction value of the ith, y (i) represents the real data value of the ith, and/or>
Representing the average of all real data values y.
Preferably, in S3, the online data set of the combustion system is obtained, and the online data set is processed online to obtain a test data set suitable for testing; the method specifically comprises the following steps:
s31, performing association and combination processing on the online data set through association conditions to obtain a combined data set; wherein the association condition comprises time, equipment number and pipeline number;
s32, calculating data characteristics of the merged data set through a difference value formula to obtain a difference value parameter;
and S33, distributing the difference parameters and the corresponding combined data sets into a group of test data, and taking the plurality of groups of test data as test data sets.
Preferably, in the step S4, the opening analog quantity of the control valve of the combustion system is obtained in a gradual approach manner according to the opening difference prediction value and the real-time opening difference value of the combustion system; the analog quantity of the control valve of the combustion system comprises an opening analog quantity of a natural gas pipe flow intelligent control valve and an opening analog quantity of an air pipe flow intelligent control valve; the method specifically comprises the following steps:
s41, judging a difference value between the predicted opening difference value and a real-time opening difference value of the combustion system, and when the difference value is equal to 0, acquiring an analog quantity of a control valve corresponding to the real-time opening difference value of the combustion system; when the difference is not equal to 0, executing S42;
s42, judging whether the real-time opening difference value of the combustion system is larger than 0, and controlling the opening analog quantity of the intelligent natural gas pipe flow control valve to step by 0.01% and the opening analog quantity of the intelligent air pipe flow control valve to step by-0.01% when the real-time opening difference value is larger than 0; when the real-time opening difference value is smaller than 0, controlling the opening analog quantity of the intelligent natural gas pipe flow control valve to step by-0.01 percent, and controlling the opening analog quantity of the intelligent air pipe flow control valve to step by 0.01 percent; outputting the opening analog quantity of the natural gas pipe flow intelligent control valve and the opening analog quantity of the air pipe flow intelligent control valve after the control modification;
and S43, updating a real-time opening difference numerical value of the combustion system according to the opening analog quantity of the intelligent natural gas pipe flow control valve and the opening analog quantity of the intelligent air pipe flow control valve after control modification, and repeating the step S41 until the opening analog quantity of the control valve of the combustion system is equal to the opening difference predicted value to obtain the opening analog quantity of the control valve of the combustion system.
An analysis system for fuel data in a combustion system of a building ceramic kiln adopts the analysis method for the fuel data in the combustion system of the building ceramic kiln, and comprises the following steps:
the data acquisition module is used for acquiring an initial data set of the combustion system, and performing initial processing on the initial data set to obtain a modeling data set suitable for modeling; wherein the initial data set includes historical device data and laboratory test data;
the model training module is used for optimizing the XGboost model by adopting a GridSearchCV algorithm, and putting modeling data into the optimized XGboost model for training to obtain an opening difference prediction model;
the model prediction module is used for acquiring an online data set of the combustion system, and performing online processing on the online data set to acquire a test data set suitable for testing; putting the test data set into an opening difference prediction model to obtain an opening difference prediction value; the opening difference prediction value represents the difference value between the opening M1 of the natural gas pipe flow and the opening M2 of the air pipe flow;
the instruction construction module is used for acquiring the opening analog quantity of the control valve of the combustion system in a gradual approach mode according to the opening difference predicted value and the real-time opening difference value of the combustion system; the analog quantity of the control valve of the combustion system comprises an opening analog quantity of a natural gas pipe flow intelligent control valve and an opening analog quantity of an air pipe flow intelligent control valve;
and the instruction issuing module is used for issuing a flow valve control instruction to the combustion system according to the opening analog quantity of the control valve of the combustion system.
One of the above technical solutions has the following beneficial effects: on the basis of effective data, an opening difference prediction value is obtained through training and prediction of an opening difference prediction model, then the opening difference prediction value and a real-time opening difference numerical value of a combustion system are used for obtaining an opening analog quantity of a control valve of the combustion system in a gradual approach mode, namely, the optimal ratio of natural gas and air is formulated, and the problems that the utilization rate of the natural gas cannot be optimal and the energy utilization rate is low in an original system are solved.
Drawings
FIG. 1 is a schematic flow diagram of a method of analyzing fuel data in a construction ceramic kiln combustion system in accordance with the present invention;
FIG. 2 is a schematic diagram of the architecture of the fuel data analysis system of the present invention in a construction ceramic kiln combustion system;
in the drawings: the system comprises a data acquisition module 1, a model training module 2, a model prediction module 3, an instruction construction module 4 and an instruction issuing module 5.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
As shown in fig. 1, a method for analyzing fuel data in a combustion system of a building ceramic kiln comprises the following steps:
s1, obtaining an initial data set of a combustion system, and carrying out initial processing on the initial data set to obtain a modeling data set suitable for modeling; wherein the initial data set includes historical device data and laboratory test data;
s2, optimizing the XGboost model by adopting a GridSearchCV algorithm, and training modeling data in the optimized XGboost model to obtain an opening difference prediction model;
s3, acquiring an online data set of the combustion system, and performing online processing on the online data set to obtain a test data set suitable for testing; putting the test data set into an opening difference prediction model to obtain an opening difference prediction value; the opening difference prediction value represents the difference value of the opening M1 of the natural gas pipe flow and the opening M2 of the air pipe flow;
s4, acquiring opening analog quantity of a control valve of the combustion system in a gradual approach mode according to the opening difference predicted value and a real-time opening difference numerical value of the combustion system; the analog quantity of the control valve of the combustion system comprises an opening analog quantity of a natural gas pipe flow intelligent control valve and an opening analog quantity of an air pipe flow intelligent control valve;
and S5, issuing a flow valve control instruction to the combustion system according to the opening degree analog quantity of the control valve of the combustion system.
The invention aims to provide a method for analyzing data such as a combustion value in a combustion system of a building ceramic kiln.
Furthermore, the system is controlled by a fully-automatic adjusting gas flow control valve, and according to the change of the natural gas combustion value, the natural gas flow in a natural gas pipe and the air flow in an air pipe in the combustion system are timely and automatically adjusted, so that the problems that heat is taken away by redundant air in the original system, and energy is wasted are solved.
In conclusion, the system integrates the automatic control principle of the gas control valve and the neural network learning model so as to comprehensively control the combustion system, so that the effects of energy conservation and emission reduction can be achieved, the manufacturing cost is reduced, and the production efficiency is improved.
To illustrate further, in this embodiment, the kiln data is acquired by automatically collecting the combustion system every 30 seconds, wherein the basic detection and control devices and data provided by the combustion system include, but are not limited to, the following data:
the natural gas phase analyzer is used for measuring the CH4 content;
the natural gas intelligent meter is used for measuring the natural gas flow X1;
the online oxygen content detection monitor is used for measuring the O2 content and the relative humidity;
the air-gas intelligent meter is used for measuring the air gas flow X2;
an exhaust gas detector for measuring the CO content;
the waste gas-gas intelligent meter is used for measuring the waste gas flow X3;
the intelligent natural gas pipe flow control valve is used for controlling the opening M1 of the flow of the natural gas pipe;
the intelligent control valve of the air pipe flow is used for controlling the opening M2 of the air pipe flow;
the initial data set and the online data set respectively comprise CH4 content, natural gas flow X1, O2 content, relative humidity, air gas flow X2, CO content, waste gas flow X3, natural gas pipe flow opening M1 and air pipe flow opening M2.
Stated further, in S1, the initial processing is performed on the initial data set to obtain a modeling data set suitable for modeling; the method specifically comprises the following steps:
s21, respectively exploring and analyzing the historical equipment data and the laboratory test data, and removing missing values and abnormal values after analysis to obtain correct historical equipment data and laboratory test data;
the method specifically comprises the following steps:
(1) and obtaining the statistical indexes of the maximum value, the maximum value digit, the minimum value position, the 25% quantile, the median, the 75% quantile, the mean, the average absolute deviation, the variance, the standard deviation, the skewness and the kurtosis of each variable by using the descriptor function of the pandas, and using the statistical indexes for the known total sample number and the statistical condition of the number of each variable.
(2) When unit inconsistency occurs in the historical filling data of the laboratory, the unit inconsistency needs to be changed into a uniform unit, for example, the oxygen content unit is unified into mg/L, and the g/L needs to be replaced by mg/L.
(3) The data deletion method for partial time data is seriously needed as follows: counting the missing unit cells of the row, and deleting the row when the number of the missing unit cells is more than 30%; if the missing data is character data, filling the data by using a mode value of the character value of the column, wherein the method comprises the step of assigning a value arranged first to a null value after counting the number of each group of characters of the column.
(4) The obvious filling error occurs in part of data information, wherein the method comprises the following steps: calculating the average value (mean) and standard value (sigma) of each column of data (datan), and rejecting the abnormal value (dif) when the abnormal value (dif) is more than 3 times of the standard value, wherein the formula is as follows: diff { data n Mean > 3 sigma, which is padded with the mode value of the column of data.
S22, carrying out correlation and combination processing on the correct historical equipment data and the laboratory test data through correlation conditions to obtain a combined data set; wherein the association condition comprises time, equipment number and pipeline number;
the basic parameter table of the associated merged data is as follows:
s23, calculating data characteristics of the merged data set through a difference value formula to obtain difference value parameters;
wherein the difference formula is:
difference parameter = data at current time-data at last time.
S24, distributing the difference parameters and the corresponding combined data sets into a group of modeling data, and taking the plurality of groups of modeling data as modeling data sets;
firstly, calculating data characteristics of the combined data set through a difference value formula to obtain a difference value parameter, and taking the difference value parameter as modeling data required by the opening difference prediction model, wherein one group of modeling data is longitudinally displayed as follows:
and S25, randomly distributing the modeling data set into a 70% training set and a 30% testing set.
Further, in the step S3, the GridSearchCV algorithm is adopted to optimize the XGBoost model, and the modeling data is put into the optimized XGBoost model for training to generate the opening difference prediction model; the method specifically comprises the following steps:
s31, carrying out parameter setting on iteration times n _ estimators of a tree model of the XGboost model, the depth max _ depth of a tree of the tree model and a loss function threshold gamma during node splitting to obtain multiple groups of parameters and generate a corresponding prediction model;
s32, training the plurality of initial prediction models by sequentially adopting a training set, and sequentially obtaining corresponding prediction data accuracy rates by sequentially adopting an evaluation function; in order to make the value of the CO content in the exhaust gas tend to zero, so that the natural gas is sufficiently combusted, the waste is reduced, and the efficiency is improved, therefore, the difference between the optimal opening M1 of the intelligent control valve for the natural gas pipe flow and the optimal opening M2 of the intelligent control valve for the air pipe flow is required. In this embodiment, other items in the training set are used as the input value X to obtain an opening difference prediction value of the output value, that is, the optimal "difference between the opening M2 and the opening M1 (DIF _ M1_ M2)", so as to achieve the best benefit.
S33, selecting a prediction model corresponding to the highest prediction data accuracy, predicting the prediction model by adopting a test set, and obtaining the corresponding prediction data accuracy through an evaluation function;
s34, if the numerical value of the accuracy rate of the predicted data is larger than 0.75 and smaller than 1, outputting the prediction model; if the value of the accuracy of the prediction data is less than 0.75 or greater than 1, the prediction model is discarded, and S31 to S33 are repeated until the prediction model conforming to S34 is obtained.
Stated further, the merit function is:
where R represents the predicted data accuracy rate,
represents the opening degree difference prediction value of the ith, y (i) represents the real data value of the ith, and/or>
Representing the average of all real data values y.
To be further described, in S3, acquiring an online data set of the combustion system, and performing online processing on the online data set to obtain a test data set suitable for testing; the method specifically comprises the following steps:
s31, performing association and combination processing on the online data set through association conditions to obtain a combined data set; the association conditions comprise time, equipment numbers and pipeline numbers;
s32, calculating data characteristics of the merged data set through a difference value formula to obtain difference value parameters;
and S33, distributing the difference parameters and the corresponding combined data sets into a group of test data, and taking the plurality of groups of test data as test data sets.
Further, S4, acquiring an opening analog quantity of a control valve of the combustion system in a gradual approach mode according to the opening difference predicted value and a real-time opening difference numerical value of the combustion system; the analog quantity of the control valve of the combustion system comprises an opening analog quantity of a natural gas pipe flow intelligent control valve and an opening analog quantity of an air pipe flow intelligent control valve; the method specifically comprises the following steps:
s41, judging a difference value between the predicted opening difference value and a real-time opening difference value of the combustion system, and when the difference value is equal to 0, acquiring an analog quantity of a control valve corresponding to the real-time opening difference value of the combustion system; when the difference is not equal to 0, executing S42;
s42, judging whether the real-time opening difference value of the combustion system is larger than 0, and controlling the opening analog quantity of the intelligent natural gas pipe flow control valve to step by 0.01% and the opening analog quantity of the intelligent air pipe flow control valve to step by-0.01% when the real-time opening difference value is larger than 0; when the real-time opening difference value is smaller than 0, controlling the opening analog quantity of the intelligent natural gas pipe flow control valve to step by-0.01 percent, and controlling the opening analog quantity of the intelligent air pipe flow control valve to step by 0.01 percent; outputting the opening analog quantity of the intelligent natural gas pipe flow control valve and the opening analog quantity of the intelligent air pipe flow control valve after the control modification;
and S43, updating a real-time opening difference numerical value of the combustion system according to the opening analog quantity of the intelligent natural gas pipe flow control valve and the opening analog quantity of the intelligent air pipe flow control valve after control modification, and repeating the step S41 until the opening analog quantity of the control valve of the combustion system is equal to the opening difference predicted value to obtain the opening analog quantity of the control valve of the combustion system.
An analysis system for fuel data in a combustion system of a building ceramic kiln adopts the analysis method for the fuel data in the combustion system of the building ceramic kiln, and comprises the following steps:
the data acquisition module 1 is used for acquiring an initial data set of the combustion system, and performing initial processing on the initial data set to obtain a modeling data set suitable for modeling; wherein the initial data set includes historical device data and laboratory test data;
the model training module 2 is used for optimizing the XGboost model by adopting a GridSearchCV algorithm, and putting modeling data into the optimized XGboost model for training to obtain an opening difference prediction model;
the model prediction module 3 is used for acquiring an online data set of the combustion system, and performing online processing on the online data set to acquire a test data set suitable for testing; putting the test data set into an opening difference prediction model to obtain an opening difference prediction value; the opening difference prediction value represents the difference value between the opening M1 of the natural gas pipe flow and the opening M2 of the air pipe flow;
the instruction construction module 4 is used for obtaining the opening analog quantity of the control valve of the combustion system in a gradual approach mode according to the opening difference predicted value and the real-time opening difference value of the combustion system; the analog quantity of the control valve of the combustion system comprises an opening analog quantity of a natural gas pipe flow intelligent control valve and an opening analog quantity of an air pipe flow intelligent control valve;
and the instruction issuing module 5 is used for issuing a flow valve control instruction to the combustion system according to the opening degree analog quantity of the control valve of the combustion system.
The system obtains an opening difference predicted value through training and predicting an opening difference prediction model on the basis of effective data, and obtains the opening analog quantity of a control valve of the combustion system by adopting a gradual approach mode through the opening difference predicted value and a real-time opening difference numerical value of the combustion system. Furthermore, the system integrates an automatic control principle of the gas control valve and a neural network learning model so as to carry out comprehensive control on the combustion system, so that the effects of energy conservation and emission reduction can be achieved, the manufacturing cost is reduced, and the production efficiency is improved. The technical principles of the present invention have been described above with reference to specific embodiments. The description is made for the purpose of illustrating the principles of the invention and should not be construed in any way as limiting the scope of the invention. Based on the explanations herein, those skilled in the art will be able to conceive of other embodiments of the present invention without inventive step, and such equivalent modifications or substitutions are intended to be included within the scope of the present invention as defined in the appended claims.
Claims (9)
1. A method for analyzing fuel data in a combustion system of a building ceramic kiln is characterized by comprising the following steps:
s1, obtaining an initial data set of a combustion system, and carrying out initial processing on the initial data set to obtain a modeling data set suitable for modeling; wherein the initial data set includes historical device data and laboratory test data;
s2, optimizing the XGboost model by adopting a GridSearchCV algorithm, and putting modeling data into the optimized XGboost model for training to obtain an opening difference prediction model;
s3, acquiring an online data set of the combustion system, and performing online processing on the online data set to obtain a test data set suitable for testing; putting the test data set into an opening difference prediction model to obtain an opening difference prediction value; the opening difference prediction value represents the difference value of the opening M1 of the natural gas pipe flow and the opening M2 of the air pipe flow;
s4, acquiring opening analog quantity of a control valve of the combustion system in a gradual approach mode according to the opening difference predicted value and a real-time opening difference numerical value of the combustion system; the analog quantity of the control valve of the combustion system comprises an opening analog quantity of a natural gas pipe flow intelligent control valve and an opening analog quantity of an air pipe flow intelligent control valve;
and S5, issuing a flow valve control instruction to the combustion system according to the opening degree analog quantity of the control valve of the combustion system.
2. The method of claim 1, wherein the combustion system comprises:
the natural gas phase analyzer is used for measuring the CH4 content;
the natural gas intelligent meter is used for measuring the natural gas flow X1;
the online oxygen content detection monitor is used for measuring the O2 content and the relative humidity;
the air-gas intelligent meter is used for measuring the air gas flow X2;
an exhaust gas detector for measuring the CO content;
the waste gas-gas intelligent meter is used for measuring the waste gas flow X3;
the intelligent natural gas pipe flow control valve is used for controlling the opening M1 of the natural gas pipe flow;
the intelligent control valve of the air pipe flow is used for controlling the opening M2 of the air pipe flow;
the initial data set and the online data set respectively comprise CH4 content, natural gas flow X1, O2 content, relative humidity, air gas flow X2, CO content, waste gas flow X3, opening M1 of natural gas pipe flow and opening M2 of air pipe flow.
3. The method for analyzing fuel data in a combustion system of a construction ceramic kiln as recited in claim 2, wherein in S1, the initial data set is initially processed to obtain a modeling data set suitable for modeling; the method specifically comprises the following steps:
s21, respectively exploring and analyzing the historical equipment data and the laboratory test data, and removing missing values and abnormal values after analysis to obtain correct historical equipment data and laboratory test data;
s22, carrying out correlation and combination processing on the correct historical equipment data and the laboratory test data through correlation conditions to obtain a combined data set; wherein the association condition comprises time, equipment number and pipeline number;
s23, calculating data characteristics of the merged data set through a difference value formula to obtain difference value parameters;
s24, distributing the difference parameters and the corresponding combined data sets into a group of modeling data, and taking the plurality of groups of modeling data as modeling data sets;
and S25, randomly distributing the modeling data sets into a 70% training set and a 30% testing set.
4. The method for analyzing fuel data in a combustion system of a construction ceramic kiln as recited in claim 3, wherein the difference formula is:
difference parameter = data at current time-data at last time.
5. The method for analyzing the fuel data in the combustion system of the architectural ceramic kiln furnace as recited in claim 4, wherein S3, a GridSearchCV algorithm is adopted to optimize an XGboost model, modeling data is put into the optimized XGboost model for training, and an opening difference prediction model is generated; the method specifically comprises the following steps:
s31, carrying out parameter setting on iteration times n _ estimators of a tree model of the XGboost model, the depth max _ depth of a tree of the tree model and a loss function threshold gamma during node splitting to obtain multiple groups of parameters and generate a corresponding prediction model;
s32, sequentially adopting a training set to train the plurality of initial prediction models, and sequentially obtaining corresponding prediction data accuracy rates through an evaluation function;
s33, selecting a prediction model corresponding to the highest prediction data accuracy, predicting the prediction model by adopting a test set, and obtaining the corresponding prediction data accuracy through an evaluation function;
s34, if the numerical value of the accuracy rate of the predicted data is more than 0.75 and less than 1, outputting the prediction model; if the value of the accuracy of the predicted data is less than 0.75 or greater than 1, the prediction model is discarded, and S31 to S33 are repeated until the prediction model meeting S34 is obtained.
6. The method of claim 5, wherein the evaluation function is:
where R represents the predicted data accuracy rate,
represents the opening degree difference prediction value of the ith, y (i) represents the real data value of the ith, and/or>
Representing the average of all real data values y.
7. The method for analyzing the fuel data in the combustion system of the architectural ceramic kiln as recited in claim 6, wherein in S3, the online data set of the combustion system is obtained, and the online data set is processed online to obtain a test data set suitable for testing; the method specifically comprises the following steps:
s31, performing association and combination processing on the online data set through association conditions to obtain a combined data set; the association conditions comprise time, equipment numbers and pipeline numbers;
s32, calculating data characteristics of the merged data set through a difference value formula to obtain difference value parameters;
and S33, distributing the difference parameters and the corresponding combined data sets into a group of test data, and taking the plurality of groups of test data as test data sets.
8. The method for analyzing the fuel data in the combustion system of the architectural ceramic kiln as recited in claim 7, wherein S4, the opening degree analog quantity of the control valve of the combustion system is obtained in a gradual approach manner according to the opening degree difference predicted value and the real-time opening degree difference value of the combustion system; the analog quantity of the control valve of the combustion system comprises the opening analog quantity of a natural gas pipe flow intelligent control valve and the opening analog quantity of an air pipe flow intelligent control valve; the method specifically comprises the following steps:
s41, judging a difference value between the predicted opening difference value and a real-time opening difference value of the combustion system, and when the difference value is equal to 0, acquiring an analog quantity of a control valve corresponding to the real-time opening difference value of the combustion system; when the difference is not equal to 0, executing S42;
s42, judging whether the real-time opening difference value of the combustion system is larger than 0, and controlling the opening analog quantity of the intelligent natural gas pipe flow control valve to step by 0.01% and the opening analog quantity of the intelligent air pipe flow control valve to step by-0.01% when the real-time opening difference value is larger than 0; when the real-time opening difference value is smaller than 0, controlling the opening analog quantity of the intelligent natural gas pipe flow control valve to step by-0.01 percent, and controlling the opening analog quantity of the intelligent air pipe flow control valve to step by 0.01 percent; outputting the opening analog quantity of the intelligent natural gas pipe flow control valve and the opening analog quantity of the intelligent air pipe flow control valve after the control modification;
and S43, updating a real-time opening difference numerical value of the combustion system according to the opening analog quantity of the intelligent natural gas pipe flow control valve and the opening analog quantity of the intelligent air pipe flow control valve after control modification, and repeating the step S41 until the opening analog quantity of the control valve of the combustion system is equal to the opening difference predicted value to obtain the opening analog quantity of the control valve of the combustion system.
9. A system for analyzing fuel data in a combustion system of a constructional ceramic kiln, which is characterized in that the method for analyzing fuel data in the combustion system of the constructional ceramic kiln as claimed in any one of claims 1 to 8 comprises:
the data acquisition module is used for acquiring an initial data set of the combustion system, and performing initial processing on the initial data set to obtain a modeling data set suitable for modeling; wherein the initial data set includes historical device data and laboratory test data;
the model training module is used for optimizing the XGboost model by adopting a GridSearchCV algorithm, and inputting modeling data into the optimized XGboost model for training to obtain an opening difference prediction model;
the model prediction module is used for acquiring an online data set of the combustion system, and performing online processing on the online data set to acquire a test data set suitable for testing; putting the test data set into an opening difference prediction model to obtain an opening difference prediction value; the opening difference prediction value represents the difference value of the opening M1 of the natural gas pipe flow and the opening M2 of the air pipe flow;
the instruction construction module is used for acquiring the opening analog quantity of the control valve of the combustion system in a gradual approach mode according to the opening difference predicted value and the real-time opening difference value of the combustion system; the analog quantity of the control valve of the combustion system comprises an opening analog quantity of a natural gas pipe flow intelligent control valve and an opening analog quantity of an air pipe flow intelligent control valve;
and the instruction issuing module is used for issuing a flow valve control instruction to the combustion system according to the opening degree analog quantity of the control valve of the combustion system.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116300666A (en) * | 2023-05-24 | 2023-06-23 | 科大智能物联技术股份有限公司 | Power plant boiler operation control method based on XGBoost optimization |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080306674A1 (en) * | 2003-09-23 | 2008-12-11 | Hill Philip G | Method For Controlling Combustion In An Internal Combustion Engine And Predicting Performance And Emissions |
| CN111377595A (en) * | 2020-05-08 | 2020-07-07 | 蚌埠凯盛工程技术有限公司 | Method and system for controlling gas supply of glass kiln in real time |
| CN114036758A (en) * | 2021-11-12 | 2022-02-11 | 华电新疆哈密煤电开发有限公司 | Boiler combustion state dynamic display method based on numerical simulation and machine learning |
| CN115081697A (en) * | 2022-06-09 | 2022-09-20 | 佛山众陶联供应链服务有限公司 | Method and equipment for predicting firing curve based on raw materials and computer storage medium |
-
2022
- 2022-09-30 CN CN202211230486.9A patent/CN115935784B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080306674A1 (en) * | 2003-09-23 | 2008-12-11 | Hill Philip G | Method For Controlling Combustion In An Internal Combustion Engine And Predicting Performance And Emissions |
| CN111377595A (en) * | 2020-05-08 | 2020-07-07 | 蚌埠凯盛工程技术有限公司 | Method and system for controlling gas supply of glass kiln in real time |
| CN114036758A (en) * | 2021-11-12 | 2022-02-11 | 华电新疆哈密煤电开发有限公司 | Boiler combustion state dynamic display method based on numerical simulation and machine learning |
| CN115081697A (en) * | 2022-06-09 | 2022-09-20 | 佛山众陶联供应链服务有限公司 | Method and equipment for predicting firing curve based on raw materials and computer storage medium |
Non-Patent Citations (2)
| Title |
|---|
| LEILEI XU 等: "Experimental and modeling study of liquid fuel injection and combustion in diesel engines with a common rail injection system", 《APPLIED ENERGY》, vol. 230, pages 287 - 304, XP085493771, DOI: 10.1016/j.apenergy.2018.08.104 * |
| 宋君花 等: "压缩天然气发动机空气管理执行器驱动控制研究", 《车用发动机》, no. 1, pages 23 - 26 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116300666A (en) * | 2023-05-24 | 2023-06-23 | 科大智能物联技术股份有限公司 | Power plant boiler operation control method based on XGBoost optimization |
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