CN111476433A - Data analysis-based flue gas emission prediction method and system - Google Patents
Data analysis-based flue gas emission prediction method and system Download PDFInfo
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Abstract
The invention discloses a flue gas emission prediction method and a flue gas emission prediction system based on data analysis, which are used for monitoring influence elements influencing flue gas emission in a waste incineration process in real time, predicting whether the pollutant concentration exceeds the standard by utilizing an L STM model according to monitoring data, counting data of each influence factor and the pollutant concentration in the flue gas in a certain time period under the condition that the pollutant concentration exceeds the standard, calculating Pearson correlation coefficients between the pollutant concentration and each corresponding influence element, visualizing the Pearson correlation coefficients of the pollutant and each influence element corresponding to the current pollutant, finding out the maximum value in a plurality of Pearson correlation coefficients, finding out the influence element corresponding to the maximum value of the Pearson correlation coefficient, and sending the found influence element to a flue gas emission monitoring client.
Description
Technical Field
The disclosure relates to the technical field of flue gas emission monitoring, in particular to a flue gas emission prediction method and a flue gas emission prediction system based on data analysis.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
With the continuous development of science and technology and the continuous improvement of the living standard of people, the quantity of household garbage and various garbage is more and more. The waste incineration is a mainstream treatment mode of current waste treatment, and a circulating fluidized bed process is mainly adopted at present. In this method, the volume of the waste is reduced by oxidation at a high temperature by appropriate thermal decomposition, combustion, melting, or other reaction, and the waste becomes a residue or a molten solid. On one hand, the waste incineration can destroy a large amount of waste, and on the other hand, a large amount of energy can be generated. The energy can be used for generating electricity so as to be recycled by people. However, when the garbage incineration is performed in the garbage disposal plant, a large amount of flue gas is generated, and the flue gas often contains a large amount of SO2If the substances are directly discharged without being controlled, the substances can seriously pollute the atmospheric environment on one hand, and can also damage the human body on the other hand.
In the course of implementing the present disclosure, the inventors found that the following technical problems exist in the prior art:
most of the current plants treat flue gas in the following ways:
1: water absorption method: the smoke is directly contacted with water and dissolved in the water by utilizing the characteristic that certain substances in the smoke are easy to dissolve in the water.
2: a dilution and diffusion method: the flue gas is directly discharged to the atmosphere through a chimney for dilution.
3: the chemical reaction method comprises the following steps: the smoke and some substances are subjected to chemical reaction, so that harmful substances are decomposed. The methods can treat the flue gas to a certain degree, but the effects are not obvious enough and have no real-time property, and the flue gas under special conditions can not be treated, and can cause serious environmental pollution.
Disclosure of Invention
In order to solve the defects of the prior art, the present disclosure provides a flue gas emission prediction method and system based on data analysis; the invention can predict whether the concentration of a certain pollutant exceeds the standard or not according to the parameters of the influencing elements of each processing link in real time, and then searches the influencing elements causing the pollutant emission to exceed the standard to regulate and control fundamentally.
In a first aspect, the present disclosure provides a method for predicting smoke emissions based on data analysis;
the flue gas emission prediction method based on data analysis comprises the following steps:
monitoring influencing elements influencing smoke emission in the waste incineration process in real time;
predicting by using an L STM prediction model according to the monitoring data and outputting a result;
and (3) analyzing and processing according to the output result of the prediction model: if the predicted output result is that the smoke pollution degree is not reached, the treatment is not carried out; if the predicted output result is that the smoke pollution degree is reached, counting the data of each influence factor and the concentration of pollutants in the smoke in a set time period; calculating Pearson correlation coefficients between the concentration of each pollutant and each corresponding influence element;
visualizing the Pearson correlation coefficient of each pollutant and each influencing factor;
finding out the maximum value in the Pearson correlation coefficients, finding out the influence element corresponding to the maximum value of the Pearson correlation coefficients, and sending the found influence element to the smoke emission monitoring client.
In a second aspect, the present disclosure also provides a flue gas emission prediction system based on data analysis;
a system for predicting smoke emissions based on data analysis, comprising:
a monitoring module configured to: monitoring influencing elements influencing smoke emission in the waste incineration process in real time;
a prediction module configured to predict from the monitoring data using an L STM prediction model and output a result;
an analysis processing module configured to: and (3) analyzing and processing according to the output result of the prediction model: if the predicted output result is that the smoke pollution degree is not reached, the treatment is not carried out; if the predicted output result is that the smoke pollution degree is reached, counting the data of each influence factor and the concentration of pollutants in the smoke in a set time period; calculating Pearson correlation coefficients between the concentration of each pollutant and each corresponding influence element;
a visualization module configured to: visualizing the Pearson correlation coefficient of each pollutant and each influencing factor;
an output module configured to: finding out the maximum value in the Pearson correlation coefficients, finding out the influence element corresponding to the maximum value of the Pearson correlation coefficients, and sending the found influence element to the smoke emission monitoring client.
In a third aspect, the present disclosure also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first aspect.
In a fourth aspect, the present disclosure also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the method of the first aspect.
In a fifth aspect, the present disclosure also provides a computer program (product) comprising a computer program for implementing the method of any one of the preceding first aspects when run on one or more processors.
Compared with the prior art, the beneficial effect of this disclosure is:
1. and (3) analyzing and processing according to the output result of the prediction model: if the predicted output result is that the smoke pollution degree is not reached, the treatment is not carried out; if the predicted output result is that the smoke pollution degree is reached, counting the data of each influence factor and the concentration of pollutants in the smoke in a set time period; the prediction model is used for carrying out prediction in advance, so that the prediction speed and efficiency can be improved, and the time waste of analyzing and processing the influence factors which do not reach the flue gas pollution index is avoided. The accuracy of prediction can be improved, and the misjudgment rate is reduced.
2. Each processing link in the waste incineration process is monitored in real time, an L STM model is used for predicting whether the content of certain pollutants in the flue gas exceeds the standard or not in real time, and when the content of the pollutants in the flue gas exceeds the standard, an alarm can be given in time.
3. And (4) analyzing by using Pearson correlation coefficients to find out factors causing high pollutant content and processing. Therefore, it is possible to prevent excessive pollutants from being discharged into the atmosphere and reduce pollution to the environment and damage to the human body.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of a method of the first embodiment;
FIG. 2 is a diagram illustrating the visualization effect of the first embodiment;
FIG. 3 is a diagram of the L STM model architecture of the first embodiment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the first embodiment, the present embodiment provides a flue gas emission prediction method based on data analysis;
as shown in fig. 1, the method for predicting smoke emission based on data analysis comprises:
s1: monitoring influencing elements influencing smoke emission in the waste incineration process in real time;
s2, predicting by using a L STM prediction model according to the monitoring data and outputting a result;
s3: and (3) analyzing and processing according to the output result of the prediction model: if the predicted output result is that the smoke pollution degree is not reached, the treatment is not carried out; if the predicted output result is that the smoke pollution degree is reached, counting the data of each influence factor and the concentration of pollutants in the smoke in a set time period; calculating Pearson correlation coefficients between the concentration of each pollutant and each corresponding influence element;
s4: visualizing the Pearson correlation coefficient of each pollutant and each influencing factor;
s5: finding out the maximum value in the Pearson correlation coefficients, finding out the influence element corresponding to the maximum value of the Pearson correlation coefficients, and sending the found influence element to the smoke emission monitoring client.
As one or more embodiments, the method further comprises:
s6: and processing and regulating relevant links corresponding to the found influence elements.
The beneficial effects of the above technical scheme are: the influence factors with high push correlation coefficient enable a worker to intuitively know which link has a problem and carry out regulation and control. Such as reducing the amount of coal, the amount of feedstock, lowering or increasing the temperature of the incinerator, etc.
Further, the influence element includes: the quantity of the fed raw materials, the wind speed of a fan and the temperature of the incinerator.
Further, the quantity of the raw materials is the quantity of the raw materials fed by the coal feeder or the feeder.
Further, the amount of the raw material is monitored by a gravity sensor.
Further, the wind speed of the fan is monitored by a wind speed sensor.
Further, the temperature of the incinerator is monitored by a temperature sensor.
Further, the gravity sensor, the wind speed sensor or the temperature sensor collects data once every set time period. The set time period is, for example, five minutes, ten minutes, or fifteen minutes.
Further, the contaminants, including one or more of the following contaminants: sulfur dioxide SO2Carbon monoxide CO or HC L.
Further, the L STM (long and short term memory model) prediction model is a recurrent neural network model, and the model structure is shown in FIG. 3.
L STM prediction model is formed by inputting sequence X ═ X1,x2,x3...xn) And implicit vector sequence H ═ H (H)1,h2,h3...hn) And the output vector sequence Y ═ Y (Y)1,y2,y3...ym) And (4) forming.
At each time step, the output of L STM is controlled by a set of gates that are controlled by the previous hidden state ht-1Current time step xtAnd input functions of input gate, output gate and forget gate.
These gates together determine the current memory cell transition and the current hidden state.
L the STM transfer function is defined as follows:
it＝σ(Wi·[ht＝1,xt]+bi)
ft＝σ(Wf·[ht-1,xt]+bf)
lt＝tanh(Wl·[ht-1,xt]+bt)
ot＝σ(Wo·[ht-1,xt]+bo)
further, σ is a sigmoid function in the interval [0,1], tanh represents a hyperbolic tangent function in the interval [ -1,1] L STM is used for the task of learning long-term dependencies.
Further, the calculating of the Pearson correlation coefficient between each pollutant concentration and each corresponding influencing element comprises one or more of the following forms:
calculating a Pearson correlation coefficient between the concentration of a certain pollutant and the corresponding raw material feeding quantity of the coal feeder; or,
calculating a Pearson correlation coefficient between the concentration of a certain pollutant and the feeding quantity of the raw materials of the corresponding feeder; or,
calculating a Pearson correlation coefficient between the concentration of a certain pollutant and the wind speed of a corresponding fan; or,
a Pearson correlation coefficient between a certain pollutant concentration and the temperature of the corresponding incinerator is calculated.
Further, the Pearson correlation coefficient is a statistic for reflecting the degree of correlation between two variables. The Pearson correlation coefficient is represented by r, and the degree of the linear correlation between the two variables is described by r. A larger absolute value of r indicates a stronger correlation.
Wherein, XiYiIs the value of each element in the two time series,
is the average of two time series elements.
Further, the influence elements influencing smoke emission in the waste incineration process are monitored in real time, and one or more time sequences of the number of the coal feeder raw materials, the number of the feeder raw materials, the wind speed of the fan, the furnace top temperature or the furnace fault temperature in a certain time period are obtained.
Further, calculating a Pearson correlation coefficient between a certain pollutant concentration and each corresponding influencing element, for example, processing the Pearson correlation coefficient between the sulfur dioxide emission concentration and each influencing element; the correlation coefficient of the sulfur dioxide and each factor is obtained.
Further, in the same coordinate system, each influence factor is taken as an abscissa, and a Pearson correlation coefficient of each influence element corresponding to a certain pollutant is taken as an ordinate; visualizing the constructed coordinate system and a histogram corresponding to the Pearson correlation coefficient of each influencing element in the coordinate system; as shown in fig. 2.
And when the concentration of the pollutants is predicted to exceed a set threshold value, an alarm signal is sent out, factors with high correlation coefficients at the monitoring moment are screened, and then the factors are pushed to a client of a worker. Therefore, related workers can rapidly check the factor, and the emission amount of sulfur dioxide is controlled in time.
Further, the L STM model is obtained by repeatedly training according to historical data, and the specific training steps comprise:
constructing L STM model;
constructing a training set, wherein the training set comprises various smoke emission influence factor contents reaching the smoke pollution degree and various smoke emission influence factor contents not reaching the smoke pollution degree;
and inputting the training set into an L STM model, training the L STM model, and obtaining the trained L STM model after the set training times are reached or the loss function reaches the minimum value.
In the same coordinate system, each influence element is taken as an abscissa, and a Pearson correlation coefficient of each influence element corresponding to a certain pollutant is taken as an ordinate; visualizing the constructed coordinate system and a histogram corresponding to each influencing element Pearson correlation coefficient in the coordinate system; the method comprises the following specific steps:
s301: marking and analyzing the obtained sulfur dioxide and the Pearson correlation coefficient of each factor by taking each influencing factor as an abscissa and the Pearson correlation coefficient (-1,1) as an ordinate, and then forming a histogram;
s302: and displaying the histogram in real time.
The second embodiment also provides a flue gas emission prediction system based on data analysis;
a system for predicting smoke emissions based on data analysis, comprising:
a monitoring module configured to: monitoring influencing elements influencing smoke emission in the waste incineration process in real time;
a prediction module configured to predict from the monitoring data using an L STM prediction model and output a result;
an analysis processing module configured to: and (3) analyzing and processing according to the output result of the prediction model: if the predicted output result is that the smoke pollution degree is not reached, the treatment is not carried out; if the predicted output result is that the smoke pollution degree is reached, counting the data of each influence factor and the concentration of pollutants in the smoke in a set time period; calculating Pearson correlation coefficients between the concentration of each pollutant and each corresponding influence element;
a visualization module configured to: visualizing the Pearson correlation coefficient of each pollutant and each influencing factor;
an output module configured to: finding out the maximum value in the Pearson correlation coefficients, finding out the influence element corresponding to the maximum value of the Pearson correlation coefficients, and sending the found influence element to the smoke emission monitoring client.
It should be noted that the monitoring module, the predicting module, the analyzing module, the visualizing module and the outputting module correspond to steps S1 to S5 in the first embodiment, and the modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In the foregoing embodiments, the descriptions of the embodiments have different emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
In a third embodiment, the present embodiment further provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the method according to the first aspect.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In a fourth embodiment, the present embodiment further provides a computer-readable storage medium for storing computer instructions, and the computer instructions, when executed by a processor, perform the steps of the method in the first embodiment.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. The method for predicting the smoke emission based on data analysis is characterized by comprising the following steps of:
monitoring influencing elements influencing smoke emission in the waste incineration process in real time;
predicting by using an L STM prediction model according to the monitoring data and outputting a result;
and (3) analyzing and processing according to the output result of the prediction model: if the predicted output result is that the smoke pollution degree is not reached, the treatment is not carried out; if the predicted output result is that the smoke pollution degree is reached, counting the data of each influence factor and the concentration of pollutants in the smoke in a set time period; calculating Pearson correlation coefficients between the concentration of each pollutant and each corresponding influence element;
visualizing the Pearson correlation coefficient of each pollutant and each influencing factor;
finding out the maximum value in the Pearson correlation coefficients, finding out the influence element corresponding to the maximum value of the Pearson correlation coefficients, and sending the found influence element to the smoke emission monitoring client.
2. The method of claim 1, further comprising:
and processing and regulating relevant links corresponding to the found influence elements.
3. The method of claim 1, wherein calculating the Pearson correlation coefficient between each contaminant concentration and each respective influencing element comprises one or more of the following forms:
calculating a Pearson correlation coefficient between the concentration of a certain pollutant and the corresponding raw material feeding quantity of the coal feeder; or,
calculating a Pearson correlation coefficient between the concentration of a certain pollutant and the feeding quantity of the raw materials of the corresponding feeder; or,
calculating a Pearson correlation coefficient between the concentration of a certain pollutant and the wind speed of a corresponding fan; or,
a Pearson correlation coefficient between a certain pollutant concentration and the temperature of the corresponding incinerator is calculated.
4. The method of claim 1, wherein the Pearson correlation coefficient is a statistic reflecting the degree of correlation between two variables; the Pearson correlation coefficient is represented by r, and the r describes the degree of the linear correlation strength between the two variables; a larger absolute value of r indicates a stronger correlation.
5. The method of claim 1, wherein the real-time monitoring of the impact elements affecting the flue gas emission during the incineration of the waste obtains a time sequence of one or more of the amount of coal feeder material dosed, the amount of feeder material dosed, the wind speed of a fan, the top temperature of the furnace, or the fault temperature of the furnace over a certain period of time.
6. The method as claimed in claim 1, wherein the L STM model is obtained by iterative training based on historical data, the training steps including:
constructing L STM model;
constructing a training set, wherein the training set comprises various smoke emission influence factor contents reaching the smoke pollution degree and various smoke emission influence factor contents not reaching the smoke pollution degree;
and inputting the training set into an L STM model, training the L STM model, and obtaining the trained L STM model after the set training times are reached or the loss function reaches the minimum value.
7. The method as claimed in claim 1, wherein, in the same coordinate system, each influencing element is taken as an abscissa and a Pearson correlation coefficient of each influencing element corresponding to a certain pollutant is taken as an ordinate; visualizing the constructed coordinate system and a histogram corresponding to each influencing element Pearson correlation coefficient in the coordinate system; the method comprises the following specific steps:
s301: marking and analyzing the obtained sulfur dioxide and the Pearson correlation coefficient of each factor by taking each influencing factor as an abscissa and the Pearson correlation coefficient (-1,1) as an ordinate, and then forming a histogram;
s302: and displaying the histogram in real time.
8. Flue gas emission prediction system based on data analysis, characterized by, include:
a monitoring module configured to: monitoring influencing elements influencing smoke emission in the waste incineration process in real time;
a prediction module configured to predict from the monitoring data using an L STM prediction model and output a result;
an analysis processing module configured to: and (3) analyzing and processing according to the output result of the prediction model: if the predicted output result is that the smoke pollution degree is not reached, the treatment is not carried out; if the predicted output result is that the smoke pollution degree is reached, counting the data of each influence factor and the concentration of pollutants in the smoke in a set time period; calculating Pearson correlation coefficients between the concentration of each pollutant and each corresponding influence element;
a visualization module configured to: visualizing the Pearson correlation coefficient of each pollutant and each influencing factor;
an output module configured to: finding out the maximum value in the Pearson correlation coefficients, finding out the influence element corresponding to the maximum value of the Pearson correlation coefficients, and sending the found influence element to the smoke emission monitoring client.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executable on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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