CN103615716A - System and method for circulating fluidized bed boiler exhaust gas temperature prediction - Google Patents

Abstract

The invention discloses a system and a method for circulating fluidized bed boiler exhaust gas temperature prediction. The system comprises an on-site intelligent instrument connected with a circulating fluidized bed boiler, a database, a data interface, a control station and an upper computer. The on-site intelligent instrument is connected with the control station, the database and the upper computer. The upper computer comprises a standardization processing module used for collecting training samples of key variables from the database and performing standardization processing, a prediction mechanism formation module used for establishing a prediction model, a prediction execution module used for real-time prediction of exhaust gas temperature, a model updating module, a signal collection module and a result displaying module. The exhaust gas temperature is predicted according to the operation conditions and operation variables of the circulating fluidized bed boiler, which is convenient for suggesting and guiding the operation, and therefore the exhaust gas temperature of the circulating fluidized bed boiler is controlled in the optimal range, the operation efficiency and operation safety of the boiler are raised effectively, and the foundation for further optimization of operation efficiency is laid.

Description

CFBB exhaust gas temperature prognoses system and method

Technical field

The present invention relates to energy project field, especially, relate to a kind of CFBB exhaust gas temperature prognoses system and method.

Background technology

CFBB has the advantages such as pollutant emission is few, fuel tolerance wide, Load Regulation ability is strong, obtains applying more and more widely in recent years in the industries such as electric power, heat supply.Along with the growing tension of the energy and the continuous enhancing of people's energy-conserving and environment-protective consciousness, user excavates in the urgent need to the operation potentiality to boiler unit, improves the operational efficiency of unit.Yet current most of CFBB all exists automaticity low, operation relies on the feature of artificial experience, makes the energy-saving potential of boiler be difficult to be taped the latent power fully, and a major reason that causes this situation is to lack rational prognoses system and method.Heat loss due to exhaust gas is a heat loss of CFBB maximum, and exhaust gas temperature is an important decisive factor of heat loss due to exhaust gas.In addition inappropriate exhaust gas temperature can cause dewfall, dust stratification and the corrosion of air preheater, affects the safe operation of boiler.Based on energy-conservation and consideration safe operation, set up the prognoses system of CFBB exhaust gas temperature, significant to the safe operation of CFBB, energy efficient operation, operating analysis and operation optimization.

Summary of the invention

The object of the invention is to for the deficiencies in the prior art, a kind of CFBB heat loss due to exhaust gas prognoses system and method are provided.

The technical solution adopted for the present invention to solve the technical problems is: a kind of CFBB exhaust gas temperature prognoses system, comprises the field intelligent instrument, database, data-interface, control station and the host computer that are connected with CFBB; Field intelligent instrument is connected with control station, database and host computer, and described host computer comprises:

Standardization module, for gather the historical record of operating condition variable and performance variable from database, the training sample matrix X that forms independent variable, gather the historical record of corresponding smoke evacuation temperature signal, form dependent variable training sample vector Y, training sample X, Y are carried out to standardization, the average that makes each variable is 0, variance is 1, obtains independent variable training sample matrix X after standardization ^*dependent variable training sample vector Y after (n * p), standardization ^*(n * 1), adopts following process to complete:

1.1) average:

${\stackrel{\‾}{x}}_j=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(1\right)$

$\stackrel{\‾}{y}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{y}_i,(i=\mathrm{1,2},...,n)---\left(2\right)$

1.2) ask standard deviation

$s_{x,j}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(3\right)$

$s_y=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(y_i-\stackrel{\‾}{y})}^2},(i=\mathrm{1,2},...,n)---\left(4\right)$

1.3) standardization

$x_{\mathrm{ij}}^{*}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_{x,j}},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(5\right)$

$y_i^{*}=\frac{y_i-\stackrel{\‾}{y}}{s_y},(i=\mathrm{1,2},...,n)---\left(6\right)$

Wherein, x _ij, y _ifor the initial value of training sample point, n is training sample number, and p is independent variable number,

for the average of training sample, s _x,j, s _yfor the standard deviation of training sample,

for the standardized value of training sample point, wherein subscript i, j represent respectively i training sample point, a j independent variable.

The described smoke evacuation temperature difference refers to the difference of exhaust gas temperature and environment temperature.

Forecasting mechanism forms module, and for setting up forecast model, implementation step is as follows:

2.1) by (7) formula, ask predictive coefficient vector β:

β=(X ^*TX ^*) ^-1X ^*TY ^* （7）

Wherein, subscript: T ,-1 is transposition, the inverse of a matrix of representing matrix respectively;

2.2) predictive coefficient vector β is transmitted and stores into prediction Executive Module.

Prediction Executive Module, for predicting exhaust gas temperature according to the performance variable of the operating condition of CFBB and setting, implementation step is as follows:

3.1) the independent variable signal of input is processed by (8) formula:

${x\left(t\right)}_j^{*}=\frac{{x\left(t\right)}_j-{\stackrel{\‾}{x}}_j}{s_{x,j}},(j=\mathrm{1,2},...,p)---\left(8\right)$

Wherein, x (t) _jfor t moment j independent variable initial value,

be the average of j independent variable training sample, s _x,jbe the standard deviation of j independent variable training sample, for t moment j independent variable nondimensionalization value, t represents that time, unit are second;

3.2) by following formula, ask the nondimensionalization predicted value of the smoke evacuation temperature difference:

$\hat{y}{\left(t\right)}^{*}=\left(\begin{array}{cccc}{x\left(t\right)}_1^{*}& {x\left(t\right)}_2^{*}& ...& {x\left(t\right)}_p^{*}\end{array}\right)\mathrm{\β}---\left(9\right)$

Wherein,

for constantly the discharge fume nondimensionalization predicted value of the temperature difference of t;

3.3) by following formula, ask the former dimension predicted value of the smoke evacuation temperature difference:

$\hat{y}\left(t\right)=\hat{y}{\left(t\right)}^{*}\·s_y+\stackrel{\‾}{y}---\left(10\right)$

Wherein,

for constantly the discharge fume former dimension predicted value of the temperature difference of t;

3.4) by following formula, ask the predicted value of exhaust gas temperature:

$t_{\mathrm{py}}=t_{\mathrm{lk}}+\hat{y}\left(t\right)---\left(11\right)$

Wherein, t _pyfor exhaust gas temperature predicted value, t _lkfor environment temperature.

As preferred a kind of scheme: described host computer also comprises: model modification module, be used for by the time interval of setting actual exhaust gas temperature and predicted value comparison, if relative error is greater than 10%, new data is added to training sample data, re-execute standardization module and forecasting mechanism and form module.

Further, described host computer also comprises:

Signal acquisition module, for the sampling time interval by setting, gathers real time data from field intelligent instrument, and from database, gathers historical data.

Result display module, for reading parameters from control station, and exhaust gas temperature predicted value is passed to control station show, and provide suggestion for operation: under current operating mode, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, so that control station staff, according to exhaust gas temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by exhaust gas temperature in time, improves boiler operating efficiency and safety in operation.Wherein, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, substitution exhaust gas temperature prognoses system, obtain corresponding exhaust gas temperature predicted value, thereby by big or small, obtain very intuitively.

As preferred another kind of scheme: described independent variable comprises: operating condition variable: main steam flow, environment temperature, feed temperature, combustion chamber draft, bed pressure, coal-fired moisture, coal-fired volatile matter, coal-fired ash content, coal-fired sulphur content; Performance variable: wind total blast volume, Secondary Air total blast volume.

A CFBB exhaust gas temperature Forecasting Methodology, described Forecasting Methodology comprises the following steps:

1) from database, gather the historical record of operating condition variable and performance variable, the training sample matrix X that forms independent variable, gather the historical record of corresponding smoke evacuation temperature signal, form dependent variable training sample vector Y, training sample X, Y are carried out to standardization, the average that makes each variable is 0, and variance is 1, obtains independent variable training sample matrix X after standardization ^*dependent variable training sample vector Y after (n * p), standardization ^*(n * 1), adopts following process to complete:

1.1) average:

${\stackrel{\‾}{x}}_j=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(1\right)$

$\stackrel{\‾}{y}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{y}_i,(i=\mathrm{1,2},...,n)---\left(2\right)$

1.2) ask standard deviation

$s_{x,j}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(3\right)$

$s_y=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(y_i-\stackrel{\‾}{y})}^2},(i=\mathrm{1,2},...,n)---\left(4\right)$

1.3) standardization

$x_{\mathrm{ij}}^{*}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_{x,j}},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(5\right)$

$y_i^{*}=\frac{y_i-\stackrel{\‾}{y}}{s_y},(i=\mathrm{1,2},...,n)---\left(6\right)$

Wherein, x _ij, y _ifor the initial value of training sample point, n is training sample number, and p is independent variable number,

for the average of training sample, s _x,j, s _yfor the standard deviation of training sample,

for the standardized value of training sample point, wherein subscript i, j represent respectively i training sample point, a j independent variable.

The described smoke evacuation temperature difference refers to the difference of exhaust gas temperature and environment temperature.

2) the standardized training sample obtaining is set up to forecast model by following process:

2.1) by (7) formula, ask predictive coefficient vector β:

β=(X ^*TX ^*) ^-1X ^*TY ^* （7）

Wherein, subscript: T ,-1 is transposition, the inverse of a matrix of representing matrix respectively;

2.2) preserve the predictive coefficient vector β obtaining.

3) using the performance variable of the operating condition variable of CFBB and setting as input signal, according to predictive coefficient vector, exhaust gas temperature to be predicted, implementation step is as follows:

3.1) the independent variable signal of input is processed by (8) formula:

${x\left(t\right)}_j^{*}=\frac{{x\left(t\right)}_j-{\stackrel{\‾}{x}}_j}{s_{x,j}},(j=\mathrm{1,2},...,p)---\left(8\right)$

Wherein, x (t) _jfor t moment j independent variable initial value,

be the average of j independent variable training sample, s _x,jbe the standard deviation of j independent variable training sample,

for t moment j independent variable nondimensionalization value, t represents that time, unit are second;

3.2) by following formula, ask the nondimensionalization predicted value of exhaust gas temperature:

$\hat{y}{\left(t\right)}^{*}=\left(\begin{array}{cccc}{x\left(t\right)}_1^{*}& {x\left(t\right)}_2^{*}& ...& {x\left(t\right)}_p^{*}\end{array}\right)\mathrm{\β}---\left(9\right)$

Wherein,

nondimensionalization predicted value for exhaust gas temperature;

3.3) by following formula, ask the former dimension predicted value of exhaust gas temperature:

$\hat{y}\left(t\right)=\hat{y}{\left(t\right)}^{*}\·s_y+\stackrel{\‾}{y}---\left(10\right)$

Wherein,

former dimension predicted value for exhaust gas temperature;

3.4) by following formula, ask the predicted value of exhaust gas temperature:

$t_{\mathrm{py}}=t_{\mathrm{lk}}+\hat{y}\left(t\right)---\left(11\right)$

Wherein, t _pyfor exhaust gas temperature predicted value, t _lkfor environment temperature.

As preferred a kind of scheme: described method also comprises: 4) by the sampling time interval of setting, collection site intelligence instrument signal, by the actual exhaust gas temperature obtaining and predicted value comparison, if relative error is greater than 10%, new data is added to training sample data, re-execute step 1), 2), so that forecast model is upgraded.

Further, in described step 3), from control station, read parameters, and exhaust gas temperature predicted value is passed to control station show, and provide suggestion for operation: under current operating mode, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, so that control station staff, according to exhaust gas temperature predicted value and suggestion for operation, timely adjusting operation condition, exhaust gas temperature is controlled to optimum range, improves boiler operating efficiency and safety in operation.Wherein, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, substitution exhaust gas temperature prognoses system, obtain corresponding exhaust gas temperature predicted value, thereby by big or small, obtain very intuitively.

As preferred another kind of scheme: described independent variable comprises: operating condition variable: main steam flow, environment temperature, feed temperature, combustion chamber draft, bed pressure, coal-fired moisture, coal-fired volatile matter, coal-fired ash content, coal-fired sulphur content; Performance variable: wind total blast volume, Secondary Air total blast volume.

Beneficial effect of the present invention is mainly manifested in: the exhaust gas temperature to CFBB is predicted, advises and guiding production operation, and exhaust gas temperature is controlled to optimum range, excavates device energy-saving potential, improves the safety in operation of device.

Accompanying drawing explanation

Fig. 1 is the hardware structure diagram of system proposed by the invention.

Fig. 2 is the functional block diagram of host computer of the present invention.

The specific embodiment

Below in conjunction with drawings and Examples, the invention will be further described.

Embodiment 1

With reference to Fig. 1, Fig. 2, a kind of CFBB exhaust gas temperature prognoses system, comprise the field intelligent instrument 2, data-interface 3, database 4, control station 5 and the host computer 6 that are connected with CFBB 1, field intelligent instrument 2 is connected with fieldbus, data/address bus is connected with data-interface 3, data-interface 3 is connected with database 4, control station 5 and host computer 6, and described host computer 6 comprises:

Standardization module 7, for gather the historical record of operating condition variable and performance variable from database, the training sample matrix X that forms independent variable, gather the historical record of corresponding smoke evacuation temperature signal, form dependent variable training sample vector Y, training sample X, Y are carried out to standardization, the average that makes each variable is 0, variance is 1, obtains independent variable training sample matrix X after standardization ^*dependent variable training sample vector Y after (n * p), standardization ^*(n * 1), adopts following process to complete:

1.1) average:

${\stackrel{\‾}{x}}_j=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(1\right)$

$\stackrel{\‾}{y}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{y}_i,(i=\mathrm{1,2},...,n)---\left(2\right)$

1.2) ask standard deviation

$s_{x,j}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(3\right)$

$s_y=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(y_i-\stackrel{\‾}{y})}^2},(i=\mathrm{1,2},...,n)---\left(4\right)$

1.3) standardization

$x_{\mathrm{ij}}^{*}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_{x,j}},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(5\right)$

$y_i^{*}=\frac{y_i-\stackrel{\‾}{y}}{s_y},(i=\mathrm{1,2},...,n)---\left(6\right)$

Wherein, x _ij, y _ifor the initial value of training sample point, n is training sample number, and p is independent variable number,

for the average of training sample, s _x,j, s _yfor the standard deviation of training sample,

for the standardized value of training sample point, wherein subscript i, j represent respectively i training sample point, a j independent variable.

The described smoke evacuation temperature difference refers to the difference of exhaust gas temperature and environment temperature.

Forecasting mechanism forms module 8, and for setting up forecast model, implementation step is as follows:

2.1) by (7) formula, ask predictive coefficient vector β:

β=(X ^*TX ^*) ^-1X ^*TY ^* （7）

Wherein, subscript: T ,-1 is transposition, the inverse of a matrix of representing matrix respectively;

2.2) predictive coefficient vector β is transmitted and stores into prediction Executive Module.

Prediction Executive Module 9, for predicting exhaust gas temperature according to the performance variable of the operating condition of CFBB and setting, implementation step is as follows:

3.1) the independent variable signal of input is processed by (8) formula:

${x\left(t\right)}_j^{*}=\frac{{x\left(t\right)}_j-{\stackrel{\‾}{x}}_j}{s_{x,j}},(j=\mathrm{1,2},...,p)---\left(8\right)$

Wherein, x (t) _jfor t moment j independent variable initial value,

be the average of j independent variable training sample, s _x,jbe the standard deviation of j independent variable training sample,

for t moment j independent variable nondimensionalization value, t represents that time, unit are second;

3.2) by following formula, ask the nondimensionalization predicted value of the smoke evacuation temperature difference:

$\hat{y}{\left(t\right)}^{*}=\left(\begin{array}{cccc}{x\left(t\right)}_1^{*}& {x\left(t\right)}_2^{*}& ...& {x\left(t\right)}_p^{*}\end{array}\right)\mathrm{\β}---\left(9\right)$

Wherein, for constantly the discharge fume nondimensionalization predicted value of the temperature difference of t;

3.3) by following formula, ask the former dimension predicted value of the smoke evacuation temperature difference:

$\hat{y}\left(t\right)=\hat{y}{\left(t\right)}^{*}\·s_y+\stackrel{\‾}{y}---\left(10\right)$

Wherein, former dimension predicted value for the temperature difference of constantly discharging fume for t;

3.4) by following formula, ask the predicted value of exhaust gas temperature:

$t_{\mathrm{py}}=t_{\mathrm{lk}}+\hat{y}\left(t\right)---\left(11\right)$

Wherein, t _pyfor exhaust gas temperature predicted value, t _lkfor environment temperature.

Described host computer 6 also comprises: signal acquisition module 11, for the sampling time interval by setting, gathers real time data from field intelligent instrument, and from database, gather historical data.

Described host computer 6 also comprises: model modification module 12, be used for by the time interval of setting actual exhaust gas temperature and predicted value comparison, if relative error is greater than 10%, new data is added to training sample data, re-execute standardization module and forecasting mechanism and form module.

Described host computer 6 also comprises: result display module 10, for reading parameters from control station, and exhaust gas temperature predicted value is passed to control station show, and provide suggestion for operation: under current operating mode, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, so that control station staff, according to exhaust gas temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by exhaust gas temperature in time, improves boiler operating efficiency and safety in operation.Wherein, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, substitution exhaust gas temperature prognoses system, obtain corresponding exhaust gas temperature predicted value, thereby by big or small, obtain very intuitively.

The hardware components of described host computer 6 comprises: I/O element, for the collection of data and the transmission of information; Data storage, the data sample that storage running is required and operational factor etc.; Program storage, the software program of storage practical function module; Arithmetic unit, performing a programme, realizes the function of appointment; Display module, shows the parameter, the operation result that arrange, and provides suggestion for operation.

Embodiment 2

With reference to Fig. 1, Fig. 2, a kind of CFBB exhaust gas temperature Forecasting Methodology, described Forecasting Methodology comprises the following steps:

1) from database, gather the historical record of operating condition variable and performance variable, the training sample matrix X that forms independent variable, gather the historical record of corresponding smoke evacuation temperature signal, form dependent variable training sample vector Y, training sample X, Y are carried out to standardization, the average that makes each variable is 0, and variance is 1, obtains independent variable training sample matrix X after standardization ^*dependent variable training sample vector Y after (n * p), standardization ^*(n * 1), adopts following process to complete:

1.1) average:

${\stackrel{\‾}{x}}_j=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(1\right)$

$\stackrel{\‾}{y}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{y}_i,(i=\mathrm{1,2},...,n)---\left(2\right)$

1.2) ask standard deviation

$s_{x,j}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j)}^2},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(3\right)$

$s_y=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(y_i-\stackrel{\‾}{y})}^2},(i=\mathrm{1,2},...,n)---\left(4\right)$

1.3) standardization

$x_{\mathrm{ij}}^{*}=\frac{x_{\mathrm{ij}}-{\stackrel{\‾}{x}}_j}{s_{x,j}},(i=\mathrm{1,2},...,n;j=\mathrm{1,2},...,p)---\left(5\right)$

$y_i^{*}=\frac{y_i-\stackrel{\‾}{y}}{s_y},(i=\mathrm{1,2},...,n)---\left(6\right)$

Wherein, x _ij, y _ifor the initial value of training sample point, n is training sample number, and p is independent variable number,

for the average of training sample, s _x,j, s _yfor the standard deviation of training sample,

for the standardized value of training sample point, wherein subscript i, j represent respectively i training sample point, a j independent variable.

The described smoke evacuation temperature difference refers to the difference of exhaust gas temperature and environment temperature.

2) the standardized training sample obtaining is set up to forecast model by following process:

2.1) by (7) formula, ask predictive coefficient vector β:

β=(X ^*TX ^*) ^-1X ^*TY ^* （7）

Wherein, subscript: T ,-1 is transposition, the inverse of a matrix of representing matrix respectively;

2.2) preserve the predictive coefficient vector β obtaining.

3) using the performance variable of the operating condition variable of CFBB and setting as input signal, according to predictive coefficient vector, exhaust gas temperature to be predicted, implementation step is as follows:

3.1) the independent variable signal of input is processed by (8) formula:

${x\left(t\right)}_j^{*}=\frac{{x\left(t\right)}_j-{\stackrel{\‾}{x}}_j}{s_{x,j}},(j=\mathrm{1,2},...,p)---\left(8\right)$

Wherein, x (t) _jfor t moment j independent variable initial value,

be the average of j independent variable training sample, s _x,jbe the standard deviation of j independent variable training sample,

for t moment j independent variable nondimensionalization value, t represents that time, unit are second;

3.2) by following formula, ask the nondimensionalization predicted value of the smoke evacuation temperature difference:

$\hat{y}{\left(t\right)}^{*}=\left(\begin{array}{cccc}{x\left(t\right)}_1^{*}& {x\left(t\right)}_2^{*}& ...& {x\left(t\right)}_p^{*}\end{array}\right)\mathrm{\β}---\left(9\right)$

Wherein,

for constantly the discharge fume nondimensionalization predicted value of the temperature difference of t;

3.3) by following formula, ask the former dimension predicted value of the smoke evacuation temperature difference:

$\hat{y}\left(t\right)=\hat{y}{\left(t\right)}^{*}\·s_y+\stackrel{\‾}{y}---\left(10\right)$

Wherein,

for constantly the discharge fume former dimension predicted value of the temperature difference of t;

3.4) by following formula, ask the predicted value of exhaust gas temperature:

$t_{\mathrm{py}}=t_{\mathrm{lk}}+\hat{y}\left(t\right)---\left(11\right)$

Wherein, t _pyfor exhaust gas temperature predicted value, t _lkfor environment temperature.

Described method also comprises: 4) by the sampling time interval of setting, collection site intelligence instrument signal, by the actual exhaust gas temperature obtaining and predicted value comparison, if relative error is greater than 10%, new data is added to training sample data, re-execute step 1), 2), so that forecast model is upgraded.

In described step 3), from control station, read parameters, and exhaust gas temperature predicted value is passed to control station show, and provide suggestion for operation: under current operating mode, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, so that control station staff, according to exhaust gas temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by exhaust gas temperature in time, improves boiler operating efficiency and safety in operation.Wherein, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, substitution exhaust gas temperature prognoses system, obtain corresponding exhaust gas temperature predicted value, thereby by big or small, obtain very intuitively.

Described independent variable comprises: operating condition variable: main steam flow, environment temperature, feed temperature, combustion chamber draft, bed pressure, coal-fired moisture, coal-fired volatile matter, coal-fired ash content, coal-fired sulphur content; Performance variable: wind total blast volume, Secondary Air total blast volume.

CFBB exhaust gas temperature prognoses system and method proposed by the invention, by above-mentioned concrete implementation step, be described, person skilled obviously can be within not departing from content of the present invention, spirit and scope to device as herein described with method of operating is changed or suitably change and combination, realize the technology of the present invention.Special needs to be pointed out is, all similar replacements and change apparent to one skilled in the artly, they all can be deemed to be included in spirit of the present invention, scope and content.

Claims (2)

1. a CFBB exhaust gas temperature prognoses system, is characterized in that, comprises the field intelligent instrument, database, data-interface, control station and the host computer that are connected with CFBB; Field intelligent instrument is connected with control station, database and host computer, and described host computer comprises:

Standardization module, for gather the historical record of operating condition variable and performance variable from database, the training sample matrix X that forms independent variable, gather the historical record of corresponding smoke evacuation temperature signal, form dependent variable training sample vector Y, training sample X, Y are carried out to standardization, the average that makes each variable is 0, variance is 1, obtains independent variable training sample matrix X after standardization ^*dependent variable training sample vector Y after (n * p), standardization ^*(n * 1), adopts following process to complete:

1.1) average:

1.2) ask standard deviation

1.3) standardization

Wherein, x _ij, y _ifor the initial value of training sample point, n is training sample number, and p is independent variable number,

for the average of training sample, s _x,j, s _yfor the standard deviation of training sample,

for the standardized value of training sample point, wherein subscript i, j represent respectively i training sample point, a j independent variable;

The described smoke evacuation temperature difference refers to the difference of exhaust gas temperature and environment temperature;

Forecasting mechanism forms module, and for setting up forecast model, implementation step is as follows:

2.1) by (7) formula, ask predictive coefficient vector β:

β=(X ^*TX ^*) ^-1X ^*TY ^* （7）

Wherein, subscript: T ,-1 is transposition, the inverse of a matrix of representing matrix respectively;

2.2) predictive coefficient vector β is transmitted and stores into prediction Executive Module;

Prediction Executive Module, for predicting exhaust gas temperature according to the performance variable of the operating condition of CFBB and setting, implementation step is as follows:

3.1) the independent variable signal of input is processed by (8) formula:

Wherein, x (t) _jfor t moment j independent variable initial value,

be the average of j independent variable training sample, s _x,jbe the standard deviation of j independent variable training sample,

for t moment j independent variable nondimensionalization value, t represents that time, unit are second;

3.2) by following formula, ask the nondimensionalization predicted value of the smoke evacuation temperature difference:

Wherein,

for constantly the discharge fume nondimensionalization predicted value of the temperature difference of t;

3.3) by following formula, ask the former dimension predicted value of the smoke evacuation temperature difference:

Wherein,

for constantly the discharge fume former dimension predicted value of the temperature difference of t;

3.4) by following formula, ask the predicted value of exhaust gas temperature:

Wherein, t _pyfor exhaust gas temperature predicted value, t _lkfor environment temperature;

Described host computer also comprises:

Signal acquisition module, for the sampling time interval by setting, gathers real time data from field intelligent instrument, and from database, gathers historical data;

Model modification module, by actual exhaust gas temperature and predicted value comparison, if relative error is greater than 10%, adds new data training sample data for the time interval by setting, and re-executes standardization module and forecasting mechanism and forms module;

Result display module, for reading parameters from control station, and exhaust gas temperature predicted value is passed to control station show, and provide suggestion for operation: under current operating mode, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, so that control station staff, according to exhaust gas temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by exhaust gas temperature in time, improves boiler operating efficiency and safety in operation; Wherein, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, substitution exhaust gas temperature prognoses system, obtain corresponding exhaust gas temperature predicted value, thereby by big or small, obtain very intuitively;

Described independent variable comprises: operating condition variable: main steam flow, environment temperature, feed temperature, combustion chamber draft, bed pressure, coal-fired moisture, coal-fired volatile matter, coal-fired ash content, coal-fired sulphur content; Performance variable: wind total blast volume, Secondary Air total blast volume.

2. the exhaust gas temperature Forecasting Methodology realizing by CFBB exhaust gas temperature prognoses system claimed in claim 1, is characterized in that, described Forecasting Methodology comprises the following steps:

1) from database, gather the historical record of operating condition variable and performance variable, the training sample matrix X that forms independent variable, gather the historical record of corresponding smoke evacuation temperature signal, form dependent variable training sample vector Y, training sample X, Y are carried out to standardization, the average that makes each variable is 0, and variance is 1, obtains independent variable training sample matrix X after standardization ^*dependent variable training sample vector Y after (n * p), standardization ^*(n * 1), adopts following process to complete:

1.1) average:

1.2) ask standard deviation

1.3) standardization

Wherein, x _ij, y _ifor the initial value of training sample point, n is training sample number, and p is independent variable number,

for the average of training sample, s _x,j, s _yfor the standard deviation of training sample,

for the standardized value of training sample point, wherein subscript i, j represent respectively i training sample point, a j independent variable;

The described smoke evacuation temperature difference refers to the difference of exhaust gas temperature and environment temperature;

2) the standardized training sample obtaining is set up to forecast model by following process:

2.1) by (7) formula, ask predictive coefficient vector β:

β=(X ^*TX ^*) ^-1X ^*TY ^* （7）

Wherein, subscript: T ,-1 is transposition, the inverse of a matrix of representing matrix respectively;

2.2) preserve the predictive coefficient vector β obtaining;

3) using the performance variable of the operating condition variable of CFBB and setting as input signal, according to predictive coefficient vector, exhaust gas temperature to be predicted, implementation step is as follows:

3.1) the independent variable signal of input is processed by (8) formula:

Wherein, x (t) _jfor t moment j independent variable initial value,

be the average of j independent variable training sample, s _x,jbe the standard deviation of j independent variable training sample,

for t moment j independent variable nondimensionalization value, t represents that time, unit are second;

3.2) by following formula, ask the nondimensionalization predicted value of the smoke evacuation temperature difference:

Wherein,

for constantly the discharge fume nondimensionalization predicted value of the temperature difference of t;

3.3) by following formula, ask the former dimension predicted value of the smoke evacuation temperature difference:

Wherein,

for constantly the discharge fume former dimension predicted value of the temperature difference of t;

3.4) by following formula, ask the predicted value of exhaust gas temperature:

Wherein, t _pyfor exhaust gas temperature predicted value, t _lkfor environment temperature;

Described method also comprises: 4) by the sampling time interval of setting, collection site intelligence instrument signal, by the actual exhaust gas temperature obtaining and predicted value comparison, if relative error is greater than 10%, new data is added to training sample data, re-execute step 1), 2), so that forecast model is upgraded;

In described step 3), from control station, read parameters, and exhaust gas temperature predicted value is passed to control station show, and provide suggestion for operation: under current operating mode, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, so that control station staff, according to exhaust gas temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by exhaust gas temperature in time, improves boiler operating efficiency and safety in operation; Wherein, how performance variable is adjusted is conducive to exhaust gas temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, substitution exhaust gas temperature prognoses system, obtain corresponding exhaust gas temperature predicted value, thereby by big or small, obtain very intuitively;

Described independent variable comprises: operating condition variable: main steam flow, environment temperature, feed temperature, combustion chamber draft, bed pressure, coal-fired moisture, coal-fired volatile matter, coal-fired ash content, coal-fired sulphur content; Performance variable: wind total blast volume, Secondary Air total blast volume.

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