CN103605287A - Bed temperature prediction system and method of circulating fluidized bed boiler - Google Patents

Abstract

The invention discloses a system and method for predicting bed temperature of a circulating fluidized bed boiler. The system includes an on-site intelligent instrument connected to the circulating fluidized bed boiler, a database, a data interface, a control station and a host computer; the on-site intelligent instrument and the control station, The database is connected to the upper computer, and the upper computer includes: a standardized processing module, which is used to collect training samples of key variables from the database and perform standardized processing; a prediction mechanism formation module, which is used to establish a prediction model; a prediction execution module, which is used for real-time prediction Bed temperature; model update module; signal acquisition module; result display module. The present invention predicts the bed temperature according to the operating conditions and operating variables of the circulating fluidized bed boiler, so as to make suggestions and guide the operation, thereby controlling the bed temperature in the optimal range and effectively ensuring the safety and security of the circulating fluidized bed boiler. Environmentally friendly operation, and lay the foundation for further optimization of operating efficiency.

Description

Circulating Fluidized Bed Temperature prognoses system and method

Technical field

The present invention relates to energy project field, especially, relate to a kind of Circulating Fluidized Bed Temperature prognoses system and method.

Background technology

Circulating Fluidized Bed Boiler 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.Circulating Fluidized Bed Temperature is one directly affect the important parameter that can boiler safe and continuous move, also directly affect desulfuration efficiency in boiler operatiopn and the generation of oxides of nitrogen simultaneously, this is external when Circulating Fluidized Bed Boiler operational efficiency is optimized, and bed temperature is the important restrictions condition that needs are considered.Set up the prognoses system of Circulating Fluidized Bed Temperature, significant to the safety of Circulating Fluidized Bed Boiler, environmental protection operation and operation optimization.

Summary of the invention

The object of the invention is to for the deficiencies in the prior art, a kind of Circulating Fluidized Bed Boiler 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 Circulating Fluidized Bed Temperature prognoses system, comprises the field intelligent instrument, database, data-interface, control station and the host computer that are connected with Circulating Fluidized Bed Boiler; 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 average bed 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=1,2,…,n；j=1,2,…,p） （1）

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

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=1,2,…,n；j=1,2,…,p） （3）

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

1.3) standardization

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

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

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.

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 execution module.

Prediction execution module, for predicting bed temperature according to the performance variable of the operating condition of Circulating Fluidized Bed Boiler 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=1,2,…,p） （8）

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 bed 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 t moment bed temperature;

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

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

Wherein,

former dimension predicted value for t moment bed 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 bed 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 bed 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 bed temperature to be controlled at optimum range most, so that control station staff, according to bed temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by bed temperature in time, effectively guarantees safety, the environmental protection operation of Circulating Fluidized Bed Boiler.Wherein, how performance variable is adjusted is conducive to bed temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, and substitution bed temperature prognoses system, obtains corresponding bed 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 Circulating Fluidized Bed 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 average bed 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=1,2,…,n；j=1,2,…,p） （1）

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

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=1,2,…,n；j=1,2,…,p） （3）

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

1.3) standardization

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

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

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.

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 Circulating Fluidized Bed Boiler and setting as input signal, according to predictive coefficient vector, bed 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=1,2,…,p） （8）

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 bed 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 t moment bed temperature;

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

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

Wherein,

former dimension predicted value for t moment bed temperature.

As preferred a kind of scheme: described method also comprises: 4) by the sampling time interval of setting, collection site intelligent instrument signal, by the actual bed 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 bed 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 bed temperature to be controlled at optimum range most, so that control station staff, according to bed temperature predicted value and suggestion for operation, timely adjusting operation condition, bed temperature is controlled to optimum range, effectively guarantees safety, the environmental protection operation of Circulating Fluidized Bed Boiler.Wherein, how performance variable is adjusted is conducive to bed temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, and substitution bed temperature prognoses system, obtains corresponding bed 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 bed temperature to Circulating Fluidized Bed Boiler is predicted, advises and guiding production operation, and bed temperature is controlled to optimum range, effectively guarantees safety, the environmental protection operation of Circulating Fluidized Bed Boiler.

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.

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 Circulating Fluidized Bed 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 Circulating Fluidized Bed Boiler 1, field intelligent instrument 2 is connected with fieldbus, data 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 average bed 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=1,2,…,n；j=1,2,…,p） （1）

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

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=1,2,…,n；j=1,2,…,p） （3）

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

1.3) standardization

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

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

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.

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 execution module.

Prediction execution module 9, for predicting bed temperature according to the performance variable of the operating condition of Circulating Fluidized Bed Boiler 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=1,2,…,p） （8）

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 bed 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 t moment bed temperature;

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

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

Wherein,

former dimension predicted value for t moment bed 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 bed 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 bed 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 bed temperature to be controlled at optimum range most, so that control station staff, according to bed temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by bed temperature in time, effectively guarantees safety, the environmental protection operation of Circulating Fluidized Bed Boiler.Wherein, how performance variable is adjusted is conducive to bed temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, and substitution bed temperature prognoses system, obtains corresponding bed 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-carrier store, the data sample that storage running is required and operational factor etc.; Program storage, the software program of storage practical function module; Arithmetical unit, executive routine, 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 Circulating Fluidized Bed 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 average bed 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=1,2,…,n；j=1,2,…,p） （1）

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

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=1,2,…,n；j=1,2,…,p） （3）

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

1.3) standardization

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

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

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.

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 Circulating Fluidized Bed Boiler and setting as input signal, according to predictive coefficient vector, bed 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=1,2,…,p） （8）

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 bed 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 t moment bed temperature;

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

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

Wherein,

former dimension predicted value for t moment bed temperature.

Described method also comprises: 4) by the sampling time interval of setting, collection site intelligent instrument signal, by the actual bed 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 bed 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 bed temperature to be controlled at optimum range most, so that control station staff, according to bed temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by bed temperature in time, effectively guarantees safety, the environmental protection operation of Circulating Fluidized Bed Boiler.Wherein, how performance variable is adjusted is conducive to bed temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, and substitution bed temperature prognoses system, obtains corresponding bed 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.

Circulating Fluidized Bed 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 (1)

1. a Circulating Fluidized Bed 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 Circulating Fluidized Bed Boiler; 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 bed 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;

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 execution module;

Prediction execution module, for predicting bed temperature according to the performance variable of the operating condition of Circulating Fluidized Bed Boiler 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 bed temperature:

Wherein,

nondimensionalization predicted value for t moment bed temperature;

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

Wherein,

former dimension predicted value for t moment bed 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 bed 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 bed 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 bed temperature to be controlled at optimum range most, so that control station staff, according to bed temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by bed temperature in time, effectively guarantees safety, the environmental protection operation of Circulating Fluidized Bed Boiler; Wherein, how performance variable is adjusted is conducive to bed temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, and substitution bed temperature prognoses system, obtains corresponding bed 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 bed temperature Forecasting Methodology that use Circulating Fluidized Bed Temperature prognoses system claimed in claim 1 realizes, 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 bed 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;

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 Circulating Fluidized Bed Boiler and setting as input signal, according to predictive coefficient vector, bed 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 bed temperature:

Wherein,

nondimensionalization predicted value for t moment bed temperature;

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

Wherein,

former dimension predicted value for t moment bed temperature;

Described method also comprises: 4) by the sampling time interval of setting, collection site intelligent instrument signal, by the actual bed 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 bed 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 bed temperature to be controlled at optimum range most, so that control station staff, according to bed temperature predicted value and suggestion for operation, adjusting operation condition, is controlled at optimum range by bed temperature in time, effectively guarantees safety, the environmental protection operation of Circulating Fluidized Bed Boiler; Wherein, how performance variable is adjusted is conducive to bed temperature to be controlled at optimum range most, and a short-cut method is by the multiple combination value of performance variable, and substitution bed temperature prognoses system, obtains corresponding bed 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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