CN103267826A - Soft measurement method for online detection of gelatin concentration - Google Patents

Soft measurement method for online detection of gelatin concentration Download PDF

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CN103267826A
CN103267826A CN2013101317090A CN201310131709A CN103267826A CN 103267826 A CN103267826 A CN 103267826A CN 2013101317090 A CN2013101317090 A CN 2013101317090A CN 201310131709 A CN201310131709 A CN 201310131709A CN 103267826 A CN103267826 A CN 103267826A
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刁芬
孟丽
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Shenyang University
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Abstract

A soft measurement method for online detection of a gelatin concentration relates to a method of detecting the gelatin concentration. The method comprises acquisition of process data, pretreatment of sample data, establishment of soft measurement model, and calculation of the gelatin solution concentration by using the model, wherein the acquisition comprises acquiring the temperature, time, and density values, which are used as auxiliary variables of modeling of the soft measurement, of the gelatin solution on site under different conditions; the pretreatment comprises treatment of abnormity data, treatment of random errors and treatment of normalization; the establishment comprises establishing an Elman neural network model topological structure, determining each single neural network model structure and training algorithm; and the calculation comprises introducing obtained weight coefficients of each single neural network model into a combined neural network model, calculating corresponding values by using the combined model, and performing an anti-normalization treatment for the values, thereby obtaining the gelatin concentration. The method realizes online real-time automatic detection for the gelatin concentration, and provides detection data for realizing automatic control during a gelatin production process.

Description

A kind of flexible measurement method of online detection gelatin concentration
Technical field
The present invention relates to a kind of method that detects gelatin concentration, particularly relate to a kind of flexible measurement method of online detection gelatin concentration.
Background technology
Gelatin enterprise is in producing the gelatin process, and glue-extracting working procedure is center operation crucial in producing, and the speed of glue-extracting working procedure progress can have influence on whole manufacturing of gelatin speed.And gelatin concentration is to carry most important technological parameter in the glue process, and it directly affects the quality of production of gelatin, has very important directive significance in manufacturing of gelatin.
At present, gelatin enterprise adopts the method for artificial timing sampling, off-line measurement to the mensuration of gelatin concentration, but because sampling time interval is longer, and labor measurement is bigger time lag, can not provide the detection data for controlling automatically, to instructing production unfavorable.Therefore, realize the online real-time detection to gelatin concentration, most important to gelatin enterprise improve production efficiency and product quality.And flexible measurement method is realize at present that gelatin concentration measures the most effective, most economical, detection method the most efficiently.
Summary of the invention
The object of the present invention is to provide a kind of flexible measurement method of online detection gelatin concentration.This method has realized the online real time automatic detection of gelatin concentration, realizes that for gelatine production course control provides the detection data automatically, can in time instruct manufacturing of gelatin, is conducive to improve the manufacturing of gelatin quality and enhances productivity.
The objective of the invention is to be achieved through the following technical solutions:
A kind of flexible measurement method of online detection gelatin concentration said method comprising the steps of: carry out the collection of process data, soft-sensing model is set up in the sampled data pre-service, utilizes model to calculate gelatin solution concentration; The collection of carrying out process data is: gelatin solution temperature, time and density value are as the auxiliary variable of soft sensor modeling under the different operating modes of collection in worksite; Sampled data pre-service: the processing, the processing of stochastic error, the normalized that comprise abnormal data; Setting up soft-sensing model is: set up combination Elman neural network model topological structure, determine each single neural network model structure and training algorithm, carry out determining of combination neural net weights; Utilize model to calculate gelatin solution concentration to be: bring the weighting coefficient of each single neural network model of trying to achieve into the combination neural net model, when built-up pattern is input as gelatin solution density, temperature and corresponding sampling time, utilize this built-up pattern can calculate respective value, after this numerical value carried out anti-normalized, just obtain the gelatin concentration value.
The flexible measurement method of described a kind of online detection gelatin concentration, in the described collection of carrying out process data, temperature reads by temperature sensor, detects the concentration value of gelatin solution by the hand-held spectroscope, and measures the density of solution with densitometer.
The flexible measurement method of described a kind of online detection gelatin concentration, the processing of described abnormal data are according to the standard deviation of Bessel formula accounting temperature (T):
Figure 546792DEST_PATH_IMAGE001
If a certain temperature samples data
Figure 188995DEST_PATH_IMAGE002
Deviation
Figure 26501DEST_PATH_IMAGE003
(
Figure 779562DEST_PATH_IMAGE004
) satisfy:
Figure 940285DEST_PATH_IMAGE005
, then think
Figure 504122DEST_PATH_IMAGE006
Be abnormal data, rejected.
The flexible measurement method of described a kind of online detection gelatin concentration, the processing of described stochastic error will be adopted the average value filtering algorithm respectively through remaining temperature, density and viscosity data after the Error processing, remove the stochastic error in the sampled data.
The flexible measurement method of described a kind of online detection gelatin concentration, described normalized will be passed through
Figure 812612DEST_PATH_IMAGE007
R temperature data after stochastic error is handled
Figure 120097DEST_PATH_IMAGE008
Normalize in [0,1] interval and be:
Figure 260277DEST_PATH_IMAGE010
Wherein,
Figure 806796DEST_PATH_IMAGE011
Be
Figure 901660DEST_PATH_IMAGE012
It is individual through the temperature data after the normalized,
Figure 850024DEST_PATH_IMAGE013
Be the minimum value of r temperature sampling data,
Figure 4931DEST_PATH_IMAGE014
Maximal value for r sampled data.
The flexible measurement method of described a kind of online detection gelatin concentration, described foundation combination Elman neural network model topological structure is combined a plurality of Elman neural network models, constitutes the combination neural net model.
The flexible measurement method of described a kind of online detection gelatin concentration, described definite each single neural network model structure and training algorithm, r after a normalized sample data is divided into two groups: one group of common initial training data set as each single neural network model, respectively each single Elman neural network is trained; Another group is used for the neural network model that verification is set up as the checking data collection.
The flexible measurement method of described a kind of online detection gelatin concentration, its combination neural net weights determine that each Determination of Weight Coefficient adopts and minimizes maximum absolute error as finding the solution the standard of the combining weights of combination neural net.
Further specify as follows:
1. the collection of process data
Gelatin solution temperature (T), time (t) and density under the different operating modes of collection in worksite (
Figure 38746DEST_PATH_IMAGE015
) value is as the auxiliary variable of soft sensor modeling.Wherein, temperature (T) can read by temperature sensor, and records the corresponding time (t), simultaneously, detects the concentration value (c) of gelatin solution by the hand-held spectroscope, and with densitometer measure solution density (
Figure 937301DEST_PATH_IMAGE015
).With gelatin solution temperature, time, density and the concentration value of synchronization record as one group of sample data (T, t, , c), every about half an hour, repeat this operation, up to collect needed sample number till.
2. sampled data pre-service
Figure 331559DEST_PATH_IMAGE016
, abnormal data processing
Respectively organize temperature value to what collect , density value
Figure 289337DEST_PATH_IMAGE018
And viscosity number
Figure 212293DEST_PATH_IMAGE019
Calculating its arithmetic mean value respectively is
Figure 709003DEST_PATH_IMAGE020
,
Figure 451831DEST_PATH_IMAGE021
With
Figure 692188DEST_PATH_IMAGE022
And calculate its deviate respectively ,
Figure 950311DEST_PATH_IMAGE024
,
Figure 695282DEST_PATH_IMAGE025
(
Figure 224483DEST_PATH_IMAGE026
), according to the standard deviation of Bessel formula accounting temperature (T):
Figure 371300DEST_PATH_IMAGE001
If a certain temperature samples data
Figure 960544DEST_PATH_IMAGE002
Deviation
Figure 927232DEST_PATH_IMAGE003
(
Figure 260124DEST_PATH_IMAGE004
) satisfy:
Figure 261447DEST_PATH_IMAGE005
, then think
Figure 21593DEST_PATH_IMAGE006
Be abnormal data, rejected.In like manner, calculate the standard deviation of density, viscosity sample data respectively
Figure 475577DEST_PATH_IMAGE027
With
Figure 612160DEST_PATH_IMAGE028
, rejecting abnormal data wherein uses the same method.
Figure 467990DEST_PATH_IMAGE007
, stochastic error processing
To pass through
Figure 399037DEST_PATH_IMAGE016
Remaining r group temperature (T) after the Error processing, density (
Figure 340317DEST_PATH_IMAGE015
) and viscosity (c) data adopt the average value filtering algorithm respectively, to remove the stochastic error in the sampled data, improve the used data precision of following model.
Figure 280591DEST_PATH_IMAGE029
, normalized
To pass through
Figure 990927DEST_PATH_IMAGE007
R temperature data after stochastic error is handled
Figure 92875DEST_PATH_IMAGE008
Normalize in [0,1] interval and be:
Figure 999837DEST_PATH_IMAGE010
Wherein,
Figure 564679DEST_PATH_IMAGE011
Be It is individual through the temperature data after the normalized, Be the minimum value of r temperature sampling data,
Figure 35478DEST_PATH_IMAGE014
Maximal value for r sampled data.Use the same method, r density, viscosity data and a corresponding r time data are normalized to respectively in [0,1] interval, obtain density, viscosity and time data after the normalization , ,
Figure 114795DEST_PATH_IMAGE032
(
Figure 184251DEST_PATH_IMAGE010
).
3. set up soft-sensing model
Figure 474418DEST_PATH_IMAGE016
, set up combination Elman neural network model topological structure
The present invention combines a plurality of Elman neural network models, constitutes the combination neural net model,
See shown in Figure 1.Total output of combination neural net model is the weighted sum of each neural network output, that is:
Figure 603917DEST_PATH_IMAGE033
Wherein X is the input data matrix of neural network,
Figure 979535DEST_PATH_IMAGE034
Be combination neural net output, Be the forecast model of combination neural net,
Figure 997355DEST_PATH_IMAGE036
Be the Elman network number of combination,
Figure 297756DEST_PATH_IMAGE037
Be
Figure 160669DEST_PATH_IMAGE038
Individual neural network prediction model,
Figure 571928DEST_PATH_IMAGE039
Be
Figure 836687DEST_PATH_IMAGE038
The non-negative weighting coefficient of individual Elman neural network satisfies normalizing, that is:
Figure 478374DEST_PATH_IMAGE007
, determine each single neural network model structure and training algorithm
The topological structure of each single Elman neural network model as shown in Figure 2, its mathematical model is:
Figure 444056DEST_PATH_IMAGE041
Figure 812589DEST_PATH_IMAGE042
Figure 205524DEST_PATH_IMAGE043
Wherein
Figure 26719DEST_PATH_IMAGE044
Connection weight matrix for context unit and hidden layer unit;
Figure 530512DEST_PATH_IMAGE045
Connection weight matrix for input block and hidden layer unit;
Figure 19131DEST_PATH_IMAGE046
Connection weight matrix for hidden layer unit and output unit; The input of expression Elman network is 3 dimensional vectors;
Figure 891458DEST_PATH_IMAGE048
With
Figure 198943DEST_PATH_IMAGE049
Be respectively the output of n dimension context unit and hidden layer unit;
Figure 542068DEST_PATH_IMAGE050
Being the output of output layer unit, is 1 dimensional vector;
Figure 276806DEST_PATH_IMAGE051
Be the neuronic transfer function of output layer;
Figure 72593DEST_PATH_IMAGE052
Transfer function for hidden layer neuron.
The Elman network carries out the correction of weights by the gradient descent method, and the study index adopts sum of squared errors function to represent:
Figure 918189DEST_PATH_IMAGE053
The learning algorithm of Elman network is:
Figure 115821DEST_PATH_IMAGE054
Figure 304543DEST_PATH_IMAGE056
Figure 953830DEST_PATH_IMAGE057
Figure 5969DEST_PATH_IMAGE058
Figure 82509DEST_PATH_IMAGE059
In the formula,
Figure 852888DEST_PATH_IMAGE060
,
Figure 368183DEST_PATH_IMAGE061
With
Figure 25560DEST_PATH_IMAGE062
Be respectively connection weight
Figure 522269DEST_PATH_IMAGE063
, With Learning rate;
Figure 548497DEST_PATH_IMAGE066
Figure 216108DEST_PATH_IMAGE067
Figure 711811DEST_PATH_IMAGE068
R after a normalized sample data is divided into two groups: one group of common initial training data set as each single neural network model, respectively each single Elman neural network is trained; Another group is used for the neural network model that verification is set up as the checking data collection.
, the determining of combination neural net weights
Total output of combination neural net
Figure 226341DEST_PATH_IMAGE070
Be the weighted sum of each single Elman neural network model output, the present invention adopts for each Determination of Weight Coefficient and minimizes maximum absolute error as finding the solution the standard of the combining weights of combination neural net.Concrete solution procedure is as follows:
If
Figure 943761DEST_PATH_IMAGE071
Be of combination neural net forecast model
Figure 525921DEST_PATH_IMAGE072
The combination neural net error of individual data point, then:
Figure 277976DEST_PATH_IMAGE073
Figure 287390DEST_PATH_IMAGE074
In the formula
Figure 492106DEST_PATH_IMAGE075
mFor checking data is concentrated the sample data number;
Figure 546836DEST_PATH_IMAGE077
Expression the iIndividual Elman neural network is
Figure 477883DEST_PATH_IMAGE078
Individual data point error;
Figure 419163DEST_PATH_IMAGE079
With
Figure 93858DEST_PATH_IMAGE080
All represent
Figure 804193DEST_PATH_IMAGE078
The sample output valve of individual data point;
Figure 171721DEST_PATH_IMAGE081
Expression the The combination neural net model predication value of individual data point.If
Figure 813104DEST_PATH_IMAGE082
The maximal value of the predicated error absolute value of expression combination neural net model, then
Figure 643525DEST_PATH_IMAGE083
Reach minimum linear combination forecasting model with the maximum error absolute value and can be expressed as following optimization problem:
Figure 916375DEST_PATH_IMAGE084
Figure 832247DEST_PATH_IMAGE085
Find the solution this optimization problem and can obtain the weighting coefficient of each single neural network.
4. utilize model to calculate gelatin solution concentration
Bring the weighting coefficient of each single neural network model of trying to achieve into the combination neural net model, when built-up pattern is input as gelatin solution density, temperature and corresponding sampling time, utilize this built-up pattern can calculate respective value, after this numerical value carried out anti-normalized, just can obtain the gelatin concentration value.
Description of drawings
Fig. 1 is each single neural network model topology diagram;
Fig. 2 is combination neural net model topology structural drawing.
Embodiment
Come below in conjunction with specific embodiments content of the present invention is further described.
(1) collection of sample data
With gelatin solution density (
Figure 848745DEST_PATH_IMAGE015
), temperature (T) and corresponding sampling time ( t) be input variable,
Gelatin solution concentration (c) is output variable.Temperature data passes through the temperature sensor collection in worksite, and records the corresponding time; Simultaneously, glue is sampled, detect by the hand-held spectroscope and obtain concentration data, and measure the density originally of sampling with densitometer.With the glue temperature of synchronization record, time, and the gelatin concentration of manual detection and density as one group of sample data, every about half an hour, repeat this operation, the 235 groups of sample datas of sampling altogether.
(2) sample Data Preprocessing
To 235 groups of temperature values that collect , density value
Figure 243003DEST_PATH_IMAGE087
And viscosity number
Figure 380592DEST_PATH_IMAGE088
Calculating its arithmetic mean value respectively is ,
Figure 740215DEST_PATH_IMAGE021
, and
Figure 620446DEST_PATH_IMAGE022
And calculate its deviate respectively ,
Figure 603632DEST_PATH_IMAGE024
,
Figure 263152DEST_PATH_IMAGE025
(
Figure 314285DEST_PATH_IMAGE089
), according to the standard deviation of Bessel formula accounting temperature (T):
Figure 426466DEST_PATH_IMAGE001
If a certain temperature samples data
Figure 588457DEST_PATH_IMAGE002
Deviation
Figure 649954DEST_PATH_IMAGE003
(
Figure 121256DEST_PATH_IMAGE090
) satisfy:
Figure 471465DEST_PATH_IMAGE005
, then think
Figure 686415DEST_PATH_IMAGE006
Be abnormal data, rejected.In like manner, calculate the standard deviation of density, viscosity sample data respectively
Figure 805681DEST_PATH_IMAGE027
With
Figure 205742DEST_PATH_IMAGE028
, rejecting abnormal data wherein uses the same method.
235 groups of data are carried out abnormal data reject 220 groups of remaining sampled datas utilization average value filtering algorithms of back, to eliminate the stochastic error that exists in the data.
220 groups of data after handling through average value filtering are carried out normalized: with 220 temperature datas
Figure 43248DEST_PATH_IMAGE091
Normalize in [0,1] interval and be:
Figure 796309DEST_PATH_IMAGE009
Figure 35660DEST_PATH_IMAGE092
Wherein, Be the minimum value of 220 temperature sampling data,
Figure 907987DEST_PATH_IMAGE014
Be the maximal value of 220 sampled datas,
Figure 464740DEST_PATH_IMAGE011
For through the after the normalized Individual temperature data.Use the same method, 220 density datas, 220 carried the glue time data and 220 viscosity datas normalize to respectively in [0,1] interval, obtain after the normalization density, carry glue time and viscosity data
Figure 277024DEST_PATH_IMAGE030
,
Figure 823543DEST_PATH_IMAGE093
With
Figure 183986DEST_PATH_IMAGE031
,
Figure 132350DEST_PATH_IMAGE092
Temperature data with the synchronization after the normalized
Figure 287257DEST_PATH_IMAGE011
, density data ,
Figure 219627DEST_PATH_IMAGE031
Data and correspondingly carry the glue time
Figure 22498DEST_PATH_IMAGE093
Sample data of data composition ( ,
Figure 869417DEST_PATH_IMAGE030
, ,
Figure 229040DEST_PATH_IMAGE031
) the final sample data collection of formation.
Concentrating arbitrarily from sample data, 170 groups of sample datas of extraction are used for neural network training to set up soft-sensing model as learning sample; Remaining 50 groups as the generalization ability of verification sample with the check soft-sensing model.
(3) combination neural net structure of models
5 single Elman neural network models are combined, constituted the combination neural net model.The input of combination neural net is identical with the input data of each single neural network model, and total output of combination neural net is the weighted sum of various neural network outputs, that is:
Figure 725749DEST_PATH_IMAGE094
(4) each single Elman neural network model structure
5 single Elman neural network models adopt 3 * 5 * 1 respectively, 3 * 6 * 1,3 * 7 * 1,3 * 8 * 1,3 * 9 * 1 structure, be that each single Elman neural network is divided into 3 layers: input layer is 3 neurons, and output layer is 1 neuron, and hidden layer adopts 5,6,7,8 and 9 neurons respectively.Hidden layer adopts
Figure 796473DEST_PATH_IMAGE095
The type transfer function, output layer adopts
Figure 787563DEST_PATH_IMAGE096
The type transfer function.
(5) training of single Elman neural network model
Respectively 5 single Elman neural networks are trained with 170 groups of training datas, the termination error of training is 1 * 10 -3, by training the weight of determining each single Elman neural network.
(6) combination neural net model combination coefficient determines
By finding the solution optimization problem
Figure 814294DEST_PATH_IMAGE084
Figure 232637DEST_PATH_IMAGE097
Obtain the optimum combination coefficient
Figure 712029DEST_PATH_IMAGE098
With
Figure 506809DEST_PATH_IMAGE099
Value.
(7) calculate gelatin solution concentration
With flexible measurement method of the present invention, be used for detecting in real time the gelatin solution concentration value, and with itself and hand-held
Spectroscope detects and obtains concentration data and compare, and partial results is as shown in table 1.The gelatin solution concentration value that calculates with flexible measurement method of the present invention and the detected value of hand-held spectroscope are very approaching, and error all within ± 2.3%, satisfies demand of practical production fully, and production is had actual directive significance.
Table 1 combination neural net part predicted value and measured value comparison sheet
Figure 388046DEST_PATH_IMAGE100
。

Claims (8)

1. the flexible measurement method of an online detection gelatin concentration is characterized in that, said method comprising the steps of: carry out the collection of process data, soft-sensing model is set up in the sampled data pre-service, utilizes model to calculate gelatin solution concentration; The collection of carrying out process data is: gelatin solution temperature, time and density value are as the auxiliary variable of soft sensor modeling under the different operating modes of collection in worksite; Sampled data pre-service: the processing, the processing of stochastic error, the normalized that comprise abnormal data; Setting up soft-sensing model is: set up combination Elman neural network model topological structure, determine each single neural network model structure and training algorithm, carry out determining of combination neural net weights; Utilize model to calculate gelatin solution concentration to be: bring the weighting coefficient of each single neural network model of trying to achieve into the combination neural net model, when built-up pattern is input as gelatin solution density, temperature and corresponding sampling time, utilize this built-up pattern can calculate respective value, after this numerical value carried out anti-normalized, just obtain the gelatin concentration value.
2. the flexible measurement method of a kind of online detection gelatin concentration according to claim 1, it is characterized in that in the described collection of carrying out process data, temperature reads by temperature sensor, detect the concentration value of gelatin solution by the hand-held spectroscope, and measure the density of solution with densitometer.
3. the flexible measurement method of a kind of online detection gelatin concentration according to claim 1 is characterized in that, the processing of described abnormal data is according to the standard deviation of Bessel formula accounting temperature (T): If a certain temperature samples data
Figure 410228DEST_PATH_IMAGE002
Deviation
Figure DEST_PATH_IMAGE003
(
Figure 580179DEST_PATH_IMAGE004
) satisfy:
Figure DEST_PATH_IMAGE005
, then think
Figure 162338DEST_PATH_IMAGE006
Be abnormal data, rejected.
4. the flexible measurement method of a kind of online detection gelatin concentration according to claim 1, it is characterized in that, the processing of described stochastic error will be adopted the average value filtering algorithm respectively through remaining temperature, density and viscosity data after the Error processing, remove the stochastic error in the sampled data.
5. the flexible measurement method of a kind of online detection gelatin concentration according to claim 1 is characterized in that, described normalized will be passed through
Figure DEST_PATH_IMAGE007
R temperature data after stochastic error is handled
Figure 835765DEST_PATH_IMAGE008
Normalize in [0,1] interval and be:
Figure 986124DEST_PATH_IMAGE010
Wherein,
Figure DEST_PATH_IMAGE011
Be
Figure 377791DEST_PATH_IMAGE012
It is individual through the temperature data after the normalized,
Figure DEST_PATH_IMAGE013
Be the minimum value of r temperature sampling data,
Figure 763642DEST_PATH_IMAGE014
Maximal value for r sampled data.
6. the flexible measurement method of a kind of online detection gelatin concentration according to claim 1 is characterized in that, described foundation combination Elman neural network model topological structure is combined a plurality of Elman neural network models, constitutes the combination neural net model.
7. the flexible measurement method of a kind of online detection gelatin concentration according to claim 1, it is characterized in that, described definite each single neural network model structure and training algorithm, r after a normalized sample data is divided into two groups: one group of common initial training data set as each single neural network model, respectively each single Elman neural network is trained; Another group is used for the neural network model that verification is set up as the checking data collection.
8. the flexible measurement method of a kind of online detection gelatin concentration according to claim 1, it is characterized in that, determining of described combination neural net weights, each Determination of Weight Coefficient adopt and minimize maximum absolute error as finding the solution the standard of the combining weights of combination neural net.
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CN103471971A (en) * 2013-09-26 2013-12-25 沈阳大学 Soft measurement method for online detecting fine powder rate of aluminum powder
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CN111912875A (en) * 2020-06-23 2020-11-10 宁波大学 Fractionating tower benzene content soft measurement method based on stack type Elman neural network
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CN111912875B (en) * 2020-06-23 2024-02-13 江苏淮河化工有限公司 Fractionation tower benzene content soft measurement method based on stacked Elman neural network

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Application publication date: 20130828