CN103245481A - Frequency conversion technology based detection method for air resistance characteristic of large-scale variable-load heat exchanger - Google Patents

Frequency conversion technology based detection method for air resistance characteristic of large-scale variable-load heat exchanger Download PDF

Info

Publication number
CN103245481A
CN103245481A CN2013101644232A CN201310164423A CN103245481A CN 103245481 A CN103245481 A CN 103245481A CN 2013101644232 A CN2013101644232 A CN 2013101644232A CN 201310164423 A CN201310164423 A CN 201310164423A CN 103245481 A CN103245481 A CN 103245481A
Authority
CN
China
Prior art keywords
frequency
output
air quantity
value
input
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN2013101644232A
Other languages
Chinese (zh)
Other versions
CN103245481B (en
Inventor
江爱朋
姜周曙
王剑
王金宏
黄国辉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Dianzi University
Original Assignee
Hangzhou Dianzi University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Dianzi University filed Critical Hangzhou Dianzi University
Priority to CN201310164423.2A priority Critical patent/CN103245481B/en
Publication of CN103245481A publication Critical patent/CN103245481A/en
Application granted granted Critical
Publication of CN103245481B publication Critical patent/CN103245481B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Landscapes

  • Feedback Control In General (AREA)

Abstract

The invention discloses a frequency conversion technology based detection method for the air resistance characteristic of a large-scale variable-load heat exchanger. The method comprises the specific steps: speculating an initial frequency predicted value f0 of a frequency converter under standard wind volume according to history data in combination with a nerve network; calculating out three parameters K, T and t of an open-loop transfer function(as shown in the specification) between frequency converter frequency and the pipeline wind volume through a least squares algorithm through acquiring the wind volume and frequency variations of the frequency converter with frequency changing from 0 to f0; then taking IATE as an optimal index and PID+(as shown in the specification) as the open-loop transfer function, utilizing the a closed-loop transfer function as a restraint equation, and performing optimization solution by a nonlinear optimization solver to obtain optimal values Kji, Ti and Td of a PID controller; and obtaining a corrected frequency value f'0 according to approximate linear feature of the wind volume and the frequency at the working point, and regulating the frequency of the frequency converter to f'0. According to the invention, the detection method avoids troubles brought by manually adjusting wind volume, is energy-saving and time-saving, is convenient and quick, and effectively improves the production efficiency.

Description

Detection method based on the varying load large heat exchanger vapour lock characteristic of converter technique
Technical field
The invention belongs to the control technology field, relate to data modeling and the process control in industrial process control field, relate in particular to a kind of detection method of the varying load large heat exchanger vapour lock characteristic based on converter technique.
Background technology
The vapour lock characteristic is the important indicator that characterizes the large heat exchanger flowing property, also is one of important indicator of large-scale plate type finned heat exchanger product necessary test before dispatching from the factory.Mainly comprise two aspects: one is for certain heat exchanger channel, under standard temperature, normal pressure and normal flow situation, and the pressure loss at these heat exchanger channel two ends; Another one is the friction factor of heat exchanger channel in the case.Because test result difference under conditions such as different temperatures, pressure lacks comparability.Therefore the required standard situation is that a normal atmosphere is depressed, the standard zero degrees celsius is the status of criterion, and the air quantity that record this moment is the standard air quantity, and the air quantity that records under other pressure and temperatures need be converted into the standard air quantity.Past, the general orifice flowmeter that adopts was by measuring the vapour lock parameter of heat interchanger under certain air quantity situation in measuring process, and converting then obtains the vapour lock characteristic of heat interchanger under normal conditions.Adopt above method to have significant disadvantages, one is that the measurement accuracy of orifice flowmeter is poor, the mode of manual control valve is adopted in the control of air quantity in addition, cannot accurately control the air quantity after conversion, the 3rd is that the measurement of employing orifice plate and not energy-conservation, inaccurate, the follow-up artificial calculated amount of manual control are big, because each heat interchanger test duration is long, work efficiency is lower.
Adopt the air quantity of Frequency Converter Control blower fan, adopt the nozzle measuring system can the resolution system power saving, the precision that nozzle is measured air quantity be much higher than orifice flowmeter.Owing to can't guarantee that current working is in standard condition in the test process, need the temperature and pressure of heat exchanging device to convert according to the equation of gas state, conversion method is:
Figure 2013101644232100002DEST_PATH_IMAGE002
, here
Figure 2013101644232100002DEST_PATH_IMAGE004
With
Figure 2013101644232100002DEST_PATH_IMAGE006
The expression standard temperature and pressure (STP),
Figure 2013101644232100002DEST_PATH_IMAGE008
Be the actual measurement air quantity,
Figure 2013101644232100002DEST_PATH_IMAGE010
With
Figure 2013101644232100002DEST_PATH_IMAGE012
Be observed pressure and temperature,
Figure 2013101644232100002DEST_PATH_IMAGE014
Then be the standard air quantity after converting.For the purpose of accurately, with the standard air quantity of the quick control of air quantity after conversion, requirement just will be surveyed air quantity by converting the standard air quantity to, control this standard air quantity then and make it equal to set air quantity in heat exchanger channel performance test process.Consider efficient and the energy-conservation requirement of test, measurement mechanism adopts converter technique to carry out air quantity and regulates.
It is that test has intermittence that the different lane testings of heat interchanger also have characteristics: after namely testing a passage, before new passage was received measurement mechanism, it was zero that measurement mechanism air channel air quantity requires.Because the model of each heat interchanger is different with type, in test process, how to determine the original frequency of frequency converter, and how according to the characteristic of system under test (SUT), adopt suitable control method that air quantity is stablized control on the setting value corresponding with standard condition, heat exchanging device measuring accuracy and rapidity have material impact.Because the nonlinear characteristic that hysteresis quality, the different passage part throttle characteristics of measuring system are widely different and frequency variation signal changes causes system to be difficult to realize measuring fast automatically.Therefore be necessary to study a kind of method of observing and controlling air quantity fast that can realize in the case automatically, make it to satisfy the industrial requirements of plate type finned heat exchanger vapour lock characteristic test.
Summary of the invention
The objective of the invention is at the deficiencies in the prior art, a kind of detection method of the varying load large heat exchanger vapour lock characteristic based on converter technique is provided.
The technical solution used in the present invention is as follows:
Step (1) is gathered dissimilar plate type finned heat exchanger design parameters and operational factor, sets up the real-time data base that comprises design of heat exchanger parameter and operational factor, and the collection of concrete parameter is obtained by the database in the heat exchanger system detection platform.
Described design of heat exchanger parameter comprises tunnel name, design air flow, the design vapour lock of heat interchanger, and operational factor comprises actual vapour lock, detected temperatures, air pressure and the actual frequency converter frequency of heat interchanger, and the preparation method of these parameters is mature technology.
Based on historical test data, the employing neural network is set up the neural network model between design standards air quantity, design vapour lock and actual vapour lock, the actual frequency converter frequency, predict that with this blower fan is in order to reach the original frequency predicted value of this air quantity under various heat exchange device channels designs standard air quantity
Figure 2013101644232100002DEST_PATH_IMAGE016
, concrete implementation step is as follows:
At first, extract the historical data under each heat exchanger channel situation, set up input sample and output sample collection, the input sample comprises design air flow, design vapour lock, the detected temperatures of heat interchanger, output sample is that actual vapour lock and heat interchanger are at the actual frequency converter frequency that reaches under the design air flow, adopt neural network that these historical datas are trained then, neural network structure comprises input layer, hidden layer (middle layer) and output layer, and the neuron number scope of hidden layer and output layer is respectively 4～10 and 2～6.In the training process with design air flow, design vapour lock and detected temperatures as input, as output, obtain neural network model by neural network learning with actual frequency converter frequency and actual vapour lock, each parameter-definition in the neural network model is as follows:
Input layer unit input vector is
Figure 2013101644232100002DEST_PATH_IMAGE018
, object vector
Figure 2013101644232100002DEST_PATH_IMAGE020
Hidden layer unit input vector
Figure 2013101644232100002DEST_PATH_IMAGE022
, output vector
Figure 2013101644232100002DEST_PATH_IMAGE024
The output layer unit input vector , output vector , k=1,2 ..., m represents the sample data number; Input layer is to the connection weight of hidden layer
Figure 2013101644232100002DEST_PATH_IMAGE030
, i=1,2 ...., n; J=1,2 ... p; Hidden layer is to the connection weight of output layer
Figure 2013101644232100002DEST_PATH_IMAGE032
, t=1,2 ... q; The output threshold value of each unit of hidden layer
Figure 2013101644232100002DEST_PATH_IMAGE034
, the output threshold value of each unit of output layer
Figure 2013101644232100002DEST_PATH_IMAGE036
The learning process step of neural network model is as follows:
A) connection weights and the threshold value of each layer of initialization are given each connection weight
Figure 56057DEST_PATH_IMAGE030
, With the output threshold value
Figure 97831DEST_PATH_IMAGE034
,
Figure 815252DEST_PATH_IMAGE036
Give the random value in the interval (1,1).
B) choose input sample and output sample.
C) with the output of importing sample, connection weight, input threshold value and output threshold calculations hidden layer and each unit of output layer.
Figure 2013101644232100002DEST_PATH_IMAGE040
Wherein, The input value of expression hidden layer unit,
Figure 2013101644232100002DEST_PATH_IMAGE046
The output valve of expression hidden layer unit,
Figure 2013101644232100002DEST_PATH_IMAGE048
The input value of expression output layer unit,
Figure 2013101644232100002DEST_PATH_IMAGE050
The output valve of expression output layer unit.
D) calculate each unit vague generalization error of output layer
Figure 2013101644232100002DEST_PATH_IMAGE052
, utilize hidden layer to the connection weight of output layer then
Figure 646679DEST_PATH_IMAGE032
, hidden layer output vector
Figure 398735DEST_PATH_IMAGE024
, each unit vague generalization error of output layer Calculate the vague generalization error of each unit of hidden layer , computing formula is as follows:
；
Figure 2013101644232100002DEST_PATH_IMAGE058
；
E) utilize the vague generalization error of each unit of output layer Revise hidden layer to the connection weight of output layer with the output valve of each unit of hidden layer
Figure 117489DEST_PATH_IMAGE032
, output threshold value :
Figure 2013101644232100002DEST_PATH_IMAGE060
；
Figure 2013101644232100002DEST_PATH_IMAGE062
；
Utilize the vague generalization error of each unit of hidden layer equally Revise input layer to the connection weight of hidden layer with the input of input layer
Figure 783329DEST_PATH_IMAGE030
, output threshold value :
Figure 2013101644232100002DEST_PATH_IMAGE064
，?
Figure 2013101644232100002DEST_PATH_IMAGE066
，
Figure 2013101644232100002DEST_PATH_IMAGE068
。
Figure 2013101644232100002DEST_PATH_IMAGE070
Represent current connection weight
Figure 292993DEST_PATH_IMAGE032
,
Figure 2013101644232100002DEST_PATH_IMAGE072
Represent revised connection weight;
Figure 2013101644232100002DEST_PATH_IMAGE074
Represent current output threshold value
Figure 847472DEST_PATH_IMAGE036
,
Figure 2013101644232100002DEST_PATH_IMAGE076
Represent revised respective threshold.
Figure 2013101644232100002DEST_PATH_IMAGE078
Represent current connection weight
Figure 213731DEST_PATH_IMAGE030
,
Figure 2013101644232100002DEST_PATH_IMAGE080
Represent revised connection weight,
Figure 2013101644232100002DEST_PATH_IMAGE082
Represent current threshold value,
Figure 2013101644232100002DEST_PATH_IMAGE084
Represent revised threshold value, N=1,2 ..., NN, wherein NN represents the study iterations set.
F) choose next input sample and output sample, turn back to step c), finish up to m training sample training.
G) calculate the cumulative errors of all samples
Figure 2013101644232100002DEST_PATH_IMAGE086
, the cumulative errors account form is , wherein, q represents the output layer unit number, m represents sample size.If sample cumulative errors
Figure 681664DEST_PATH_IMAGE086
Less than preset value
Figure 2013101644232100002DEST_PATH_IMAGE090
, perhaps current study iterations is greater than the study iterations of setting, and learning training finishes so.Otherwise choose sample input and target output again, turn back to step c) then.
After learning process finishes, weights and threshold value by the neural network each several part that obtains, foundation can reflect the neural network model of input and output, by given input information (design air flow, design vapour lock and detected temperatures), obtains the predicted value of frequency converter original frequency.
Step (2): according to design air flow, design vapour lock and the detected temperatures of real exchanger passage, the neural network model that utilizes step (1) to set up obtains the original frequency predicted value of this heat interchanger channel condition low-converter
Figure 449769DEST_PATH_IMAGE016
Step (3): the original frequency predicted value that obtains according to step (2)
Figure 971886DEST_PATH_IMAGE016
, the blower fan frequency is adjusted to from 0
Figure 700808DEST_PATH_IMAGE016
80%, be designated as
Figure 2013101644232100002DEST_PATH_IMAGE092
, when the blower fan frequency reaches And behind stable operation 10～20s, record and survey the standard air quantity and be designated as this moment
Figure 2013101644232100002DEST_PATH_IMAGE094
With the blower fan frequency adjustment be
Figure 339917DEST_PATH_IMAGE016
100%, be designated as , when the blower fan frequency reaches
Figure 298514DEST_PATH_IMAGE096
And after stablizing 10～20s, record and survey the standard air quantity and be designated as this moment
Figure 2013101644232100002DEST_PATH_IMAGE098
And the blower fan frequency to
Figure 2013101644232100002DEST_PATH_IMAGE100
And stablize in 10～20s process, with actual measurement standard air quantity, actual measurement vapour lock and the blower fan frequency of cycle 0.5～1s record heat exchanger channel, then, actual measurement standard air quantity and blower fan frequency that record in this process is obtained deduct respectively
Figure 2013101644232100002DEST_PATH_IMAGE102
With
Figure 2013101644232100002DEST_PATH_IMAGE104
, obtain the air quantity changing value
Figure 2013101644232100002DEST_PATH_IMAGE106
, the frequency change value , wherein
Figure 2013101644232100002DEST_PATH_IMAGE110
With Respectively the of expression record
Figure 2013101644232100002DEST_PATH_IMAGE114
Individual air quantity changes and the frequency change value.
Step (4): according to the mobile control of pipeline gas model feature, set up the transport function between frequency and the air quantity.The air quantity control system transport function is that one order inertia adds delay link, therefore the transport function between frequency and the standard air quantity can be made as
Figure 2013101644232100002DEST_PATH_IMAGE116
, wherein
Figure 2013101644232100002DEST_PATH_IMAGE118
Represent open loop enlargement factor, time constant and time delay respectively,
Figure 2013101644232100002DEST_PATH_IMAGE120
Be plural number.The frequency change value that obtains according to step (3)
Figure DEST_PATH_IMAGE108A
, will Give positive initial value respectively, pass through transport function Calculate in sample point frequency change value
Figure DEST_PATH_IMAGE125
Output under the effect , then with
Figure DEST_PATH_IMAGE129
Be target, the employing least-squares algorithm simulates three parameters in the transport function
Figure DEST_PATH_IMAGE118AA
, obtain the concrete transport function of this heat interchanger passage.
Step (5): set up the concrete transport function of heat interchanger passage in step (4) after, air quantity is adjusted to preset standard air quantity, the parameter of the PID that adjusts in order to ensure fast and stable.Be optimum index with IATE integration performance, with parameter in the PID controller
Figure 2013101644232100002DEST_PATH_IMAGE132
,
Figure 2013101644232100002DEST_PATH_IMAGE134
, Be variable, with PID+
Figure 2013101644232100002DEST_PATH_IMAGE138
As open-loop transfer function, be equation of constraint with the closed loop transfer function,, with
Figure DEST_PATH_IMAGE132A
,
Figure DEST_PATH_IMAGE134A
,
Figure DEST_PATH_IMAGE136A
Be on the occasion of being variable bound, adopt the nonlinear optimization solution technique to be optimized and find the solution, obtain optimum PID controller parameter
Figure DEST_PATH_IMAGE132AA
, ,
Figure DEST_PATH_IMAGE136AA
Value, wherein ,
Figure DEST_PATH_IMAGE134AAA
,
Figure DEST_PATH_IMAGE136AAA
Represent ratio, integration and differential parameter respectively.
Step (6): obtain the original frequency predicted value according to step (2)
Figure DEST_PATH_IMAGE142
And the frequency that obtains of step (3)
Figure DEST_PATH_IMAGE104A
, calculate approximate linear parameter between air quantity and the frequency, and the modified value of definite original frequency , with the blower fan frequency adjustment to modified value , and stable
Figure DEST_PATH_IMAGE146
Second.According to the principle of similitude, frequency
Figure DEST_PATH_IMAGE148
With air quantity
Figure DEST_PATH_IMAGE150
Exist approximation relation to be
Figure DEST_PATH_IMAGE152
, wherein
Figure DEST_PATH_IMAGE154
With
Figure DEST_PATH_IMAGE156
The linear relationship parameter that obtains for needs.With the original frequency predicted value
Figure DEST_PATH_IMAGE142A
With
Figure DEST_PATH_IMAGE104AA
And corresponding air quantity, bring above relational expression into and obtain parameter
Figure DEST_PATH_IMAGE154A
With
Figure DEST_PATH_IMAGE156A
Obtaining With
Figure DEST_PATH_IMAGE156AA
After, according to
Figure DEST_PATH_IMAGE162
Relation, the order
Figure DEST_PATH_IMAGE150A
Be the established standards air quantity, obtain the modified value of original frequency under the established standards air quantity
Figure DEST_PATH_IMAGE144AA
Step (7): the blower fan frequency setting is existed Frequency point, equifrequent reach setting value and stable
Figure DEST_PATH_IMAGE146A
After second.Adopt the increment type PID controller that the actual measurement air quantity of heat exchanger channel is controlled at established standards air quantity place then, obtain the vapour lock characteristic of heat exchanger channel under the established standards air quantity.The output form of PID is:
；
Figure DEST_PATH_IMAGE167
Here three of the increment type PID controller parameters
Figure DEST_PATH_IMAGE132AAAA
,
Figure DEST_PATH_IMAGE134AAAA
,
Figure DEST_PATH_IMAGE136AAAA
For obtain in step (5) those three parameters.
Figure DEST_PATH_IMAGE169
The expression sampling period,
Figure DEST_PATH_IMAGE171
Represent the setting value of corresponding step number and the error between the value of feedback, ,
Figure DEST_PATH_IMAGE175
,
Figure DEST_PATH_IMAGE177
Represent current frequency values, frequency change value and next step frequency values respectively.By increment type PID control, the actual measurement airflow value can be controlled automatically in the established standards airflow value, control accuracy is in 0.5%.Be exactly vapour lock characteristic under established standards air quantity by the vapour lock characteristic that measures this moment, and the comparison by between actual measurement vapour lock and the design vapour lock can get heat interchanger vapour lock characteristic performance index.
Beneficial effect of the present invention:
The present invention not only replaces manual inspection heat interchanger vapour lock characteristic method in the past fully, has automatic detection, calculates and characteristic of high test precision automatically.Robotization and the rapidity of this method are very good, can adapt to different loads and air quantity requirement, when improving measuring accuracy, reducing labor workload, can accelerate heat interchanger test lot number greatly.
Embodiment
Based on the detection method of the varying load large heat exchanger vapour lock characteristic of converter technique, to the different channel characteristics of various heat exchange device, carry out measuring fast automatically the vapour lock characteristic.The present invention at first carries out data mining according to historical test data, the employing neural network is set up the relation between design of heat exchanger parameter and the original frequency, obtain the control characteristic of heat exchanger channel then by the automatic identification of the control characteristic parameter of various heat exchange device passage, adopt the adjust pid parameter of self-actuated controller of optimal performance index, standard value after adopting the increment type PID controller with the conversion of air channel air quantity then controls to established standards air quantity place, obtains its vapour lock characteristic by the vapour lock value that detects this moment.
Concrete steps of the present invention are as follows:
Step (1) is gathered dissimilar plate type finned heat exchanger design parameters and operational factor, sets up the real-time data base that comprises design of heat exchanger parameter and operational factor, and the collection of concrete parameter is obtained by the database in the heat exchanger system detection platform.
Described design of heat exchanger parameter comprises tunnel name, design air flow, the design vapour lock of heat interchanger, and operational factor comprises actual vapour lock, detected temperatures, air pressure and the actual frequency converter frequency of heat interchanger, and the preparation method of these parameters is mature technology.
Based on historical test data, the employing neural network is set up the neural network model between design standards air quantity, design vapour lock and actual vapour lock, the actual frequency converter frequency, predict that with this blower fan is in order to reach the original frequency predicted value of this air quantity under various heat exchange device channels designs standard air quantity
Figure DEST_PATH_IMAGE142AA
, concrete implementation step is as follows:
At first, extract the historical data under each heat exchanger channel situation, set up input sample and output sample collection, the input sample comprises design air flow, design vapour lock, the detected temperatures of heat interchanger, output sample is that actual vapour lock and heat interchanger are at the actual frequency converter frequency that reaches under the design air flow, adopt neural network that these historical datas are trained then, neural network structure comprises input layer, hidden layer (middle layer) and output layer, and the neuron number scope of hidden layer and output layer is respectively 4～10 and 2～6.In the training process with design air flow, design vapour lock and detected temperatures as input, as output, obtain neural network model by neural network learning with actual frequency converter frequency and actual vapour lock, each parameter-definition in the neural network model is as follows:
Input layer unit input vector is , object vector
Figure DEST_PATH_IMAGE182
Hidden layer unit input vector
Figure DEST_PATH_IMAGE184
, output vector
Figure DEST_PATH_IMAGE186
The output layer unit input vector , output vector , k=1,2 ..., m represents the sample data number; Input layer is to the connection weight of hidden layer
Figure DEST_PATH_IMAGE192
, i=1,2 ...., n; J=1,2 ... p; Hidden layer is to the connection weight of output layer
Figure DEST_PATH_IMAGE194
, t=1,2 ... q; The output threshold value of each unit of hidden layer
Figure DEST_PATH_IMAGE196
, the output threshold value of each unit of output layer
Figure DEST_PATH_IMAGE198
The learning process step of neural network model is as follows:
H) connection weights and the threshold value of each layer of initialization are given each connection weight ,
Figure DEST_PATH_IMAGE201
With the output threshold value
Figure DEST_PATH_IMAGE196A
,
Figure DEST_PATH_IMAGE198A
Give the random value in the interval (1,1).
I) choose input sample and output sample.
J) with the output of importing sample, connection weight, input threshold value and output threshold calculations hidden layer and each unit of output layer.
Figure DEST_PATH_IMAGE205
Wherein,
Figure DEST_PATH_IMAGE209
The input value of expression hidden layer unit,
Figure DEST_PATH_IMAGE211
The output valve of expression hidden layer unit, The input value of expression output layer unit,
Figure DEST_PATH_IMAGE215
The output valve of expression output layer unit.
K) calculate each unit vague generalization error of output layer
Figure DEST_PATH_IMAGE217
, utilize hidden layer to the connection weight of output layer then , hidden layer output vector , each unit vague generalization error of output layer Calculate the vague generalization error of each unit of hidden layer
Figure DEST_PATH_IMAGE219
, computing formula is as follows:
Figure DEST_PATH_IMAGE221
；
；
L) utilize the vague generalization error of each unit of output layer
Figure DEST_PATH_IMAGE217AA
Revise hidden layer to the connection weight of output layer with the output valve of each unit of hidden layer
Figure DEST_PATH_IMAGE194AA
, output threshold value
Figure DEST_PATH_IMAGE198AA
:
Figure DEST_PATH_IMAGE226
；
Figure DEST_PATH_IMAGE228
；
Utilize the vague generalization error of each unit of hidden layer equally
Figure DEST_PATH_IMAGE219A
Revise input layer to the connection weight of hidden layer with the input of input layer
Figure DEST_PATH_IMAGE192AA
, output threshold value
Figure DEST_PATH_IMAGE196AA
:
Figure DEST_PATH_IMAGE231
，?
Figure DEST_PATH_IMAGE233
，
Figure DEST_PATH_IMAGE235
。
Figure DEST_PATH_IMAGE237
Represent current connection weight
Figure DEST_PATH_IMAGE194AAA
,
Figure DEST_PATH_IMAGE239
Represent revised connection weight;
Figure DEST_PATH_IMAGE241
Represent current output threshold value
Figure DEST_PATH_IMAGE198AAA
, Represent revised respective threshold.
Figure DEST_PATH_IMAGE245
Represent current connection weight
Figure DEST_PATH_IMAGE192AAA
,
Figure DEST_PATH_IMAGE247
Represent revised connection weight,
Figure DEST_PATH_IMAGE249
Represent current threshold value,
Figure DEST_PATH_IMAGE251
Represent revised threshold value, N=1,2 ..., NN, wherein NN represents the study iterations set.
M) choose next input sample and output sample, turn back to step c), finish up to m training sample training.
N) calculate the cumulative errors of all samples
Figure DEST_PATH_IMAGE253
, the cumulative errors account form is
Figure DEST_PATH_IMAGE255
, wherein, q represents the output layer unit number, m represents sample size.If sample cumulative errors
Figure DEST_PATH_IMAGE253A
Less than preset value
Figure DEST_PATH_IMAGE258
, perhaps current study iterations is greater than the study iterations of setting, and learning training finishes so.Otherwise choose sample input and target output again, turn back to step c) then.
After learning process finishes, weights and threshold value by the neural network each several part that obtains, foundation can reflect the neural network model of input and output, by given input information (design air flow, design vapour lock and detected temperatures), obtains the predicted value of frequency converter original frequency.
Step (2): according to design air flow, design vapour lock and the detected temperatures of real exchanger passage, the neural network model that utilizes step (1) to set up obtains the original frequency predicted value of this heat interchanger channel condition low-converter
Figure DEST_PATH_IMAGE142AAA
Step (3): the original frequency predicted value that obtains according to step (2)
Figure DEST_PATH_IMAGE142AAAA
, the blower fan frequency is adjusted to from 0
Figure DEST_PATH_IMAGE142AAAAA
80%, be designated as
Figure DEST_PATH_IMAGE104AAA
, when the blower fan frequency reaches
Figure DEST_PATH_IMAGE104AAAA
And behind stable operation 10～20s, record and survey the standard air quantity and be designated as this moment
Figure DEST_PATH_IMAGE102A
With the blower fan frequency adjustment be 100%, be designated as
Figure DEST_PATH_IMAGE100A
, when the blower fan frequency reaches
Figure DEST_PATH_IMAGE100AA
And after stablizing 10～20s, record and survey the standard air quantity and be designated as this moment
Figure DEST_PATH_IMAGE261
And the blower fan frequency from
Figure DEST_PATH_IMAGE104AAAAA
Arrive
Figure DEST_PATH_IMAGE100AAA
And stablize in 10～20s process, with actual measurement standard air quantity, actual measurement vapour lock and the blower fan frequency of cycle 0.5～1s record heat exchanger channel, then, actual measurement standard air quantity and blower fan frequency that record in this process is obtained deduct respectively
Figure DEST_PATH_IMAGE102AA
With
Figure DEST_PATH_IMAGE104AAAAAA
, obtain the air quantity changing value , the frequency change value
Figure DEST_PATH_IMAGE108AA
, wherein With
Figure DEST_PATH_IMAGE112A
Respectively the of expression record
Figure DEST_PATH_IMAGE114A
Individual air quantity changes and the frequency change value.
Step (4): according to the mobile control of pipeline gas model feature, set up the transport function between frequency and the air quantity.The air quantity control system transport function is that one order inertia adds delay link, therefore the transport function between frequency and the standard air quantity can be made as
Figure DEST_PATH_IMAGE116AA
, wherein Represent open loop enlargement factor, time constant and time delay respectively,
Figure DEST_PATH_IMAGE120A
Be plural number.The frequency change value that obtains according to step (3)
Figure DEST_PATH_IMAGE108AAA
, will
Figure DEST_PATH_IMAGE118AAAA
Give positive initial value respectively, pass through transport function
Figure DEST_PATH_IMAGE116AAA
Calculate in sample point frequency change value
Figure DEST_PATH_IMAGE125A
Output under the effect
Figure DEST_PATH_IMAGE127A
, then with
Figure DEST_PATH_IMAGE129A
Be target, the employing least-squares algorithm simulates three parameters in the transport function , obtain the concrete transport function of this heat interchanger passage.
Step (5): set up the concrete transport function of heat interchanger passage in step (4) after, air quantity is adjusted to preset standard air quantity, the parameter of the PID that adjusts in order to ensure fast and stable.Be optimum index with IATE integration performance, with parameter in the PID controller
Figure DEST_PATH_IMAGE132AAAAA
,
Figure DEST_PATH_IMAGE134AAAAA
,
Figure DEST_PATH_IMAGE136AAAAA
Be variable, with PID+
Figure DEST_PATH_IMAGE138A
As open-loop transfer function, be equation of constraint with the closed loop transfer function,, with
Figure DEST_PATH_IMAGE132AAAAAA
,
Figure DEST_PATH_IMAGE134AAAAAA
, Be on the occasion of being variable bound, adopt the nonlinear optimization solution technique to be optimized and find the solution, obtain optimum PID controller parameter
Figure DEST_PATH_IMAGE132AAAAAAA
,
Figure DEST_PATH_IMAGE134AAAAAAA
, Value, wherein , ,
Figure DEST_PATH_IMAGE136AAAAAAAA
Represent ratio, integration and differential parameter respectively.
Step (6): obtain the original frequency predicted value according to step (2)
Figure DEST_PATH_IMAGE142AAAAAAA
And the frequency that obtains of step (3)
Figure DEST_PATH_IMAGE104AAAAAAA
, calculate approximate linear parameter between air quantity and the frequency, and the modified value of definite original frequency
Figure DEST_PATH_IMAGE144AAAA
, with the blower fan frequency adjustment to modified value , and stable
Figure DEST_PATH_IMAGE146AA
Second.According to the principle of similitude, frequency
Figure DEST_PATH_IMAGE148A
With air quantity
Figure DEST_PATH_IMAGE150AA
Exist approximation relation to be
Figure DEST_PATH_IMAGE152A
, wherein
Figure DEST_PATH_IMAGE154AAA
With
Figure DEST_PATH_IMAGE156AAA
The linear relationship parameter that obtains for needs.With the original frequency predicted value
Figure DEST_PATH_IMAGE142AAAAAAAA
With
Figure DEST_PATH_IMAGE104AAAAAAAA
And corresponding air quantity, bring above relational expression into and obtain parameter
Figure DEST_PATH_IMAGE154AAAA
With Obtaining With
Figure DEST_PATH_IMAGE156AAAAA
After, according to Relation, the order
Figure DEST_PATH_IMAGE150AAA
Be the established standards air quantity, obtain the modified value of original frequency under the established standards air quantity
Figure DEST_PATH_IMAGE144AAAAAA
Step (7): the blower fan frequency setting is existed
Figure DEST_PATH_IMAGE144AAAAAAA
Frequency point, equifrequent reach setting value and stable
Figure DEST_PATH_IMAGE146AAA
After second.Adopt the increment type PID controller that the actual measurement air quantity of heat exchanger channel is controlled at established standards air quantity place then, obtain the vapour lock characteristic of heat exchanger channel under the established standards air quantity.The output form of PID is:
Figure DEST_PATH_IMAGE165A
；
Figure DEST_PATH_IMAGE167A
Here three of the increment type PID controller parameters ,
Figure DEST_PATH_IMAGE134AAAAAAAAA
,
Figure DEST_PATH_IMAGE136AAAAAAAAA
For obtain in step (5) those three parameters. The expression sampling period,
Figure DEST_PATH_IMAGE171A
Represent the setting value of corresponding step number and the error between the value of feedback, , , Represent current frequency values, frequency change value and next step frequency values respectively.By increment type PID control, the actual measurement airflow value can be controlled automatically in the established standards airflow value, control accuracy is in 0.5%.Be exactly vapour lock characteristic under established standards air quantity by the vapour lock characteristic that measures this moment, and the comparison by between actual measurement vapour lock and the design vapour lock can get heat interchanger vapour lock characteristic performance index.

Claims (1)

1. based on the detection method of the varying load large heat exchanger vapour lock characteristic of converter technique, it is characterized in that this method comprises the steps:
Step (1) is gathered dissimilar plate type finned heat exchanger design parameters and operational factor, sets up the real-time data base that comprises design of heat exchanger parameter and operational factor, and the collection of concrete parameter is obtained by the database in the heat exchanger system detection platform;
Described design of heat exchanger parameter comprises tunnel name, design air flow, the design vapour lock of heat interchanger, and operational factor comprises actual vapour lock, detected temperatures, air pressure and the actual frequency converter frequency of heat interchanger;
Based on historical test data, the employing neural network is set up the neural network model between design standards air quantity, design vapour lock and actual vapour lock, the actual frequency converter frequency, predict that with this blower fan is in order to reach the original frequency predicted value of this air quantity under various heat exchange device channels designs standard air quantity
Figure 2013101644232100001DEST_PATH_IMAGE002
, concrete implementation step is as follows:
At first, extract the historical data under each heat exchanger channel situation, set up input sample and output sample collection, the input sample comprises design air flow, design vapour lock, the detected temperatures of heat interchanger, output sample be actual vapour lock and heat interchanger at the actual frequency converter frequency that reaches under the design air flow, adopt neural network that these historical datas are trained then; Neural network structure comprises input layer, hidden layer and output layer, and the neuron number scope of hidden layer and output layer is respectively 4～10 and 2～6; In the training process with design air flow, design vapour lock and detected temperatures as input, as output, obtain neural network model by neural network learning with actual frequency converter frequency and actual vapour lock, each parameter-definition in the neural network model is as follows:
Input layer unit input vector is
Figure 2013101644232100001DEST_PATH_IMAGE004
, object vector
Figure 2013101644232100001DEST_PATH_IMAGE006
Hidden layer unit input vector, output vector
Figure 2013101644232100001DEST_PATH_IMAGE008
The output layer unit input vector , output vector
Figure DEST_PATH_IMAGE012
, k=1,2 ..., m represents the sample data number; Input layer is to the connection weight of hidden layer
Figure DEST_PATH_IMAGE014
, i=1,2 ..., n; J=1,2 ... p; Hidden layer is to the connection weight of output layer
Figure DEST_PATH_IMAGE016
, t=1,2 ... q; The output threshold value of each unit of hidden layer
Figure DEST_PATH_IMAGE018
, the output threshold value of each unit of output layer
Figure DEST_PATH_IMAGE020
The learning process step of neural network model is as follows:
Connection weights and the threshold value of each layer of initialization are given each connection weight
Figure 312329DEST_PATH_IMAGE014
, and output threshold value
Figure 229469DEST_PATH_IMAGE018
,
Figure 822255DEST_PATH_IMAGE020
Give the random value in the interval (1,1);
Choose input sample and output sample;
Output with input sample, connection weight, input threshold value and output threshold calculations hidden layer and each unit of output layer;
Figure DEST_PATH_IMAGE022
Figure DEST_PATH_IMAGE024
Wherein,
Figure DEST_PATH_IMAGE026
The input value of expression hidden layer unit,
Figure DEST_PATH_IMAGE028
The output valve of expression hidden layer unit,
Figure DEST_PATH_IMAGE030
The input value of expression output layer unit,
Figure DEST_PATH_IMAGE032
The output valve of expression output layer unit;
Calculate each unit vague generalization error of output layer
Figure DEST_PATH_IMAGE034
, utilize hidden layer to the connection weight of output layer then
Figure 466732DEST_PATH_IMAGE016
, hidden layer output vector
Figure 343421DEST_PATH_IMAGE008
, each unit vague generalization error of output layer
Figure 477468DEST_PATH_IMAGE034
Calculate the vague generalization error of each unit of hidden layer
Figure DEST_PATH_IMAGE036
, computing formula is as follows:
Figure DEST_PATH_IMAGE038
；
Figure DEST_PATH_IMAGE040
；
Utilize the vague generalization error of each unit of output layer
Figure 619868DEST_PATH_IMAGE034
Revise hidden layer to the connection weight of output layer with the output valve of each unit of hidden layer
Figure 881085DEST_PATH_IMAGE016
, output threshold value
Figure 549964DEST_PATH_IMAGE020
:
Figure DEST_PATH_IMAGE042
；
Figure DEST_PATH_IMAGE044
；
Equally, utilize the vague generalization error of each unit of hidden layer
Figure 917229DEST_PATH_IMAGE036
Revise input layer to the connection weight of hidden layer with the input of input layer
Figure 671558DEST_PATH_IMAGE014
, output threshold value
Figure 221619DEST_PATH_IMAGE018
:
Figure DEST_PATH_IMAGE046
，? ，
Figure DEST_PATH_IMAGE050
；
Wherein,
Figure DEST_PATH_IMAGE052
Represent current connection weight
Figure 136484DEST_PATH_IMAGE016
,
Figure DEST_PATH_IMAGE054
Represent revised connection weight;
Figure DEST_PATH_IMAGE056
Represent current output threshold value
Figure 628645DEST_PATH_IMAGE020
,
Figure DEST_PATH_IMAGE058
Represent revised respective threshold; Represent current connection weight
Figure 745637DEST_PATH_IMAGE014
,
Figure DEST_PATH_IMAGE062
Represent revised connection weight,
Figure DEST_PATH_IMAGE064
Represent current threshold value,
Figure DEST_PATH_IMAGE066
Represent revised threshold value, N=1,2 ..., NN, wherein NN represents the study iterations set;
Choose next input sample and output sample, turn back to step c), finish up to m training sample training;
Calculate the cumulative errors of all samples , the cumulative errors account form is , wherein, q represents the output layer unit number, m represents sample size; If sample cumulative errors
Figure 394662DEST_PATH_IMAGE068
Less than preset value
Figure DEST_PATH_IMAGE072
, perhaps current study iterations is greater than the study iterations of setting, and learning training finishes so; Otherwise choose sample input and target output again, turn back to step c) then;
After learning process finished, by weights and the threshold value of the neural network each several part that obtains, foundation can reflect the neural network model of input and output, by given input sample information, obtains the predicted value of frequency converter original frequency;
Step (2) is according to design air flow, design vapour lock and the detected temperatures of real exchanger passage, and the neural network model that utilizes step (1) to set up obtains the original frequency predicted value of this heat interchanger channel condition low-converter
Figure 913499DEST_PATH_IMAGE002
The original frequency predicted value that step (3) obtains according to step (2)
Figure 248665DEST_PATH_IMAGE002
, the blower fan frequency is adjusted to from 0
Figure 977587DEST_PATH_IMAGE002
80%, be designated as
Figure DEST_PATH_IMAGE074
, when the blower fan frequency reaches
Figure 367986DEST_PATH_IMAGE074
And behind stable operation 10～20s, record and survey the standard air quantity and be designated as this moment
Figure DEST_PATH_IMAGE076
With the blower fan frequency adjustment be 100%, be designated as
Figure DEST_PATH_IMAGE078
, when the blower fan frequency reaches
Figure 512977DEST_PATH_IMAGE078
And after stablizing 10～20s, record and survey the standard air quantity and be designated as this moment
Figure DEST_PATH_IMAGE080
And the blower fan frequency from
Figure 525932DEST_PATH_IMAGE074
Arrive
Figure 408437DEST_PATH_IMAGE078
And stablize in 10～20s process, with actual measurement standard air quantity, actual measurement vapour lock and the blower fan frequency of cycle 0.5～1s record heat exchanger channel, then, actual measurement standard air quantity and blower fan frequency that record in this process is obtained deduct respectively
Figure 72506DEST_PATH_IMAGE076
With
Figure 15054DEST_PATH_IMAGE074
, obtain the air quantity changing value , the frequency change value , wherein
Figure DEST_PATH_IMAGE086
With
Figure DEST_PATH_IMAGE088
Respectively the of expression record
Figure DEST_PATH_IMAGE090
Individual air quantity changes and the frequency change value;
Step (4) flows according to pipeline gas and controls model feature, sets up the transport function between frequency and the air quantity; The air quantity control system transport function is that one order inertia adds delay link, therefore the transport function between frequency and the standard air quantity can be made as , wherein
Figure DEST_PATH_IMAGE094
Represent open loop enlargement factor, time constant and time delay respectively,
Figure DEST_PATH_IMAGE096
Be plural number; The frequency change value that obtains according to step (3) , will
Figure 191049DEST_PATH_IMAGE094
Give positive initial value respectively, pass through transport function
Figure 663619DEST_PATH_IMAGE092
Calculate in sample point frequency change value
Figure DEST_PATH_IMAGE098
Output under the effect
Figure DEST_PATH_IMAGE100
, then with
Figure DEST_PATH_IMAGE102
Be target, the employing least-squares algorithm simulates three parameters in the transport function
Figure 652435DEST_PATH_IMAGE094
, obtain the concrete transport function of this heat interchanger passage;
After step (5) has been set up the concrete transport function of heat interchanger passage in step (4), air quantity is adjusted to preset standard air quantity, the parameter of the PID that adjusts in order to ensure fast and stable; Be optimum index with IATE integration performance, with parameter in the PID controller
Figure DEST_PATH_IMAGE104
,
Figure DEST_PATH_IMAGE106
, Be variable, with PID+ As open-loop transfer function, be equation of constraint with the closed loop transfer function,, with
Figure 764616DEST_PATH_IMAGE104
,
Figure 988924DEST_PATH_IMAGE106
,
Figure 112738DEST_PATH_IMAGE108
Be on the occasion of being variable bound, adopt the nonlinear optimization solution technique to be optimized and find the solution, obtain optimum PID controller parameter
Figure 397089DEST_PATH_IMAGE104
,
Figure 121200DEST_PATH_IMAGE106
, Value, wherein
Figure 330781DEST_PATH_IMAGE104
,
Figure 582771DEST_PATH_IMAGE106
,
Figure 482594DEST_PATH_IMAGE108
Represent ratio, integration and differential parameter respectively;
The original frequency predicted value that step (6) obtains according to step (2)
Figure 861754DEST_PATH_IMAGE002
And the frequency that obtains of step (3)
Figure 163422DEST_PATH_IMAGE074
, calculate approximate linear parameter between air quantity and the frequency, and the modified value of definite original frequency
Figure DEST_PATH_IMAGE112
, with the blower fan frequency adjustment to modified value
Figure 897898DEST_PATH_IMAGE112
, and stable
Figure DEST_PATH_IMAGE114
Second; According to the principle of similitude, frequency
Figure DEST_PATH_IMAGE116
With air quantity
Figure DEST_PATH_IMAGE118
Exist approximation relation to be
Figure DEST_PATH_IMAGE120
, wherein
Figure DEST_PATH_IMAGE122
With
Figure DEST_PATH_IMAGE124
The linear relationship parameter that obtains for needs; With the original frequency predicted value With
Figure 592501DEST_PATH_IMAGE074
And corresponding air quantity, bring above relational expression into and obtain parameter With
Figure 585877DEST_PATH_IMAGE124
Obtaining
Figure 257030DEST_PATH_IMAGE122
With After, according to
Figure DEST_PATH_IMAGE126
Relation, the order
Figure 254253DEST_PATH_IMAGE118
Be the established standards air quantity, obtain the modified value of original frequency under the established standards air quantity
Figure 284526DEST_PATH_IMAGE112
Step (7) exists the blower fan frequency setting
Figure 380657DEST_PATH_IMAGE112
Frequency point, equifrequent reach setting value and stable
Figure 403846DEST_PATH_IMAGE114
After second; Adopt the increment type PID controller that the actual measurement air quantity of heat exchanger channel is controlled at established standards air quantity place then, obtain the vapour lock characteristic of heat exchanger channel under the established standards air quantity; The output form of PID is:
Figure DEST_PATH_IMAGE128
；
Figure DEST_PATH_IMAGE130
Here three of the increment type PID controller parameters
Figure 331351DEST_PATH_IMAGE104
, ,
Figure 866685DEST_PATH_IMAGE108
For obtain in step (5) those three parameters; The expression sampling period,
Figure DEST_PATH_IMAGE134
Represent the setting value of corresponding step number and the error between the value of feedback,
Figure DEST_PATH_IMAGE136
,
Figure DEST_PATH_IMAGE138
,
Figure DEST_PATH_IMAGE140
Represent current frequency values, frequency change value and next step frequency values respectively; By increment type PID control, the actual measurement airflow value can be controlled automatically in the established standards airflow value, control accuracy is in 0.5%; Be exactly vapour lock characteristic under established standards air quantity by the vapour lock characteristic that measures this moment, and the comparison by between actual measurement vapour lock and the design vapour lock can get heat interchanger vapour lock characteristic performance index.
CN201310164423.2A 2013-05-07 2013-05-07 Frequency conversion technology based detection method for air resistance characteristic of large-scale variable-load heat exchanger Active CN103245481B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201310164423.2A CN103245481B (en) 2013-05-07 2013-05-07 Frequency conversion technology based detection method for air resistance characteristic of large-scale variable-load heat exchanger

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201310164423.2A CN103245481B (en) 2013-05-07 2013-05-07 Frequency conversion technology based detection method for air resistance characteristic of large-scale variable-load heat exchanger

Publications (2)

Publication Number Publication Date
CN103245481A true CN103245481A (en) 2013-08-14
CN103245481B CN103245481B (en) 2015-07-15

Family

ID=48925149

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201310164423.2A Active CN103245481B (en) 2013-05-07 2013-05-07 Frequency conversion technology based detection method for air resistance characteristic of large-scale variable-load heat exchanger

Country Status (1)

Country Link
CN (1) CN103245481B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104569625A (en) * 2015-01-20 2015-04-29 中国人民解放军国防科学技术大学 Large antenna directional diagram measuring method based on rotary auxiliary antenna
CN105929683A (en) * 2016-06-23 2016-09-07 东南大学 Differential adjustable PID controller parameter project adjusting method

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS59205523A (en) * 1983-05-09 1984-11-21 Omron Tateisi Electronics Co Combustion controlling device
US6460359B1 (en) * 1998-05-26 2002-10-08 Atlas Copco Airpower, Nv Method and device for cool-drying
CN101392939A (en) * 2008-11-18 2009-03-25 天津大学 Nonlinear prediction and control method for independence energy supply temperature of buildings
CN102096373A (en) * 2010-12-07 2011-06-15 昆明理工大学 Microwave drying PID (proportion integration differentiation) control method based on increment improved BP (back propagation) neural network
CN102645315A (en) * 2012-04-28 2012-08-22 杭州电子科技大学 Automatic, fast and accurate detection method for air resistance characteristics of large heat exchanger
CN102680037A (en) * 2012-06-06 2012-09-19 上海华东电脑系统工程有限公司 Air quantity differential pressure calibration method applied to liquid cooling type frame

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS59205523A (en) * 1983-05-09 1984-11-21 Omron Tateisi Electronics Co Combustion controlling device
US6460359B1 (en) * 1998-05-26 2002-10-08 Atlas Copco Airpower, Nv Method and device for cool-drying
CN101392939A (en) * 2008-11-18 2009-03-25 天津大学 Nonlinear prediction and control method for independence energy supply temperature of buildings
CN102096373A (en) * 2010-12-07 2011-06-15 昆明理工大学 Microwave drying PID (proportion integration differentiation) control method based on increment improved BP (back propagation) neural network
CN102645315A (en) * 2012-04-28 2012-08-22 杭州电子科技大学 Automatic, fast and accurate detection method for air resistance characteristics of large heat exchanger
CN102680037A (en) * 2012-06-06 2012-09-19 上海华东电脑系统工程有限公司 Air quantity differential pressure calibration method applied to liquid cooling type frame

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
徐丁等: "板翅式换热器翅片表面流动特性测试系统", 《杭州电子科技大学学报》, vol. 30, no. 4, 31 August 2010 (2010-08-31) *
李相发等: "大型板翅式换热器气阻特性测控系统的开发", 《第30届中国控制会议》, 31 December 2011 (2011-12-31) *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104569625A (en) * 2015-01-20 2015-04-29 中国人民解放军国防科学技术大学 Large antenna directional diagram measuring method based on rotary auxiliary antenna
CN104569625B (en) * 2015-01-20 2015-11-04 中国人民解放军国防科学技术大学 A kind of large-scale antenna directional diagram measuring method based on rotatable auxiliary antenna
CN105929683A (en) * 2016-06-23 2016-09-07 东南大学 Differential adjustable PID controller parameter project adjusting method
CN105929683B (en) * 2016-06-23 2019-01-18 东南大学 A kind of differential is adjustable PID controller parameter engineering turning model and method

Also Published As

Publication number Publication date
CN103245481B (en) 2015-07-15

Similar Documents

Publication Publication Date Title
CN104533701B (en) A kind of automatic setting method of Turbine Governor System control parameter
CN109583585B (en) Construction method of power station boiler wall temperature prediction neural network model
CN103912966B (en) A kind of earth source heat pump refrigeration system optimal control method
CN102096373B (en) Microwave drying PID (proportion integration differentiation) control method based on increment improved BP (back propagation) neural network
CN104651559B (en) Blast furnace liquid iron quality online forecasting system and method based on multivariable online sequential extreme learning machine
CN102176221B (en) Coke furnace temperature predicting method based on dynamic working conditions in coke furnace heating and burning process
CN111829003B (en) Power plant combustion control system and control method
CN107272412A (en) A kind of identifying approach of intermittent wind tunnel flow field control
CN107632524B (en) Communication machine room temperature model prediction control method and system
CN112987566A (en) Aerodynamic-thermal supercoiled nonlinear fractional order sliding-mode model-free control method
CN201476905U (en) Neural network PID temperature controlled thermocouple automatic verification system
WO2015171196A1 (en) Virtual flow measurement system
CN102998720A (en) Method and device for calibrating dynamic response characteristic of sonde humidity by double flow method
Feng et al. Predictive control model for variable air volume terminal valve opening based on backpropagation neural network
CN103279030B (en) Dynamic soft measuring modeling method and device based on Bayesian frame
CN102419827A (en) Radial basis function (RBF) neural network-based boiling heat exchanging prediction method
CN202210005U (en) Heat energy meter flow full-automatic detection device
CN103245481B (en) Frequency conversion technology based detection method for air resistance characteristic of large-scale variable-load heat exchanger
CN102645315B (en) Automatic, fast and accurate detection method for air resistance characteristics of large heat exchanger
CN114721263A (en) Intelligent regulation and control method for cement decomposing furnace based on machine learning and intelligent optimization algorithm
CN108106679B (en) Method and system for measuring inlet air volume of coal mill of power station
CN104034378A (en) Constant-current thermal gas mass flow meter and measuring method implemented by same
CN107730045A (en) A kind of baseline load thermal inertia modification method based on discrete inertia force system
CN105090084B (en) Draught fan online monitoring system and method
CN104571086B (en) Temperature controller emulation test method based on transmission function

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20130814

Assignee: ZHEJIANG YINBO INTELLIGENT TECHNOLOGY Co.,Ltd.

Assignor: HANGZHOU DIANZI University

Contract record no.: X2020330000114

Denomination of invention: Detection method of gas resistance characteristics of large heat exchanger with variable load based on frequency conversion technology

Granted publication date: 20150715

License type: Common License

Record date: 20201216

EE01 Entry into force of recordation of patent licensing contract