CN108268359A - The optimization method of air compression station based on deep learning - Google Patents
The optimization method of air compression station based on deep learning Download PDFInfo
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- CN108268359A CN108268359A CN201711486859.8A CN201711486859A CN108268359A CN 108268359 A CN108268359 A CN 108268359A CN 201711486859 A CN201711486859 A CN 201711486859A CN 108268359 A CN108268359 A CN 108268359A
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Abstract
The present invention provides a kind of optimization method of the air compression station based on deep learning, the optimization method of the air compression station based on deep learning includes the following steps：(A1) using Internet of Things is monitored, the operating parameter of each air compressor machine in air compression station is obtained；(A2) using operating parameter described in deep learning Algorithm Analysis, the operational mode characteristic model of each air compressor machine in air compression station is extracted；(A3) using operational mode characteristic model described in genetic algorithm optimization, unlatching number of units and the opening time of air compressor machine are obtained.The present invention has the advantages that power consumption is low etc..
Description
Technical field
The present invention relates to the optimization methods of air compression station, the more particularly to air compression station based on deep learning.
Background technology
Compressed air is widely used in the every field in industrial production as a kind of energy of clean environment firendly.But as two
The secondary energy, the power consumption of compressed air in itself are huge.According to statistics, the 10% of China's commercial power is used for air compressor machine.But one
Since straight, enterprise does not have the energy consumption of compressed air system enough attention, causes the operation of air compressor machine energy consumption unreasonable, compression
The energy waste of air system.It for enterprise, is generally all supplied by the way of air compression station, air compression station has multiple air compressors group
Into.
At present, all it is by the original control mode of machine (the included control of limits pressure up and down), substantially on air-compressor set
Joint control is not carried out, can only check the operating parameter of separate unit air compressor machine.Compressed air main application sets cushion gas, instrument is used
Gas, purging etc..If cushion gas is being mainly used for gas equipment, if cushion gas is by after oil removing, water removal and dust, specializing in instrument
With gas, other purposes then include purging, cleaning etc..
The air compressor system planning of most of enterprise is less reasonable, effective coordination control is lacked between unit, there are bright
Aobvious waste, main problem include：
1. air compressor machine is opened arbitrarily, high efficiency does not efficiently use.
2. the variation of environment is not accounted for, the influence for pneumatics engine efficiency.If to the air-compressor set of air compression station into
The power consumption of enterprise will be greatly decreased in row optimal control.
Invention content
In order to solve the deficiency in above-mentioned prior art, the present invention provides a kind of air compression stations based on deep learning
Optimization method, be effectively improved the operational efficiency of air compressor machine, and reduce energy consumption.
A kind of optimization method of the air compression station based on deep learning, the optimization method of the air compression station based on deep learning
Include the following steps：
(A1) using Internet of Things is monitored, the operating parameter of each air compressor machine in air compression station is obtained；
(A2) using operating parameter described in deep learning Algorithm Analysis, the operational mode for extracting each air compressor machine in air compression station is special
Levy model；
(A3) using operational mode characteristic model described in genetic algorithm optimization, unlatching number of units and the unlatching of air compressor machine are obtained
Time.
According to the optimization method of the above-mentioned air compression station based on deep learning, optionally, the sky based on deep learning
The optimization method at pressure station further comprises the steps：
(A4) Internet of Things and Hadoop framework of increasing income, structure air compression station optimization operating system are utilized.
According to the optimization method of the above-mentioned air compression station based on deep learning, it is preferable that the operating parameter includes：Operation
Time, admission pressure, outlet pressure, air compression station environment temperature, inlet temperature, outlet temperature, flow and power.
According to the optimization method of the above-mentioned air compression station based on deep learning, it is preferable that the deep learning algorithm utilizes
Depth autocoder, the depth autocoder include：Encoder, decoder and hidden layer；
The encoder is encoded using following relational expression：
H=f (x)=Sf(Wx+bj)
Wherein, the feature vector that x is made of operating parameter, weights of the W for input vector, bjRepresent j-th of neuron
Threshold value or be known as bias, the hidden layer value that h is；
Decoder is decoded using following relational expression：
Y=g (h)=Sg(Wh+bh)
Wherein, h for hidden layer vector, here as input, W be corresponding weight vector, bhFor threshold value, SgIt is decoder
Activation primitive；
Training process to depth autocoder is that parameter W, b are found on training sample set Dj, bhThe minimum of composition
Reconstructed error, the expression formula of reconstructed error are：
Wherein, x is the input of above-mentioned formula, and g (f (x)) is that the decoder of above-mentioned formula exports, and L is reconstructed error function.
According to the optimization method of the above-mentioned air compression station based on deep learning, it is preferable that described to depth autocoder
Training process include the following steps：
(B1) operating parameter of the input as training, it is unsupervised to train first self-encoding encoder；
(B2) using the output of first self-encoding encoder as the input of next self-encoding encoder, second own coding is trained
Device；
(B3) step (B2) is repeated, until completing the training of preset quantity hidden layer；
(B4) using the power H of air compression station as output, increase a reverse transmittance nerve network on the last one hidden layer
Prediction model realizes the weight fine tuning to the prediction model.
According to the optimization method of the above-mentioned air compression station based on deep learning, it is preferable that in step (A3), optimization includes
Following steps：
(C1) grid and valuation functions of the operating parameter of structure air compressor machine, valuation functions use
(C2) to the unlatching number of units of air compressor machine, opening sequence, using air compression station initial launch number of units and operation order as just
Initial value assesses fitness individual corresponding to every chromosome, using the power H of air-compressor set as fitness；
(C3) principle higher in accordance with fitness, bigger select probability P, selected from population two individual as it is paternal with
Maternal；
(C4) chromosome of parent both sides is extracted, is intersected, generates filial generation.
(C5) to the chromosome of filial generation into row variation.
(C6) step (C2)-(C4) is repeated, until the generation of new population, when iterations reach setting number, acquisition is empty
Press opens number of units, opening time.
According to the optimization method of the above-mentioned air compression station based on deep learning, it is preferable that in step (A3), utilize heredity
Algorithm is solved in the case of air compression station power minimum, obtains the unlatching number of units and sequence of air compressor machine.
Compared with prior art, the device have the advantages that being：
By way of wireless internet of things, air-compressor set Optimal Control System is built, while utilize depth and genetic algorithm,
Optimize air compressor machine and open number of units, operation duration, so as to optimize air-compressor set entirety efficiency, energy saving, environmental protection.
Description of the drawings
With reference to attached drawing, the disclosure will be easier to understand.Skilled addressee readily understands that be：This
A little attached drawings are used only for the technical solution illustrated the present invention, and are not intended to and protection scope of the present invention is construed as limiting.
In figure：
Fig. 1 is the flow chart of the optimization method of the air compression station according to embodiments of the present invention based on deep learning.
Specific embodiment
Fig. 1 and following description describe the present invention optional embodiment with instruct those skilled in the art how to implement and
Reproduce the present invention.In order to instruct technical solution of the present invention, simplified or some conventional aspects be omitted.Those skilled in the art should
The understanding is originated from the modification of these embodiments or replacement will within the scope of the invention.Under those skilled in the art should understand that
Stating feature can combine to form multiple modifications of the invention in various ways.The invention is not limited in following optional as a result,
Embodiment, and be only limited by the claims and their equivalents.
Embodiment：
Fig. 1 schematically illustrates the flow of the optimization method of the air compression station based on deep learning of the embodiment of the present invention
Figure, as shown in Figure 1, the optimization method of the air compression station based on deep learning includes the following steps：
(A1) using Internet of Things is monitored, the operating parameter of each air compressor machine in air compression station is obtained, such as run time, air inlet pressure
Power, outlet pressure, air compression station environment temperature, inlet temperature, outlet temperature, flow and power；
(A2) using operating parameter described in deep learning Algorithm Analysis, the operational mode for extracting each air compressor machine in air compression station is special
Levy model；Specially：
The deep learning algorithm utilizes depth autocoder, and the depth autocoder includes：Encoder, decoding
Device and hidden layer；
The encoder is encoded using following relational expression：
H=f (x)=Sf(Wx+bj)
Wherein, the feature vector that x is made of operating parameter, weights of the W for input vector, bjRepresent j-th of neuron
Threshold value or be known as bias, the hidden layer value that h is；
Decoder is decoded using following relational expression：
Y=g (h)=Sg(Wh+bh)
Wherein, h for hidden layer vector, here as input, W be corresponding weight vector, bhFor threshold value, sgIt is decoder
Activation primitive；
Training process to depth autocoder is that parameter W, b are found on training sample set Dj, bhThe minimum of composition
Reconstructed error, the expression formula of reconstructed error are：
Wherein, x is the input of above-mentioned formula, and g (f (x)) is that the decoder of above-mentioned formula exports, and L is reconstructed error function.
According to the optimization method of the above-mentioned air compression station based on deep learning, it is preferable that described to depth autocoder
Training process include the following steps：
(B1) operating parameter of the input as training, it is unsupervised to train first self-encoding encoder；
(B2) using the output of first self-encoding encoder as the input of next self-encoding encoder, second own coding is trained
Device；
(B3) step (B2) is repeated, until completing the training of preset quantity hidden layer；
(B4) using the power H of air compression station as output, increase a reverse transmittance nerve network on the last one hidden layer
Prediction model realizes the weight fine tuning to the prediction model；
(A3) using operational mode characteristic model described in genetic algorithm optimization, in the case of obtaining air compression station power minimum,
The unlatching number of units of air compressor machine and opening time, specially：
(C1) grid and valuation functions of the operating parameter of structure air compressor machine, valuation functions use
(C2) to the unlatching number of units of air compressor machine, opening sequence, using air compression station initial launch number of units and operation order as just
Initial value assesses fitness individual corresponding to every chromosome, using the power H of air-compressor set as fitness；
(C3) principle higher in accordance with fitness, bigger select probability P, selected from population two individual as it is paternal with
Maternal；
(C4) chromosome of parent both sides is extracted, is intersected, generates filial generation.
(C5) to the chromosome of filial generation into row variation.
(C6) step (C2)-(C4) is repeated, until the generation of new population, when iterations reach setting number, acquisition is empty
Press opens number of units, opening time；
(A4) Internet of Things and Hadoop framework of increasing income, structure air compression station optimization operating system are utilized.
Claims (7)
1. a kind of optimization method of the air compression station based on deep learning, the optimization method packet of the air compression station based on deep learning
Include following steps：
(A1) using Internet of Things is monitored, the operating parameter of each air compressor machine in air compression station is obtained；
(A2) using operating parameter described in deep learning Algorithm Analysis, the operational mode character modules of each air compressor machine in air compression station are extracted
Type；
(A3) using operational mode characteristic model described in genetic algorithm optimization, when obtaining the unlatching number of units of air compressor machine and opening
Between.
2. the optimization method of the air compression station according to claim 1 based on deep learning, it is characterised in that：It is described to be based on deeply
The optimization method for spending the air compression station of study further comprises the steps：
(A4) Internet of Things and Hadoop framework of increasing income, structure air compression station optimization operating system are utilized.
3. the optimization method of the air compression station according to claim 1 based on deep learning, it is characterised in that：The operation ginseng
Number includes：Run time, admission pressure, outlet pressure, air compression station environment temperature, inlet temperature, outlet temperature, flow and work(
Rate.
4. the optimization method of the air compression station according to claim 1 based on deep learning, it is characterised in that：The depth
Algorithm is practised using depth autocoder, the depth autocoder includes：Encoder, decoder and hidden layer；
The encoder is encoded using following relational expression：
H=f (x)=Sf(Wx+bj)
Wherein, the feature vector that x is made of operating parameter, weights of the W for input vector, bjRepresent the threshold of j-th of neuron
Value is known as biasing, the hidden layer value that h is；
Decoder is decoded using following relational expression：
Y=g (h)=Sg(Wh+bh)
Wherein, h for hidden layer vector, here as input, W be corresponding weight vector, bhFor threshold value, SgIt is swashing for decoder
Function living；
Training process to depth autocoder is that parameter W, b are found on training sample set Dj, bhThe minimum reconstruct of composition
Error, the expression formula of reconstructed error are：
Wherein, x is the input of above-mentioned formula, and g (f (x)) is that the decoder of above-mentioned formula exports, and L is reconstructed error function.
5. the optimization method of the air compression station according to claim 4 based on deep learning, it is characterised in that：It is described to depth
The training process of autocoder includes the following steps：
(B1) operating parameter of the input as training, it is unsupervised to train first self-encoding encoder；
(B2) using the output of first self-encoding encoder as the input of next self-encoding encoder, second self-encoding encoder is trained；
(B3) step (B2) is repeated, until completing the training of preset quantity hidden layer；
(B4) using the power H of air compression station as output, increase a reverse transmittance nerve network prediction on the last one hidden layer
Model realizes the weight fine tuning to the prediction model.
6. the optimization method of the air compression station according to claim 1 based on deep learning, it is characterised in that：In step (A3)
In, optimization includes the following steps：
(C1) grid and valuation functions of the operating parameter of structure air compressor machine, valuation functions use
(C2) to the unlatching number of units of air compressor machine, opening sequence, using air compression station initial launch number of units and operation order as initial value,
Fitness individual corresponding to every chromosome is assessed, using the power H of air-compressor set as fitness；
(C3) principle higher in accordance with fitness, bigger select probability P selects two individuals as paternal and female from population
Side；
(C4) chromosome of parent both sides is extracted, is intersected, generates filial generation.
(C5) to the chromosome of filial generation into row variation.
(C6) step (C2)-(C4) is repeated, until the generation of new population, when iterations reach setting number, acquisition air compressor machine
Open number of units, opening time.
7. the optimization method of the air compression station according to claim 1 based on deep learning, it is characterised in that：In step (A3)
In, it is solved in the case of air compression station power minimum using genetic algorithm, obtains the unlatching number of units and sequence of air compressor machine.
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Cited By (8)
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| CN109322816A (en) * | 2018-09-30 | 2019-02-12 | 西门子电力自动化有限公司 | Supply method, apparatus, equipment, medium and the program of compressed air |
| WO2020165043A1 (en) * | 2019-02-14 | 2020-08-20 | Bayer Cropscience Lp | Machine learning based algorithmic procedure |
| CN112901449A (en) * | 2021-03-17 | 2021-06-04 | 英赛孚工业智能科技(苏州)有限公司 | Air compressor system energy consumption optimization method based on machine learning |
| CN113669242A (en) * | 2021-08-03 | 2021-11-19 | 新奥数能科技有限公司 | Power control method and device of air compressor system and computer equipment |
| CN113670642A (en) * | 2021-08-25 | 2021-11-19 | 广东鑫钻节能科技股份有限公司 | Cloud automatic detection system and method based on air compression station protection device |
| CN113807015A (en) * | 2021-09-17 | 2021-12-17 | 南方电网科学研究院有限责任公司 | Parameter optimization method, device, equipment and storage medium for compressed air energy storage system |
| CN114893402A (en) * | 2022-04-06 | 2022-08-12 | 合肥工业大学 | Parallel air compressor energy consumption regulation and control method and system based on artificial intelligence |
| CN117311430A (en) * | 2023-11-29 | 2023-12-29 | 广东鑫钻节能科技股份有限公司 | Energy-saving digital energy air compression station control system |
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| CN114893402A (en) * | 2022-04-06 | 2022-08-12 | 合肥工业大学 | Parallel air compressor energy consumption regulation and control method and system based on artificial intelligence |
| CN117311430A (en) * | 2023-11-29 | 2023-12-29 | 广东鑫钻节能科技股份有限公司 | Energy-saving digital energy air compression station control system |
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