CN108921364A - Sewage treatment plant's blast engine energy saving consumption-reducing method based on artificial intelligence - Google Patents
Sewage treatment plant's blast engine energy saving consumption-reducing method based on artificial intelligence Download PDFInfo
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- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 34
- 239000010865 sewage Substances 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000012549 training Methods 0.000 claims abstract description 22
- 238000012360 testing method Methods 0.000 claims abstract description 20
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 15
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000007637 random forest analysis Methods 0.000 claims description 4
- 238000003066 decision tree Methods 0.000 claims description 3
- 238000007477 logistic regression Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000004579 scanning voltage microscopy Methods 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 8
- 238000010801 machine learning Methods 0.000 abstract description 8
- 238000005265 energy consumption Methods 0.000 abstract description 7
- 239000002699 waste material Substances 0.000 abstract description 3
- 238000005273 aeration Methods 0.000 abstract description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 4
- 238000007664 blowing Methods 0.000 description 4
- 239000001301 oxygen Substances 0.000 description 4
- 229910052760 oxygen Inorganic materials 0.000 description 4
- 230000031018 biological processes and functions Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 239000010802 sludge Substances 0.000 description 2
- 241000254158 Lampyridae Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000012843 least square support vector machine Methods 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 239000008213 purified water Substances 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
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- G—PHYSICS
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Abstract
The present invention discloses a kind of sewage treatment plant's blast engine energy saving consumption-reducing method based on artificial intelligence.The method includes the following steps：1) data prediction, is carried out based on the true operation data of the existing history of sewage treatment plant；2) pretreated data are divided into training data sample and test data sample；3) artificial intelligence model is constructed using training data sample and test data sample；4) blow rate required in adjustment artificial intelligence model input parameter is constantly recycled according to effluent index situation, until the optimal effluent index value of effluent index infinite approach of model output.The method of the present invention finds out the relevance between data using artificial intelligence machine learning algorithm, and the blower air quantity value for comparatively facilitating sewage treatment process is found out instead of artificial experience, more accurate and effective.And the present invention removes adjustment blower switch and frequency by the optimal blow rate required that artificial intelligence technology obtains, can solve the excessive energy consumption waste problem of sewage plant aeration quantity, energy saving, saves operating cost.
Description
Technical Field
The artificial intelligence model is used for modeling the blast volume in the sewage treatment process to reduce the blast volume, thereby saving the energy consumption of the blower.
Background
Aiming at sewage treatment plants of biological processes such as an activated sludge process and the like, 50% of energy consumption generally comes from an oxidation ditch oxygen supply blower (mainly electric energy consumption), and in the sewage treatment system with large lag and large delay, the problem of excessive energy waste caused by blower gas explosion often exists; the AI artificial intelligence technology is a technology for researching a computer to simulate some thinking process and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.) of a human, and an artificial intelligence machine learning algorithm (including but not limited to a neural network algorithm, a support vector machine, a random forest, etc.) can train and learn by itself through established data to find out relevance and dependence in the data, thereby replacing manual decision making.
Patents of artificial intelligence technology combined with sewage treatment process improvement research in the industry mostly center on DO (dissolved oxygen) control in the process, such as CN 106354019 a, "an accurate dissolved oxygen control method based on RBF neural network"; CN 107085372A 'an energy-saving sewage treatment optimizing control method based on improved firefly algorithm and least square support vector machine'; CN 107358021 a, "DO prediction model establishment method based on optimized BP neural network", all target dissolved oxygen control, and use specific algorithms in artificial intelligence technology machine learning algorithms, but there are many machine learning algorithms, and they are only tools, and it is not necessarily best to use which algorithm to model a specific sewage treatment plant. The invention covers all machine learning algorithms of artificial intelligence as modeling tools, and simultaneously does not use DO as a solving control target, but directly uses the blast volume as a research control target.
Disclosure of Invention
The invention aims to introduce artificial intelligence technology into the biological process links such as the activated sludge process of the sewage treatment plant to perform modeling analysis on the historical data of the sewage treatment plant, reduce the blast volume of a blower and save energy consumption under the condition of ensuring that the purified water after sewage treatment does not reach the standard. The method aims to provide the optimal target blast volume of the blower in the biological process link of the sewage plant (not only can ensure that sewage treatment reaches the standard, but also can reasonably save the energy consumption of the blower) through artificial intelligence technology self-training and self-learning, thereby achieving the purposes of saving energy and reducing consumption of the sewage plant.
In order to achieve the aim, the invention relates to an energy-saving and consumption-reducing method of a blower of a sewage treatment plant based on artificial intelligence, which comprises the following steps:
1) performing data preprocessing based on the existing historical real operation data of the sewage treatment plant;
2) dividing the preprocessed data into training data samples and testing data samples;
3) establishing an artificial intelligence model by taking the effluent index as an output parameter and other indexes as input parameters; training the training data sample by using an artificial intelligence model;
4) testing the test data sample by using the trained artificial intelligence model;
5) and (5) adjusting model parameters, and repeating the step 3 and the step 4 to obtain an optimal model.
6) And continuously and circularly adjusting the blast volume in the input parameters of the model according to the water outlet index condition until the water outlet index output by the model is infinitely close to the optimal water outlet index value.
Wherein,
the step 1) is specifically as follows:
21) taking historical data of a sewage treatment plant, and preprocessing the data, wherein the preprocessing mainly comprises deleting repeated data, deleting invalid data, supplementing missing data, performing 3delta processing and the like.
22) And performing time re-matching on the processed data.
23) Dividing the data processed in the second step into training data samples and testing data samples, and according to the number of the training samples: test sample number 9: 1.
The algorithm adopted by the artificial intelligence model comprises but is not limited to logistic regression, BP neural network, SVM, decision tree and random forest.
The method of the invention uses the artificial intelligence machine learning algorithm to find out the relevance among the data, replaces the artificial experience to find out the blower air volume value which is more beneficial to the sewage treatment process, and is more accurate and effective. In addition, the invention adjusts the switch and the frequency of the air blower according to the optimal air blowing quantity obtained by the artificial intelligence technology, can solve the problem of energy consumption waste caused by overlarge aeration quantity of a sewage plant, saves energy and saves operation cost.
Drawings
FIG. 1 is a flow chart of the work flow of artificial intelligence algorithm model building;
figure 2 is a flow chart of the present invention,
Detailed Description
The invention is further described with reference to the following figures and specific embodiments.
The invention relates to a method for modeling and analyzing historical data of a sewage treatment plant based on an artificial intelligence machine learning algorithm, which intelligently learns an optimal blast volume and air volume value on the premise of ensuring that water outlet indexes reach standards. The specific implementation steps are as follows:
step one, taking historical data of a sewage treatment plant, and preprocessing the data, wherein the preprocessing mainly comprises deleting repeated data, deleting invalid data, supplementing missing data, performing 3delta processing and the like.
And step two, carrying out time re-matching on the data processed in the step one, namely, backwardly offsetting the effluent index data for 8 hours on the data time (the time is determined according to the empirical reaction time of the biochemical pool of the sewage treatment plant or the optimal time offset is obtained according to the following model training result after software codes are adopted for traversal matching in a time range).
Step three, dividing the data processed in the step two into training data samples and testing data samples, and according to the number of the training samples: test sample number 9: 1.
And step four, establishing an artificial intelligent machine learning model by taking the effluent indexes (such as effluent COD and effluent NH4) as output indexes and other indexes as water inlet indexes, wherein the algorithm adopted by the model includes but is not limited to logistic regression, BP neural network, SVM, decision tree, random forest and the like.
And step five, training the model in the step four by adopting a training sample, and adjusting and measuring model parameters to ensure that the training fitting degree of the model is higher.
And step six, testing the model in the step five by adopting the test sample, so that the accuracy of the test sample is higher.
And step seven, adjusting the training parameters of the model, repeating the step five and the step six, checking whether parameter setting with higher accuracy of the test sample exists (over-fitting or under-fitting is prevented), and storing the model result with the highest accuracy of the training and test samples.
And step eight, taking the real-time index acquisition data of the sewage treatment plant as input, and calling the storage result model in the step seven to obtain the effluent index data.
And step nine, if the water outlet index data calculated in the step eight is higher than the optimal water outlet standard data, increasing the blast volume data by one thousandth, and repeating the step eight until the calculated water outlet index data is the optimal standard data, and the blast volume value is the optimal blast volume.
And step ten, if the water outlet index data calculated in the step eight is lower than the optimal water outlet standard data, reducing the blast volume data by one thousandth, and repeating the step eight until the calculated water outlet index data is the optimal standard data, and the blast volume value at the moment is the optimal blast volume.
Step eleven, manually adjusting the on-off or frequency of the blower according to the optimal blowing quantity or adjusting the on-off or frequency of the blower through an intelligent control system, and controlling the blowing quantity to achieve the optimal blowing quantity.
The above description is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention, and the claimed invention is not limited thereto, and those skilled in the art can easily conceive equivalent variations based on the disclosure of the present invention and the protection scope of the present invention.
Claims (4)
1. An energy-saving and consumption-reducing method for blowers of a sewage treatment plant based on artificial intelligence is characterized by comprising the following steps:
1) performing data preprocessing based on the existing historical real operation data of the sewage treatment plant;
2) dividing the preprocessed data into training data samples and testing data samples;
3) constructing an artificial intelligence model by using the training data samples and the test data samples;
4) and continuously and circularly adjusting the blast volume in the input parameters of the artificial intelligent model according to the water outlet index condition until the water outlet index output by the model is infinitely close to the optimal water outlet index value.
2. The energy saving and consumption reduction method for blower of sewage treatment plant based on artificial intelligence as claimed in claim 1,
the step 1) is specifically as follows:
21) taking historical data of a sewage treatment plant, and preprocessing the data, wherein the preprocessing mainly comprises deleting repeated data, deleting invalid data, supplementing missing data, performing 3delta processing and the like.
22) And performing time re-matching on the processed data.
23) Dividing the data processed in the second step into training data samples and testing data samples, and according to the number of the training samples: test sample number 9: 1.
3. The energy saving and consumption reduction method for blowers of sewage treatment plants based on artificial intelligence as claimed in claim 1, wherein the step of constructing artificial intelligence model comprises:
31) establishing an artificial intelligence model by taking the effluent index as an output parameter and other indexes as input parameters; training the training data sample by using an artificial intelligence model;
32) testing the test data sample by using the trained artificial intelligence model;
33) and adjusting model parameters, and repeating the step 31 and the step 32 to obtain an optimal model.
4. The energy saving and consumption reducing method for blowers of sewage treatment plants based on artificial intelligence as claimed in claim 1, wherein the algorithm adopted by the artificial intelligence model includes but is not limited to logistic regression, BP neural network, SVM, decision tree, random forest.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110298010A (en) * | 2019-06-27 | 2019-10-01 | 李达维 | The ecological environment of ecological engineering of landscape measures system |
| CN110773578A (en) * | 2019-11-15 | 2020-02-11 | 中冶华天工程技术有限公司 | System and method for adjusting parameters of hot-rolled threaded steel post-rolling cooling water tank based on artificial intelligence |
| CN111522316A (en) * | 2020-05-12 | 2020-08-11 | 中冶华天工程技术有限公司 | Optimal dynamic selection method for belt conveying process of stock yard |
| CN111652445A (en) * | 2020-06-11 | 2020-09-11 | 广东科创工程技术有限公司 | Sewage equipment optimized operation control method based on Gaussian distribution |
| CN111950166A (en) * | 2020-08-26 | 2020-11-17 | 广州博依特智能信息科技有限公司 | Cost optimization method for household paper making machine based on data mining |
| CN112286155A (en) * | 2020-10-30 | 2021-01-29 | 陕西大唐高科机电科技有限公司 | Electromechanical device operating system and method based on network |
| CN113217448A (en) * | 2021-06-01 | 2021-08-06 | 广东鑫钻节能科技股份有限公司 | Energy-saving control system for air blower |
| CN114563988A (en) * | 2022-01-26 | 2022-05-31 | 浙江中控信息产业股份有限公司 | Water plant intelligent PAC adding method and system based on random forest algorithm |
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| CN114563988A (en) * | 2022-01-26 | 2022-05-31 | 浙江中控信息产业股份有限公司 | Water plant intelligent PAC adding method and system based on random forest algorithm |
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Application publication date: 20181130 |