CN109711642B - Big data-based desulfurization system operation optimization method and system - Google Patents
Big data-based desulfurization system operation optimization method and system Download PDFInfo
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- 238000006477 desulfuration reaction Methods 0.000 title claims abstract description 70
- 230000023556 desulfurization Effects 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000005457 optimization Methods 0.000 title claims abstract description 40
- 238000007405 data analysis Methods 0.000 claims abstract description 15
- 238000011156 evaluation Methods 0.000 claims description 13
- 239000000463 material Substances 0.000 claims description 13
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000005265 energy consumption Methods 0.000 claims description 7
- 238000012550 audit Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 238000000611 regression analysis Methods 0.000 abstract description 2
- 238000013178 mathematical model Methods 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 239000003344 environmental pollutant Substances 0.000 description 3
- 231100000719 pollutant Toxicity 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000004566 building material Substances 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000003750 conditioning effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract
The invention discloses a big data-based desulfurization system operation optimization method, which is based on a desulfurization system theoretical model, performs regression analysis and big data analysis on actual operation data of a power station, builds a mathematical model, searches a local and global optimal solution of the model, and then provides an operation optimization method with the assistance of the theoretical model. The invention provides an operation optimization improvement scheme for a desulfurization system based on a theoretical model, and solves the problem of unreasonable energy resource utilization of the conventional desulfurization system.
Description
Technical Field
The invention relates to the technical field of control of atmospheric pollutants of coal-fired boilers, in particular to a method for providing a strategy for operation optimization of a desulfurization system based on big data analysis.
Background
Nowadays, coal-fired power plants are used as traditional pollution discharge households, and the coal-fired power plants are close to the direction of fine management, so that the resource utilization efficiency of the power plants is improved, and the energy consumption is reduced.
However, in the actual operation process, because the power station equipment is numerous, when people face the problem of how to allocate equipment to achieve the best working efficiency and fully protect equipment assets, accurate decision is often difficult, which often causes redundant energy consumption and resource waste. Meanwhile, with the development of hardware equipment, more and more coal-fired power stations begin to use equipment such as variable frequency pumps and the like which need accurate adjustment, and how to realize accurate adjustment and reduce resource and energy consumption is another practical problem faced by the power stations. In addition, for years, in the actual operation process, a power station stores a large amount of precious actual operation data but is not fully utilized, so that not only are the network and physical space wasted, but also the data resources are wasted.
The development of big data and artificial intelligence technology provides ideas and directions for the power station to solve the problems. Big data technology has been of increasing interest, research and application by coal burning power plants because of its powerful data analysis capabilities. The deep analysis of the operation data of the power station system by using a big data technology becomes one of important contents of reducing pollution discharge and improving efficiency of the coal-fired power station.
According to the method, external factors and internal environment urge the coal-fired power station to accurately adjust, and the efficiency is improved. In order to solve the problems of unreasonable energy utilization and high pollutant removal cost of a desulfurization system, the invention provides a method for providing a strategy scheme for the optimized operation of the desulfurization system based on a big data analysis technology, and aims to improve the energy utilization efficiency of the desulfurization system of a coal-fired power station, reduce the pollutant removal cost and save energy.
Disclosure of Invention
The invention aims to provide a desulfurization system operation optimization method and a desulfurization system based on big data. Analyzing the actual operation data with huge quantity by means of a big data analysis method to obtain the correlation degree between different factors and efficiency, selecting the factors with proper quantity, building a mathematical prediction model, and searching the local and global optimal solution of the model by adopting a proper algorithm. The invention provides an operation optimization improvement scheme for a desulfurization system based on a theoretical model, and solves the problem of unreasonable energy resource utilization of the conventional desulfurization system.
In order to achieve the above object, with reference to fig. 1, the present invention provides a big data based desulphurization system operation optimization method, including:
s1: based on the energy balance and material conservation theory, a theoretical model of the desulfurization system is created according to actual equipment components of the coal-fired power plant, the evaluation index of desulfurization efficiency is set, and the operation data of the coal-fired power plant in a first set time period is extracted as modeling data.
Preferably, the evaluation index of the desulfurization efficiency includes energy consumption and material consumption consumed in the desulfurization process. In some examples, several materials or energy sources with high loss or high value can be selected as the desulfurization efficiency evaluation index to simplify the model.
More preferably, the desulfurization efficiency evaluation index includes a monetary value equivalent to energy and materials consumed in the desulfurization step. The energy consumption and the material consumption consumed in the desulfurization process are equivalent to money, so that the desulfurization efficiency evaluation index can be further simplified, and subsequent modeling and data processing are facilitated; as much material loss and energy loss as possible can be added as evaluation factors, and materials or energy does not need to be deleted; meanwhile, even if the energy source changes or the material price changes, the desulfurization efficiency evaluation index can be quickly adjusted in an equivalent mode, and the correction amount of the model is reduced.
S2: the modeling data is preprocessed, and a plurality of factors influencing the desulfurization efficiency under different working conditions are analyzed on the basis of a desulfurization system theoretical model.
In a further embodiment, in step S2, the method for preprocessing the modeling data and analyzing several factors affecting the desulfurization efficiency under different operating conditions based on the theoretical model of the desulfurization system includes the following steps:
s201: preprocessing the modeling data, including:
auditing the credibility to eliminate unreasonable data in the credibility; supplementing missing data; and denoising the parameter data with the fluctuation frequency exceeding a set frequency threshold, for example, denoising the parameter data with the larger fluctuation frequency such as working voltage and the like, and then utilizing the parameter data.
S202: and dividing different working conditions according to the load.
S203: and performing regression calculation on the preprocessed modeling data on the basis of different working conditions to obtain a plurality of factors influencing the desulfurization efficiency under different working conditions.
S3: and performing big data analysis on the preprocessed modeling data to obtain key factors under different working conditions, creating a mathematical prediction model, and searching a global optimal solution of the mathematical prediction model under different working conditions to serve as an operation optimization strategy of the desulfurization system under different working conditions.
In a further embodiment, in step S3, the method for performing big data analysis on the preprocessed modeling data to obtain key factors under different working conditions, creating a mathematical prediction model, and finding a global optimal solution of the mathematical prediction model under different working conditions, as an operation optimization strategy of the desulfurization system under different working conditions, includes the following steps:
s301: and performing big data analysis on the preprocessed modeling data to obtain key factors under different working conditions, and creating a mathematical prediction model by using the key factors under different working conditions.
S302: and extracting the operation data of the coal-fired power plant in the second set time period as audit data, auditing the mathematical prediction model, setting an error threshold value, entering the step S303 if the audit is passed, and returning to the step S301 if the audit is passed.
S303: and calculating the optimal solution of the mathematical prediction model under different working conditions to obtain an operation optimization strategy of the desulfurization system under different working conditions.
In a further embodiment, the method further comprises:
in step S302, if the number of times of non-passing of the audit reaches the set number threshold, a warning is issued.
The problem is eliminated by issuing an alert to remind the user to go back to an earlier step, such as going back to step S1 to recheck whether the theoretical model is correct, or whether there is a missing modeled data, etc.
The method for acquiring the key factors under the corresponding working condition comprises the following steps:
and (2) acquiring key factors under corresponding working conditions by adopting a gray correlation method, or acquiring influence values of the factors on the desulfurization efficiency under different working conditions, and defining the factors with the influence values exceeding a set influence threshold as the key factors under the corresponding working conditions.
S4: and analyzing on the basis of a theoretical model of the desulfurization system, verifying the correctness of the operation optimization strategy, if the operation optimization strategy is correct, deriving the operation optimization strategy, and otherwise, searching the global optimal solution of the mathematical prediction model under different working conditions again.
In a further embodiment, the method further comprises:
and (4) adopting a genetic algorithm to calculate the optimal solution of the mathematical prediction model under different working conditions.
In a further embodiment, in step S4, a neural network and/or a vector machine method is used to create a corresponding mathematical prediction model using the key factors under different operating conditions.
Based on the method, the invention also provides a big data-based desulfurization system operation optimization system, which comprises the following modules:
1) and the module is used for establishing a theoretical model of the desulfurization system according to the actual equipment components of the coal-fired power plant based on the energy balance and material conservation theory.
2) And the module is used for setting the desulfurization efficiency evaluation index.
3) And the module is used for extracting the operation data of the coal-fired power plant in the first set time period as modeling data.
4) A module for preprocessing modeling data.
5) And the module is used for analyzing a plurality of factors influencing the desulfurization efficiency under different working conditions on the basis of a theoretical model of the desulfurization system.
6) And the module is used for carrying out big data analysis on the preprocessed modeling data so as to obtain key factors under different working conditions.
7) A module for creating a mathematical prediction model.
8) The method is used for searching the global optimal solution of the mathematical prediction model under different working conditions and is used as a module of an operation optimization strategy of the desulfurization system under different working conditions.
9) And the module is used for analyzing on the basis of the theoretical model of the desulfurization system and verifying the correctness of the operation optimization strategy, if the operation optimization strategy is correct, the operation optimization strategy is derived, and otherwise, the global optimal solution of the mathematical prediction model under different working conditions is searched again.
Compared with the prior art, the technical scheme of the invention has the following remarkable beneficial effects:
1) in the practical application of the power station, the CCS adjusting system inevitably mixes manual decision, and the manual decision is difficult to realize accuracy, so that the situation of energy and resource waste is often caused, and the analysis method based on big data has the characteristic of accurate adjustment, so that the characteristic of providing guarantee for reducing energy consumption and saving energy.
2) In the traditional power station regulation process, manual decision often plays an important role, and the manual decision cannot be accurately determined in real time, and the traditional power station regulation system cannot necessarily make an accurate and optimal decision due to excessive dependence on the manual decision. The decision generated based on the big data analysis technology is established on the basis of actual operation data of the power station, mathematical accuracy is taken as a strong dependence, and the introduction of a theoretical model in the decision process provides further guarantee for the effectiveness of the decision.
3) During operation of a power plant, the matching between equipment may not be fully considered in a conventional CCS conditioning system, and may have changed due to long-term operation and maintenance of the equipment. Based on this basis, big data analysis offers the possibility for new matching methods.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
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The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a big data based desulfurization system operation optimization method of the present invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
With reference to fig. 1, the present invention provides a big data-based desulfurization system operation optimization method, which includes the following steps:
1) establishing a coal-fired power plant desulfurization system theoretical model and obtaining required data: and (4) building a theoretical model according to the actual equipment assembly and the specific structure of the power station, and building material conservation. To remove SO per unit mass2The consumed money is an efficiency evaluation index, and data required by the project are analyzed and cooperate with the power station to obtain operation data in a certain time period.
2) Preprocessing the obtained data: and performing credibility audit: removing unreasonable data, supplementing missing data by adopting a proper method, and denoising parameter data (such as pressure) with frequent fluctuation.
3) Performing regression analysis on the preprocessed data: and performing regression calculation on the data according to the efficiency indexes, and comparing and analyzing factors influencing efficiency difference under different conditions.
4) Carrying out artificial intelligence analysis on the data: and analyzing and obtaining a plurality of factors which are most critical to the efficiency by using a proper method such as grey correlation analysis, and the like, and then setting a threshold value to select a certain number of factors for further analysis.
5) Building a mathematical prediction model: and establishing a mathematical prediction model by using key factors and methods such as a neural network, a vector machine and the like.
6) And (3) auditing a prediction model: and (4) auditing the prediction model by using actual operation data of the power station in another time period, setting a proper error line, entering the step 8) if the auditing is passed, and returning to the step 5) if the auditing is passed.
7) And searching a local optimal solution of the prediction model by using methods such as a genetic algorithm and the like, and providing an optimization and improvement strategy of the system under different working conditions.
8) And 7) analyzing the big data processing in the step 7) on the basis of the theoretical model to obtain an operation optimization improvement strategy.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily defined to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (6)
1. A big data-based desulfurization system operation optimization method is characterized by comprising the following steps:
s1: based on an energy balance and material conservation theory, creating a desulfurization system theoretical model according to actual equipment components of the coal-fired power plant, setting a desulfurization efficiency evaluation index, and extracting operation data of the coal-fired power plant in a first set time period as modeling data;
s2: preprocessing modeling data, and analyzing a plurality of factors influencing the desulfurization efficiency under different working conditions on the basis of a desulfurization system theoretical model;
s3: performing big data analysis on the preprocessed modeling data to obtain key factors under different working conditions, creating a mathematical prediction model, and searching a global optimal solution of the mathematical prediction model under different working conditions as an operation optimization strategy of the desulfurization system under different working conditions;
s4: analyzing on the basis of a theoretical model of a desulfurization system, verifying the correctness of the operation optimization strategy, if the operation optimization strategy is correct, deriving the operation optimization strategy, and otherwise, searching the global optimal solution of the mathematical prediction model under different working conditions again;
in step S2, the method for preprocessing the modeling data and analyzing a plurality of factors affecting the desulfurization efficiency under different working conditions based on the theoretical model of the desulfurization system includes the following steps:
s201: preprocessing the modeling data, including:
checking the credibility to remove unreasonable data in the credibility; supplementing missing data; denoising the parameter data with the fluctuation frequency exceeding a set frequency threshold;
s202: dividing different working conditions according to the load;
s203: performing regression calculation on the preprocessed modeling data on the basis of different working conditions to obtain a plurality of factors influencing the desulfurization efficiency under different working conditions;
in step S3, the method for performing big data analysis on the preprocessed modeling data to obtain key factors under different working conditions, creating a mathematical prediction model, and finding a global optimal solution of the mathematical prediction model under different working conditions, as an operation optimization strategy of the desulfurization system under different working conditions, includes the following steps:
s301: performing big data analysis on the preprocessed modeling data to obtain key factors under different working conditions, and creating a mathematical prediction model by using the key factors under different working conditions;
s302: extracting the operation data of the coal-fired power station in a second set time period as auditing data, auditing the mathematical prediction model, setting an error threshold value, entering a step S303 if the auditing is passed, and returning to the step S301 if the auditing is passed;
s303: calculating the optimal solution of the mathematical prediction model under different working conditions to obtain an operation optimization strategy of the desulfurization system under different working conditions;
the method further comprises the following steps: calculating the optimal solution of the mathematical prediction model under different working conditions by adopting a genetic algorithm;
the method further comprises the following steps: and obtaining key factors under corresponding working conditions by adopting a gray correlation method.
2. The big data-based desulfurization system operation optimization method according to claim 1, wherein the desulfurization efficiency evaluation index includes energy consumption and material consumption consumed by a desulfurization process.
3. The big data-based desulfurization system operation optimization method according to claim 2, wherein the desulfurization efficiency evaluation index includes a monetary value equivalent to energy and materials consumed by a desulfurization process.
4. The big data based desulfurization system operation optimization method according to claim 1, further comprising:
in step S302, if the number of times of non-passing of the audit reaches the set number threshold, a warning is issued.
5. The big data based desulphurization system operation optimization method according to claim 1, wherein in step S4, a neural network and/or a vector machine method is used to create the corresponding mathematical prediction model by using key factors under different conditions.
6. The big data based desulfurization system operation optimization system applying the method of claim 1, wherein the system comprises:
the module is used for establishing a theoretical model of the desulfurization system according to actual equipment components of the coal-fired power plant based on an energy balance and material conservation theory;
a module for setting a desulfurization efficiency evaluation index;
the module is used for extracting the operation data of the coal-fired power plant in a first set time period as modeling data;
a module for preprocessing modeling data;
the module is used for analyzing a plurality of factors influencing the desulfurization efficiency under different working conditions on the basis of a theoretical model of a desulfurization system;
the module is used for carrying out big data analysis on the preprocessed modeling data so as to obtain key factors under different working conditions;
a module for creating a mathematical prediction model;
the module is used for searching the global optimal solution of the mathematical prediction model under different working conditions and is used as an operation optimization strategy of the desulfurization system under different working conditions;
and the module is used for analyzing on the basis of the theoretical model of the desulfurization system and verifying the correctness of the operation optimization strategy, if the operation optimization strategy is correct, the operation optimization strategy is derived, and otherwise, the global optimal solution of the mathematical prediction model under different working conditions is searched again.
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| CN108320042A (en) * | 2017-12-05 | 2018-07-24 | 浙江中控软件技术有限公司 | The optimization method and device of circulation |
| CN109146141A (en) * | 2018-07-25 | 2019-01-04 | 上海交通大学 | A kind of coal fired power plant rotary air preheater air leak rate prediction technique |
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| CN102073272A (en) * | 2011-02-24 | 2011-05-25 | 北京恩耐特分布能源技术有限公司 | System and method for optimizing overall planning of distributed energy |
| CN108320042A (en) * | 2017-12-05 | 2018-07-24 | 浙江中控软件技术有限公司 | The optimization method and device of circulation |
| CN109146141A (en) * | 2018-07-25 | 2019-01-04 | 上海交通大学 | A kind of coal fired power plant rotary air preheater air leak rate prediction technique |
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