CN110263988A - A kind of data run optimization method based on power plant desulphurization system - Google Patents
A kind of data run optimization method based on power plant desulphurization system Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- 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
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
A kind of method the invention discloses power plant desulphurization system based on data-optimized operation this method comprises: obtaining the actual operating data in power station actual motion system certain period of time, and carries out pretreatment and regression analysis to the actual operating data;Different operating conditions are divided according to operating load, and carries out numerical simulation and obtains the flow field of desulphurization system entirety, temperature, SO under different operating conditions2Component distribution situation;The actual operating data described in coal fired power plant desulphurization system carries out big data analysis, obtains the optimum interval for determining operation data under operating condition;Big data optimizing result is practiced to the accuracy to examine optimizing result in analog result.The present invention first distinguishes operating condition before establishing neural network prediction model, this reduces data volume, and relationship is relatively unambiguous and simple between energy consumption, desulphurizing ratio output parameter and impact factor, advantageously ensures that precision.
Description
Technical field
The present invention relates to coal-burning boiler technical field of air pollution control, and in particular to a kind of based on power plant desulphurization system
Data run optimization method.
Background technique
Currently, environmental pollution has become one of the main roadblock for hindering the high quality sustainable development of national economy health,
And pollutant emission standard is continuously improved in keypoint treatment object of the coal fired power plant as environmental problem, country.To meet pollutant
Discharge standard, coal fired power plant often take the countermeasure of abundant materials decontamination, and which results in the waste of resource to a certain extent.
Meanwhile in desulphurization system simultaneously be related to heat and mass, acid-base neutralization, moisture evaporation, drop coalescence be crushed etc. ask
Topic, practical operation situation is complicated, although having many scholars using related software to desulfurization flow field in tower, temperature field, SO2Distribution
The problems such as carried out detailed analog study, however based on a large amount of actual operating datas, combined with numerical simulation to de-
It is to rarely have that sulphur system, which carries out network analysis,.
In addition, power station in many years operational process, after various operating conditions, stores the actual operating data of a large amount of preciousnesses.
It can be not only the significant wastage to data resource, simultaneously there is no this valuable data resource is utilized in most power stations at present
Also valuable network and physical space are occupied.
Under this situation, the progress of big data, intelligent Computation Technology provides new for the fine-grained management in power station
Developing direction.Big data analysis technology is gradually more and more fire coals because of remarkable advantages such as its is accurate, speed is fast, visualizations
Power plant's concern, research and application.Depth analysis is carried out using actual operating data of the big data technology to desulphurization system, it has also become
One of coal fired power plant pollutant process, running Optimization, the important content improved efficiency.
Summary of the invention
Goal of the invention: the present invention is in view of the above-mentioned problems, provide one kind based on theory analysis, supplemented by numerical simulation
It helps, provides the method for strategy by means of big data analysis for desulphurization system running optimizatin, be for solving previous desulphurization system
Meet National Emission Standard and there are problems that the energy, the wasting of resources.
Technical solution: the present invention is based on pollutant control system theoretical model, with the simulation of desulphurization system practical flow field
Mathematical model is built as core by auxiliary, using actual operating data.Three kinds of models cooperate with each other, complement each other.This method tool
Body practices step
(1) actual operating data in power station actual motion system certain period of time is obtained, and to the actual motion number
According to carry out pretreatment and its regression analysis;
(2) different operating conditions are divided according to operating load, carries out numerical simulation and obtains desulphurization system entirety under different operating conditions
Flow field, temperature, SO2Component distribution situation;
(3) actual operating data described in coal fired power plant desulphurization system carries out big data analysis, specifically includes:
(31) regression analysis is carried out to fixed floor data: according to removal unit mass SO2Consumed currency is to pretreatment
Data carry out recurrence calculating afterwards, and analyzing influence determines the factor of energy consumption difference under operating condition;
(32) it screens impact factor: analyzing and determine operation data the most key to energy consumption under operating condition, screening is wherein
Meet the operation data of preset threshold as impact factor collection, the impact factor collection is obtained power station actual operating data
Subset;
(33) it builds mathematical forecasting model: being input with the impact factor collection supplemental characteristic, constructed using neural network
Mathematical forecasting model is exported using the part actual operating data amount obtained after the pretreatment as training data as Energy Consumption Evaluation
Index;
(34) prediction model is audited: using power station with other actual operating data amounts under operating condition, in addition to training data
Output prediction is carried out to same operating condition prediction model, suitable error line, the accuracy of judgment models are set, if audit is by entering step
Suddenly (35), otherwise separate regression steps (33);
(35) it algorithm optimizing: is found using genetic algorithm and determines prediction model global optimum disaggregation or optimal under operating condition
Section.
(4) data are analyzed into gained optimal solution set or optimum interval brings gained simulation model, audit optimal solution set, optimal into
The correctness in section.
Further, comprising:
In the step (1), actual operating data includes: unit load, coal, FGD import tolerance, the outlet FGD tolerance,
Sulfur content, temperature, speed, loop slurry flow, serum density, absorption tower slurries PH, absorption tower liquid level of slurry, supplement in desulfurizing tower
Serum density, flow, oxidation fan component, temperature, power consumption and blender power consumption.
Further, comprising:
In the step (2), numerical simulation is specifically included:
(21) according to the practical structures and size of power station actual motion system equipment, being established using Gambit software includes pot
Furnace burner hearth, horizontal flue, each decontamination apparatus and back-end ductwork total system three-dimensional physical model;
(22) using the heterogeneity condition of desulphurization system inlet as the entrance side of the total system three-dimensional physical model
Boundary's condition, using Fluent software, simulation calculates the flow field of desulphurization system entirety, temperature, SO under different operating conditions2Component is distributed feelings
Condition;
(23) by the numerical simulation result and actual operation parameters date comprision, modifying factor is introduced, audits institute
The reliability of total system three-dimensional physical model and analog result is stated, if audit passes through, enters the big data analysis, otherwise,
Re-start numerical simulation.
Further, comprising:
In the step (1), pretreatment includes: firstly, carrying out confidence level audit to obtained operation data, rejecting is wherein
Unreasonable data;Secondly, supplementing missing data using weighting fill method;Finally, being gone to frequent supplemental characteristic is fluctuated
It makes an uproar processing.
Further, comprising:
This method further include: numerical simulation is carried out to the final optimization pass parameter section in step (35), with the phase of optimal solution
The input that parameter is numerical simulation is closed, judges whether big data analysis parameters obtained optimum interval can satisfy sulfur limitation effect, if
Meet, then integrate parameter value section under different operating conditions, obtain final optimization pass parameter section, otherwise, replacement input parameter is again
Carry out numerical simulation.
Further, comprising:
In the step (32), analyze determine the operation data the most key to energy consumption uses under operating condition method for
Grey relation entropy analysis method specifically includes:
(321) data initialization;X0={ X0(i) | i=1,2 ..., m }, Xk={ Xk(i) | i=1,2 ..., m }, m table
Show that sequence length, n indicate dimension.
X'0={ X0(i)/X0(1) | i=1,2 ..., m }, X'k={ Xk(i)/Xk(1) | i=1,2 ..., m }
(322) data are calculated and arranges differential matrix
Wherein, yij=| X0(j)-Xi(j)|
(323) two-stage lowest difference Y is soughtmin, two-stage maximum difference Ymax
ymin=min (min (Y)), ymax=max (max (Y))
(324) incidence matrix ζ is calculated
Wherein, ζij=(ymin+εymax)/(yij+εymax), i=1,2 ..., n;J=1,2 ..., m.0 < ε < 1, ε=
0.5;
(325) grey correlation matrix E is calculatedr:
Pi,j=ζij/ζi
Er(Xi)=H (Ri)/Hm,Hm=ln (m).
Further, comprising:
In the step (2), operating load divide the method that different operating conditions specifically use for using conventional method by accounting for volume
Constant load ratio divide or is distinguished using clustering method according to data distribution.
The utility model has the advantages that compared with prior art, the present invention its remarkable advantage is: 1, the present invention is establishing neural network prediction
Operating condition is distinguished first before model, data volume is smaller, and the relationship between output parameters and impact factor such as energy consumption, desulphurizing ratio
It is relatively unambiguous and simple, so often centainly being screened to input parameter, it ensure that precision;2, operating condition of the invention differentiation makes
It obtains parameter fluctuation obviously to weaken, data volume is obviously reduced, this will greatly reduce the complexity of data analysis, help to obtain spy
Determine the stable solution and optimal solution under operating condition;3, the present invention is auxiliary with numerical simulation, using desulphurization system big data analysis as means
The method of strategy is provided for desulphurization system running optimizatin, it is intended to be improved the efficiency of energy utilization of coal fired power plant desulphurization system, be reduced
Pollutant removing cost, it is energy saving;4, the present invention builds determining operating condition mathematical forecasting model by big data analysis, and analysis is each
Relationship between a operation data provides running optimizatin improvement strategy for desulphurization system, solves previous desulphurization system due to abundant
Energy resources caused by materials utilize unreasonable problem, achieve the effect that Optimized Operation power station resource.
Detailed description of the invention
Fig. 1 is method flow diagram described in a wherein embodiment of the invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with the accompanying drawings and the specific embodiments
The present invention will be described in detail.
This method of the present invention is analysis and research object with power station desulphurization system, by the simulation of Fluent software, big data point
The method of analysis carries out profound analysis to the system power station actual operating data, can not only obtain power station can not provide but
The data information of important in inhibiting, and mathematical forecasting model can be built and obtain model part, globally optimal solution, simultaneously
It is auxiliary with theoretical model, triplicity provides running optimizatin improvement strategy for desulphurization system actual motion.
As shown in fig.1, the data run optimization method of the present invention based on power plant desulphurization system, specifically includes:
S1 data needed for) establishing coal fired power plant desulphurization system theoretical model and obtaining: according to power station physical device component and
Specific configuration builds theoretical model, establishes its energy balance, conservation of matter theoretical model, is 1:1 with prototype size ratio.
To remove unit mass SO2Consumed currency is energy consumption evaluation indexes, and data needed for analysis project are simultaneously closed with power station
Make, obtains electric power station system structural parameters and certain period of time operation data.The energy consumption evaluation indexes are defined by the formula.
Energy consumption=a × feed consumption+b × power consumption-c × output
In formula, output is mainly gypsum, a, b, and c represents the market unit price of three kinds of materials.
Acquired operation data should include but is not limited to following parameter: unit load, coal, FGD into (out) implication amount,
Sulfur content, temperature, speed, loop slurry flow, serum density in desulfurizing tower, absorption tower slurries PH, absorption tower liquid level of slurry, supplement
Serum density, flow, oxidation fan component, temperature, power consumption, blender power consumption.
S2 Preprocessing) is carried out to power station actual operating data: confidence level audit being carried out to obtained operation data, is picked
Except wherein unreasonable data;Missing data is supplemented using weighting fill method;To fluctuate frequent supplemental characteristic (such as pressure) into
Row denoising, denoising method can use SG filter method.
And different operating conditions are divided according to operating load, it is of the invention in one embodiment, tradition side can be used
Method is divided in rated load ratio is accounted for, and such as 50%, the rated loads such as 70%;Clustering method can also be taken according to number
It is distinguished according to distribution.
S3) to S2) pretreatment after data carry out regression analysis: with S1) described in energy consumption evaluation indexes and traditional index
Desulphurizing ratio, calcium sulfur ratio are that target component carries out recurrence calculating to data.
It returns to calculate and can obtain desulfurization degree, the parameters such as calcium sulfur ratio.Due to desulphurization plant design capacity and actual motion mistake
The factors such as the abundant materials of journey carry out desulfurization degree and are often higher than national minimum discharge requirement, and excessively high desulphurizing ratio has directly energy consumption
The influence connect.Under the premise of meeting minimum discharge standard, the ratio that desulphurizing ratio can reduce is often S5) it is pre- in big data analysis
Survey the required precision of model.
S4 significant parameter can not be provided comprehensively by) obtaining power station by simulation, in one of them of the invention
In embodiment, specially desulphurization system practical flow field, temperature field, SO2Component distribution situation.
Numerical simulation is in S2) in carry out under classed load.Specific step is as follows for simulation:
(1) according to power station actual motion system equipment practical structures and size, being established using Gambit software includes boiler
Burner hearth, horizontal flue, each decontamination apparatus and back-end ductwork total system three-dimensional physical model;
(2) the non-homogeneous of desulphurization system inlet is obtained in conjunction with power station related data by experiment and on-the-spot test means
Property entrance boundary condition of the condition as computation model, including but not limited to VELOCITY DISTRIBUTION, Temperature Distribution, constituent concentration distribution.
Using Fluent software, the flow field of desulphurization system whole (entrance, desulfurizing tower, outlet), temperature, SO under different operating conditions are obtained2Group
Divide distribution situation;
(3) by analog result and power station actual operation parameters date comprision, modifying factor is introduced, audits built object
Manage the reliability of model and analog result.Audit passes through, then enters and carry out big data analysis in next step, otherwise re-start S4);
S5 big data analysis) big data analysis: is carried out to coal fired power plant desulphurization system actual operating data.
For distinguishing operating condition, following steps are specifically included:
(51) regression analysis is carried out to fixed floor data: according to energy consumption evaluation indexes described in S1) to S2) after pretreatment
Data carry out recurrence calculating, and based on theoretical model, preliminary analysis influences to determine the factor of energy consumption difference under operating condition.It is distinguishing
Under the premise of operating condition, logical relation is simple, and data volume is smaller, therefore can be divided using multinomial logistic regression method energy consumption
Analysis, using obtained operation data as independent variable, using energy consumption as dependent variable.
(52) it screens impact factor: utilizing correlation technique, the present embodiment uses grey relation entropy analysis method, analyzes and determines operating condition
Under the operation data the most key to energy consumption, threshold values screening wherein several factors are set up, of the invention one of real
It applies in example, threshold values selected by possibility is different under different operating conditions, and impact factor number is different, the shadow as next step mode input
Factor set is rung, impact factor collection is the subset of obtained power station parameter set.
Grey relation entropy analysis method specifically includes:
(521) data initialization;X0={ X0(i) | i=1,2 ..., m }, Xk={ Xk(i) | i=1,2 ..., m }
X'0={ X0(i)/X0(1) | i=1,2 ..., m }, X'k={ Xk(i)/Xk(1) | i=1,2 ..., m }
(522) data are calculated and arranges differential matrix
Wherein, yij=| X0(j)-Xi(j)|
(523) two-stage lowest difference Y is soughtmin, two-stage maximum difference Ymax
ymin=min (min (Y)), ymax=max (max (Y))
(524) incidence matrix ζ is calculated
Wherein, ζij=(ymin+εymax)/(yij+εymax), i=1,2 ..., n;J=1,2 ..., m.0 < ε < 1, ε=
0.5;
(525) grey correlation matrix E is calculatedr:
Pi,j=ζij/ζi
Er(Xi)=H (Ri)/Hm,Hm=ln (m)
(53) mathematical forecasting model is built: based on impact factor collection supplemental characteristic, by neural network, vector machine etc.
Method builds mathematical forecasting model.Mathematical forecasting model is input with impact factor collection parameter, with S2) it is real after data prediction
Border operation data is training data, and in the present embodiment, the data volume for choosing 95% is training data, and residue 5% is pre- for auditing
Model is surveyed, output is energy consumption evaluation indexes.Furthermore, it is possible to add the parameters such as desulphurizing ratio, calcium sulfur ratio as multi output model.Such as benefit
Energy consumption, desulphurizing ratio dual factors output model are built with neural network, help to obtain the optimal solution set for meeting numerous parameters in this way,
If not building, related optimizing result can be automatically deleted in the dummy run phase;
(54) prediction model is audited: using power station with model buildings data non-under operating condition (remaining 5% data) to same operating condition
Prediction model carries out output prediction, and suitable error line, the accuracy of judgment models is arranged.If audit, which passes through, enters step (55),
Otherwise separate regression steps (53).If repeatedly for built model not by audit, separate regression steps (52) check physical model and institute
Need data whether gaps and omissions;
(55) it is found using appropriate algorithm and determines prediction model part and globally optimal solution under operating condition.The embodiment of the present invention
In, using genetic algorithm or artificial bee colony algorithm, it may be assumed that on built prediction model basis, in conjunction with genetic algorithm or
Artificial bee colony algorithm is found each using genetic algorithm or the obtained Key Influential Factors data of artificial bee colony algorithm as mode input
A optimal value interval of parameter.To avoid separated data from impacting optimizing result, calcium sulfur ratio, desulphurizing ratio parameter can use
Related threshold is set.
S6) in conjunction with theoretical model and the optimal value interval of gained in S1), judge whether gained optimizing solution meets theory about
Beam determines system each Key Influential Factors optimum interval in the case where determining operating condition;
S7) to S6) obtained in optimum interval using Fluent software carry out numerical simulation, with the relevant parameter of optimal solution
The correctness of big data analysis parameters obtained optimum interval is judged referring to the analog result of S4) for the input of numerical simulation, if
Correctly, S8 is carried out), otherwise, return S6);
S8 parameter value section under different operating conditions) is integrated, obtains final optimization pass parameter section, on this basis, the present invention
Embodiment, analyze dynamic relationship between optimum interval and each impact factor, in the embodiment of the present invention, utilize the side of regression analysis
Method is using energy consumption as dependent variable, respectively to input the optimal value of factor under different operating conditions as independent variable;Utilize each parameter under different operating conditions
Relationship builds neural network model between optimal value and load, so can further obtain divided in operating condition it is not yet existing
The optimal value of each parameter of operating condition, obtains final optimization pass improvement strategy.
The present invention will be described in detail referring to the drawings implements.The implementation case carries out real under premised on this technology method
It applies, gives detailed embodiment and operating process, but protection scope of the present invention is not limited only to following embodiments.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Finally it should be noted that: the above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, to the greatest extent
Invention is explained in detail referring to above-described embodiment for pipe, it should be understood by those ordinary skilled in the art that: still
It can be with modifications or equivalent substitutions are made to specific embodiments of the invention, and without departing from any of spirit and scope of the invention
Modification or equivalent replacement, should all cover within the scope of the claims of the present invention.
Claims (7)
1. a kind of data run optimization method based on power plant desulphurization system, which is characterized in that this method comprises:
(1) obtain power station actual motion system certain period of time in actual operating data, and to the actual operating data into
Row pretreatment and regression analysis;
(2) different operating conditions are divided according to operating load, numerical simulation obtains the flow field of desulphurization system entirety, temperature under different operating conditions
Degree, SO2Component distribution situation;
(3) actual operating data described in coal fired power plant desulphurization system carries out big data analysis, obtains running number under determining operating condition
According to optimum interval, specifically include:
(31) regression analysis is carried out to fixed floor data: according to removal unit mass SO2Consumed currency is to data after pretreatment
Recurrence calculating is carried out, the factor of energy consumption difference under the analyzing influence operating condition;
(32) it screens impact factor: analyzing and determine operation data the most key to energy consumption under operating condition, selection wherein meets
For the operation data of preset threshold as impact factor collection, the impact factor collection is the son of obtained power station actual operating data
Collection;
(33) it builds mathematical forecasting model: being input with the impact factor collection supplemental characteristic, mathematics is constructed using neural network
Prediction model is exported using the part actual operating data amount obtained after the pretreatment as training data as energy consumption evaluation indexes;
(34) audit prediction model: using power station under operating condition, other actual operating data amounts in addition to training data are to same
Operating condition prediction model carries out output prediction, and suitable error line, the accuracy of judgment models is arranged, if audit is by entering step
(35), otherwise separate regression steps (33);
(35) it algorithm optimizing: is found using genetic algorithm and determines the prediction model part and globally optimal solution under operating condition, obtained
The optimal value interval of parameters.
(4) by data analysis optimizing result in conjunction with numerical simulation result, mould once again is carried out to optimizing result by analog result
It is quasi-, judge whether it can reach default sulphur removal target.
2. the data run optimization method according to claim 1 based on power plant desulphurization system, which is characterized in that the step
Suddenly in (1), actual operating data includes: unit load, and coal, FGD import tolerance, FGD export tolerance, sulfur content, temperature, speed
Serum density, absorption tower slurries PH, absorption tower liquid level of slurry, supplement serum density, stream in degree, loop slurry flow, desulfurizing tower
Amount, oxidation fan component, temperature, power consumption and blender power consumption.
3. the data run optimization method according to claim 1 based on power plant desulphurization system, which is characterized in that the step
Suddenly in (2), numerical simulation is specifically included:
(21) according to the practical structures and size of power station actual motion system equipment, being established using Gambit software includes Boiler Furnace
Thorax, horizontal flue, each decontamination apparatus and back-end ductwork total system three-dimensional physical model;
(22) using the heterogeneity condition of desulphurization system inlet as the entrance boundary item of the total system three-dimensional physical model
Part, using Fluent software, simulation calculates the flow field of desulphurization system entirety, temperature, SO under different operating conditions2Component distribution situation;
(23) by the numerical simulation result and actual operation parameters date comprision, modifying factor is introduced, is audited described complete
The reliability of system three-dimensional physical model and analog result enters the big data analysis, otherwise, again if audit passes through
Carry out numerical simulation.
4. the data run optimization method according to claim 1 based on power plant desulphurization system, which is characterized in that the step
Suddenly in (1), pretreatment includes: to reject wherein unreasonable data firstly, to the progress confidence level audit of obtained operation data;Its
It is secondary, missing data is supplemented using weighting fill method;Finally, carrying out denoising to frequent supplemental characteristic is fluctuated.
5. the data run optimization method according to claim 1 based on power plant desulphurization system, which is characterized in that this method
Further include: numerical simulation is carried out to the final optimization pass parameter section in step (35), using the relevant parameter of optimal solution set as numerical value
The input of simulation judges whether big data analysis parameters obtained can satisfy SO referring to the analog result of the step (2)2's
Removing requires, if can be to integrate parameter value section under different operating conditions, obtain final optimization pass parameter section, otherwise, replacement is joined
Number re-starts numerical simulation.
6. the data run optimization method according to claim 1 based on power plant desulphurization system, which is characterized in that the step
Suddenly in (32), the method that the operation data the most key to energy consumption uses under the determining operating condition of analysis is grey relation entropy analysis
Method specifically includes:
(321) assume to be compared sequence are as follows: X0={ X0(i) | i=1,2 ..., m }, comparing sequence is Xk={ Xk(i) | i=1,
2 ..., m }, k=1,2 ... n, m indicate that sequence length, n indicate dimension;
Data initialization: X'0={ X0(i)/X0(1) | i=1,2 ..., m }, X 'k={ Xk(i)/Xk(1) | i=1,2 ..., m }
(322): calculating data and arrange differential matrix
Wherein, yij=| X0(j)-Xi(j)|
(323) two-stage lowest difference Y is soughtmin, two-stage maximum difference Ymax
ymin=min (min (Y)), ymax=max (max (Y))
(324) incidence matrix ζ is calculated
Wherein, ζij=(ymin+εymax)/(yij+εymax), i=1,2 ..., n;J=1,2 ..., m.0 < ε < 1, ε=0.5;
(325) grey correlation matrix E is calculatedr:
Pi,j=ζij/ζi
Er(Xi)=H (Ri)/Hm,Hm=ln (m).
7. the data run optimization method according to claim 1 based on power plant desulphurization system, which is characterized in that the step
Suddenly in (2), operating load divides the method that different operating conditions specifically use to carry out using conventional method in rated load ratio is accounted for
It divides or is distinguished using clustering method according to data distribution.
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CN109636001A (en) * | 2018-11-13 | 2019-04-16 | 北京国电龙源环保工程有限公司 | Desulfurization pulp feeding system pH value adjusting method, system and computer-readable medium based on big data |
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CN112216351A (en) * | 2020-09-23 | 2021-01-12 | 成都佳华物链云科技有限公司 | Desulfurization optimization method and device, electronic equipment and storage medium |
CN112216351B (en) * | 2020-09-23 | 2024-02-13 | 成都佳华物链云科技有限公司 | Desulfurization optimization method and device, electronic equipment and storage medium |
WO2022210827A1 (en) * | 2021-03-31 | 2022-10-06 | 三菱重工業株式会社 | Control method for wet flue gas desulfurisation device, control device for wet flue gas desulfurisation device, remote monitoring system comprising said control device for wet flue gas desulfurisation device, information processing device, and information processing system |
CN113313325A (en) * | 2021-06-21 | 2021-08-27 | 西安热工研究院有限公司 | Desulfurization system operation optimization method, system, equipment and storage medium |
CN113361971A (en) * | 2021-07-13 | 2021-09-07 | 浙江菲达环保科技股份有限公司 | Combined control method and system for gypsum dehydration and desulfurization wastewater |
CN113469449A (en) * | 2021-07-13 | 2021-10-01 | 浙江菲达环保科技股份有限公司 | Optimizing control method and system for desulfurization system |
CN113361971B (en) * | 2021-07-13 | 2023-04-07 | 浙江菲达环保科技股份有限公司 | Combined control method and system for gypsum dehydration and desulfurization wastewater |
CN113426264A (en) * | 2021-07-15 | 2021-09-24 | 国电环境保护研究院有限公司 | Intelligent operation control method and control platform for flue gas purification island |
CN114413247A (en) * | 2022-01-14 | 2022-04-29 | 西安热工研究院有限公司 | Boiler combustion heating surface overtemperature monitoring and active inhibition system |
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