CN116432515A - Boiler efficiency operation instruction optimization method and system based on artificial intelligence - Google Patents
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Abstract
The invention discloses a boiler efficiency operation instruction optimization method and system based on artificial intelligence, comprising the following steps: acquiring historical data of related measuring points of boiler operation; performing correlation calculation on the obtained historical data of the boiler operation related measuring points, and screening to obtain a boiler efficiency related operation instruction; processing the acquired historical data of the relevant boiler operation measuring points to obtain training data; constructing a boiler efficiency simulation model, and training the constructed boiler efficiency simulation model based on training data to obtain a trained boiler efficiency simulation model; and acquiring real-time data of relevant measuring points of boiler operation, inputting the real-time data into a trained boiler efficiency simulation model, optimizing and adjusting relevant operation instruction data of boiler efficiency by using a genetic algorithm, obtaining an optimal adjustment strategy of the boiler and displaying a recommended value. The invention can provide the best operation guidance for boiler operation under various working conditions. The double optimization targets of economy and safety of the unit are met, and energy conservation and emission reduction of the unit are realized.
Description
Technical Field
The invention belongs to the technical field of thermal power generation control, and particularly relates to a boiler efficiency operation instruction optimization method and system based on artificial intelligence.
Background
Along with the gradual improvement of the energy-saving and emission-reducing standard, the thermal power generating unit is currently faced with the improvement of risks and pressures in various aspects such as environmental protection, economy, safety, market and the like; how to improve the comprehensive performance index of the thermal power generating unit, and realizing energy conservation and emission reduction is a problem that the thermal power generating unit needs to be considered for a long time in the future on the premise of ensuring the safe, economical, stable and environment-friendly operation of the unit.
In order to meet the requirements, the operation mode of the thermal power unit is optimized, the operation environment of the thermal power unit is improved by adopting a novel method and technology, and the thermal power unit is one of effective means for effectively improving the operation efficiency of the thermal power unit. In the running process of the thermal power generating unit, the adjustment of boiler combustion is a foundation for ensuring the stable and economic running of the whole unit.
However, the running state of the thermal power generating unit continuously changes, the internal reaction is complex and changeable, the running adjustment is controllable, the quantity of the running adjustment is numerous, the running adjustment is dependent on the experience of operators, and the optimal solution cannot be found according to different working conditions. The traditional modeling optimization algorithm only considers a small amount of states and action spaces, and is difficult to accurately model a huge and complex boiler combustion process.
The prior art document 1 discloses a method for optimizing and predicting the operation of a coal-fired boiler based on an improved neural network and a genetic algorithm, wherein the neural network optimized based on the genetic algorithm is used for modeling experimental data of boiler parameters (fuel quantity, air supply quantity, oxygen quantity, temperature difference quantity and coal type characteristics), and further the genetic algorithm is used for optimizing adjustable parameters of the boiler (fuel quantity, air supply quantity, oxygen quantity and temperature difference quantity) under a certain working condition, so that the production operation state of the boiler is optimized. Prior art document 1 does not consider that there may be a correlation between the measurement points before modeling optimization, and it establishes a boiler prediction model objective function factor including current and post-optimization furnace efficiency, current and post-optimization emission values, boiler efficiency, and NOx concentration; in the prior art 1, the calculation amount for algorithm optimization is large and the calculation speed is low, and the main purposes of the algorithm optimization are to improve the combustion efficiency and reduce the NOx emission, so that the optimization range is limited.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides the boiler efficiency operation instruction optimization method based on artificial intelligence, which can optimize the operation mode of the thermal power unit, improve the operation environment of the unit and effectively improve the operation efficiency of the thermal power unit.
The invention adopts the following technical scheme.
An artificial intelligence-based boiler efficiency operation instruction optimization method comprises the following steps:
step 2, performing correlation calculation on the historical data of the boiler operation related measuring points obtained in the step 1, and screening to obtain a boiler efficiency related operation instruction;
step 3, processing the acquired historical data of the relevant measuring points of the boiler operation to obtain training data;
step 4, constructing a boiler efficiency simulation model, and training the constructed boiler efficiency simulation model based on training data to obtain a trained boiler efficiency simulation model;
and step 5, acquiring real-time data of relevant measurement points of the boiler operation, inputting the real-time data into a trained boiler efficiency simulation model, optimizing and adjusting relevant operation instruction data of the boiler efficiency by using a genetic algorithm, obtaining an optimal adjustment strategy of the boiler and displaying a recommended value.
Preferably, in step 1, the obtained boiler operation related measurement point variables further include: the method comprises the steps of inlet oxygen amount, outlet flue gas temperature, main steam pressure, reheat steam temperature, water supply temperature, desuperheater, steam turbine high-pressure cylinder exhaust main pipe pressure, boiler efficiency, water-cooled wall temperature difference data, total coal supply amount, boiler load, actual power, total air quantity signal, total primary air quantity, water supply flow, main steam flow, wind powder temperature, current, main steam temperature, reheat steam pressure, chamber total air quantity, primary air quantity, post flue gas oxygen content, blower inlet temperature, fly ash carbon content and inlet flue gas temperature.
Preferably, the water wall temperature difference data further includes: the temperature difference of the lower water wall temperature of the left wall, the temperature difference of the lower water wall temperature of the right wall, the temperature difference of the lower water wall temperature of the front wall, the temperature difference of the water wall temperature of the rear wall, the temperature difference of the upper water wall temperature of the left wall, the temperature difference of the upper water wall temperature of the right wall, the temperature difference of the upper water wall temperature of the front wall, the temperature difference of the water wall of the horizontal flue of the boiler and the temperature difference of the water wall of the bottom of the horizontal flue of the boiler.
Preferably, the step 2 further includes:
step 2-1, calculating a correlation index of boiler efficiency based on historical data of boiler operation correlation measuring points;
and 2-2, screening the relevant measuring points of the boiler operation according to the calculated relevant indexes of the boiler efficiency to obtain relevant operation instructions of the boiler efficiency.
Preferably, the obtained boiler efficiency related operation instruction data includes: air preheater inlet oxygen content, main steam pressure, reheat steam pressure, boiler feed water temperature, steam turbine high pressure cylinder exhaust main pipe pressure and boiler efficiency.
Preferably, the step 3 further includes:
step 3-1, carrying out unified format processing on the historical data of the boiler operation related measuring points obtained in the step 1;
and 3-2, performing data cleaning processing on the data in the uniform format, wherein the cleaned data is used as training data.
Preferably, the step 4 further includes:
constructing a boiler efficiency simulation model based on a GBDT algorithm;
and (3) taking the processed boiler operation related measuring point data obtained in the step (3) as input, and taking the boiler efficiency as output, training the boiler efficiency simulation model, and obtaining the trained boiler efficiency simulation model.
Preferably, step 5-1, inputting real-time data of relevant measurement points of boiler operation into a trained boiler efficiency simulation model to obtain a current boiler efficiency value, and setting an optimization rule;
the optimization rule of the boiler efficiency optimizing model comprises the following steps: setting the upper limit of the temperature difference of the water wall at each part of the boiler, and ensuring that the temperature difference of the water wall at each part of the boiler is smaller than the set upper limit;
and 5-2, optimizing the operation instruction data related to the boiler efficiency by using a genetic algorithm based on an optimization rule to obtain the operation instruction data related to the optimal boiler efficiency.
Preferably, the step 5-2 further comprises:
step 5-2-1: under the condition that the limit condition of the optimization rule set in the step 5-1 is met, randomly initializing K groups of boiler efficiency related operation instruction data before the optimization operation starts;
step 5-2-2: calculating an optimized target value of the K groups of operation instructions until the optimized target value is smaller than a threshold value, and selecting a boiler efficiency related operation instruction recommended value with the minimum M groups of optimized target values from the K groups;
step 5-2-3: performing mutation operation of a genetic algorithm on the M groups of values, adding random offset to floating point number or integer type operation instructions, and negating Boolean type operation instructions;
step 5-2-4: and (3) carrying out generation offspring operation in a genetic algorithm on each group of operation instructions obtained in the step (5-2-1), wherein the generation offspring operation comprises the following steps: randomly selecting two groups of operation instructions, and calculating an average value to generate N groups of operation instruction data related to the boiler efficiency of the offspring;
step 5-2-5: and (3) repeating the steps 5-2-2 to 5-2-4 on each group of boiler efficiency related operation instruction data obtained after the processing of the step 5-2-4 until the iteration times meet the requirement or the change value of the optimization target value calculated in the step 5-2-2 between two iterations reaches a set threshold value, and outputting the boiler efficiency related operation instruction data which obtains the minimum target value as an optimization result.
The invention also provides a boiler efficiency operation instruction optimization system based on artificial intelligence, which comprises: the system comprises a data acquisition module, a data cleaning module, a correlation calculation module, a model training module and a boiler efficiency optimizing module;
the data acquisition module can acquire historical data and real-time data of a boiler operation related measuring point in a historical working condition from the time sequence database;
the data cleaning module is used for processing the data of the obtained relevant measuring points of the boiler operation to obtain training data;
the correlation calculation module can perform correlation calculation on the historical data of the boiler operation correlation measurement points acquired by the data acquisition module to acquire a boiler efficiency correlation operation instruction;
the model training module can construct a boiler efficiency simulation model based on the GBDT algorithm, and the boiler efficiency simulation model is trained by combining training data obtained by the data cleaning module to obtain a trained boiler efficiency simulation model;
the boiler efficiency optimizing module can perform optimizing exploration in the range of the limiting conditions of the optimizing rule based on the output value of the boiler efficiency simulation model and by combining a genetic algorithm, and obtain the boiler efficiency related operation instruction data of the minimum target value.
The invention also provides a terminal, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate according to the instructions to perform the steps of the artificial intelligence based boiler efficiency operation instruction optimization method.
The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the artificial intelligence based boiler efficiency operation instruction optimization method.
Compared with the prior art, the invention has the beneficial effects that the big data technology and the artificial intelligence technology are combined, the water wall temperature difference simulation model and the boiler efficiency simulation model are constructed by utilizing a machine learning method based on long-term operation data of the thermal power plant unit, the model optimizing operation is carried out by taking a genetic algorithm as a core, and related operation instructions which ensure that the boiler efficiency is not reduced and is close to the optimal boiler efficiency are searched under various working conditions; the invention can search the operation instruction of each boiler efficiency operation instruction which can ensure that the boiler efficiency is not reduced and is close to the optimal under various working conditions, provides more finely divided power supply coal consumption and is convenient for operation analysis instruction. The double optimization targets of economy and safety of the unit are met, and energy conservation and emission reduction of the unit are realized.
Drawings
FIG. 1 is a flow chart of an artificial intelligence based method for optimizing boiler efficiency operating instructions in accordance with the present invention;
FIG. 2 is a flow chart of online optimization guidance for optimization of boiler efficiency-related state parameters in the present invention;
FIG. 3 is a schematic diagram of the model structure of the GBDT algorithm of the present invention;
FIG. 4 is a schematic diagram of a model structure of a genetic algorithm in the present invention;
FIG. 5 is a schematic diagram of an artificial intelligence based boiler efficiency operating instruction optimization system in accordance with the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present invention.
As shown in fig. 1, the invention provides a boiler efficiency operation instruction optimization method based on artificial intelligence, which comprises the following steps:
specifically, the time interval t may be set to 3s;
the acquired history data may select data within the past 1 year.
Further, the boiler operation related measuring points obtained in the step 1 further include: air preheater inlet oxygen content, air preheater outlet flue gas temperature, main steam pressure, reheat steam temperature, water supply temperature, desuperheater water flow, low desuperheater water flow, steam turbine high pressure cylinder exhaust main pipe pressure, boiler efficiency, water wall temperature difference data, total coal supply quantity, boiler load, actual power, total air quantity signal, total primary air quantity, water supply flow, main steam flow, air powder temperature, current, boiler main steam temperature, reheat steam pressure, hearth total air quantity, primary air quantity, post flue gas oxygen content, blower inlet temperature, fly ash carbon content and inlet flue gas temperature;
wherein, water-cooled wall temperature difference data still includes: the temperature difference of the lower water wall temperature of the left wall, the temperature difference of the lower water wall temperature of the right wall, the temperature difference of the lower water wall temperature of the front wall, the temperature difference of the water wall temperature of the rear wall, the temperature difference of the upper water wall temperature of the left wall, the temperature difference of the upper water wall temperature of the right wall, the temperature difference of the upper water wall temperature of the front wall, the temperature difference of the water wall of the horizontal flue of the boiler and the temperature difference of the water wall of the bottom of the horizontal flue of the boiler.
Step 2, performing correlation calculation on the historical data of the boiler operation related measuring points obtained in the step 1 to obtain a boiler efficiency related operation instruction;
specifically, the calculation of the boiler efficiency related operation instruction data through the correlation calculation comprises the following steps:
step 2-1, calculating a correlation index of boiler efficiency based on historical data of boiler operation correlation measuring points;
specifically, the corr function (correlation coefficient function) in the pandas library is used for calculating the correlation index of the boiler efficiency, and the specific codes are as follows:
| corr(method＝′pearson′，min_periods＝1) |
parameter description:
method of: the optional value is { 'pearson', 'kendall', 'sporman', }
pearson: the pearson correlation coefficient is used for measuring whether two data sets are on a line or not, namely, the correlation coefficient calculation is carried out on linear data, and errors exist on nonlinear data;
kendall: the Kendell correlation coefficient is used for reflecting indexes of the correlation of the classification variables, namely data of non-positive distribution aiming at the correlation coefficient of the unordered sequence;
clearman: spearman correlation coefficients, nonlinear, non-positive-distributed data correlation coefficients;
the correlation calculation is performed on the historical data of the boiler operation correlation measuring points to obtain the values of the correlation indexes pearson, kendall, spearman of the boiler efficiency of each boiler operation correlation measuring point, and the correlation indexes obtained by the calculation of part of measuring points are shown in the following table 1.
Table 1: correlation analysis table of partial boiler correlation measuring points and boiler efficiency
Step 2-2, screening relevant measuring points of boiler operation according to the calculated relevant indexes of boiler efficiency to obtain relevant operation instructions of boiler efficiency;
by performing correlation analysis by using corr functions in the pandas library, as shown in the above table 1, each correlation index is positive, the positive number represents positive correlation, the negative number represents negative correlation, the larger the value is, the larger the represented correlation is, the three measuring points with absolute values of the correlation index being larger than 0.2 are selected as measuring points with strong correlation with boiler efficiency, namely, the source points in the above table 1, and the obtained measuring points with strong correlation with boiler efficiency are used as boiler efficiency correlation operation instructions.
As shown in fig. 2, in the present invention, the boiler efficiency related operation instruction obtained by correlation calculation and screening includes: air preheater inlet oxygen content, main steam pressure, reheat steam pressure, boiler feed water temperature, steam turbine high pressure cylinder exhaust main pipe pressure and boiler efficiency;
step 3, processing the acquired historical data of the relevant measuring points of the boiler operation to obtain training data;
step 3 further comprises:
step 3-1, carrying out unified format processing on the historical data of the boiler operation related measuring points obtained in the step 1;
step 3-2, performing data cleaning treatment on the data in the unified format, wherein the cleaned data is used as training data;
and according to the business rules and the safety operation specifications, cleaning the data in the unified format, and deleting the data which does not accord with the business rules and the safety operation specifications to obtain cleaned data which accords with the safety operation.
The following data cleaning process is performed on all the data in the unified format, specifically: and (3) presetting the high and low thresholds of the variables of each measuring point according to the safety operation specification, if a certain measuring point of certain data exceeds the range of the high and low thresholds, setting the upper and lower limit ranges of each measuring point by a person skilled in the art according to actual conditions, removing the value of the measuring point of the certain data, and complementing the data by using an interpolation method.
Step 4, constructing a boiler efficiency simulation model, and training the constructed boiler efficiency simulation model based on training data to obtain a trained boiler efficiency simulation model;
step 4 further comprises:
as shown in fig. 3, constructing a boiler efficiency simulation model based on the GBDT algorithm;
taking the processed boiler operation related measuring point data obtained in the step 3 as input, and taking the boiler efficiency as output, training the boiler efficiency simulation model to obtain a trained boiler efficiency simulation model;
preferably, the boiler efficiency related measuring points comprise inlet oxygen amount, outlet flue gas temperature, main steam pressure, reheat steam temperature, water supply temperature, desuperheater low-temperature water flow, steam turbine high-pressure cylinder exhaust main pipe pressure, boiler efficiency, water wall temperature difference data, total coal supply amount, boiler load, actual power, total air quantity signal, total primary air quantity, water supply flow, main steam flow, wind powder temperature, current, main steam temperature, reheat steam pressure, total air quantity of a hearth, primary air quantity, oxygen content of post flue gas, blower inlet temperature, fly ash carbon content and inlet flue gas temperature;
and step 5, acquiring real-time data of relevant measurement points of the boiler operation, inputting the real-time data into a trained boiler efficiency simulation model, optimizing and adjusting relevant operation instruction data of the boiler efficiency by using a genetic algorithm, obtaining an optimal adjustment strategy of the boiler and displaying a recommended value.
Step 5-1, inputting real-time data of relevant measurement points of boiler operation into a trained boiler efficiency simulation model to obtain a current boiler efficiency value, and setting an optimization rule;
the optimization rules of the boiler efficiency optimizing model comprise: setting the upper limit of the temperature difference of the water wall at each part of the boiler, and ensuring that the temperature difference of the water wall at each part of the boiler is smaller than the set upper limit; the water wall temperature difference of each part of the boiler comprises: the temperature difference of the lower water wall temperature of the left wall, the temperature difference of the lower water wall temperature of the right wall, the temperature difference of the lower water wall temperature of the front wall, the temperature difference of the water wall temperature of the rear wall, the temperature difference of the upper water wall temperature of the left wall, the temperature difference of the upper water wall temperature of the right wall, the temperature difference of the upper water wall temperature of the front wall, the temperature difference of the water wall of the horizontal flue of the boiler and the temperature difference of the water wall of the bottom of the horizontal flue of the boiler;
preferably, the upper limit of the temperature difference of the water-cooled walls of all parts of the boiler is set to be 50 ℃, namely the temperature difference of the water-cooled walls of all parts is smaller than 50 ℃.
Step 5-2, optimizing the operation instruction data related to the boiler efficiency by using a genetic algorithm based on an optimization rule to obtain the operation instruction data related to the optimal boiler efficiency;
in the step 5-2, optimization exploration is performed on the operation instruction data related to the boiler efficiency within the range of the limitation condition of the optimization rule based on the genetic algorithm, so that the optimization target reaches the minimum value.
Specifically, the optimization objective is a reduction value of the boiler efficiency, the reduction value of the boiler efficiency is a difference value between the optimized boiler efficiency value and the current boiler efficiency value, and then the judgment is performed according to the reduction value of the boiler efficiency, and a penalty value is given to the situation that the boiler efficiency after the optimization is reduced, so that the optimizing operation is required to be searched towards the direction that the boiler efficiency is not reduced as far as possible.
Specifically, the calculation formula of the reduction value of the boiler efficiency is:
effiencicy_temp＝opt_effiencicy--cur_effiencicy
wherein, the efficiency_temp is a reduction value of boiler efficiency, opt_efficiency is an optimized boiler efficiency value, and cur_efficiency is a current boiler efficiency value;
the penalty value is calculated by the following formula:
wherein V is 2 Representing a penalty value.
Each optimizing operation continuously adjusts limiting conditions and boiler efficiency optimizing values based on the previous optimizing result, and gives recommended values of operation instruction data related to the boiler efficiency;
after the optimizing, if the working condition does not change greatly, the optimizing operation of each subsequent time should be performed in a smaller searching range based on the previous optimizing result, for example, the result of the last optimizing instruction (such as the inlet oxygen amount) is A, and the optimizing range of the instruction at this time can be between 0.98A and 1.02A.
Furthermore, under the limiting condition of meeting the optimization rule, before the optimization operation starts, K groups of operation instruction data related to the boiler efficiency are randomly initialized, and the boiler efficiency under the operation instruction is obtained.
Preferably, in the step 5-2, as shown in fig. 4, the optimization discovery operation using the genetic algorithm is as follows:
step 5-2-1: and under the condition that the limit condition of the optimization rule set in the step 5-1 is met, randomly initializing K groups of boiler efficiency related operation instruction data before the optimization operation starts.
Step 5-2-2: and calculating the optimized target value of the K groups of operation instructions until the optimized target value is smaller than a threshold value, and selecting the recommended value of the boiler efficiency related operation instruction with the minimum M groups of optimized target values from the K groups.
Step 5-2-3: and performing mutation operation of a genetic algorithm on the M groups of values, wherein in the aspect, a random offset is added to a floating point number or an integer type operation instruction, and the operation instruction of a Boolean type is inverted.
Step 5-2-4: and (3) carrying out generation offspring operation in a genetic algorithm on each group of operation instructions obtained in the step (5-2-1), wherein the generation offspring operation specifically comprises the following steps: and randomly selecting two groups of operation instructions, and calculating an average value to generate N groups of operation instruction data related to the boiler efficiency of the offspring.
Step 5-2-5: and (3) repeating the steps 5-2-2 to 5-2-4 on each group of boiler efficiency related operation instruction data obtained after the processing of the step 5-2-4 until the iteration times meet the requirement or the change value of the optimization target value calculated in the step 5-2-2 reaches a set threshold between two iterations, and outputting the boiler efficiency related operation instruction data with the minimum target value as an optimization result.
As shown in fig. 5, the present invention further provides an artificial intelligence based boiler efficiency operation instruction optimization system, and the above-mentioned artificial intelligence based boiler efficiency operation instruction optimization method can be implemented based on the system, where the system specifically includes: the system comprises a data acquisition module, a data cleaning module, a correlation calculation module, a model training module and a boiler efficiency optimizing module;
the data acquisition module can acquire historical data and real-time data of a boiler operation related measuring point in a historical working condition from the time sequence database;
the data cleaning module is used for processing the data of the obtained relevant measuring points of the boiler operation to obtain training data;
the correlation calculation module can perform correlation calculation on the historical data of the boiler operation correlation measurement points acquired by the data acquisition module to acquire a boiler efficiency correlation operation instruction;
the model training module can construct a boiler efficiency simulation model based on the GBDT algorithm, and the boiler efficiency simulation model is trained by combining training data obtained by the data cleaning module to obtain a trained boiler efficiency simulation model;
the boiler efficiency optimizing module can perform optimizing exploration in the range of the limiting conditions of the optimizing rule based on the output value of the boiler efficiency simulation model and by combining a genetic algorithm, and obtain the boiler efficiency related operation instruction data of the minimum target value.
Compared with the prior art, the invention has the beneficial effects that the invention is applied to the thermal power generation system through the artificial intelligence technology, and before optimization, the correlation calculation is carried out on the measuring points related to the boiler efficiency of the boiler, the measuring points with strong correlation with the boiler efficiency are extracted and used as the operation instructions for carrying out the model optimizing process by using the genetic algorithm subsequently; the objective function of the constructed model is a reduction value of boiler efficiency, the reduction value is a difference value between the optimized boiler efficiency value and the current boiler efficiency value, optimization exploration is carried out on the premise that limiting conditions are met, judgment is carried out according to the reduction value of the boiler efficiency, a penalty value is given to the condition that the boiler efficiency is reduced after optimization, the optimization operation is required to be explored towards the direction that the boiler efficiency is not reduced as far as possible, and after optimization is carried out once, if working conditions are not changed greatly, the next optimization operation is carried out on the basis of the last optimizing result, optimization is carried out in a smaller exploration range, operation guidance of each operation instruction which enables the boiler efficiency not to be reduced and is close to the optimal is sought under various working conditions, more finely divided power supply coal consumption can be provided, and operation analysis guidance is convenient. The double optimization targets of economy and safety of the unit are met, and energy conservation and emission reduction of the unit are realized.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (e.g., connected through the internet using an internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (12)
1. An artificial intelligence-based boiler efficiency operation instruction optimization method is characterized by comprising the following steps:
step 1, acquiring historical data of related measuring points of boiler operation from a time sequence database according to a time interval t;
step 2, performing correlation calculation on the historical data of the boiler operation related measuring points obtained in the step 1, and screening to obtain a boiler efficiency related operation instruction;
step 3, processing the acquired historical data of the relevant measuring points of the boiler operation to obtain training data;
step 4, constructing a boiler efficiency simulation model, and training the constructed boiler efficiency simulation model based on training data to obtain a trained boiler efficiency simulation model;
and step 5, acquiring real-time data of relevant measurement points of the boiler operation, inputting the real-time data into a trained boiler efficiency simulation model, optimizing and adjusting relevant operation instruction data of the boiler efficiency by using a genetic algorithm, obtaining an optimal adjustment strategy of the boiler and displaying a recommended value.
2. The method for optimizing boiler efficiency operating instructions based on artificial intelligence of claim 1,
in the step 1, the obtained variable of the measurement point related to the operation of the boiler further comprises: the method comprises the steps of inlet oxygen amount, outlet flue gas temperature, main steam pressure, reheat steam temperature, water supply temperature, desuperheater, steam turbine high-pressure cylinder exhaust main pipe pressure, boiler efficiency, water-cooled wall temperature difference data, total coal supply amount, boiler load, actual power, total air quantity signal, total primary air quantity, water supply flow, main steam flow, wind powder temperature, current, main steam temperature, reheat steam pressure, chamber total air quantity, primary air quantity, post flue gas oxygen content, blower inlet temperature, fly ash carbon content and inlet flue gas temperature.
3. The method for optimizing boiler efficiency operating instructions based on artificial intelligence of claim 2,
the water wall temperature difference data further comprises: the temperature difference of the lower water wall temperature of the left wall, the temperature difference of the lower water wall temperature of the right wall, the temperature difference of the lower water wall temperature of the front wall, the temperature difference of the water wall temperature of the rear wall, the temperature difference of the upper water wall temperature of the left wall, the temperature difference of the upper water wall temperature of the right wall, the temperature difference of the upper water wall temperature of the front wall, the temperature difference of the water wall of the horizontal flue of the boiler and the temperature difference of the water wall of the bottom of the horizontal flue of the boiler.
4. The method for optimizing boiler efficiency operating instructions based on artificial intelligence of claim 1,
the step 2 further includes:
step 2-1, calculating a correlation index of boiler efficiency based on historical data of boiler operation correlation measuring points;
and 2-2, screening the relevant measuring points of the boiler operation according to the calculated relevant indexes of the boiler efficiency to obtain relevant operation instructions of the boiler efficiency.
5. The method for optimizing boiler efficiency operating instructions based on artificial intelligence of claim 2,
the obtained boiler efficiency related operation instruction data comprises: air preheater inlet oxygen content, main steam pressure, reheat steam pressure, boiler feed water temperature, steam turbine high pressure cylinder exhaust main pipe pressure and boiler efficiency.
6. The method for optimizing boiler efficiency operating instructions based on artificial intelligence of claim 1,
the step 3 further includes:
step 3-1, carrying out unified format processing on the historical data of the boiler operation related measuring points obtained in the step 1;
and 3-2, performing data cleaning processing on the data in the uniform format, wherein the cleaned data is used as training data.
7. The method for optimizing boiler efficiency operating instructions based on artificial intelligence of claim 1,
the step 4 further includes:
constructing a boiler efficiency simulation model based on a GBDT algorithm;
and (3) taking the processed boiler operation related measuring point data obtained in the step (3) as input, and taking the boiler efficiency as output, training the boiler efficiency simulation model, and obtaining the trained boiler efficiency simulation model.
8. The method for optimizing boiler efficiency operating instructions based on artificial intelligence of claim 1,
step 5-1, inputting real-time data of relevant measurement points of boiler operation into a trained boiler efficiency simulation model to obtain a current boiler efficiency value, and setting an optimization rule;
the optimization rule of the boiler efficiency optimizing model comprises the following steps: setting the upper limit of the temperature difference of the water wall at each part of the boiler, so that the temperature difference of the water wall at each part of the boiler is smaller than the set upper limit;
and 5-2, optimizing the operation instruction data related to the boiler efficiency by using a genetic algorithm based on an optimization rule to obtain the operation instruction data related to the optimal boiler efficiency.
9. The method for optimizing boiler efficiency operating instructions based on artificial intelligence of claim 8,
the step 5-2 further comprises:
step 5-2-1: under the condition that the limit condition of the optimization rule set in the step 5-1 is met, randomly initializing K groups of boiler efficiency related operation instruction data before the optimization operation starts;
step 5-2-2: calculating an optimized target value of the K groups of operation instructions until the optimized target value is smaller than a threshold value, and selecting a boiler efficiency related operation instruction recommended value with the minimum M groups of optimized target values from the K groups;
step 5-2-3: performing mutation operation of a genetic algorithm on the M groups of values, adding random offset to floating point number or integer type operation instructions, and negating Boolean type operation instructions;
step 5-2-4: and (3) carrying out generation offspring operation in a genetic algorithm on each group of operation instructions obtained in the step (5-2-1), wherein the generation offspring operation comprises the following steps: randomly selecting two groups of operation instructions, and calculating an average value to generate N groups of operation instruction data related to the boiler efficiency of the offspring;
step 5-2-5: and (3) repeating the steps 5-2-2 to 5-2-4 on each group of boiler efficiency related operation instruction data obtained after the processing of the step 5-2-4 until the iteration times meet the requirement or the change value of the optimization target value calculated in the step 5-2-2 between two iterations reaches a set threshold value, and outputting the boiler efficiency related operation instruction data which obtains the minimum target value as an optimization result.
10. An artificial intelligence based boiler efficiency operation instruction optimization system utilizing the artificial intelligence based boiler efficiency operation instruction optimization method of any one of claims 1-9, comprising: the system comprises a data acquisition module, a data cleaning module, a correlation calculation module, a model training module and a boiler efficiency optimizing module;
the data acquisition module can acquire historical data and real-time data of a boiler operation related measuring point in a historical working condition from the time sequence database;
the data cleaning module is used for processing the data of the obtained relevant measuring points of the boiler operation to obtain training data;
the correlation calculation module can perform correlation calculation on the historical data of the boiler operation correlation measurement points acquired by the data acquisition module to acquire a boiler efficiency correlation operation instruction;
the model training module can construct a boiler efficiency simulation model based on the GBDT algorithm, and the boiler efficiency simulation model is trained by combining training data obtained by the data cleaning module to obtain a trained boiler efficiency simulation model;
the boiler efficiency optimizing module can perform optimizing exploration in the range of the limiting conditions of the optimizing rule based on the output value of the boiler efficiency simulation model and by combining a genetic algorithm, and obtain the boiler efficiency related operation instruction data of the minimum target value.
11. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-9.
12. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1-9.
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| CN117251957B (en) * | 2023-11-16 | 2024-02-13 | 江苏千桐科技有限公司 | Simulation optimization system and method for boiler accessory process |
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