A method, system and electronic equipment for determining operating parameters of a coal-ammonia co-firing boiler

Abstract

The present application discloses a method, system and electronic equipment for determining the operating parameters of a coal-ammonia co-firing boiler. Before the coal-ammonia co-firing boiler is operated, the coal-ammonia co-firing test bench and the coal-ammonia co-firing boiler are first combined to obtain a third model, and the third model is used to perform predictions. Based on the obtained predicted emission data, the operating parameters to be determined are optimized to obtain target operating parameters, so that the target operating parameters can be used to optimize the operation of the coal-ammonia co-firing boiler, improve combustion efficiency, and reduce pollutant emissions.

Description

Technical Field

[0001] The present application relates to the field of coal combustion technology, and in particular to a method, system and electronic equipment for determining operating parameters of a coal-ammonia co-firing boiler. Background Art

[0002] Energy consumption and environmental pollution are important issues of global concern. Traditional energy, such as coal, releases a large amount of greenhouse gases when burned, exacerbating climate change.

[0003] In order to reduce carbon emissions and improve the environment, low-carbon and clean energy technologies have become a research hotspot. Coal-ammonia co-firing is a low-carbon technology that replaces a certain proportion of pulverized coal with combustible ammonia and enters the boiler for combustion. It has the advantages of environmental protection, emission reduction, improved combustion efficiency, and reduced operating costs.

[0004] However, the existing coal-ammonia co-combustion system still has problems such as low combustion efficiency and high pollutant emissions. Summary of the invention

[0005] In view of this, the present application provides a method, system and electronic equipment for determining the operating parameters of a coal-ammonia co-firing boiler, and the specific scheme is as follows:

[0006] A method for determining operating parameters of a coal-ammonia co-firing boiler, comprising:

[0007] A third model is obtained by using the first model and the second model, and the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data;

[0008] Inputting the operating parameters to be determined into the first model to obtain the emission data to be determined output by the first model;

[0009] Inputting the emission data to be determined into the third model to obtain predicted emission data output by the third model;

[0010] The operating parameters to be determined are optimized based on the predicted emission data to determine target operating parameters so as to operate the coal-ammonia co-firing boiler using the target operating parameters.

[0011] Furthermore, the obtaining of the third model by using the first model and the second model includes:

[0012] Determine input data having an associated relationship from a first input data set of the first model and a second input data set of the second model;

[0013] Determine that the output data corresponding to the input data having the association relationship in the first output data set is a third output data set, and determine that the output data corresponding to the input data having the association relationship in the second output data set is a fourth output data set;

[0014] The third output data set is used as input data of the third model, and the fourth output data set is used as output data of the third model to perform model training to obtain the third model.

[0015] Further, determining the input data having an associated relationship from the first input data set of the first model and the second input data set of the second model includes:

[0016] Input data having at least some of the same data is determined from the first input data set of the first model and the second input data set of the second model, wherein the at least some of the data includes: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio.

[0017] Furthermore, the optimizing the to-be-determined operating parameters based on the predicted emission data to determine the target operating parameters includes:

[0018] determining a predicted total cost based at least on the predicted emissions data;

[0019] If it is determined that the predicted total cost meets the target condition, the to-be-determined operating parameter is determined as the target operating parameter.

[0020] Further, the determining the predicted total cost based at least on the predicted emission data includes:

[0021] Determine predicted raw material consumption data and predicted pollutant emission data based on the predicted emission data, wherein the predicted raw material consumption data includes: predicted coal consumption data and predicted ammonia consumption data;

[0022] The predicted total cost is determined based at least on the predicted raw material consumption data and the predicted pollutant emission data.

[0023] Further, if it is determined that the predicted total cost meets the target condition, determining the to-be-determined operating parameter as the target operating parameter includes:

[0024] comparing a plurality of predicted total costs, the plurality of predicted total costs being obtained based on a plurality of sets of operating parameters to be determined;

[0025] A minimum predicted total cost among the multiple predicted total costs is determined, and a group of to-be-determined operating parameters corresponding to the minimum predicted total cost is determined as the target operating parameters.

[0026] Further, each operating parameter included in each group of operating parameters to be determined is within a target range of the operating parameter, and different operating parameters included in each group of operating parameters to be determined are within different target ranges.

[0027] Furthermore, it also includes:

[0028] Determine key information of the coal-ammonia co-firing boiler during the coal-ammonia co-firing process;

[0029] According to the key information of the coal-ammonia co-firing boiler in the coal-ammonia co-firing process, a coal-ammonia co-firing experimental platform is built to experiment on the coal-ammonia co-firing process.

[0030] A system for determining operating parameters of a coal-ammonia co-firing boiler, comprising:

[0031] The first obtaining unit is used to obtain a third model using the first model and the second model, and the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by model training using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench, and the second model is obtained by model training using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data;

[0032] a second obtaining unit, configured to input the operating parameter to be determined into the first model, and obtain the emission data to be determined output by the first model;

[0033] A third obtaining unit, used for inputting the emission data to be determined into the third model to obtain the predicted emission data output by the third model;

[0034] A determination unit is used to optimize the operating parameters to be determined based on the predicted emission data, and determine target operating parameters so as to operate the coal-ammonia co-firing boiler using the target operating parameters.

[0035] An electronic device, comprising:

[0036] A processor, used to obtain a third model using the first model and the second model, wherein the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by model training using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench, and the second model is obtained by model training using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data; input the operating parameters to be determined into the first model to obtain the emission data to be determined output by the first model; input the emission data to be determined into the third model to obtain the predicted emission data output by the third model; optimize the operating parameters to be determined based on the predicted emission data, and determine the target operating parameters, so as to operate the coal-ammonia co-firing boiler using the target operating parameters;

[0037] The memory is used to store the programs and data required by the processor to execute the above processing process.

[0038] It can be seen from the above technical scheme that the method, system and electronic device for determining the operating parameters of a coal-ammonia co-firing boiler disclosed in the present application use the first model and the second model to obtain a third model, and the third model is used to characterize the correspondence between the output data of the first model and the output data of the second model; the first model is obtained by model training using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench, and the second model is obtained by model training using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data; the operating parameters to be determined are input into the first model to obtain the emission data to be determined output by the first model; the emission data to be determined are input into the third model to obtain the predicted emission data output by the third model; the operating parameters to be determined are optimized based on the predicted emission data, and the target operating parameters are determined, so as to operate the coal-ammonia co-firing boiler using the target operating parameters. Before the coal-ammonia co-firing boiler is put into operation, this scheme first combines the coal-ammonia co-firing test bench and the coal-ammonia co-firing boiler to obtain a third model, and uses the third model to make predictions, and optimizes the operating parameters to be determined based on the obtained predicted emission data to obtain target operating parameters, so as to use the target operating parameters to optimize the operation of the coal-ammonia co-firing boiler, improve combustion efficiency, and reduce pollutant emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A flow chart of a method for determining operating parameters of a coal-ammonia co-firing boiler disclosed in an embodiment of the present application;

[0041] Figure 2 This is a structural schematic diagram of a coal-ammonia co-combustion test bench disclosed in an embodiment of the present application;

[0042] Figure 3 A flow chart of a method for determining operating parameters of a coal-ammonia co-firing boiler disclosed in an embodiment of the present application;

[0043] Figure 4 A flow chart of a method for determining operating parameters of a coal-ammonia co-firing boiler disclosed in an embodiment of the present application;

[0044] Figure 5 A complete schematic diagram corresponding to a method for determining operating parameters of a coal-ammonia co-firing boiler disclosed in an embodiment of the present application;

[0045] Figure 6 A schematic diagram of the structure of a system for determining operating parameters of a coal-ammonia co-firing boiler disclosed in an embodiment of the present application;

[0046] Figure 7 A schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0048] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0049] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0050] The present application discloses a method for determining the operating parameters of a coal-ammonia co-firing boiler, and its flow chart is as follows: Figure 1 As shown, including:

[0051] Step S11, using the first model and the second model to obtain a third model, the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data;

[0052] Step S12: inputting the operating parameters to be determined into the first model to obtain the emission data to be determined output by the first model;

[0053] Step S13, inputting the emission data to be determined into the third model to obtain the predicted emission data output by the third model;

[0054] Step S14: Optimize the operating parameters to be determined based on the predicted emission data and determine the target operating parameters so as to operate the coal-ammonia co-firing boiler using the target operating parameters.

[0055] Energy consumption and environmental pollution are important issues of global concern. Traditional energy, such as coal, releases a large amount of greenhouse gases when burned, exacerbating climate change.

[0056] In order to reduce carbon emissions and improve the environment, low-carbon and clean energy technologies have become a research hotspot. Coal-ammonia co-firing is a low-carbon technology that replaces a certain proportion of pulverized coal with combustible ammonia and enters the boiler for combustion. It has the advantages of environmental protection, emission reduction, improved combustion efficiency, and reduced operating costs.

[0057] However, the existing coal-ammonia co-combustion system still has problems such as low combustion efficiency and high pollutant emissions.

[0058] Optimizing the coal-ammonia co-firing system and reducing carbon emissions are crucial to energy transformation and environmental improvement. Based on this, in this solution, the third model is used to predict the emission data of the coal-ammonia co-firing boiler when it is operating under the operating parameters to be determined, and the operating parameters to be determined are optimized based on the predicted emission data to obtain the target operating parameters, so as to ensure that the coal-ammonia co-firing boiler operates under the target operating parameters, which can improve the combustion efficiency, reduce pollutant emissions, and achieve the optimization of coal-ammonia co-firing; in addition, the third model is obtained based on the first model and the second model, that is, it is obtained using the coal-ammonia co-firing test bench and the coal-ammonia co-firing boiler, so that more data required for the third model can be obtained through the coal-ammonia co-firing test bench, without all data being obtained by running on the coal-ammonia co-firing boiler, which improves the processing efficiency and reduces the cost of prediction.

[0059] Specifically, the first model and the second model can be obtained first, wherein the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training. The first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data.

[0060] The coal-ammonia co-firing test bench is built according to the coal-ammonia co-firing boiler. By using the coal-ammonia co-firing test bench to carry out coal-ammonia co-firing experiments, the co-firing process of the coal-ammonia co-firing boiler can be simulated and the corresponding emission data can be obtained. That is, the first input data set is input into the coal-ammonia co-firing test bench. After the coal-ammonia co-firing test bench conducts the coal-ammonia co-firing experiment, the first output data set is obtained.

[0061] The operating conditions of the coal-ammonia co-combustion experiment conducted using the coal-ammonia co-combustion test bench can be determined as multiple groups of first input data included in the first input data set. The first input data set includes multiple groups of first input data, each group of first input data includes at least: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and may also include: industrial analysis (percentage of received basis moisture in coal, percentage of ash in coal, percentage of volatile matter in coal, percentage of fixed carbon in coal and received basis low calorific value of coal in coal), fuel flow, combustion temperature (e.g., from 900°C to 1500°C, with a compensation of 50°C for each change), heat ratio of coal-ammonia blending (e.g., from 0.1-0.9, with a step size of 0.1), total combustion air volume (e.g., the total amount of primary and secondary air controls the excess air coefficient to be 1.2 unchanged), in addition, the primary and secondary air ratios may be: 1:3, 1:3.5, 1:4, 1:4.5, 1:5, etc.; the coal-ammonia blending ratio may vary, the coal quality may vary, and the coal quality may be determined by elemental analysis of coal. That is, experimental data under various coal qualities and various working conditions are obtained to construct multiple groups of first input data sets.

[0062] Each set of first input data will have a corresponding set of first output data in the first output data set, and each set of first output data at least includes: emission data, such as: concentrations of various pollutants emitted, such as: ammonia concentration, carbon monoxide concentration, sulfur dioxide concentration, nitrogen oxide concentration, etc. The emission data can also include: fly ash carbon content, etc.

[0063] Coal-ammonia co-firing boiler is a boiler in actual application. Coal-ammonia co-firing boiler can also be used for coal-ammonia co-firing test. The test conditions may include: using a variety of single coals commonly used in boilers, the heat ratio of coal-ammonia co-firing in each layer of burners can be selected from 0.1, 0.4, 0.7, and 0.9, the primary and secondary air ratios can be: 1:3.5, 1:4, 1:4.5, and the boiler compliance can be set to 40%, 60%, 80% or 100% of the rated load. By carrying out boiler coal-ammonia co-firing tests respectively, the following boiler data can be obtained, the concentrations of ammonia, carbon monoxide, sulfur dioxide, and nitrogen oxides in the tail flue gas, the exhaust temperature and oxygen content of the flue gas, the delayed flow rate and pressure during the combustion process, the flue gas temperature and hot air temperature at the boiler furnace outlet, the carbon content of fly ash, etc.

[0064] The working conditions of the coal-ammonia co-firing test using a coal-ammonia co-firing boiler can determine multiple groups of second input data included in the second input data set. The second input data set can include multiple groups of second input data, each group of second input data at least includes: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and can also include: calorific value, industrial analysis, burnout air ratio, boiler compliance, etc. Each group of second input data will have a corresponding group of second output data in the second output data set, and each group of second output data at least includes: emission data, such as: the concentration of various pollutants emitted, such as: ammonia concentration, carbon monoxide concentration, sulfur dioxide concentration, nitrogen oxide concentration, etc. The emission data can also include: fly ash carbon content, exhaust gas temperature and flue gas oxygen content, boiler furnace outlet flue gas temperature, hot air temperature, etc.

[0065] Generally, it is easier and more cost-effective to conduct experiments on a coal-ammonia co-firing test bench, while the cost of conducting experiments on a coal-ammonia co-firing boiler is higher. Therefore, the amount of data in the first input data set may be greater than the amount of data in the second input data set, so as to predict the combustion results of the coal-ammonia co-firing boiler by using the data obtained from the coal-ammonia co-firing test bench.

[0066] Furthermore, when collecting the input and output of the coal-ammonia co-firing test bench and the coal-ammonia co-firing boiler to obtain the first input data set, the first output data set, the second input data set and the second output data set, the collected data sets can be first processed, such as: after data cleaning, outliers are removed to ensure the accuracy and consistency of the data; after data normalization, data in different ranges are normalized to eliminate deviations caused by differences in data magnitude; after feature extraction, useful feature parameters are extracted from the collected raw data, such as: fuel supply rate, fly ash carbon content, combustion temperature, superheater outlet temperature, furnace pressure, flue gas oxygen content, furnace temperature, to form an input data set and an output data set as input features for building a model.

[0067] After obtaining the first input data set and the first output data set, the first input data set and the corresponding first output data set can be used for model training to obtain the first model, that is, the first model characterizes the corresponding relationship between the input and output of the coal-ammonia co-combustion test bench, and a set of input data is input into the first model, and the obtained output data characterizes that the coal-ammonia raw material corresponding to the input data is input into the coal-ammonia co-combustion test bench, and the relevant data of the coal-ammonia co-combustion test bench after the coal-ammonia co-combustion experiment is directly calculated using the first model, without the need to use the coal-ammonia co-combustion test bench for experiments, and the experimental results of the coal-ammonia co-combustion experiment can be predicted;

[0068] Correspondingly, after obtaining the second input data set and the second output data set, the second input data set and the corresponding second output data set can be used for model training to obtain the second model, that is, the second model characterizes the corresponding relationship between the input and output of the coal-ammonia co-firing boiler, and a set of input data is input into the second model. The output data obtained characterizes the input of the coal-ammonia raw material corresponding to the input data into the coal-ammonia co-firing boiler. The relevant data of the coal-ammonia co-firing boiler after coal-ammonia co-firing can be directly calculated using the second model, and there is no need to use the coal-ammonia co-firing boiler for experiments, and the experimental results of the coal-ammonia co-firing experiment can be predicted.

[0069] The third model is constructed using the output data of the first model and the output data of the second model to characterize the correspondence between the output data of the first model and the output data of the second model, that is, to characterize the correspondence between the experimental results of the coal-ammonia co-firing test bench and the output results of the coal-ammonia co-firing boiler.

[0070] After obtaining the third model, when it is necessary to determine the operating parameters of the coal-ammonia co-firing boiler, the operating parameters to be determined can be input into the first model to obtain the emission data to be determined output by the first model. The emission data to be determined is the predicted experimental results obtained when a coal-ammonia co-firing experiment is carried out using the coal-ammonia co-firing test bench with the operating parameters to be determined. The data is input into the third model that characterizes the correspondence between the output data of the first model and the second model, and the predicted results of the operating data to be determined when coal-ammonia co-firing is carried out on the coal-ammonia co-firing boiler, that is, the predicted emission data, can be obtained.

[0071] The operating parameters to be determined are optimized based on the predicted emission data to obtain the optimized target operating parameters, and the target operating parameters are determined as the operating parameters of the coal-ammonia co-firing boiler for coal-ammonia co-firing. This can optimize the coal-ammonia co-firing process of the coal-ammonia co-firing boiler, reduce the cost of coal-ammonia co-firing, improve combustion efficiency, and reduce pollutant emissions.

[0072] Since the coal-ammonia co-firing boiler is used for experiments to obtain input data and output data, and the second model is constructed using the input data and output data, and the cost consumed in the coal-ammonia co-firing boiler experimental process is relatively high, the input data and output data obtained based on the coal-ammonia co-firing boiler experiment are relatively small. Therefore, in this embodiment, the second model is not directly used to predict the results of the coal-ammonia co-firing boiler, but the first model and the third model are used to predict the results of the coal-ammonia co-firing boiler. Since the cost of using the coal-ammonia co-firing test bench for experiments is relatively low, the accuracy of the first model is relatively high. Therefore, the first model and the third model are used for prediction to improve the accuracy of the prediction.

[0073] The method for determining the operating parameters of a coal-ammonia co-firing boiler disclosed in the present embodiment uses the first model and the second model to obtain a third model, and the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data; the operating parameters to be determined are input into the first model to obtain the emission data to be determined output by the first model; the emission data to be determined are input into the third model to obtain the predicted emission data output by the third model; the operating parameters to be determined are optimized based on the predicted emission data, and the target operating parameters are determined, so as to operate the coal-ammonia co-firing boiler using the target operating parameters. Before the coal-ammonia co-firing boiler is put into operation, this scheme first combines the coal-ammonia co-firing test bench and the coal-ammonia co-firing boiler to obtain a third model, and uses the third model to make predictions, and optimizes the operating parameters to be determined based on the obtained predicted emission data to obtain target operating parameters, so as to use the target operating parameters to optimize the operation of the coal-ammonia co-firing boiler, improve combustion efficiency, and reduce pollutant emissions.

[0074] Furthermore, the construction process of the coal-ammonia co-combustion test bench disclosed in this embodiment may be:

[0075] Determine the key information of the coal-ammonia co-firing boiler in the coal-ammonia co-firing process, and build a coal-ammonia co-firing test bench according to the key information of the coal-ammonia co-firing boiler in the coal-ammonia co-firing process to conduct experiments on the coal-ammonia co-firing process.

[0076] Among them, the coal-ammonia co-combustion steps of the coal-ammonia co-combustion test bench in the coal-ammonia co-combustion process must be consistent with the coal-ammonia co-combustion steps of the coal-ammonia co-combustion boiler in the coal-ammonia co-combustion process. Therefore, the key information of the coal-ammonia co-combustion boiler in the coal-ammonia co-combustion process may be the coal-ammonia co-combustion steps, or, determine the capacity of each component in the coal-ammonia co-combustion boiler, and reduce each component in the coal-ammonia co-combustion boiler in proportion to its capacity, so as to build a coal-ammonia co-combustion test bench corresponding to the coal-ammonia co-combustion boiler.

[0077] The coal-ammonia co-combustion test bench built can simulate the operation of a coal-ammonia co-combustion boiler. The coal-ammonia co-combustion test bench may include: a combustion tube furnace, an electric heating equipment, a furnace tube inlet sleeve, a cyclone separator, a flue gas analyzer, etc.

[0078] Among them, the combustion tube furnace is constructed with high-temperature refractory materials to ensure a stable combustion environment; the fuel supply system sprays the coal-ammonia mixed raw material evenly into the furnace through the fuel nozzle to ensure sufficient combustion of the fuel; the combustion control system adjusts the fuel supply rate, air flow and combustion speed to achieve the best combustion effect; the electric heating equipment is composed of electric heating elements arranged around the combustion tube furnace to provide the required combustion temperature, and its temperature control system maintains a constant temperature in the furnace by controlling the power and power supply current of the electric heating elements; the furnace tube inlet sleeve is composed of two nested circular tubes, the inner tube is the primary air duct, and the space between the outer tube and the inner tube is the secondary air channel The mixing area of ​​the coal-ammonia mixed raw material and the primary air ensures the uniform mixing of the fuel and the air. The primary air supply system sends the pre-controlled primary air into the sleeve for sufficient mixing with the coal-ammonia mixed raw material. The secondary air supply system sends the pre-controlled secondary air into the sleeve through the secondary air inlet to further adjust the oxygen supply during the combustion process to optimize the combustion effect. The cyclone separator uses centrifugal force to separate the fly ash in the furnace, and then the loss on ignition method can be used to determine the combustible content of the fly ash. The flue gas analyzer understands the gas composition produced during the combustion process by analyzing the concentrations of ammonia, carbon monoxide, nitrogen oxides and sulfur dioxide in the flue gas.

[0079] The structural diagram of the coal-ammonia co-combustion test bench can be shown as follows Figure 2 As shown, it includes: a primary air inlet 21, a secondary air inlet 22, an electric heating element 23, a furnace 24 of a combustion tube furnace, a cyclone separator 25, a temperature controller 26 and an analyzer 27 ( Figure 2 Only some components are shown for reference. Other components of the coal-ammonia co-combustion test bench are not shown. Figure 2 ).

[0080] The coal-ammonia mixed fuel doped with air enters the furnace 24 of the combustion tube furnace through the primary air inlet 21 for combustion. 22 is the secondary air inlet. Electric heating elements 23 are arranged around the tube furnace to provide the required combustion temperature. The heating temperature is controlled in real time by the temperature controller 26. The combustion products enter the cyclone separator 25, and the ash is discharged from the bottom and used to measure the carbon content of the fly ash. The separated pure flue gas enters the flue gas analyzer 27 from the top to further measure the concentration of ammonia, carbon monoxide, nitrogen oxides, sulfur dioxide and other components in the flue gas.

[0081] This embodiment discloses a method for determining operating parameters of a coal-ammonia co-firing boiler, and its flow chart is as follows: Figure 3 As shown, including:

[0082] Step S31, determining input data with a correlation relationship from the first input data set of the first model and the second input data set of the second model; the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data;

[0083] Step S32: determining that the output data corresponding to the input data having an associated relationship in the first output data set is a third output data set, and determining that the output data corresponding to the input data having an associated relationship in the second output data set is a fourth output data set;

[0084] Step S33: using the third output data set as input data of the third model, using the fourth output data set as output data of the third model, performing model training, and obtaining the third model;

[0085] Step S34, inputting the operating parameters to be determined into the first model to obtain the emission data to be determined output by the first model;

[0086] Step S35, inputting the emission data to be determined into the third model to obtain the predicted emission data output by the third model;

[0087] Step S36: Optimize the operating parameters to be determined based on the predicted emission data and determine the target operating parameters so as to operate the coal-ammonia co-firing boiler using the target operating parameters.

[0088] The third model is obtained by using the first model and the second model. The third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model. Then, when running the prediction, the operating parameters to be determined are input into the first model to obtain the emission data to be determined output by the first model. The emission data to be determined are input into the third model to obtain the predicted emission data output by the third model. The predicted emission data output by the third model is the predicted emission data of the operating parameters to be determined after combustion in the coal-ammonia co-firing boiler. The operating parameters to be determined are optimized based on the predicted emission data so that the coal-ammonia co-firing boiler can perform coal-ammonia co-firing according to the optimized target operating parameters.

[0089] After constructing the first model using the first input data set and the first output data set of the coal-ammonia co-firing test bench, and constructing the second model using the second input data set and the second output data set of the coal-ammonia co-firing boiler, it is necessary to use the first model and the second model to obtain the third model. The third model is a new prediction model that uses the experimental data of the coal-ammonia co-firing test bench to expand the prediction range of the coal-ammonia co-firing boiler.

[0090] Specifically, the input data with the associated relationship is extracted from the first input data set corresponding to the first model and the second input data set corresponding to the second model, such as: if it is determined that the first input data a in the first input data set and the second input data b in the second input data set have an associated relationship, the first input data a is extracted from the first input data set, and the second input data b is extracted from the second input data set;

[0091] Determine that the output data corresponding to the input data having an associated relationship in the first output data set is the third output data set. Since each group of first input data has a corresponding group of first output data in the first output data set, then, after determining a group of first input data in the first input data set that has an associated relationship with the second input data in the second input data set, determine from the first output data set a group of first output data corresponding to the group of first input data that has an associated relationship with the second input data in the second input data set. If there are multiple groups of input data in the first input data set that have an associated relationship with the second input data set, then the multiple groups of first input data determined will correspond to multiple groups of first output data, and the multiple groups of first output data can constitute the third output data set. For example, the first input data a in the first input data set has an associated relationship with the second input data b in the second input data set, and the first output data a' corresponding to the first input data a in the first output data set is extracted. If there are multiple groups of first input data a, then the multiple groups of first output data a' constitute the third output data set.

[0092] Correspondingly, the output data in the second output data set corresponding to the input data having an associated relationship is determined to be the fourth output data set, such as: the second input data b in the second input data set has an associated relationship with the first input data a in the first input data set, and the second output data b' corresponding to the second input data b in the second output data set is extracted. If the second input data b is multiple groups, the multiple groups of second output data b' constitute the fourth output data set.

[0093] The third output data set and the fourth output data set are used for model training, wherein the data in the third output data set is used as input data of the third model, and the data in the fourth output data set is used as output data of the third model, and the third model is obtained after training.

[0094] Furthermore, input data with an associated relationship is extracted from the first input data set corresponding to the first model and the second input data set corresponding to the second model, wherein the input data with an associated relationship may be: input data with at least part of the same data.

[0095] That is, the first input data set includes multiple sets of first input data, and the second input data set includes multiple sets of second input data. If it is determined that the first input data a and the second input data b are the same, it can be determined that the first input data a and the second input data b are input data with an associated relationship.

[0096] In addition, since each group of first input data included in the first input data set includes multiple data, it is determined that the first input data a and the second input data b have an associated relationship, and it can be specifically determined that at least some parameters in the first input data a are the same as at least some parameters in the second input data b, and at least some parameters can be specific parameters in the first input data and the second input data. For example, if the coal quality information, coal-ammonia blending ratio, and primary and secondary air ratio in the first input data a are the same as the coal quality information, coal-ammonia blending ratio, and primary and secondary air ratio in the second input data b, it can be determined that the first input data a and the second input data b have an associated relationship, and whether other data in the first input data a and the second input data b except the coal quality information, coal-ammonia blending ratio, and primary and secondary air ratio are the same does not affect the implementation of this embodiment.

[0097] The method for determining the operating parameters of a coal-ammonia co-firing boiler disclosed in this embodiment introduces the above-mentioned machine learning and neural network technologies, and can achieve automatic optimization and intelligent decision-making on the operation of the coal-ammonia co-firing boiler through training and prediction of a large amount of experimental data, thereby improving operating efficiency and responsiveness. In addition, a coal-ammonia co-firing test bench with lower experimental costs is used to obtain experimental data to guide the operation simulation of a coal-ammonia co-firing boiler with higher experimental costs. The third model formed can achieve a wider range of predictions under the condition of less operating data of the coal-ammonia co-firing boiler, thereby reducing the prediction cost.

[0098] The method for determining the operating parameters of a coal-ammonia co-firing boiler disclosed in this embodiment determines the input data with an associated relationship in the first input data set and the second input data set, and determines the output data corresponding to the input data with an associated relationship in the first input data set as the third output data set, determines the output data corresponding to the input data with an associated relationship in the second input data set as the fourth output data set, uses the third output data set as the input data of the third model, and uses the fourth output data set as the output data of the third model, performs model training, and obtains the third model. The model training is performed by the output data of the first model and the output data of the second model, and the third model that can characterize the corresponding relationship between the output data of the first model and the output data of the second model is obtained, so that when verifying the operating parameters to be determined, it is first input into the first model to obtain the output of the first model, and then the output of the first model is input into the third model to obtain the predicted co-firing result of the third model output that can characterize the operating parameters to be determined after the coal-ammonia co-firing boiler is subjected to coal-ammonia co-firing, that is, the predicted emission data, and the combustion result of the operating parameters to be determined in the coal-ammonia co-firing boiler is predicted by the first model and the third model, so as to ensure the accuracy of the prediction.

[0099] This embodiment discloses a method for determining operating parameters of a coal-ammonia co-firing boiler, and its flow chart is as follows: Figure 4 As shown, including:

[0100] Step S41, using the first model and the second model to obtain a third model, the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data;

[0101] Step S42: inputting the operating parameters to be determined into the first model to obtain the emission data to be determined output by the first model;

[0102] Step S43, inputting the emission data to be determined into the third model to obtain the predicted emission data output by the third model;

[0103] Step S44, determining a predicted total cost based at least on the predicted emission data;

[0104] Step S45: If it is determined that the predicted total cost meets the target condition, the to-be-determined operating parameter is determined as the target operating parameter.

[0105] The third model is obtained by using the first model and the second model. The third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model. Then, when running the prediction, the operating parameters to be determined are input into the first model to obtain the emission data to be determined output by the first model. The emission data to be determined are input into the third model to obtain the predicted emission data output by the third model. The predicted emission data output by the third model is the predicted emission data of the operating parameters to be determined after combustion in the coal-ammonia co-firing boiler. The operating parameters to be determined are optimized based on the predicted emission data so that the coal-ammonia co-firing boiler can perform coal-ammonia co-firing according to the optimized target operating parameters.

[0106] In the process of optimizing the operating parameters to be determined based on the predicted emission data and determining the target operating parameters, the predicted total cost can be first determined based on the predicted emission data, that is, the predicted emission data can be: information on the predicted pollutants emitted and information such as the carbon content of fly ash. The pollutants emitted can include: ammonia, carbon monoxide, sulfur dioxide, nitrogen oxides, etc. in the flue gas emitted by the coal-ammonia co-firing boiler. The information on the pollutants emitted can be: the concentration of different types of pollutants emitted. The treatment costs of different pollutants can be determined based on the concentration of different types of pollutants emitted. In addition, based on the content of pollutants such as ammonia and nitrogen oxides, the consumption of ammonia in the coal-ammonia raw material input to the coal-ammonia co-firing boiler can be determined, and then the cost of ammonia in the coal-ammonia raw material can be determined. Based on the concentration of carbon monoxide, the carbon content of fly ash and other data, the consumption of coal in the coal-ammonia raw material input to the coal-ammonia co-firing boiler can be determined, and then the cost of coal in the coal-ammonia raw material can be determined, and then the total cost required to operate the coal-ammonia co-firing boiler according to the operating parameters to be determined is predicted.

[0107] That is: based on the predicted emission data, the predicted raw material consumption data and the predicted pollutant emission data are determined, and the predicted raw material consumption data include: predicted coal consumption data and predicted ammonia consumption data; at least the predicted total cost is determined based on the predicted raw material consumption data and the predicted pollutant emission data.

[0108] Specific:

[0109]

[0110] in, To predict the total cost, For coal cost, is the cost of ammonia, is the carbon emission cost, is the desulfurization cost, is the denitrification cost, The cost of auxiliary system.

[0111] Coal costs It can be: the product of coal consumption and coal price; the cost of ammonia It can be: the product of ammonia consumption and ammonia price; carbon emission cost = The product of the carbon concentration in the pollutants emitted and the carbon dioxide tax rate; the desulfurization cost The product of the concentration of sulfur dioxide in the discharged pollutants and the desulfurization cost rate; the denitrification cost The product of the concentration of nitrogen oxides in the discharged pollutants and the denitrification cost rate; the auxiliary system cost It is the sum of the cost of the primary fan, the cost of the secondary fan, the cost of the induced draft fan, the cost of the coal mill and the cost of the electrostatic precipitator.

[0112] After obtaining the predicted total cost, determine whether the predicted total cost meets the target conditions. If it meets the target conditions, the operating parameters to be determined can be determined as the target operating parameters. If it does not meet the target conditions, it can continue to judge whether other operating parameters to be determined meet the target conditions, and finally determine a set of operating parameters from other operating parameters to be determined as target operating parameters, so that the coal-ammonia co-firing boiler can carry out coal-ammonia co-firing according to the determined target operating parameters, thereby achieving the purpose of improving co-firing efficiency and reducing pollutant emissions by reducing costs.

[0113] Determining whether the predicted total cost meets the target condition can be determined as follows: determining whether the predicted total cost is lower than a certain threshold. If it is determined that it is lower than the certain threshold, it can be determined that the predicted total cost meets the target condition. If it is determined that it is not lower than the certain threshold, it can be determined that the predicted total cost does not meet the target condition.

[0114] At this time, when it is determined that the predicted total cost is not lower than the specific threshold, it is possible to determine whether each cost in the calculated predicted total cost exceeds the specified range of the item, and when it is determined that a certain cost exceeds the specified range, the operating parameters corresponding to the cost are adjusted. For example, if the carbon emission cost is higher than the specified range, the percentage of carbon in the coal corresponding to the carbon emission cost can be adjusted, that is, the coal quality information of the coal can be adjusted; for example, if the cost of ammonia is higher than the specified range, the coal-ammonia mixture ratio can be adjusted to reduce the proportion of ammonia in the coal-ammonia mixture ratio.

[0115] By comparing the predicted total cost with a specific threshold, the predicted total cost can be optimized to reduce pollutant emissions and improve co-combustion efficiency by reducing costs.

[0116] Alternatively, determining whether the predicted total cost meets the target condition may also be: comparing multiple predicted total costs, where the multiple predicted total costs are obtained based on multiple groups of operating parameters to be determined; determining the minimum predicted total cost among the multiple predicted total costs, and determining a group of operating parameters to be determined corresponding to the minimum predicted total cost as the target operating parameters.

[0117] Multiple groups of operating parameters to be determined are input into the first model to obtain the emission data to be determined corresponding to each group of operating parameters to be determined, and each group of emission data to be determined is input into the third model to obtain the predicted emission data corresponding to each group of operating parameters to be determined, and each group of predicted emission data in the multiple groups of predicted emission data is used to calculate multiple predicted total costs, and then the predicted total costs are compared to obtain the minimum value of the multiple predicted total costs, that is, the minimum predicted total cost, and the group of operating parameters to be determined corresponding to the minimum predicted total cost is determined as the target operating parameters.

[0118] That is, multiple groups of operating parameters to be determined are determined respectively, and multiple groups of predicted emission data are obtained respectively by using the operating parameters to be determined, the first model and the third model, so as to determine multiple predicted total costs, each group of operating parameters to be determined corresponds to a group of predicted emission data and a predicted total cost, that is, the operating parameters to be determined, the predicted emission data and the predicted total cost are one-to-one corresponding.

[0119] After comparing multiple predicted total costs and obtaining the minimum predicted total cost, based on the one-to-one correspondence between the operating parameters to be determined, the predicted emission data and the predicted total cost, a set of operating parameters to be determined corresponding to the minimum predicted total cost can be determined and directly determined as the target operating parameters.

[0120] Furthermore, it should be noted that the determination of multiple groups of operating parameters to be determined can be: setting each operating parameter included in each group of operating parameters to be determined to be within a target range of the operating parameters, and different operating parameters included in each group of operating parameters to be determined to be within different target ranges.

[0121] Each operating parameter is limited to be within a target range of the operating parameter, wherein the operating parameters that need to be limited to the target range may include: parameters of coal quality information, coal-ammonia blending ratio, and related parameters of primary and secondary air ratios.

[0122] Among them, the parameters for coal quality information may include: the percentage of basic moisture in coal , the percentage of ash in coal , the percentage of volatile matter in coal , the percentage of fixed carbon in coal And the received low calorific value of coal In addition, the relevant parameters of the coal-ammonia blending ratio can be directly expressed as ; The parameters related to the ratio of primary and secondary air may include: the ratio of primary air to total air volume .

[0123] The percentage of received basis moisture in coal can be set is: greater than or equal to 5 and less than or equal to 30; set the percentage of ash in coal is: greater than or equal to 5 and less than or equal to 40; set the percentage of volatile matter in coal is: greater than or equal to 5 and less than or equal to 55; sets the percentage of fixed carbon in coal The minimum calorific value of coal is: greater than or equal to 40 and less than or equal to 70; the minimum calorific value of coal is set to The coal-ammonia blending ratio is: greater than or equal to 13 and less than or equal to 27; is: greater than or equal to 0 and less than or equal to 0.7; set the ratio of primary air to total air volume is: greater than or equal to 0.1 and less than or equal to 0.3.

[0124] The above description is merely an illustrative description and is not intended to be a specific limitation.

[0125] In the process of optimizing the operating parameters, the above operating parameters (i.e., the percentage of received basis moisture in coal, the percentage of ash in coal, the percentage of volatile matter in coal, the percentage of fixed carbon in coal, the received basis lower calorific value of coal in coal, the coal-ammonia blending ratio, and the proportion of primary air in the total air volume) are actually optimized. However, the optimization of the above operating parameters requires that the operating parameters are always kept within their specific ranges and optimized within their corresponding specific ranges to achieve the purpose of minimizing the total predicted cost, i.e.: .

[0126] The complete schematic diagram corresponding to the method for determining the operating parameters of a coal-ammonia co-firing boiler disclosed in this embodiment is as follows: Figure 5 As shown, each group of first input data in the first input data set I1 is input into the first model to obtain the first output data set O1, and each group of second input data in the second input data set I2 is input into the second model to obtain the second output data set O2. After data preprocessing, the data in the first output data set O1 and the second output data set O2 are extracted from the third output data O3 and the fourth output data O4 corresponding to the input data with an associated relationship, and the third output data O3 is used as the input data of the third model, and the fourth output data O4 is used as the output data of the third model. The model is trained by a deep neural network algorithm to obtain a third model for characterizing the corresponding relationship between the output of the coal-ammonia co-firing test bench and the output of the coal-ammonia co-firing boiler. Multiple groups of operating parameters to be determined are input into the first model. After obtaining the results, the results are input into the third model to obtain multiple groups of predicted emission data. The predicted emission data are used to calculate the cost, and then the target operating parameters are finally determined through the optimization of the operating parameters.

[0127] The method for determining the operating parameters of a coal-ammonia co-firing boiler disclosed in the present embodiment achieves the determination of the optimal operating parameters, that is, the determination of the target operating parameters, by optimizing the operating parameters. When the coal-ammonia co-firing boiler uses the target operating parameters to carry out coal-ammonia co-firing, it effectively reduces the fuel cost, auxiliary machine operating cost, desulfurization, denitrification and dust removal cost and carbon emission cost. The optimized coal-ammonia co-firing boiler can improve the economic benefits on the basis of reducing the operating costs.

[0128] The method for determining the operating parameters of a coal-ammonia co-firing boiler disclosed in this embodiment, after obtaining the third model using the first model and the second model, inputs the operating parameters to be determined into the first model to obtain the emission data to be determined output by the first model, and inputs the emission data to be determined into the third model to obtain the predicted emission data output by the third model. When optimizing the operating parameters to be determined based on the predicted emission data, the predicted total cost is determined based on the predicted emission data, and whether the operating parameters to be determined can be determined as the target operating parameters is determined based on the predicted total cost, thereby achieving the determination of the operating parameters of the coal-ammonia co-firing boiler. This scheme uses the predicted emission data obtained by the first model and the third model to predict the total cost, so as to determine whether the total cost can meet the optimization target, and through the prediction of the total cost, the effect of coal-ammonia co-firing using the operating parameters to be determined by the coal-ammonia co-firing boiler is predicted, so that the coal-ammonia co-firing boiler can finally operate under the target operating parameters, reduce pollutant emissions, and meet the demand of reducing costs.

[0129] This embodiment discloses a system for determining operating parameters of a coal-ammonia co-firing boiler, and its structural diagram is shown in FIG. Figure 6 As shown, including:

[0130] A first obtaining unit 61 , a second obtaining unit 62 , a third obtaining unit 63 and a determining unit 64 .

[0131] The first obtaining unit 61 is used to obtain a third model using the first model and the second model, and the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data;

[0132] The second obtaining unit 62 is used to input the to-be-determined operating parameters into the first model to obtain the to-be-determined emission data output by the first model;

[0133] The third obtaining unit 63 is used to input the emission data to be determined into the third model to obtain the predicted emission data output by the third model;

[0134] The determination unit 64 is used to optimize the operating parameters to be determined based on the predicted emission data, and determine the target operating parameters so as to operate the coal-ammonia co-firing boiler using the target operating parameters.

[0135] Furthermore, the first obtaining unit is used for:

[0136] Determine input data with an associated relationship from the first input data set of the first model and the second input data set of the second model; determine the output data corresponding to the input data with an associated relationship in the first output data set as the third output data set, and determine the output data corresponding to the input data with an associated relationship in the second output data set as the fourth output data set; use the third output data set as input data of the third model, and use the fourth output data set as output data of the third model, perform model training, and obtain a third model.

[0137] Furthermore, the first obtaining unit is used for:

[0138] Input data having at least some of the same data is determined from the first input data set of the first model and the second input data set of the second model, wherein at least some of the data include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio.

[0139] Furthermore, the determination unit is used to:

[0140] The predicted total cost is determined based on at least the predicted emission data; if it is determined that the predicted total cost meets the target condition, the operating parameters to be determined are determined as the target operating parameters.

[0141] Furthermore, the determination unit is used to:

[0142] Based on the predicted emission data, the predicted raw material consumption data and the predicted pollutant emission data are determined, and the predicted raw material consumption data include: predicted coal consumption data and predicted ammonia consumption data; the predicted total cost is determined at least based on the predicted raw material consumption data and the predicted pollutant emission data.

[0143] Furthermore, the determination unit is used to:

[0144] Compare multiple predicted total costs, where the multiple predicted total costs are obtained based on multiple groups of operating parameters to be determined; determine the minimum predicted total cost among the multiple predicted total costs, and determine the group of operating parameters to be determined corresponding to the minimum predicted total cost as the target operating parameters.

[0145] Further, each operating parameter included in each group of operating parameters to be determined is within a target range of the operating parameter, and different operating parameters included in each group of operating parameters to be determined are within different target ranges.

[0146] Furthermore, the coal-ammonia co-firing boiler operating parameter determination system disclosed in this embodiment may also include:

[0147] A unit is constructed to determine key information of a coal-ammonia co-firing boiler in the coal-ammonia co-firing process; a coal-ammonia co-firing test bench is constructed according to the key information of the coal-ammonia co-firing boiler in the coal-ammonia co-firing process to conduct experiments on the coal-ammonia co-firing process.

[0148] The system for determining the operating parameters of a coal-ammonia co-firing boiler disclosed in this embodiment is implemented based on the method for determining the operating parameters of a coal-ammonia co-firing boiler disclosed in the above embodiment, which will not be described in detail here.

[0149] The coal-ammonia co-firing boiler operating parameter determination system disclosed in the present embodiment uses the first model and the second model to obtain the third model, and the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data; the operating parameters to be determined are input into the first model to obtain the emission data to be determined output by the first model; the emission data to be determined are input into the third model to obtain the predicted emission data output by the third model; the operating parameters to be determined are optimized based on the predicted emission data, and the target operating parameters are determined, so as to operate the coal-ammonia co-firing boiler using the target operating parameters. Before the coal-ammonia co-firing boiler is put into operation, this scheme first combines the coal-ammonia co-firing test bench and the coal-ammonia co-firing boiler to obtain a third model, and uses the third model to make predictions, and optimizes the operating parameters to be determined based on the obtained predicted emission data to obtain target operating parameters, so as to use the target operating parameters to optimize the operation of the coal-ammonia co-firing boiler, improve combustion efficiency, and reduce pollutant emissions.

[0150] This embodiment discloses an electronic device, and its structural diagram is as follows: Figure 7 As shown, including:

[0151] Processor 71 and memory 72.

[0152] Among them, the processor 71 is used to obtain a third model using the first model and the second model, and the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data; input the operating parameters to be determined into the first model to obtain the emission data to be determined output by the first model; input the emission data to be determined into the third model to obtain the predicted emission data output by the third model; optimize the operating parameters to be determined based on the predicted emission data, and determine the target operating parameters, so as to operate the coal-ammonia co-firing boiler using the target operating parameters;

[0153] The memory 72 is used to store the programs and data required by the processor to execute the above processing.

[0154] The electronic device disclosed in this embodiment is implemented based on the method for determining the operating parameters of the coal-ammonia co-firing boiler disclosed in the above embodiment, which will not be described in detail here.

[0155] The electronic device disclosed in this embodiment uses the first model and the second model to obtain the third model, and the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; the first model is obtained by using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench for model training, and the second model is obtained by using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler for model training; the first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data; the operating parameters to be determined are input into the first model to obtain the emission data to be determined output by the first model; the emission data to be determined are input into the third model to obtain the predicted emission data output by the third model; the operating parameters to be determined are optimized based on the predicted emission data, and the target operating parameters are determined, so as to operate the coal-ammonia co-firing boiler using the target operating parameters. Before the coal-ammonia co-firing boiler is put into operation, this scheme first combines the coal-ammonia co-firing test bench and the coal-ammonia co-firing boiler to obtain a third model, and uses the third model to make predictions, and optimizes the operating parameters to be determined based on the obtained predicted emission data to obtain target operating parameters, so as to use the target operating parameters to optimize the operation of the coal-ammonia co-firing boiler, improve combustion efficiency, and reduce pollutant emissions.

[0156] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0157] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0158] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0159] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. A method for determining operating parameters of a coal-ammonia co-firing boiler, characterized in that: include: Using the first model and the second model to obtain a third model, the third model is used to characterize the corresponding relationship between the output data of the first model and the output data of the second model; The first model is obtained by training the model using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench, and the second model is obtained by training the model using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler; The first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data; Inputting the operating parameters to be determined into the first model to obtain the emission data to be determined output by the first model; Inputting the emission data to be determined into the third model to obtain the predicted emission data output by the third model; The operating parameters to be determined are optimized based on the predicted emission data to determine target operating parameters so as to operate the coal-ammonia co-firing boiler using the target operating parameters.

2. The method according to claim 1, characterized in that The method of obtaining the third model by using the first model and the second model comprises: Determine input data having an associated relationship from a first input data set of the first model and a second input data set of the second model; Determine that the output data corresponding to the input data having the association relationship in the first output data set is a third output data set, and determine that the output data corresponding to the input data having the association relationship in the second output data set is a fourth output data set; The third output data set is used as input data of the third model, and the fourth output data set is used as output data of the third model to perform model training to obtain the third model.

3. The method according to claim 2, characterized in that The step of determining input data having an associated relationship from the first input data set of the first model and the second input data set of the second model comprises: Input data having at least some of the same data is determined from the first input data set of the first model and the second input data set of the second model, wherein the at least some of the data includes: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio.

4. The method according to claim 1, characterized in that The step of optimizing the operating parameters to be determined based on the predicted emission data to determine the target operating parameters includes: determining a predicted total cost based at least on the predicted emissions data; If it is determined that the predicted total cost meets the target condition, the to-be-determined operating parameter is determined as the target operating parameter.

5. The method according to claim 4, characterized in that The determining of the predicted total cost based at least on the predicted emissions data comprises: Determine predicted raw material consumption data and predicted pollutant emission data based on the predicted emission data, wherein the predicted raw material consumption data includes: predicted coal consumption data and predicted ammonia consumption data; The predicted total cost is determined based at least on the predicted raw material consumption data and the predicted pollutant emission data.

6. The method according to claim 4, characterized in that If it is determined that the predicted total cost meets the target condition, determining the to-be-determined operating parameter as the target operating parameter includes: comparing a plurality of predicted total costs, the plurality of predicted total costs being obtained based on a plurality of sets of operating parameters to be determined; A minimum predicted total cost among the multiple predicted total costs is determined, and a group of to-be-determined operating parameters corresponding to the minimum predicted total cost is determined as the target operating parameters.

7. The method according to claim 6, characterized in that Each operating parameter included in each set of operating parameters to be determined is within a target range of the operating parameter, and different operating parameters included in each set of operating parameters to be determined are within different target ranges.

8. The method according to claim 1, characterized in that: Also includes: Determine key information of the coal-ammonia co-firing boiler during the coal-ammonia co-firing process; According to the key information of the coal-ammonia co-firing boiler in the coal-ammonia co-firing process, a coal-ammonia co-firing experimental platform is built to experiment on the coal-ammonia co-firing process.

9. A system for determining operating parameters of a coal-ammonia co-firing boiler, characterized in that: include: A first obtaining unit, configured to obtain a third model using the first model and the second model, wherein the third model is configured to represent a corresponding relationship between output data of the first model and output data of the second model; The first model is obtained by training the model using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench, and the second model is obtained by training the model using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler; The first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data; a second obtaining unit, configured to input the operating parameter to be determined into the first model, and obtain the emission data to be determined output by the first model; A third obtaining unit, used for inputting the emission data to be determined into the third model to obtain the predicted emission data output by the third model; A determination unit is used to optimize the operating parameters to be determined based on the predicted emission data, and determine target operating parameters so as to operate the coal-ammonia co-firing boiler using the target operating parameters.

10. An electronic device, characterized in that: include: A processor, configured to obtain a third model using the first model and the second model, wherein the third model is configured to represent a corresponding relationship between output data of the first model and output data of the second model; The first model is obtained by training the model using the first input data set and the corresponding first output data set of the coal-ammonia co-firing test bench, and the second model is obtained by training the model using the second input data set and the corresponding second output data set of the coal-ammonia co-firing boiler; The first input data set and the second input data set at least include: coal quality information, coal-ammonia blending ratio and primary and secondary air ratio, and the first output data set and the second output data set at least include: emission data; input the to-be-determined operating parameters into the first model to obtain the to-be-determined emission data output by the first model; input the to-be-determined emission data into the third model to obtain the predicted emission data output by the third model; optimize the to-be-determined operating parameters based on the predicted emission data to determine the target operating parameters, so as to operate the coal-ammonia co-firing boiler using the target operating parameters; The memory is used to store the programs and data required by the processor to execute the above processing process.

Images