Dynamic coal blending combustion optimization method and device, electronic equipment and storage medium

Abstract

The invention provides a dynamic coal blending combustion optimization method and device, electronic equipment and a storage medium, and relates to the technical field of electric power systems, branch bin combustion control is implemented and combustion operation parameters are optimized in real time by establishing a multi-stage blending verification system and a feed-forward type coal blending adjustment model, and therefore the dynamic coal blending combustion optimization method and device can be applied to the field of electric power systems. The problems that in the prior art, due to the fact that blending accuracy is insufficient, a coal blending scheme lacks dynamic adjustment, combustion control pertinence is not high, and abnormal working conditions are not dealt with in time can be solved, and the technical effects that accurate coal blending is achieved, unit combustion economical efficiency and peak regulation capacity are improved, and safe and stable operation of a boiler is guaranteed are achieved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of power systems, and particularly relates to a dynamic coal blending and burning optimization method and device, electronic equipment and a storage medium. BACKGROUND

[0002] As a core link of power production, fuel management of thermal power plants is widely used in economic optimization and operation stability guarantee of coal-fired units. With the intensification of load fluctuation brought by power market reform and new energy grid connection, the traditional static coal blending and burning technology has been difficult to meet the peak shaving demand. The existing technology usually adopts fixed proportion of mixed coal and cooperates with single combustion adjustment strategy, but is limited by the technical bottlenecks such as insufficient accuracy of coal quality parameter acquisition, dynamic response lag of combustion working condition and weak multi-variable coupling control ability, resulting in that the blending and burning scheme is difficult to balance economy and safety. Specifically, the technical system covers the whole process from coal procurement, pulverizing system configuration to combustion parameter regulation, including key links such as coal quality characteristic analysis, mixed proportion design, burner arrangement optimization, etc., among which multi-coal blending and burning characteristic analysis and load dynamic matching strategy become the current research focus.

[0003] However, in the existing coal blending and burning method, the static mixed proportion is directly used and the experience-based operation adjustment is relied on, and the dynamic correlation modeling of load fluctuation and coal quality parameters is lacked, which may lead to working condition instability phenomena such as combustion center deviation and uneven flame temperature distribution. Based on this, the existing technology exposes the technical defect of insufficient systematic and collaborative control ability when dealing with problems such as low efficiency of multi-coal combination test, poor combustion stability and fluctuation of environmental protection parameters, which restricts the economic improvement and safe operation guarantee of coal-fired units in complex operating environment SUMMARY The present disclosure provides a dynamic coal blending and burning optimization method and device, electronic equipment and a storage medium. The main purpose is to at least solve one of the technical problems in the related art to some extent.

[0004] According to a first aspect of the present disclosure, a dynamic coal blending and burning optimization method is provided, comprising: A multi-stage blending and verification system is established, and the mixed coal quality is gradually mixed and monitored in real time through unloading ditch belt blending, bucket wheel machine secondary blending and three-stage sampling system; Based on unit load prediction and boiler combustion characteristics, a feedforward coal blending adjustment model is developed, and a blending proportion scheme is dynamically generated in combination with a coal characteristic database and equipment state parameters; Implementing sub-bin combustion control, the mixed coal with different blending proportions is distributed to corresponding grinding groups for differential combustion according to the boiler grinding group arrangement and load demand; Real-time adjustment of combustion operation parameters, according to the mixed coal characteristics and combustion condition monitoring data, dynamically optimize the secondary air volume configuration, combustion center position and furnace temperature distribution, to deal with flame deviation, slagging and other abnormal conditions.

[0005] Optionally, the establishment of the multi-level blending verification system comprises: After the unloading ditch belt blending, the mixed coal sample is collected by using the belt sampler and the calorific value, volatile matter, ash content and moisture parameters are detected; After the secondary blending of the bucket wheel machine, the coal sample is collected by the sampling machine and the mixing uniformity is verified.

[0006] Optionally, the feedforward coal blending adjustment model is developed based on the unit load prediction and boiler combustion characteristics, comprising: According to the predicted load, the boiler equipment state, the coal yard storage and transportation situation and the coal car unloading situation, the blending coal proportion of each mill group is adjusted; Combined with the historical data in the coal characteristics database, the single coal is blended according to the blending coal proportion to make up for the short board of volatile matter or calorific value.

[0007] Optionally, the implementation of the sub-bin combustion control further comprises: The high calorific value coal is distributed to the mill group at the center of the boiler combustion, and the low calorific value coal is distributed to the edge mill group to balance the combustion stability; According to the unit load fluctuation range of the day, the mill group coal supply proportion is dynamically adjusted to ensure that the hourly coal consumption is within the preset range.

[0008] Optionally, the real-time adjustment of the combustion operation parameters comprises: When the volatile matter of the mixed coal changes more than the first percentage range, the secondary air damper opening is adjusted by layering, so that the furnace temperature distribution deviation is controlled within the preset temperature range; When the moisture content of the mixed coal changes more than the second percentage range, the combustion center position is adjusted and the secondary air volume is optimized, so that the fly ash combustible content is reduced to below the preset percentage.

[0009] Optionally, it further comprises: Based on the test data, a combustion economy evaluation model is established, and by comparing the boiler efficiency, smoke loss and environmental protection parameters, the optimal blending scheme is selected and the standardized operation procedure is generated.

[0010] According to the second aspect of the present disclosure, a dynamic coal blending and combustion optimization device is provided, comprising: The monitoring unit is used for establishing a multi-level blending verification system, and the mixed coal quality is gradually mixed and monitored in real time through the unloading ditch belt blending, the bucket wheel machine secondary blending and the three-stage sampling system; The generating unit is configured to formulate a feed-forward coal blending adjustment model based on the unit load prediction and the boiler combustion characteristics, and dynamically generate a blending ratio scheme in combination with a coal type characteristic database and equipment state parameters; The distribution unit is configured to implement a compartmentalized combustion control, and distribute the mixed coal with different blending ratios to corresponding mills for differential combustion according to the mill arrangement and load demand of the boiler. The adjustment unit is configured to adjust the combustion operation parameters in real time, dynamically optimize the secondary air volume configuration, combustion center position and furnace temperature distribution according to the mixed coal type characteristic changes and combustion condition monitoring data, so as to cope with abnormal conditions such as flame deviation and slagging.

[0011] Optionally, the monitoring unit is further configured to: After blending through the unloading groove belt, a belt sampler is used to collect a mixed coal sample and detect the calorific value, volatile matter, ash content and moisture content parameters; After the secondary blending by the bucket wheel machine, a sampler is used to collect a coal sample into the furnace and verify the uniformity of mixing.

[0012] Optionally, the generating unit is further configured to: According to the predicted load, the boiler equipment state, the coal yard storage and transportation situation and the coal car unloading situation, the blending ratio of each mill is adjusted; In combination with the historical data in the coal type characteristic database, a single coal type is blended according to the blending ratio to make up for the short board of volatile matter or calorific value.

[0013] Optionally, the distribution unit is further configured to: The high-calorific-value coal type is distributed to the mill group at the combustion center position of the boiler, and the low-calorific-value coal type is distributed to the edge mill group to balance the combustion stability; According to the unit load fluctuation range of the day, the mill coal supply ratio is dynamically adjusted to ensure that the combustion condition is stable when the hourly coal consumption is within the preset range.

[0014] Optionally, the adjustment unit is further configured to: When the volatile matter of the mixed coal type changes by more than a first percentage range, the secondary air damper opening is adjusted by layering, so that the furnace temperature distribution deviation is controlled within a preset temperature range; When the moisture content of the mixed coal type changes by more than a second percentage range, the combustion center position is adjusted and the secondary air volume is optimized, so that the combustible content of fly ash is reduced to below a preset percentage.

[0015] Optionally, it further comprises: The screening unit is configured to establish a combustion economic evaluation model based on test data, screen the optimal blending scheme by comparing the boiler efficiency, flue gas loss and environmental protection parameters, and generate a standardized operation procedure.

[0016] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0017] According to a fourth aspect of the present disclosure, a non-transitory computer readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to perform the method of the first aspect.

[0018] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method of the first aspect.

[0019] The dynamic coal blending optimization method and device, electronic device and storage medium provided by the present disclosure establish a multi-level blending verification system and a feedforward coal blending adjustment model, implement bin combustion control and real-time optimization of combustion operation parameters, and therefore can solve the problems of insufficient blending accuracy, lack of dynamic adjustment of coal blending scheme, weak pertinence of combustion control and untimely response to abnormal working conditions in the prior art, and achieve the technical effects of precise coal blending, improved combustion economy and peak regulation capacity of the unit, and safe and stable operation of the boiler.

[0020] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings serve to better understand the present scheme and do not limit the present disclosure. Among them: Figure 1 A flowchart of a dynamic coal blending optimization method provided by an embodiment of the present disclosure; Figure 2 A structural schematic diagram of a dynamic coal blending optimization device provided by an embodiment of the present disclosure; Figure 3 A schematic block diagram of an example electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] Exemplary embodiments of the present disclosure are described herein below with reference to the accompanying drawings, in which various details are set forth to facilitate an understanding of the present disclosure. It should be readily understood, however, that various changes and modifications can be made to the embodiments described herein, without departing from the spirit and scope of the present disclosure. Likewise, the description set forth herein is not intended to be exhaustive or to be limited to the precise steps or construction details described. As such, it should be understood that various changes and modifications can be made to the embodiments described herein, without departing from the spirit and scope of the present disclosure.

[0023] A dynamic blending and burning optimization method and device, an electronic device, and a storage medium are described below with reference to the accompanying drawings for embodiments of the present disclosure.

[0024] Figure 1 A flowchart of a dynamic blending and burning optimization method provided by embodiments of the present disclosure is shown.

[0025] As shown in Figure 1 the method includes the following steps: Step 101, a multi-stage blending verification system is established to gradually mix and real-time monitor the mixed coal quality through unloading ditch belt blending, secondary blending by a bucket wheel machine, and a three-stage sampling system.

[0026] In embodiments of the present disclosure, to solve the problem that the blending uniformity is difficult to control in the traditional blending and burning process, and the overall blending accuracy is insufficient due to the lag of coal quality monitoring, this step integrates the multi-stage continuous blending operation and the sampling monitoring function of the corresponding stage by establishing a multi-stage blending verification system, gradually realizes the gradual homogenization of the mixed coal quality by using blending equipment with different functions, and at the same time, relies on the sampling system arranged in stages to collect real-time data and detect indicators of the coal quality after each stage of blending, to ensure that the coal quality parameters are always within a controllable range during the blending process, and to avoid the blending effect deviation that may be caused by single-stage blending or single-point monitoring. As an implementation, the first stage blending can be completed by a belt in the unloading ditch area, and then secondary blending is performed by a bucket wheel machine, and a three-stage sampling system such as an automobile into the plant sampling machine, a belt sampling machine, and a middle sampling machine is matched to sample and detect the raw coal before blending, the belt first blending coal, and the into the furnace coal after secondary blending by the bucket wheel machine, to verify whether the blending indicators of each stage meet the preset requirements.

[0027] The multi-stage blending verification system effectively solves the problems of poor blending uniformity and difficult timely feedback of the coal quality state in the traditional blending, significantly improves the accuracy of blending and burning, and at the same time, ensures that the blending effect of each step is traceable through the staged verification, to provide a reliable coal quality basis for subsequent stable combustion of the unit.

[0028] Step 102, based on the unit load prediction and the boiler combustion characteristics, a feedforward blending adjustment model is developed to dynamically generate a blending ratio scheme in combination with the coal type characteristic database and the equipment state parameters.

[0029] In the embodiments of the present disclosure, in order to solve the problems that in the traditional coal blending combustion, the scheme adjustment lags behind the working condition change, it is difficult to adapt to the unit load fluctuation and the boiler combustion demand, resulting in low combustion efficiency or increased operation risk, the step is to integrate the unit load prediction result and the boiler combustion characteristic parameter to construct a feedforward coal blending adjustment model. The model takes working condition prediction as the core, calls various coal quality basic parameters stored in the coal characteristic database, combines the current actual operation state parameters of the equipment, dynamically calculates and generates the coal blending combustion proportion scheme suitable for the predicted working condition, realizes the change of the coal blending strategy from "passive response" to "active adaptation", and ensures that the coal blending scheme always keeps in coordination with the unit load and the boiler combustion demand. As an implementation manner, based on the load prediction data of the thermal power plant unit participating in peak shaving, combined with the boiler pulverizing system type, the burner guide arrangement angle, the secondary air damper arrangement layering mode and other combustion characteristic parameters, the coal characteristic database (storing parameters such as calorific value, volatile matter, ash content, moisture content and the like of each coal) containing coal from multiple regions is called, and the current state parameters of the mill group, bucket wheel machine and other equipment are also included, and the feedforward coal blending adjustment model dynamically generates the blending proportion scheme to adapt to the unit load change in advance.

[0030] The feedforward coal blending adjustment model effectively solves the problem of lagging of the traditional coal blending scheme adjustment by fusing the load prediction, the boiler combustion characteristic, the coal database and the equipment state parameter, realizes the prospective optimization of the coal blending strategy, can accurately adapt to the load variation caused by the unit peak shaving, and can also guarantee the matching of the coal quality and the boiler combustion characteristic, thereby laying a foundation for the safe and stable operation of the unit and the improvement of the combustion economy.

[0031] Step 103, implement the sub-bin combustion control, according to the boiler mill group arrangement and the load demand, the mixed coal with different blending proportions is distributed to the corresponding mill group for differential combustion.

[0032] In the embodiments of the present disclosure, to solve the problem that in the traditional coal blending combustion, all grinding groups use single blended coal, which leads to the difficulty in adapting the operation characteristics of different grinding groups and the change of unit load, and further affects the combustion stability and economy, the step is implemented by implementing the sub-bin combustion control. Taking the actual arrangement structure of the boiler grinding group and the real-time load demand of the unit as the core basis, the mixed coal with different blending ratios is accurately distributed to the corresponding grinding group, so that each grinding group obtains the coal quality that is adapted to its own operating parameters and current load demand, thereby realizing differentiated combustion control and avoiding the mismatch between single coal blending and multi-grinding group and variable load conditions. As an implementation manner, according to the arrangement characteristics of the grinding group of the thermal power plant boiler, combined with the difference of 60 tons to 160 tons of coal consumption per hour when the unit participates in peak regulation, the blended coal suitable for high load (such as mixed coal with a higher proportion of high calorific value coal) is distributed to the corresponding high load operation grinding group, and the economic type of blended coal suitable for low load (such as mixed coal reasonably blended with low cost coal) is distributed to the low load operation grinding group, so as to ensure the accurate cooperation of the combustion state and load demand of each grinding group.

[0033] The sub-bin combustion control effectively solves the problem that the traditional single coal blending combustion is difficult to adapt to the characteristics of multi-grinding group and load fluctuation by differentiating the distribution of mixed coal according to the arrangement of grinding group and load demand, which not only ensures the stability of the combustion conditions of each grinding group of the boiler, but also optimizes the coal quality configuration according to the load demand, and further improves the adaptation ability of the unit combustion economy and peak regulation condition.

[0034] Step 104, real-time adjustment of combustion operating parameters, dynamic optimization of secondary air volume configuration, combustion center position and furnace temperature distribution according to the change of mixed coal characteristics and combustion condition monitoring data, to cope with abnormal conditions such as flame deviation and slagging.

[0035] In the embodiments of the present disclosure, to solve the problem that in the traditional combustion operation process, parameter adjustment lags behind the change of mixed coal characteristics and the fluctuation of combustion conditions, causing abnormal conditions such as flame deviation and slagging, and thus affecting the stability of boiler combustion and the safety of unit operation, this step builds a dynamic adjustment mechanism of combustion operation parameters, taking the real-time characteristic change (such as the dynamic fluctuation of parameters such as calorific value, volatile matter, and ash content) of mixed coal in the combustion process and the combustion condition data (such as furnace temperature, pressure, and flame shape) obtained in real time by monitoring equipment as the core basis, to optimize and adjust the secondary air volume configuration, combustion center position, and furnace temperature distribution in real time during the operation of the boiler, to ensure that the combustion system is always adapted to the current coal quality and operating conditions, effectively avoiding the occurrence or expansion of abnormal conditions; as an implementation, for the differences in combustion conditions caused by the type of pulverizing system and the different guide angles of the burner arrangement of the thermal power plant boiler, combined with the characteristic change (such as the parameter fluctuation when high and low calorific value coal is mixed) of mixed coal during blending and the monitored flame deviation and slagging precursor data, the secondary air damper layer opening is dynamically adjusted to optimize the air volume configuration, the combustion center position is adjusted by adjusting the operation state of the burner, and the furnace heat load distribution is controlled to optimize the temperature field, to realize the timely disposal of abnormal conditions.

[0036] This real-time adjustment of combustion operation parameters effectively solves the problem of lagging behind in traditional parameter adjustment and not responding in time to abnormal conditions by accurately associating the characteristic change of mixed coal with the monitoring data of combustion conditions, which can not only adapt to the fluctuation of coal quality and conditions in real time to ensure the continuous and stable combustion of the boiler, but also avoid risks such as flame deviation and slagging, providing key support for the safe operation of the unit and the improvement of combustion economy.

[0037] The dynamic coal blending and combustion optimization method and device, electronic equipment and storage medium provided by the present disclosure implement bin combustion control and real-time optimization of combustion operation parameters by establishing a multi-level blending verification system and a feedforward coal blending adjustment model, so as to solve the problems of insufficient blending accuracy, lack of dynamic adjustment of coal blending scheme, weak targeting of combustion control, and not responding in time to abnormal conditions in the prior art, and achieve the technical effects of precise coal blending, improved combustion economy and peak regulation capacity of the unit, and safe and stable operation of the boiler.

[0038] As a specific implementation form of the present disclosure, on the basis of the basic scheme, the multi-level blending verification system is further limited to include: after blending by the unloading ditch belt, a belt sampler is used to collect mixed coal samples and detect calorific value, volatile matter, ash content, and moisture parameters; after secondary blending by the bucket wheel machine, a sampler is used to collect the coal samples into the furnace and verify the uniformity of mixing.

[0039] Specifically, in the implementation process of establishing a multi-level blending verification system, first, rely on the existing unloading ditch two belts of the thermal power plant to complete the initial blending of different coal types. After the mixed coal is conveyed to the preset sampling point by the belt, start the #2 belt sampler configured in the plant to automatically collect the mixed coal sample after the initial blending. The collection process strictly follows the preset sampling frequency and sampling amount standard to ensure the representativeness of the coal sample. After the collection is completed, the coal sample is sent to the detection system for quantitative detection of four core parameters: calorific value, volatile matter, ash content, and moisture content. By comparing the detection results with the preset blending indicators at this stage (such as the preset calorific value range and volatile matter threshold under a certain blending ratio), it is determined whether the initial blending meets the basic requirements. If the parameters exceed the allowable deviation, the coal type conveying speed and ratio of the two belts in the unloading ditch are adjusted immediately. After the initial blending is qualified, the mixed coal is conveyed to the #3 belt bucket wheel machine, which performs secondary blending according to the set blending logic. After the secondary blending is completed, the central sampler deployed in the plant collects the coal sample entering the furnace. After collection, the calorific value, volatile matter, ash content, and moisture content parameters are also detected, and the uniformity of the mixed coal is verified by calculating the dispersion (such as the standard deviation and coefficient of variation of each parameter) of the parameters of different sampling points in the same batch. If the dispersion is within the preset uniformity qualified range (such as the calorific value variation coefficient ≤ 5%), it is determined that the mixed uniformity of the coal entering the furnace is qualified, allowing it to enter the subsequent combustion link.

[0040] By using specific sampling equipment (such as #2 belt sampler and central sampler) to detect key parameters and verify uniformity in stages, parameter deviations can be detected and corrected in time after initial blending, preventing unqualified mixed coal from entering secondary blending. The uniformity verification before entering the furnace can prevent unevenly blended coal from entering the furnace, effectively reducing the problem of unstable combustion caused by coal quality fluctuations, and providing reliable coal quality data support for subsequent precise control of combustion conditions.

[0041] As a specific implementation form of the present disclosure, based on the basic scheme, a feedforward coal blending adjustment model is further defined based on unit load prediction and boiler combustion characteristics, including: adjusting the blending coal ratio of each mill group according to the predicted load, boiler equipment state, coal yard storage and transportation conditions, and coal car unloading conditions; combining historical data in the coal type characteristic database, a single coal type is blended according to the blending coal ratio to make up for the short board of volatile matter or calorific value.

[0042] Specifically, in the specific implementation process of formulating the feedforward coal blending adjustment model based on unit load prediction and boiler combustion characteristics, first, the predicted load data of the thermal power plant unit are obtained (combined with the coal consumption fluctuation range of 60 tons to 160 tons per hour when the unit participates in peak regulation), the boiler equipment state information (including the operation condition of the pulverizing system, the actual calibration parameters of the burner guide arrangement angle, the start-stop state and load upper limit of each mill group), the coal yard storage condition (such as the inventory of each coal type, the storage area and the change of water content), and the coal car unloading plan (such as the unloading batch, arrival time and actual unloading amount of each coal type in the next 24 hours) are synchronously collected, and the above data are input into the feedforward coal blending adjustment model in real time. According to the matching relationship between load demand and resource supply, the model calculates and outputs the corresponding blending coal proportion of each mill group (for example, during the high load period, the 1# and 2# main mill groups are allocated with 60% A coal + 40% B coal mixed coal, and during the low load period, the 3# and 4# auxiliary mill groups are allocated with 70% C coal + 30% D coal mixed coal). At the same time, the model calls the historical data of each single coal type in the coal type characteristic database (such as the low volatile matter record of D coal and the low calorific value record of C coal), and formulates a targeted blending strategy for the coal type with a short board in characteristics, so as to ensure that the defects of a single coal type are accurately compensated through blending.

[0043] Through multi-dimensional data (load, equipment, storage and transportation, and unloading) linkage adjustment of mill group blending proportion, the coal blending interruption caused by insufficient coal supply or equipment load mismatch can be avoided, and continuous and stable coal supply under deep peak regulation condition can be ensured. At the same time, the historical data are relied on to make up for the short board of single coal type characteristics, which can effectively avoid the problems of flame deflection and low combustion efficiency caused by the defects of coal type itself, and further improve the economy and safety of boiler operation.

[0044] As a specific implementation form of the present disclosure, on the basis of the basic scheme, the implementation of the sub-bin combustion control further comprises: allocating high calorific value coal to the mill group at the center position of the boiler combustion, and allocating low calorific value coal to the edge mill group to balance the combustion stability; dynamically adjusting the mill group coal supply proportion according to the load fluctuation range of the unit on the same day, and ensuring the stability of the combustion condition when the hourly coal consumption is within the preset range.

[0045] Specifically, in the specific process of implementing the sub-bin combustion control, first, combined with the guide arrangement angle of the boiler burner of the thermal power plant, the layered arrangement characteristics of the secondary air damper, and the actual arrangement position of the mill group, the mill group corresponding to the center area of the boiler combustion (such as #2 and #3 mill groups close to the core area of the furnace heat load) and the mill group corresponding to the edge area (such as #1 and #4 mill groups with relatively low heat load on both sides of the furnace) are determined, the high-calorific-value coal in the coal property database is preferentially allocated to the mill group in the center of the combustion, and the high-calorific-value coal is burned to ensure the heat load demand of the center of the furnace, relying on the characteristics of high heat release and high temperature of the high-calorific-value coal; at the same time, the low-calorific-value coal is allocated to the edge mill group, and the layered opening adjustment of the secondary air damper corresponding to the edge mill group is matched to enable the low-calorific-value coal to be smoothly burned in the edge area, avoiding the imbalance problem of excessive heat load in the center area or insufficient combustion in the edge area. On this basis, the coal supply ratio of each mill group is dynamically adjusted according to the load fluctuation range of the unit on the same day (combined with the coal consumption range of 60-160 tons per hour of the unit in the plant): when the load is in the high range (such as 120-160 tons of coal consumption per hour), the high-calorific-value coal supply ratio of the mill group in the center of the combustion is increased (such as from the initial 50% to 70%) to match the heat output demand under high load; when the load is in the low range (such as 60-90 tons of coal consumption per hour), the high-calorific-value coal ratio of the center mill group is reduced to 40%, while the low-calorific-value coal supply ratio (such as 60%) of the edge mill group is maintained stable, to ensure that when the coal consumption per hour is in the preset range, the temperature distribution of the boiler furnace is uniform, the flame is not deflected, and the combustion condition is always stable.

[0046] By allocating the mill group according to the calorific value of the coal, the heat load distribution of the furnace is balanced in the spatial dimension, avoiding local overheating or insufficient combustion caused by concentrated combustion of a single coal; at the same time, the coal supply ratio is dynamically adjusted according to the load, accurately adapting to the coal consumption change of 60-160 tons per hour, effectively ensuring the stability of the combustion under different peak regulation conditions, and further reducing the risk of slagging and flame drifting caused by load fluctuation.

[0047] As a specific implementation form of the present disclosure, on the basis of the basic scheme, the real-time adjustment of the combustion operating parameter further includes: when the volatile matter of the mixed coal changes by more than a first percentage range, the layered adjustment of the secondary air damper opening is performed to control the furnace temperature distribution deviation within a preset temperature range; when the moisture content of the mixed coal changes by more than a second percentage range, the center of the combustion is adjusted and the secondary air volume is optimized to reduce the combustible content of the fly ash to below a preset percentage.

[0048] Specifically, in the specific implementation process of real-time adjustment of the combustion operation parameter, firstly, relying on the existing secondary air damper layering structure and combustion condition monitoring system of the thermal power plant boiler, a first percentage range (such as ±15%, determined based on the volatile matter reference value of each purchased coal in the coal characteristic database) of the volatile matter of the mixed coal and a second percentage range (such as ±8%, similarly set based on the moisture reference value of each coal) of the moisture content are pre-set, and a pre-set furnace temperature distribution deviation control target (such as ±50℃) and an upper limit (such as 8%) of the fly ash combustible content are pre-set. When the monitoring system detects that the volatile matter of the mixed coal changes beyond the first percentage range (for example, the volatile matter increases by 18% than the reference value due to the increase of the proportion of A coal in the mixed coal), the corresponding damper opening is adjusted layer by layer according to the layering characteristics of the secondary air damper: the upper layer secondary air damper opening is increased by 10% to strengthen the disturbance of the upper part of the furnace, the middle layer damper opening is adjusted by 8% to maintain the stability of the middle part of the combustion, and the lower layer damper opening is reduced by 5% to avoid local overheating of the lower part, so that the temperature difference of each region of the furnace is controlled within the pre-set range of ±50℃ by layering precise air control; when the monitoring system detects that the moisture content of the mixed coal changes beyond the second percentage range, the burner guide angle is adjusted downward by 3° to lower the combustion center position, and the total secondary air volume is increased by 12% to enhance the heat carrying capacity of the airflow and accelerate the evaporation rate of the moisture, so that the fly ash combustible content is stably reduced to below the pre-set percentage of 8% through the synergistic optimization of the combustion center and the air volume.

[0049] Through the precise operation of layering air control, combustion center and air volume synergistic optimization respectively for volatile matter and moisture changes, the furnace temperature distribution deviation can be effectively controlled to avoid the problems of local overheating or insufficient combustion caused by volatile matter fluctuation, and the fly ash combustible content can be reduced when high-moisture coal is mixed and burned, thereby ensuring the stability of the boiler combustion and further improving the combustion economy of the unit, which meets the core target of ensuring the safe operation of the unit and improving the combustion economy.

[0050] As a specific implementation form of the present disclosure, based on the basic scheme, the present disclosure further limits that the embodiment of the present disclosure further comprises: establishing a combustion economy evaluation model based on test data, comparing the boiler efficiency, flue gas loss and environmental protection parameters to select the optimal blending scheme and generate a standardized operation procedure.

[0051] Specifically, the core data under different blending schemes is collected in real time at a preset frequency, wherein the boiler efficiency is calculated by the counterbalance method combined with the furnace heat loss, heat loss, etc., the exhaust loss is derived based on the exhaust temperature monitoring data and the flue gas composition analysis results (such as oxygen content, carbon dioxide content), and the environmental protection parameters mainly collect the emission concentration data of pollutants such as nitrogen oxides and sulfur dioxide. After classifying and arranging these test data, they are used as the basic input of the combustion economy evaluation model. Then the evaluation model is built, taking "maximizing boiler efficiency, minimizing exhaust loss, and meeting environmental protection parameters" as the three-dimensional objective function. According to the requirements of the subject on combustion economy and environmental protection compliance, 40% weight is allocated to boiler efficiency, 30% weight to exhaust loss, and 30% weight to environmental protection parameters. The comprehensive economic score of each blending scheme is calculated by weighted calculation. On this basis, the comprehensive scores of all test blending schemes are compared, and the scheme with the highest comprehensive score and meeting the safe operation requirements of the unit (without combustion instability, slagging, etc.) is selected as the optimal blending scheme. Finally, the optimal scheme is taken as the core, combined with the effective equipment operation parameters (such as the start-stop timing of the pulverizing system, the layered opening of the secondary air damper, and the sampling monitoring frequency) verified in the test, the post operation process (fuel blending operation steps, combustion adjustment steps), and the abnormal disposal measures (such as the blending ratio adjustment method when the environmental protection parameters exceed the standard), to generate the standardized operation procedures, and to clarify the operation standard value and the allowable deviation range of each link.

[0052] The quantitative combustion economy evaluation model is built by test data, avoiding the subjectivity of relying on experience to screen the blending scheme, ensuring the scientificity and reliability of the optimal scheme. At the same time, the standardized operation procedures are generated based on the optimal scheme, which can unify the operation standards of each post, reduce the fluctuation of blending effect caused by operation differences, stabilize the boiler efficiency, reduce the exhaust loss, ensure the environmental protection parameters to meet the standard continuously, and fully meet the core goal of improving the combustion economy and ensuring the safe operation of the unit.

[0053] It should be noted that the embodiments of the present disclosure can include multiple steps, which are numbered for the convenience of description, but these numbers do not limit the execution time slots and execution order between the steps; the steps can be implemented in any order, and the embodiments of the present disclosure do not limit this.

[0054] Corresponding to the dynamic coal blending and combustion optimization method described above, the present disclosure also proposes a dynamic coal blending and combustion optimization device. Since the device embodiments of the present disclosure correspond to the method embodiments described above, for details not disclosed in the device embodiments, please refer to the method embodiments described above, which will not be described in detail in the present disclosure.

[0055] Figure 2 A structural schematic diagram of a dynamic coal blending and combustion optimization device provided by an embodiment of the present disclosure is shown inFigure 2 as shown, comprising: The monitoring unit 21 is configured to establish a multi-stage blending verification system, and gradually mix and monitor the mixed coal quality through the unloading groove belt blending, the second blending of the bucket wheel machine, and the three-stage sampling system; The generating unit 22 is configured to formulate a feedforward coal blending adjustment model based on the unit load prediction and the boiler combustion characteristics, and dynamically generate a blending ratio scheme in combination with the coal property database and the equipment state parameters; The distribution unit 23 is configured to implement the sub-chamber combustion control, and distribute the mixed coal with different blending ratios to the corresponding mill groups for differential combustion according to the mill group arrangement and the load demand of the boiler; The adjusting unit 24 is configured to adjust the combustion operation parameters in real time, dynamically optimize the secondary air volume configuration, the combustion center position, and the furnace temperature distribution according to the mixed coal property changes and the combustion condition monitoring data, so as to cope with abnormal conditions such as flame deviation and slagging.

[0056] The dynamic coal blending and combustion optimization device provided by the present disclosure establishes a multi-stage blending verification system and a feedforward coal blending adjustment model, implements sub-chamber combustion control, and optimizes combustion operation parameters in real time, so as to solve the problems of insufficient blending accuracy, lack of dynamic adjustment of coal blending scheme, weak pertinence of combustion control, and untimely response to abnormal conditions in the prior art, and achieve the technical effects of precise coal blending, improved combustion economy and peak regulation capacity of the unit, and safe and stable operation of the boiler.

[0057] Further, in one possible implementation manner of the present embodiment, the monitoring unit 21 is further configured to: After the unloading groove belt blending, a belt sampler is used to collect a mixed coal sample and detect the calorific value, volatile matter, ash content, and moisture parameters; After the second blending of the bucket wheel machine, a sampler is used to collect a coal sample into the furnace and verify the mixing uniformity.

[0058] Further, in one possible implementation manner of the present embodiment, the generating unit 22 is further configured to: According to the predicted load, the boiler equipment state, the coal yard storage and transportation condition, and the coal car unloading condition, the blending coal ratio of each mill group is adjusted; In combination with the historical data in the coal property database, a single coal is subjected to targeted blending according to the blending coal ratio to make up for the short board of volatile matter or calorific value.

[0059] Further, in one possible implementation manner of the present embodiment, the distribution unit 23 is further configured to: The high-calorific-value coal is distributed to the mill group at the combustion center position of the boiler, and the low-calorific-value coal is distributed to the edge mill group to balance the combustion stability; According to the unit load fluctuation range of the day, the coal supply ratio of the mill group is dynamically adjusted to ensure that the hourly coal consumption is within the preset range and the stable combustion condition is ensured.

[0060] Further, in a possible implementation manner of the embodiment, the adjustment unit 24 is further configured to: When the volatile matter of the mixed coal changes by more than the first percentage range, the secondary air door opening is adjusted by layering to control the furnace temperature distribution deviation within the preset temperature range; When the moisture content of the mixed coal changes by more than the second percentage range, the combustion center position is adjusted and the secondary air volume is optimized to reduce the fly ash combustible content to below the preset percentage.

[0061] Further, in a possible implementation manner of the embodiment, as Figure 2 shown, the method further comprises: The screening unit 25 is configured to establish a combustion economy evaluation model based on the test data, screen the optimal blending scheme by comparing the boiler efficiency, flue gas loss and environmental protection parameters, and generate a standardized operation procedure.

[0062] It should be noted that the foregoing explanation and description of the method embodiment are also applicable to the device of the embodiment, and the principle is the same, which is not limited in the embodiment.

[0063] According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0064] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0065] As Figure 3As shown, the electronic device 300 includes a computing unit 301 that can perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-Only Memory) 302 or a computer program loaded into a RAM (Random Access Memory) 303 from a storage unit 308. Various programs and data required for the operation of the electronic device 300 can also be stored in the RAM 303. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0066] Various components in the electronic device 300 are connected to the I / O interface 305, including an input unit 306 such as a keyboard, a mouse, and the like, an output unit 307 such as various types of displays, a speaker, and the like, a storage unit 308 such as a magnetic disk, an optical disk, and the like, and a communication unit 309 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0067] The computing unit 301 can be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, and the like. The computing unit 301 performs various methods and processes described above, such as the dynamic blending optimization method. For example, in some embodiments, the dynamic blending optimization method can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the aforementioned dynamic blending optimization method by any other appropriate means, such as by means of firmware.

[0068] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a Field Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a System on a Chip (SOC), a Complex Programmable Logic Device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0069] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general or special purpose computer, such that the program code, when executed by the processor or controller, causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0070] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory), or flash memory, an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0071] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0072] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, and a blockchain network.

[0073] The computer system can include clients and servers. This relationship can be between a client and a server that are typically distant from each other and typically interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of large management difficulty and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server can also be a server of a distributed system, or a server combined with a blockchain.

[0074] It should be noted that artificial intelligence is a discipline that studies enabling computers to simulate some thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.) of people, both hardware and software technologies. Artificial intelligence hardware technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing, etc.; artificial intelligence software technology mainly includes computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, knowledge graph technology, etc. several major directions.

[0075] The first, second, and the like various numerical numbers involved in the present disclosure are only for the convenience of differentiation in the description, and do not limit the scope of the embodiments of the present disclosure, nor represent the order of precedence.

[0076] At least one of the present disclosure can also be described as one or more, and the plurality can be two, three, four or more, which is not limited by the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D". The technical features described by "first", "second", "third", "A", "B", "C" and "D" have no order or size order.

[0077] It should be understood that the steps shown above can be reordered, added or deleted. For example, the steps described in the present disclosure can be executed in parallel, sequentially or in different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, which is not limited herein.

[0078] The above detailed description does not limit the scope of the disclosure. Various modifications, combinations, sub-combinations and alternatives can be made to the detailed description. Any modification, equivalent replacement and improvement etc. made within the spirit and principle of the disclosure shall be included in the scope of the disclosure.

Claims

1. A dynamic coal blending and combustion optimization method, characterized in that, include: A multi-level blending verification system was established, and the blended coal was gradually mixed and monitored in real time through unloading trench belt blending, bucket wheel excavator secondary blending, and a three-level sampling system. Based on unit load forecasting and boiler combustion characteristics, a feedforward coal blending adjustment model is developed, and a blending ratio scheme is dynamically generated by combining a coal type characteristic database and equipment status parameters. Implement compartmentalized combustion control, and allocate mixed coal with different blending ratios to corresponding mill groups for differentiated combustion according to the boiler mill group layout and load demand; The combustion operation parameters are adjusted in real time. Based on the changes in the characteristics of the mixed coal and the monitoring data of the combustion conditions, the secondary air volume configuration, the combustion center position and the furnace temperature distribution are dynamically optimized to cope with abnormal conditions such as flame deviation and slagging.

2. The method according to claim 1, characterized in that, The establishment of a multi-level blending verification system includes: After being blended via the unloading trench conveyor belt, a belt sampler was used to collect mixed coal samples and test parameters such as calorific value, volatile matter, ash content, and moisture content. After secondary blending by the bucket wheel machine, coal samples are collected from the furnace using a sampler to verify the uniformity of the mixture.

3. The method according to claim 1, characterized in that, The feedforward coal blending adjustment model, based on unit load forecasting and boiler combustion characteristics, includes: Adjust the blending ratio of each grinding group according to the predicted load, boiler equipment status, coal yard storage and transportation conditions, and coal car unloading conditions; Based on historical data from the coal type characteristics database, a single coal type is blended in a targeted manner according to the specified blending coal ratio to compensate for its shortcomings in volatile matter or calorific value.

4. The method according to claim 1, characterized in that, The implementation of compartmentalized combustion control also includes: High-calorific-value coal is allocated to the central combustion grinding group of the boiler, while low-calorific-value coal is allocated to the edge grinding group to balance combustion stability. Based on the daily load fluctuation range of the unit, the coal supply ratio of the mill is dynamically adjusted to ensure stable combustion conditions when the hourly coal consumption is within the preset range.

5. The method according to claim 1, characterized in that, The real-time adjustment of combustion operating parameters includes: When the volatile matter content of the mixed coal changes beyond the first percentage range, the opening of the secondary air damper is adjusted in layers to keep the furnace temperature distribution deviation within the preset temperature range. When the moisture content of the mixed coal exceeds the second percentage range, the combustion center position is adjusted and the secondary air volume is optimized to reduce the combustible content of fly ash to below the preset percentage.

6. The method according to claim 1, characterized in that, Also includes: A combustion economy evaluation model was established based on experimental data. By comparing boiler efficiency, flue gas loss and environmental parameters, the optimal blending scheme was selected and standardized operating procedures were generated.

7. A dynamic coal blending and combustion optimization device, characterized in that, include: The monitoring unit is used to establish a multi-level blending verification system, which performs progressive blending and real-time monitoring of the mixed coal quality through unloading trench belt blending, bucket wheel excavator secondary blending, and a three-level sampling system. The generation unit is used to formulate a feedforward coal blending adjustment model based on unit load forecasting and boiler combustion characteristics, and dynamically generate blending ratio schemes by combining coal type characteristic database and equipment status parameters. The distribution unit is used to implement compartmentalized combustion control, which distributes mixed coal with different blending ratios to the corresponding mills for differentiated combustion according to the boiler mill arrangement and load demand. The adjustment unit is used to adjust the combustion operation parameters in real time. Based on the changes in the characteristics of the mixed coal and the monitoring data of the combustion conditions, it dynamically optimizes the secondary air volume configuration, the combustion center position and the furnace temperature distribution to cope with abnormal conditions such as flame deviation and slagging.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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