Intelligent soot blowing control method and system in multi-coal combustion state

Abstract

The invention discloses an intelligent soot blowing control method and system in a multi-coal combustion state, and relates to the technical field of boiler control, and the intelligent soot blowing control method comprises the steps that a digital model of a boiler control system is established, and input and output measuring point positions are configured; collecting inlet and outlet flue gas and working medium parameters of each heating surface of the boiler through a sensor; on the basis of the real-time parameters, operation performance indexes of the boiler are calculated; according to the operation performance indexes, monitoring the dirt state, and calculating the cleaning factor, the heat transfer coefficient and the total heat transfer amount of each heating surface; and based on the monitoring result of the soot state, determining the optimal soot blowing time and soot blowing period by using an intelligent algorithm, and automatically controlling the soot blowing operation of each heating surface. According to the intelligent soot blowing control method in the multi-coal combustion state, the operation efficiency of the boiler is improved, the energy consumption is reduced, the service life of equipment is prolonged, meanwhile, the adaptability and stability of the system to the multi-coal combustion state are enhanced, and the overall performance and economic benefits of the boiler are remarkably optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of boiler control, and in particular to an intelligent soot blowing control method and system under multi-coal combustion conditions. Background Art

[0002] As an important equipment in industrial production, the efficient and stable operation of the boiler has a great impact on energy utilization and production costs. During the operation of the boiler, the accumulation of dust and unburned particles generated by combustion on the heating surface will form an ash layer, resulting in reduced heat transfer efficiency and combustion efficiency, which in turn affects the overall performance and economy of the boiler. In order to maintain the clean state of the heating surface, the traditional soot blowing method is widely used.

[0003] Existing sootblowing control methods mainly include timed sootblowing and automatic sootblowing based on fixed thresholds. The timed sootblowing method performs sootblowing operations according to predetermined time intervals, lacks real-time response to actual operating conditions, and may lead to unnecessary energy consumption and equipment wear. The automatic sootblowing method based on fixed thresholds triggers sootblowing operations by setting specific performance indicator thresholds (such as temperature, pressure, etc.). However, under the combustion conditions of multiple coal types, due to differences in the combustion characteristics and ash content of different coal types, fixed thresholds are often unable to adapt to complex and changeable operating conditions, resulting in inaccurate timing and frequency of sootblowing operations, affecting the operating efficiency of the boiler and the life of the equipment.

[0004] In addition, existing sootblowing control methods mostly rely on single sensor data or simple algorithms, lack comprehensive analysis of multiple operating parameters and intelligent decision-making capabilities, and are difficult to achieve comprehensive evaluation and optimization control of boiler operating conditions. This is particularly evident under multi-coal combustion conditions. The combustion behavior and ash characteristics of different coal types have complex and diverse effects on the boiler heating surface, and traditional methods are difficult to effectively deal with. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that the traditional timed sootblowing method cannot be adjusted according to the real-time status of boiler operation, which easily leads to unnecessary sootblowing operations, increased energy consumption and equipment wear. At the same time, the sootblowing method based on a fixed threshold performs poorly under the conditions of multi-coal combustion and cannot adapt to the fluctuations in operating parameters caused by different coal types.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent soot blowing control method under multi-coal combustion conditions, comprising:

[0008] Establish a digital model of the boiler control system and configure the positions of input and output measuring points; collect the inlet and outlet flue gas and working fluid parameters of each heating surface of the boiler through sensors; calculate the operating performance indicators of the boiler based on real-time parameters; monitor the ash and fouling status according to the operating performance indicators, and calculate the cleanliness factor, heat transfer coefficient and total heat transfer of each heating surface; based on the monitoring results of the ash and fouling status, use an intelligent algorithm to determine the optimal soot blowing time and soot blowing cycle, and automatically control the soot blowing operation of each heating surface.

[0009] As a preferred scheme of the intelligent sootblowing control method under multi-coal combustion conditions described in the present invention, wherein: the establishment of a digital model of the boiler control system includes creating a simulation environment for the boiler control system; inputting the physical properties of each subsystem according to the design parameters of the boiler; the subsystem includes a furnace control system, a convection heating surface control system, an air preheater control system and a smoke exhaust control system; the models of each subsystem are connected through signal lines to form a digital twin model of the overall boiler control system; the input and output measuring point positions are configured in the digital model, including temperature sensors and pressure sensors at the furnace inlet and outlet, flow sensors at the inlets and outlets of each level of the heating surface, temperature sensors at the inlet and outlet of the air preheater, and a flow sensor at the smoke exhaust outlet.

[0010] As a preferred scheme of the intelligent sootblowing control method under multi-coal combustion conditions described in the present invention, wherein: the calculation of the operating performance indicators of the boiler includes calculating the overall thermal efficiency of the boiler based on the fuel flow and the fuel calorific value, which is the ratio of the input fuel energy to the output steam energy; calculating the average temperature of the flue gas at the furnace outlet based on the data of the furnace outlet temperature sensor; calculating the average temperature of the flue gas at the heating surface outlet based on the data of the outlet temperature sensors of each level of the heating surface; calculating the fuel consumption rate based on the data of the fuel flow sensor to reflect the fuel usage per unit time; calculating the water flow consumption rate based on the data of the water flow sensor to reflect the water usage per unit time; calculating the heat input parameters of the boiler based on the data of the furnace inlet temperature and pressure sensor; calculating the air preheating efficiency based on the data of the air preheater inlet and outlet temperatures; calculating the heat loss of the smoke exhaust system based on the data of the smoke exhaust outlet flow and temperature.

[0011] As a preferred solution of the intelligent soot blowing control method under multi-coal combustion state described in the present invention, the cleaning factor is expressed as:

[0012]

[0013] Among them, CF j represents the cleaning factor of the jth heating surface, η th Represents the overall thermal efficiency of the boiler, η ap Indicates the air preheating efficiency, L muc Indicates mechanical incomplete combustion loss, Qex represents the heat loss of the smoke exhaust system, Q in represents heat input, T h,out,j represents the outlet flue gas temperature of the jth heating surface, T h,out,avg Indicates the average temperature of flue gas at the outlet of the heating surface.

[0014] As a preferred solution of the intelligent soot blowing control method under multi-coal combustion state described in the present invention, the heat transfer coefficient is expressed as:

[0015]

[0016] Among them, HTC j represents the heat transfer coefficient of the jth heated surface, A j represents the heat transfer area of ​​the jth heating surface.

[0017] As a preferred solution of the intelligent soot blowing control method under the multi-coal combustion state described in the present invention, the total heat transfer is expressed as:

[0018] Q total,j =HTC j ×A j ×(T h,out ,jT f,out ,j)

[0019] Among them, Q total,j Represents the total heat transfer of the jth heated surface.

[0020] As a preferred scheme of the intelligent sootblowing control method under multi-coal combustion conditions described in the present invention, wherein: the determination of the optimal sootblowing timing and sootblowing cycle includes taking the cleaning factor, heat transfer coefficient and total heat transfer of each heating surface as input parameters, and inputting them into the fuzzy logic controller; in the fuzzy logic controller, three membership functions are defined for each input parameter, corresponding to low, medium and high levels respectively; in the fuzzy logic controller, a fuzzy rule base is established; fuzzy reasoning is performed on the input parameters by a fuzzy reasoning method, and fuzzy control output is generated according to the fuzzy rule base; the fuzzy control output is defuzzified by the centroid method, and converted into specific control instructions for sootblowing intensity and sootblowing frequency; the sootblowing device of each heating surface is automatically adjusted according to the control instructions for sootblowing intensity and sootblowing frequency obtained by the defuzzification process.

[0021] Another object of the present invention is to provide an intelligent sootblowing control system under multiple coal combustion conditions, which can solve the problems of insufficient real-time performance, poor adaptability and insufficient intelligent decision-making ability in existing sootblowing control methods by constructing an intelligent sootblowing control system under multiple coal combustion conditions.

[0022] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent sootblowing control system under multi-coal combustion conditions, comprising: a model building module, used to establish a digital model of the boiler control system and configure the input and output measurement point positions; a data acquisition module, used to collect the inlet and outlet flue gas and working fluid parameters of each heating surface of the boiler through sensors; an index analysis module, used to calculate the operating performance index of the boiler based on real-time parameters; an ash pollution judgment module, used to monitor the ash pollution state according to the operating performance index, and calculate the cleaning factor, heat transfer coefficient and total heat transfer of each heating surface; a sootblowing control module, used to determine the optimal sootblowing time and sootblowing cycle based on the monitoring results of the ash pollution state using an intelligent algorithm, and automatically control the sootblowing operation of each heating surface.

[0023] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the intelligent soot blowing control method under the combustion state of multiple coal types when executing the computer program.

[0024] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the intelligent soot blowing control method under the combustion state of multiple coal types as described above.

[0025] Beneficial effects of the present invention: The intelligent sootblowing control method under multi-coal combustion conditions provided by the present invention monitors the ash contamination state of the boiler heating surface and intelligently adjusts the sootblowing intensity and frequency based on multi-dimensional indicators such as cleaning factor and heat transfer coefficient, thereby avoiding the inefficiency and waste of traditional empirical or timing control. By optimizing the sootblowing strategy, the present invention improves the heat transfer efficiency of the boiler, reduces energy consumption and equipment wear, and extends the service life of the equipment. In addition, the method can adapt to the combustion characteristics of different types of coal, ensure the stable operation of the boiler under complex working conditions, and significantly improve the overall economy and environmental protection performance of the boiler. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0027] Figure 1 An overall flow chart of an intelligent soot blowing control method under multi-coal combustion conditions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0030] Example 1

[0031] Reference Figure 1 , is an embodiment of the present invention, and provides an intelligent soot blowing control method under multi-coal combustion conditions, comprising:

[0032] Establish a digital model of the boiler control system and configure the input and output measurement point locations.

[0033] The inlet and outlet flue gas and working fluid parameters of each heating surface of the boiler are collected through sensors.

[0034] Based on real-time parameters, calculate the boiler's operating performance indicators.

[0035] According to the operating performance indicators, the dust and dirt status is monitored and the cleanliness factor, heat transfer coefficient and total heat transfer of each heating surface are calculated.

[0036] Based on the monitoring results of the soot pollution status, an intelligent algorithm is used to determine the best soot blowing time and soot blowing cycle, and the soot blowing operation of each heating surface is automatically controlled.

[0037] The digital model of the boiler control system is established, which includes creating a simulation environment for the boiler control system; inputting the physical characteristics of each subsystem according to the design parameters of the boiler; the subsystems include a furnace control system, a convection heating surface control system, an air preheater control system and a smoke exhaust control system; connecting the models of each subsystem through a signal line to form a digital twin model of the overall boiler control system; configuring the positions of input and output measuring points in the digital model, including temperature sensors and pressure sensors at the furnace inlet and outlet, flow sensors at the inlet and outlet of each level of the heating surface, temperature sensors at the inlet and outlet of the air preheater, and a flow sensor at the smoke exhaust outlet.

[0038] It should be noted that the establishment of the digital model not only includes the simulation environment of each subsystem of the boiler, but also involves the detailed input and modeling of various parameters of boiler operation in order to form a complete digital twin model of the boiler. In this process, it is first necessary to accurately input the physical characteristics of each subsystem according to the design parameters of the boiler. These subsystems include the furnace control system, the convection heating surface control system, the air preheater control system and the smoke exhaust control system. Each subsystem has different characteristics and has complex thermodynamic, fluid dynamic and mechanical coupling relationships with each other. In order to accurately reflect the overall operation of the boiler, the digital model connects these subsystems through signal lines to form an overall boiler control system model. Through this model, the system can reflect the operating status of each subsystem in real time and provide real-time data support for subsequent control decisions.

[0039] It should be pointed out that the positions of the input and output measuring points configured in the digital model are crucial, and are directly related to the comprehensiveness and accuracy of data collection. Temperature sensors and pressure sensors at the furnace inlet and outlet can provide real-time data on the combustion conditions in the furnace; flow sensors at the inlet and outlet of the heating surface at all levels can accurately capture the changes in water vapor flow, and thus reflect the heat exchange efficiency of the boiler; temperature sensors at the inlet and outlet of the air preheater help understand the effect of air preheating; and flow sensors at the exhaust outlet help evaluate the energy efficiency of the exhaust system. By configuring these input and output measuring points, it is possible to ensure comprehensive and accurate data input from all key links, ensuring the efficient operation of the digital model and the actual control effect.

[0040] Furthermore, the digital twin model of the boiler control system not only realizes real-time data collection and feedback, but also provides theoretical support for system optimization. Through the simulation and analysis of the boiler model, the boiler operation performance under different working conditions can be predicted, providing a reference for subsequent control decisions. For example, the operating characteristics of the boiler under different fuels, different loads, and different environmental conditions can be accurately reflected in the simulation model, helping to predict possible system failures or efficiency bottlenecks, so as to adjust the control strategy in advance and maximize the boiler performance.

[0041] The calculation of the operating performance index of the boiler includes calculating the overall thermal efficiency of the boiler based on the fuel flow and the fuel calorific value, which is the ratio of the input fuel energy to the output steam energy; calculating the average temperature of the flue gas at the furnace outlet based on the data of the furnace outlet temperature sensor; calculating the average temperature of the flue gas at the heating surface outlet based on the data of the outlet temperature sensors of each level of the heating surface; calculating the fuel consumption rate based on the data of the fuel flow sensor to reflect the fuel usage per unit time; calculating the water flow consumption rate based on the data of the water flow sensor to reflect the water usage per unit time; calculating the heat input parameters of the boiler based on the data of the furnace inlet temperature and pressure sensor; calculating the air preheating efficiency based on the data of the air preheater inlet and outlet temperatures; and calculating the heat loss of the smoke exhaust system based on the data of the smoke exhaust outlet flow and temperature.

[0042] The operating performance index of the boiler is a quantitative assessment of the overall thermal efficiency of the boiler, the fuel utilization efficiency, and the performance of each part of the system. The present invention realizes real-time monitoring and accurate evaluation of boiler performance by calculating and analyzing multiple important operating parameters, thereby providing an intelligent decision-making basis for sootblowing operations. Specifically, by inputting fuel flow and fuel calorific value data, the overall thermal efficiency of the boiler can be calculated, that is, the ratio between the energy of the input fuel and the energy of the output steam. This indicator reflects the thermal energy conversion efficiency of the boiler and is the core parameter for evaluating the operating effect of the boiler.

[0043] On this basis, the present invention also calculates the average temperature of the flue gas at the furnace outlet by collecting the furnace outlet temperature data, reflecting the combustion efficiency of the furnace and the heat content of the combustion products. The data calculation of the heating surface outlet temperature can help further evaluate the heat transfer effect of each heating surface of the boiler, so as to determine whether the heat exchange inside the boiler is efficient. By calculating the fuel consumption rate based on the fuel flow sensor data, the fuel usage per unit time can be quantitatively analyzed to provide a basis for optimizing the fuel utilization rate. In addition, the water flow consumption rate calculated by the water flow sensor data helps to understand the efficiency of the water circulation inside the boiler, ensuring that the heat can be fully transferred to the steam, thereby improving the overall thermal efficiency of the boiler.

[0044] It should be noted that the heat input of the boiler is calculated through the data of the furnace inlet temperature and pressure sensor, which can provide guidance for optimizing boiler operating conditions and reducing energy waste. The air preheating efficiency can be further calculated through the inlet and outlet temperature data of the air preheater, and then the air utilization efficiency of the boiler during startup and stable operation can be understood. Finally, the heat loss of the smoke exhaust system is another important performance indicator. The heat loss of the smoke exhaust system is calculated through the flow and temperature data of the smoke exhaust outlet, which can provide data support for subsequent energy-saving improvements.

[0045] Furthermore, the calculation of all these performance indicators not only provides data support for real-time monitoring of the boiler, but also provides a theoretical basis for the optimization and adjustment of the control strategy. For example, when the overall thermal efficiency of the boiler is low or the heat loss of the flue gas is too large, the system can automatically adjust the soot blowing time and cycle to improve the boiler efficiency by reducing heat loss, reduce energy consumption, and improve the economy of the boiler. The overall thermal efficiency of the boiler is expressed as:

[0046]

[0047] Among them, η th It represents the overall thermal efficiency of the boiler. Indicates water flow (water usage per unit time), H w represents the enthalpy of water, Indicates fuel flow rate (fuel consumption per unit time), H f Indicates the calorific value of fuel.

[0048] Mechanical incomplete combustion loss, expressed as:

[0049]

[0050] Among them, L muc Indicates mechanical incomplete combustion loss, represents the fuel flow rate, α represents the proportion of incompletely burned fuel, H f Indicates the calorific value of fuel.

[0051] The average temperature of flue gas at the furnace outlet is expressed as:

[0052]

[0053] Among them, T f,out,avg represents the average temperature of flue gas at the furnace outlet, n represents the number of furnace partitions, T f,out,i Represents the outlet flue gas temperature of the i-th furnace partition.

[0054] The average temperature of flue gas at the outlet of the heating surface is expressed as:

[0055]

[0056] Among them, T h,out,avg represents the average temperature of flue gas at the outlet of the heating surface, m represents the number of heating surfaces, T h,out,j Represents the outlet flue gas temperature of the jth heating surface.

[0057] The fuel consumption rate is expressed as:

[0058]

[0059] in, Indicates the fuel consumption rate, Indicates the fuel flow rate.

[0060] The water flow consumption rate is expressed as:

[0061]

[0062] in, represents the water consumption rate, Indicates water flow.

[0063] Heat input parameter, expressed as:

[0064]

[0065] Among them, Q in represents the heat input, Indicates the fuel flow rate, H f Indicates the calorific value of fuel.

[0066] Air preheating efficiency, expressed as:

[0067]

[0068] Among them, η ap represents the air preheating efficiency, T ap,out Indicates the air preheater outlet temperature, T ap,in Indicates the air preheater inlet temperature, T ap,ref Indicates the reference temperature.

[0069] The heat loss of the smoke exhaust system is expressed as:

[0070]

[0071] Among them, Q ex represents the heat loss of the smoke exhaust system, Indicates the exhaust outlet flow rate, C p represents the specific heat capacity of exhaust gas, T ex,out Indicates the exhaust outlet temperature.

[0072] The cleaning factor is expressed as,

[0073]

[0074] Among them, CF j represents the cleaning factor of the jth heating surface, η th Represents the overall thermal efficiency of the boiler, η ap Indicates the air preheating efficiency, L muc Indicates the mechanical incomplete combustion loss, Q ex represents the heat loss of the smoke exhaust system, Q in represents heat input, T h,out,jrepresents the outlet flue gas temperature of the jth heating surface, T h,out,avg Indicates the average temperature of flue gas at the outlet of the heating surface.

[0075] The heat transfer coefficient is expressed as,

[0076]

[0077] Among them, HTC j represents the heat transfer coefficient of the jth heated surface, A j represents the heat transfer area of ​​the jth heating surface.

[0078] The total heat transfer is expressed as,

[0079] Q total,j =HTC j ×A j ×(T h,out ,jT f,out ,j)

[0080] Among them, Q total,j Represents the total heat transfer of the jth heated surface.

[0081] The method for determining the optimal sootblowing timing and sootblowing cycle includes inputting the cleaning factor, heat transfer coefficient and total heat transfer of each heating surface as input parameters into a fuzzy logic controller; defining three membership functions for each input parameter in the fuzzy logic controller, corresponding to low, medium and high levels respectively; establishing a fuzzy rule base in the fuzzy logic controller; performing fuzzy reasoning on the input parameters by using a fuzzy reasoning method, and generating a fuzzy control output according to the fuzzy rule base; defuzzifying the fuzzy control output by using a centroid method, and converting it into a specific control instruction for sootblowing intensity and sootblowing frequency; and automatically adjusting the sootblowing device of each heating surface according to the control instructions for sootblowing intensity and sootblowing frequency obtained by the defuzzification process.

[0082] It should be noted that, first of all, the present invention forms a dynamic monitoring capability for each subsystem of the boiler by establishing a digital model of the boiler, based on the boiler's design parameters and real-time operating data. Each important component of the boiler, such as the heating surface, furnace, and air preheater, is included in the monitoring range of the digital model. Different from the timed soot cleaning strategy in the traditional method, the present invention can accurately obtain various performance indicators of the boiler operation, including flue gas temperature, flow, pressure, etc., through the collection of real-time data. These parameters reflect the operating status and cleanliness of the boiler, and provide an accurate basis for subsequent soot blowing operations.

[0083] Specifically, the present invention dynamically monitors the accumulation of ash and scale on the heating surface of the boiler through real-time calculation of the cleaning factor. As an important indicator reflecting the cleanliness of the heating surface, the value of the cleaning factor is directly linked to the heat transfer efficiency and operating performance of the boiler. When the cleaning factor is low, it means that there is more ash and scale on the heating surface and the heat transfer efficiency is reduced. At this time, the frequency and intensity of the soot blowing operation need to be increased to ensure that the thermal efficiency of the boiler does not decrease due to the accumulation of ash and scale. This dynamic adjustment based on real-time monitoring makes the soot blowing operation more accurate and avoids the energy waste caused by lack of experience or fixed cycles in traditional methods.

[0084] On this basis, the calculation of the heat transfer coefficient and the total heat transfer further improves the accuracy of the sootblowing operation. The heat transfer coefficient directly reflects the heat exchange efficiency of the boiler, while the total heat transfer indicates the actual heat exchange capacity of each heating surface. Through these indicators, the heat transfer status of each part of the boiler can be evaluated in real time, and a quantitative basis can be provided for the timing and cycle of sootblowing. When the heat transfer coefficient is low, it means that the ash scale on the heating surface has had a significant impact on the heat exchange, and its heat transfer capacity must be restored by increasing the sootblowing frequency. Through this precise monitoring and adjustment, the present invention not only reduces the frequency of sootblowing operations, but also ensures the high efficiency of sootblowing operations, avoiding energy waste caused by excessive sootblowing.

[0085] The innovation of the present invention also lies in the use of a fuzzy logic controller (FLC), which intelligently adjusts the sootblowing intensity and frequency through fuzzy reasoning of key parameters such as cleaning factor, heat transfer coefficient and total heat transfer. Unlike traditional timing or empirical control methods, the fuzzy logic controller can convert various input parameters (such as cleaning factor, heat transfer coefficient) into different levels of membership, and generate the best sootblowing operation instructions based on the current operating status of the boiler. The application of fuzzy control not only makes sootblowing control have intelligent decision-making capabilities, but also can avoid misoperation or untimely operation caused by human factors, significantly improving control accuracy and response speed.

[0086] Through fuzzy reasoning and defuzzification, the timing and cycle of soot blowing can be automatically adjusted according to the real-time status of boiler operation. Specifically, the fuzzy reasoning process can synthesize the fuzzy results of each input parameter, comprehensively evaluate the operating status of the boiler, and then defuzzify the fuzzy results through the center of gravity method to convert the fuzzy results into specific soot blowing intensity and frequency. This process not only ensures the real-time and accuracy of the soot blowing operation, but also can dynamically adjust the soot blowing operation according to the different operating status of the boiler, avoiding the inefficiency and waste caused by fixed cycle control.

[0087] The present invention has strong adaptability under the combustion conditions of multiple coal types. Different coal types have different combustion characteristics, such as ash content, combustion temperature, etc. These factors directly affect the thermal efficiency and ash accumulation of the boiler. The traditional soot blowing control method is usually unable to be dynamically adjusted according to the changes in coal types. The present invention can flexibly adjust the calculation method of the cleaning factor and the heat transfer coefficient according to parameters such as the ash content and combustion temperature of different coal types by real-time monitoring of the combustion characteristics of the coal types, and then adjust the soot blowing operation strategy. For example, when using high-ash coal types, the heating surface of the boiler is prone to accumulate more ash. At this time, the soot blowing system will automatically increase the soot blowing frequency to maintain the thermal efficiency of the boiler; when using low-ash coal types, the soot blowing frequency can be appropriately reduced to avoid unnecessary energy consumption. This adaptive adjustment based on coal types makes the soot blowing operation more in line with the actual operating state and combustion conditions of the boiler, ensuring that the boiler can operate stably and efficiently under the combustion environment of different coal types.

[0088] Through the comprehensive application of the above technical features, the present invention has demonstrated significant advantages over traditional methods in optimizing boiler sootblowing operations. Traditional timing control methods cannot be dynamically adjusted according to the real-time operating status of the boiler and the characteristics of the coal type, which often leads to energy waste or equipment wear. The present invention monitors and analyzes real-time parameters through intelligent algorithms, and accurately controls the intensity and frequency of sootblowing, which not only improves the heat transfer efficiency of the boiler and reduces energy waste, but also effectively extends the service life of the equipment. Especially in the application scenario of multiple coal types, the present invention can optimize and adjust according to the combustion characteristics of different coal types, ensure the stable operation of the boiler under complex working conditions, and further improve the economy and environmental protection of the boiler.

[0089] In the intelligent sootblowing control method of the present invention, the calculation of the operating performance index not only provides basic data for the monitoring of the boiler, but also provides an important basis for the adjustment of the sootblowing timing and cycle. Indicators such as the cleaning factor, heat transfer coefficient and total heat transfer reflect the accumulation of soot scale on the heating surface of the boiler, and the intelligent adjustment of the sootblowing timing and frequency depends on the changes in these indicators. Specifically, when the cleaning factor and heat transfer coefficient are low, it means that there is a lot of soot scale on the heating surface of the boiler, which affects the heat exchange efficiency. At this time, it will automatically determine the need to increase the frequency and intensity of the sootblowing operation based on these indicators to restore the heat transfer efficiency of the heating surface and prevent the overall thermal efficiency of the boiler from decreasing.

[0090] Furthermore, the calculation of heat transfer coefficient and total heat transfer can help accurately evaluate the heat transfer capacity of each heating surface of the boiler. Once the heat transfer coefficient is found to be lower than the set standard, it means that the accumulation of ash and scale has significantly affected the heat exchange. At this time, the timing and intensity of the soot blowing operation will be automatically adjusted through the intelligent algorithm. In the case of multiple coal combustion, due to the differences in ash content, combustion temperature and other characteristics of different coal types, these changes can be monitored in real time, and the calculation method of the cleaning factor and heat transfer coefficient can be dynamically adjusted, so as to more accurately optimize the soot blowing control strategy and ensure that the boiler can still maintain efficient operation under variable combustion conditions.

[0091] It should be noted that, through the introduction of the fuzzy logic controller, the present invention can intelligently adjust the intensity and frequency of soot blowing through real-time monitoring of performance indicators (cleaning factor, heat transfer coefficient and total heat transfer). Through fuzzy reasoning and defuzzification, these indicators are converted into specific control instructions, thereby providing an accurate adjustment scheme for actual operation. This process ensures that the soot blowing operation can not only respond to the operating status of the boiler, but also automatically adapt to the needs of different working conditions, avoiding excessive or insufficient soot blowing, and significantly improving the economy and environmental protection of the boiler.

[0092] Example 2

[0093] One embodiment of the present invention provides an intelligent soot blowing control system under multiple coal combustion conditions, comprising:

[0094] Model building module, used to build the digital model of the boiler control system and configure the input and output measurement point locations;

[0095] The data acquisition module is used to collect the inlet and outlet flue gas and working medium parameters of each heating surface of the boiler through sensors;

[0096] Index analysis module, used to calculate the boiler's operating performance index based on real-time parameters;

[0097] A dust and dirt judgment module is used to monitor the dust and dirt status and calculate the cleaning factor, heat transfer coefficient and total heat transfer of each heating surface according to the operating performance index;

[0098] The soot blowing control module is used to determine the best soot blowing time and soot blowing cycle based on the monitoring result of the soot pollution state by using an intelligent algorithm, and automatically control the soot blowing operation of each heating surface.

[0099] Example 3

[0100] An embodiment of the present invention is different from the first two embodiments in that:

[0101] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0103] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0104] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0105] Example 4

[0106] An embodiment of the present invention provides an intelligent soot blowing control method under multi-coal combustion conditions. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0107] The experiment was conducted in a high-power industrial boiler, which was configured with a furnace, convection heating surface, air preheater and exhaust system. Three different types of coal were selected for mixed combustion tests to ensure that the ash content of the coals was significantly different and the test results were representative. The boiler system is equipped with temperature sensors, pressure sensors, flow sensors and other equipment to monitor the operating status of each heating surface of the boiler in real time. The experimental data was processed and the control strategy was simulated through the MATLAB / Simulink simulation platform, and a digital twin model was built and connected to the actual boiler control system. The data collected by the sensor is fed back to the simulation platform in real time for real-time analysis and control.

[0108] The experimental steps of the traditional sootblowing method are as follows: at the beginning of the experiment, the boiler operation time is set to 100 hours, and low-ash coal, medium-ash coal and high-ash coal are selected, with a coal mixture ratio of 50%, 30% and 20%. After the boiler system is started, the fuel injection amount and air flow rate are adjusted to maintain the boiler under normal operating conditions. Data acquisition uses temperature sensors, pressure sensors and flow sensors to record boiler operation data every 12 hours, including furnace temperature, flue gas temperature, fuel consumption, exhaust gas temperature and other parameters. During the entire experiment, sootblowing operations are performed every 12 hours, with a fixed sootblowing amount and time, and the sootblowing intensity is set to medium, with a duration of 5 minutes. At the end of the experiment, parameters such as the total thermal efficiency, fuel consumption, equipment wear, cleaning factor and heat transfer coefficient of the boiler are recorded.

[0109] The experimental steps of the intelligent sootblowing method of the present invention are as follows. The experiment sets the boiler operation time to 100 hours, and the coal type configuration is a mixed combustion of low-ash coal, medium-ash coal and high-ash coal. The coal type mixing ratio is the same as the traditional method. After the boiler system is started, the fuel injection amount and the air flow rate are set to the same initial parameters. Data acquisition collects data of each heating surface of the boiler in real time through temperature sensors, pressure sensors and flow sensors, and the collection frequency is once per minute. During the experiment, the cleaning factor, heat transfer coefficient and total heat transfer index are calculated by the MATLAB / Simulink simulation platform to monitor the performance of each heating surface of the boiler in real time. The calculation formula is as follows: The cleaning factor, heat transfer coefficient and total heat transfer are calculated using the above-mentioned formulas. Based on these calculations, a fuzzy logic controller is used to optimize the sootblowing strategy according to real-time data. Each parameter is determined by fuzzy reasoning. The corresponding sootblowing intensity and frequency are then defuzzified using the center of gravity method, and the operating intensity and timing of the sootblowing device are finally adjusted. The experimental results are shown in Table 1.

[0110] Table 1 Comparison of experimental results

[0111]

[0112] Traditional methods often rely on empirical or timed sootblowing operations, which usually cannot be adjusted according to the real-time soot accumulation of the boiler, resulting in excessive or insufficient sootblowing. In contrast, the present invention accurately monitors key indicators such as the real-time cleanliness factor, heat transfer coefficient, and total heat transfer of each heating surface of the boiler, and implements intelligent control based on these data. Intelligent algorithms (such as fuzzy logic controllers) can adaptively adjust the sootblowing intensity and frequency according to the operating status of the boiler, thereby avoiding inefficient and wasteful sootblowing operations. In the experiment, the intelligent sootblowing method reduced unnecessary heat losses, improved thermal efficiency, and reduced energy consumption by optimizing the sootblowing strategy in real time.

[0113] The present invention can adjust the sootblowing timing in real time by dynamically monitoring the cleaning factor of the heating surface, effectively reducing the impact of soot on the heating surface and maintaining a high heat transfer efficiency. Under the traditional method, the dirt accumulation on the heating surface is not removed in time, resulting in a gradual decrease in heat transfer efficiency. The intelligent sootblowing method ensures that the heating surface is always kept in the best clean state during boiler operation by precisely controlling the sootblowing operation. Experimental results show that the intelligent sootblowing method improves the heat transfer coefficient of the boiler and enhances the overall heat exchange capacity of the boiler, thereby effectively improving the thermal efficiency and overall economy of the boiler.

[0114] Since the intelligent sootblowing operation can accurately adjust the sootblowing intensity and frequency, unnecessary heat loss is avoided. Experimental results show that the intelligent sootblowing method can significantly reduce fuel consumption and exhaust heat loss. Under the traditional method, the heat loss of the boiler is relatively fixed, and the actual cleanliness of the heating surface is often not taken into account during the sootblowing operation, thereby wasting fuel. By adjusting the sootblowing operation in real time, the method of the present invention not only improves the combustion efficiency of the boiler, but also reduces the heat loss in the exhaust during long-term operation, reflecting the dual advantages of energy saving and environmental protection.

[0115] The intelligent sootblowing method can reduce excessive sootblowing and equipment wear by controlling the sootblowing intensity and frequency, thereby extending the service life of boiler equipment. In traditional methods, since the sootblowing frequency is fixed and the actual state of the heating surface is not taken into account, excessive or insufficient sootblowing is prone to occur, resulting in increased wear and tear of equipment parts and shortening the service life of the equipment. The intelligent sootblowing method can achieve dynamic adjustment to ensure that sootblowing operations are only performed when necessary, reducing equipment wear and extending the service life of the boiler heating surface.

[0116] The technical solution of the present invention is designed with the characteristics of multiple coal types combustion in mind. The changes in the combustion characteristics, ash content and sulfur content of different coal types will affect the operating state of the boiler and the change in the cleaning factor. Traditional methods fail to adapt to the combustion characteristics of different coal types, which may lead to excessive or insufficient sootblowing. The intelligent sootblowing method can accurately control the sootblowing operation of each heating surface through real-time monitoring of coal types and dynamic analysis of ash pollution status, thereby coping with the challenges of boiler operation under different coal combustion conditions and improving the stability and adaptability of the boiler.

[0117] During the experiment, the sootblowing strategy was able to quickly adapt and adjust during boiler load fluctuations or coal type switching. Under traditional methods, boiler load fluctuations may lead to temperature instability, while the intelligent sootblowing system can adjust the sootblowing operation in real time, avoiding the problem of inefficiency in the boiler during load fluctuations. In addition, the intelligent control system can maintain efficient operation of the boiler under different fuels and operating conditions by continuously learning the boiler operation mode, greatly improving the adaptability and stability of the boiler.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent soot blowing control method under multi-coal combustion conditions, characterized in that: include: Establish a digital model of the boiler control system and configure the input and output measurement point locations; The inlet and outlet flue gas and working medium parameters of each heating surface of the boiler are collected through sensors; Calculate the boiler's operating performance indicators based on real-time parameters; According to the operating performance indicators, the dust pollution status is monitored and the cleanliness factor, heat transfer coefficient and total heat transfer of each heating surface are calculated; Based on the monitoring results of the soot pollution status, an intelligent algorithm is used to determine the best soot blowing time and soot blowing cycle, and the soot blowing operation of each heating surface is automatically controlled.

2. The intelligent soot blowing control method under multi-coal combustion state according to claim 1, characterized in that: The said establishing the digital model of the boiler control system includes creating a simulation environment of the boiler control system; Input the physical characteristics of each subsystem according to the boiler design parameters; The subsystem includes a furnace control system, a convection heating surface control system, an air preheater control system and a smoke exhaust control system; Connect the models of each subsystem through signal lines to form a digital twin model of the overall boiler control system; The input and output measuring point positions are configured in the digital model, including temperature sensors and pressure sensors at the furnace inlet and outlet, flow sensors at the inlet and outlet of each level of the heating surface, temperature sensors at the inlet and outlet of the air preheater, and flow sensors at the exhaust outlet.

3. The intelligent soot blowing control method under multi-coal combustion state according to claim 2, characterized in that: The calculation of the boiler operating performance index includes calculating the overall thermal efficiency of the boiler based on the fuel flow rate and the fuel calorific value, which is the ratio of the input fuel energy to the output steam energy; Based on the data of the furnace outlet temperature sensor, the average temperature of the flue gas at the furnace outlet is calculated; Based on the data of the outlet temperature sensors at each level of the heating surface, the average temperature of the flue gas at the outlet of the heating surface is calculated; Based on the data from the fuel flow sensor, the fuel consumption rate is calculated to reflect the fuel usage per unit time; Based on the data from the water flow sensor, the water flow consumption rate is calculated to reflect the water usage per unit time; Calculate the boiler's heat input parameters based on data from furnace inlet temperature and pressure sensors; Calculate the air preheating efficiency based on the air preheater inlet and outlet temperature data; Calculate the heat loss of the smoke exhaust system based on the smoke exhaust outlet flow rate and temperature data.

4. The intelligent soot blowing control method under multiple coal combustion conditions according to claim 3, characterized in that: The cleaning factor is expressed as, Among them, CF j represents the cleaning factor of the jth heating surface, η th Represents the overall thermal efficiency of the boiler, η ap Indicates the air preheating efficiency, L muc Indicates the mechanical incomplete combustion loss, Q ex represents the heat loss of the smoke exhaust system, Q in represents heat input, T h，out，j represents the outlet flue gas temperature of the jth heating surface, T h，out，avg Indicates the average temperature of flue gas at the outlet of the heating surface.

5. The intelligent soot blowing control method under multi-coal combustion state according to claim 4, characterized in that: The heat transfer coefficient is expressed as, Among them, HTC j represents the heat transfer coefficient of the jth heated surface, A j represents the heat transfer area of ​​the jth heating surface.

6. The intelligent soot blowing control method under multi-coal combustion state according to claim 5, characterized in that: The total heat transfer is expressed as, Q total，j =HTC j ×A j ×(T h，out ,j-T f，out ,j) Among them, Q total，j Represents the total heat transfer of the jth heated surface.

7. The intelligent soot blowing control method under multi-coal combustion state according to claim 6, characterized in that: Determining the optimal soot blowing time and soot blowing cycle includes inputting the cleaning factor, heat transfer coefficient and total heat transfer of each heating surface as input parameters into the fuzzy logic controller; In the fuzzy logic controller, three membership functions are defined for each input parameter, corresponding to low, medium, and high levels; In the fuzzy logic controller, a fuzzy rule base is established; Perform fuzzy reasoning on input parameters through fuzzy reasoning method, and generate fuzzy control output according to fuzzy rule base; The fuzzy control output is defuzzified by the centroid method and converted into specific control instructions for soot blowing intensity and frequency; The soot blowing devices of each heating surface are automatically adjusted according to the control instructions of soot blowing intensity and soot blowing frequency obtained by defuzzification processing.

8. A system using the intelligent soot blowing control method under multiple coal combustion conditions as claimed in any one of claims 1 to 7, characterized in that: include: Model building module, used to build the digital model of the boiler control system and configure the input and output measurement point locations; The data acquisition module is used to collect the inlet and outlet flue gas and working medium parameters of each heating surface of the boiler through sensors; Index analysis module, used to calculate the boiler's operating performance index based on real-time parameters; A dust and dirt judgment module is used to monitor the dust and dirt status and calculate the cleaning factor, heat transfer coefficient and total heat transfer of each heating surface according to the operating performance index; The soot blowing control module is used to determine the best soot blowing time and soot blowing cycle based on the monitoring result of the soot pollution state by using an intelligent algorithm, and automatically control the soot blowing operation of each heating surface.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent sootblowing control method under multi-coal combustion conditions described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent sootblowing control method under multi-coal combustion conditions described in any one of claims 1 to 7 are implemented.

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