Time-sharing measuring device and measuring method for carbon content in flue fly ash

Abstract

The invention relates to a time-sharing measurement device for carbon content in flue fly ash. The device mainly comprises a sampler, a detection part and a control module, the sampler is provided with a plurality of sampling pipes, each sampling pipe is provided with an adjusting valve, time-sharing sampling is achieved through PLC logic control, and full-time-period fine coverage is ensured. The separation component separates fly ash from gas and collects a fly ash sample, and the detection component integrates a spectral analysis unit and a data processing unit, performs spectral analysis on the fly ash sample and calculates the carbon content. According to the method, multiple objective functions such as the fly ash carbon content, the combustion efficiency and the system stability are optimized at the same time by selecting multi-dimensional equipment process parameters and fly ash measurement data and adopting an evolutionary optimization algorithm, global optimization is achieved, the dependency relationship among the process parameters is fully considered, falling into a local optimal solution is avoided, the optimization efficiency is improved, and the method is suitable for popularization and application. The method can systematically coordinate the contradictory relation among all targets, achieves the maximization of the operation efficiency of the system while detecting the fly ash, and enables the combustion system to operate stably.

Description

Technical Field

[0001] The present application relates to the technical field of fly ash carbon content measurement, and in particular to a time-sharing measurement device and method for flue fly ash carbon content. Background Art

[0002] In the field of modern industrial production, especially in energy-intensive industries such as coal-fired power generation, the carbon content of boiler fly ash is a key indicator for measuring combustion efficiency and energy utilization efficiency, and its accurate, online and real-time monitoring is becoming increasingly important. With the deepening of global attention to energy conservation, emission reduction and efficient use of resources, optimizing the combustion process, improving energy conversion efficiency and reducing pollutant emissions have become core issues in the development of the industry. Accurate measurement of the carbon content of boiler fly ash can directly reflect the economic efficiency of unit operation, guide the timely adjustment and optimization of combustion conditions, thereby reducing coal consumption and improving economic benefits.

[0003] Although a variety of online measurement technologies for fly ash carbon content have been proposed and applied in practice, including microwave absorption method, infrared reflection method, laser induced breakdown spectroscopy, etc., these methods are generally sensitive to changes in fly ash mineral content and coal type, resulting in large deviations in measurement results and lack of universality. Specifically, changes in fly ash mineral content and coal type will cause the above methods to produce deviations of 30%, 50% and 40% respectively, seriously affecting the accuracy and reliability of the measurement. In addition, traditional measurement methods such as the ignition loss method, although simple in principle, are cumbersome to operate, time-consuming and labor-intensive, and cannot provide real-time data, making it difficult to meet the needs of modern industrial production for real-time monitoring and adjustment of the combustion process.

[0004] In terms of online measurement technology, although the capacitance method and microwave method have achieved continuous monitoring of the carbon content of fly ash to a certain extent, these methods also face many challenges. The capacitance method is easily disturbed by factors such as the impact force of falling ash and the temperature of fly ash, resulting in unstable measurement accuracy; the microwave method is difficult to ensure long-term stable operation and high-precision measurement due to problems such as easy clogging of the device and the great influence of environmental factors on the measurement results. More importantly, most of the existing measurement devices can only provide the overall average value of the carbon content of fly ash, and cannot meet the needs of time-sharing measurement of changes in the carbon content of fly ash in different time periods. In actual production, the combustion conditions change significantly over time. Understanding the time distribution characteristics of the carbon content of fly ash is crucial for in-depth analysis of the combustion process, accurate positioning of the causes of fluctuations in combustion efficiency, and formulation of targeted optimization strategies.

[0005] In summary, developing a device that can overcome the limitations of existing technologies and achieve high-precision, online, real-time and time-sharing measurement of the carbon content of flue fly ash is of great significance for improving combustion efficiency and optimizing energy utilization, and is a technical problem that needs to be urgently solved in the current industrial production field. Summary of the invention

[0006] The purpose of this application is to provide a device for measuring the carbon content in flue gas fly ash at different times, which can achieve sampling at different times, efficient separation, accurate detection and automatic control, solve the problems of cumbersome operation, easy blockage of sampling pipes, low accuracy and inability to measure at different times in the prior art, and can provide richer data support for optimizing the combustion process. The above purpose is achieved through the following technical solutions. The device for measuring the carbon content in flue gas fly ash at different times of this application includes a sampler, a detection component and a control module;

[0007] The sampler includes more than two sampling pipes, a separation component and a cleaning component. The openings of the sampling pipes are arranged at multiple different positions. Each sampling pipe is provided with a regulating valve, and the regulating valve controls the flow rate and opening and closing of the sampling pipe, and ensures that at most only one sampling pipe opening is open within a period of time. Each sampling pipe includes at least one separation component, and the separation component separates fly ash from gas and collects the fly ash. The cleaning component includes a purging pipe, and the purging pipe provides clean gas to independently clean different sampling pipes and separation components;

[0008] The detection component includes a spectral analysis unit and a data processing unit. The spectral analysis unit performs spectral analysis on the collected fly ash to obtain the spectral characteristic information of the fly ash; the data processing unit calculates the carbon content of the fly ash according to the spectral characteristic information and the corresponding relationship model between the spectral characteristics and the carbon content.

[0009] In one embodiment, the opening of the sampling pipe is conical.

[0010] In one embodiment, the gas outlet of the separation component is connected to the gas inlet of the cleaning component.

[0011] In one embodiment, it further includes a multi-channel control valve. When one sampling pipe is sampling, the multi-channel control valve controls the gas outlet of the separation component to communicate with the purging pipe of another sampling pipe.

[0012] In one embodiment, the control module further includes a data statistics unit. The data statistics unit includes a statistical model, and the statistical model establishes a corresponding relationship between the process parameters of the equipment and the carbon content in the fly ash according to the obtained fly ash measurement data.

[0013] In one embodiment, the control module further includes a data statistics unit. The data statistics unit includes a statistical model, and the statistical model establishes a corresponding relationship between the process parameters of the equipment and the carbon content in the fly ash according to the obtained fly ash measurement data.

[0014] In one embodiment, the control module further includes an equipment process adjustment unit. The equipment process adjustment unit includes a process adjustment model, and automatically outputs an adjustment signal for the equipment process according to the obtained process parameters of the equipment and the fly ash measurement data.

[0015] In one embodiment, it further includes performing a mutation operation on the new individuals generated after crossover.

[0016] In one embodiment, it further includes calculating the fitness of the new individuals, and selecting the new generation of individuals according to the fitness, and retaining the individuals with higher fitness to enter the next generation.

[0017] In addition, the present application further provides a method for measuring the carbon content in flue gas fly ash in real time, including:

[0018] Using the aforementioned device for measuring the carbon content in flue gas fly ash in real time, the regulating valve is arranged at the inlet end of the sampling pipe. One sampling pipe in the sampler is opened through the regulating valve, and the other sampling pipes are closed;

[0019] The gas passing through the sampling pipe enters the separation component to separate the fly ash from the gas and collect the fly ash;

[0020] Performing spectral analysis on the collected fly ash to obtain the spectral characteristic information of the fly ash; calculating the carbon content of the fly ash according to the spectral characteristic information and the corresponding relationship model between the spectral characteristics and the carbon content;

[0021] Wherein, when collecting and detecting in one sampling pipe, the other sampling pipes are cleaned.

[0022] In one embodiment, while closing one sampling pipe, another sampling pipe that has been cleaned is opened for sampling and detection.

[0023] Compared with the prior art, the present application has the following beneficial effects:

[0024] Through the unique design of setting two or more sampling pipes, and combining the electric regulating valve with the advanced control system, the present application realizes the accurate sampling of flue gas fly ash in real time. It ensures the accurate acquisition of the carbon content in fly ash at different time periods, greatly enriches the data dimension of the combustion process analysis, makes it possible to conduct refined evaluation and optimization adjustment of the combustion efficiency, and provides data support for improving the overall combustion economy.

[0025] By adopting the separator and the cleaning component, it realizes the seamless and efficient transfer of fly ash from the flue to the analysis system. It not only greatly improves the conveying efficiency of fly ash, but also through the effective action of the cyclone separator, realizes the complete separation of fly ash from the gas, reduces the loss and pollution of fly ash in the conveying and subsequent treatment processes, thereby ensuring the accuracy and reliability of the measurement results.

[0026] By using an advanced spectral analyzer combined with an efficient data processing module, it is possible to achieve rapid and high-precision detection of the carbon content in fly ash, complete the analysis of a large number of samples in a short time, and through the built-in intelligent algorithm, generate a dynamic curve graph of the carbon content in fly ash changing with time in real time, providing an intuitive and easy-to-understand combustion process monitoring interface for operators, facilitating timely adjustment of the combustion strategy and optimizing the combustion effect.

[0027] The entire measurement device control system is uniformly scheduled and managed, realizing full-process automatic control from sampling, conveying, separating to detection and analysis. It can also be combined with the production process control system to automatically adjust the production process, not only simplifying the operation process, reducing human operation errors, reducing the labor intensity of operators, but also improving work efficiency. In summary, through a series of improved technical features, this application realizes the time-sharing precise measurement, efficient and stable processing, rapid and high-precision detection, and highly automated control of the carbon content in flue gas fly ash, providing a new technical solution for improving combustion efficiency, optimizing energy utilization, and reducing operating costs. Brief Description of the Drawings

[0028] Figure 1 is a schematic structural diagram of a time-sharing measurement device for carbon content in flue gas fly ash in an embodiment of this application;

[0029] Figure 2 is a schematic structural diagram of a time-sharing measurement device for carbon content in flue gas fly ash in another embodiment of this application;

[0030] Figure 3 is a three-dimensional parameter space trajectory diagram of the traditional method;

[0031] Figure 4 is a three-dimensional parameter space trajectory diagram of the algorithm in the time-sharing measurement device for carbon content in flue gas fly ash of this application;

[0032] Figure 5 is a dual-objective optimization comparison diagram of the algorithm and the traditional method in the time-sharing measurement device for carbon content in flue gas fly ash of this application;

[0033] Figures 6-8 are respectively the dynamic stability comparison diagrams of the algorithm and the traditional method in the time-sharing measurement device for carbon content in flue gas fly ash of this application under different process parameters.

[0034] Explanation of the Reference Numerals in the Drawings: 100, sampler; 110, sampling tube; 120, separation component; 130, cleaning component; 131, purge tube; 140, regulating valve; 200, detection component; 210, spectral analysis unit; 220, data processing unit; 300, control module; 400, flue. Detailed Description of the Embodiments

[0035] To make the above objects, features, and advantages of the present application more apparent and understandable, the following will describe in detail the specific embodiments of the present application with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only for explaining the present application and not for limiting the present application. Additionally, it should be noted that for the convenience of description, only the parts related to the present application rather than all the structures are shown in the drawings. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0036] The terms "comprise" and "have" and any variations thereof in the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0037] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0038] In coal-fired power generation and many industrial combustion processes, as a by-product of combustion, the carbon content of flue gas fly ash is not only a key indicator for evaluating combustion efficiency and energy utilization level, but also directly related to the feasibility and economic benefits of subsequent fly ash resource utilization. Traditional methods for measuring the carbon content of fly ash, such as the loss-on-ignition method, although they can provide certain references, have limitations such as cumbersome operation, long time consumption, and inability to monitor in real time, making it difficult to meet the requirements of modern industry for refined management of the combustion process. Existing online measurement technologies, such as the capacitance method and the microwave method, are also restricted in their wide application due to problems such as susceptibility to environmental factors, insufficient measurement accuracy, or easy clogging and failure of the device. In view of this, it is particularly important to develop a device that can efficiently, accurately, and in real time measure the carbon content of flue gas fly ash at different times. The flue gas fly ash carbon content time-sharing measurement device described in the present application is designed specifically for the above problems, aiming to achieve precise and continuous monitoring of the carbon content of flue gas fly ash through innovative technical means, providing a basis for optimizing and adjusting the combustion process and comprehensively utilizing fly ash. Next, the flue gas fly ash carbon content time-sharing measurement device of the present application will be introduced in detail. Please refer to Figures 1 to 2As shown in the figure, the fly ash carbon content in-situ measurement device in a preferred embodiment of the present application includes a sampler 100, a detection component 200, and a control module 300. The sampler 100 includes more than two sampling tubes 110, a separation component 120, and a cleaning component 130. The openings of the sampling tubes 110 are arranged at multiple different positions. Each sampling tube 110 is provided with a regulating valve 140. The regulating valve 140 controls the flow rate and opening / closing of the sampling tube 110, and ensures that at most only one sampling tube 110 is open within a period of time. Each sampling tube 110 includes at least one separation component 120. The separation component 120 separates fly ash from gas and collects the fly ash. The cleaning component 130 includes a purge tube 131. The purge tube 131 provides clean gas to independently clean different sampling tubes 110 and separation components 120. The detection component 200 includes a spectral analysis unit 210 and a data processing unit 220. The spectral analysis unit 210 performs spectral analysis on the collected fly ash to obtain spectral characteristic information of the fly ash. The data processing unit 220 calculates the carbon content of the fly ash according to the spectral characteristic information and the corresponding relationship model between the spectral characteristics and the carbon content.

[0039] As a device for monitoring the combustion process, the fly ash carbon content in-situ measurement device for the flue 400 is mainly composed of three core parts: a sampler 100, a detection component 200, and a control module 300, which can achieve efficient, accurate, and real-time measurement of the carbon content of fly ash. First, the sampler 100 is configured with more than two sampling tubes 110. The openings of these sampling tubes 110 are arranged at multiple different positions in the flue 400 to capture fly ash samples in different regions and at different time periods. Each sampling tube 110 is equipped with a regulating valve 140, which can flexibly control the flow rate of the sampling tube 110, open and close the sampling tube 110, and is designed to ensure that at most only one sampling tube 110 is in the open state within a period of time, thus achieving the goal of time-sharing sampling. Each sampling tube 110 also has at least one separation component 120 built in, which is used to separate fly ash from gas and accurately collect fly ash samples, providing high-quality raw materials for subsequent analysis.

[0040] In the prior art, real-time detection usually selects intermittent time periods or accumulates fly ash for a period of time for detection. This may result in no detection of fly ash at some time points, or taking the fly ash of a period of time as the result of one time point, leading to poor real-time performance. The present application combines the design of multiple positions and multiple sampling tubes 110, which can achieve the integrity of time-point sampling without omission, and can also achieve a fine division of time periods, so as to achieve a fine coverage of the entire sampling time period.

[0041] Since the present application uses multiple sampling tubes 110 and multiple positions, to ensure the accuracy of the test, it is necessary to ensure that the sampling tube 110 paths and the separation device do not affect each other during multiple tests, ensure the cleanliness and efficiency of the sampling process, effectively prevent cross-contamination and blockage problems during the sampling process. The sampler 100 is also equipped with a cleaning component 130. The purge tube 131, as the core component, provides clean gas to independently clean different sampling tubes 110 and the separation component 120. In terms of the detection component 200, this device integrates two major modules: a spectral analysis unit 210 and a data processing unit 220. The spectral analysis unit 210 uses advanced spectral analysis technology to conduct a detailed spectral analysis on the collected fly ash samples and extract the spectral characteristic information of the fly ash. The data processing unit 220 then calculates the carbon content data of the fly ash through algorithms based on this spectral characteristic information and in combination with the pre-established correspondence model between spectral characteristics and carbon content.

[0042] The present application first realizes real-time detection and time-sharing sampling. By configuring multiple sampling tubes 110 and using PLC logic control to achieve time-sharing sampling, this device can complete the detection of fly ash within a smaller time unit, thus getting rid of the time limit of traditional detection methods and achieving real-time detection in the true sense. It not only improves the timeliness of detection but also provides the possibility for refined analysis of the combustion process, enabling operators to adjust the combustion strategy in a timely manner according to real-time data and optimize the combustion efficiency. The introduction of the cleaning component 130, especially the design of the purge tube 131, effectively avoids pollution problems during the sampling process. The regular purge of clean gas not only keeps the sampling tubes 110 and the separation component 120 clean but also ensures the purity of the fly ash samples, thereby improving the accuracy of detection. At the same time, due to avoiding cross-contamination, the stability and reliability of the detection results are also improved. In summary, the fly ash carbon content time-sharing measurement device for the flue 400 realizes accurate and real-time measurement of the fly ash carbon content in the flue 400 through its unique sampler 100 design, efficient detection component 200, and intelligent control module 300.

[0043] The opening of the sampling tube 110 is designed as a conical structure. The conical opening takes into account the complex airflow environment in the flue 400. Through unique geometric shape optimization, it minimizes the interference with the airflow distribution in the flue 400. Its tapered shape can effectively guide the airflow in the flue 400, reducing the vortex and turbulence phenomena caused by the presence of the sampling tube 110. This design avoids the airflow disorder that may be caused by the opening of traditional straight tubes, thus ensuring that fly ash particles can flow in the flue 400 in their natural state, improving the probability and accuracy of fly ash particles being captured during the sampling process. The design of the conical opening also optimizes the airflow passability, enabling the gas in the flue 400 to flow more smoothly through the sampling tube 110, reducing the pressure loss caused by airflow obstruction. This not only helps to maintain a stable airflow state in the flue 400 but also reduces the increase in energy consumption that may be caused by the increase in airflow resistance, improving the operating efficiency of the entire system. The design of the conical opening also considers the convenience of cleaning and maintenance. Its smooth surface and gradually shrinking shape make it difficult for fly ash particles to accumulate at the opening during the sampling process, reducing the blockage problem caused by fly ash accumulation. At the same time, this design also facilitates the operator to use cleaning tools to regularly clean the sampling tube 110, ensuring the continuous and efficient progress of the sampling process.

[0044] The gas outlet of the separation component 120 can be stably and hermetically connected to the gas inlet of the cleaning component 130 through a connection structure. It can be selectively connected to a gas storage tank to store standby gas according to actual needs, or directly connected to the purge pipe 131 to achieve an immediate purging function, enabling the gas transmission path between the separation component 120 and the cleaning component 130 to be quickly adjusted according to specific application scenarios and operation requirements. Whether it is connected to a gas storage tank for gas storage or directly connected to the purge pipe 131 for immediate purging, it improves the flexibility and adaptability of the system. The separation component 120 and the cleaning component 130 are integrated through a standardized and modular connection method, which helps to promote the modular design of the entire system.

[0045] The system is also integrated with a multi-channel control valve. During the operation of the system, when a sampling tube 110 is in the sampling working state, the multi-channel control valve can quickly respond according to the preset logic and automatically control the gas outlet of the separation component 120 to communicate with the purge tube 131 corresponding to another sampling tube 110 in the non-sampling state (i.e., the idle state). Through this intelligent switching mechanism, the efficient coordination and parallel processing of sampling and purge operations are realized, which can improve the overall efficiency of sampling and purge, and achieve parallel operation. The traditional sampling and purge processes are often sequential, that is, after one sampling tube 110 completes sampling, the other sampling tube 110 can be purged. This method has obvious waste of waiting time. The setting of the multi-channel control valve breaks this limitation, enabling the sampling and purge operations to be carried out simultaneously. For example, when one sampling tube 110 continuously collects fly ash, another sampling tube 110 can be purged synchronously to remove residual fly ash and impurities in the tube and prepare for the next sampling, greatly shortening the entire sampling cycle and improving the overall operation efficiency of the system. Reducing the process connection time, the fast switching function of the multi-channel control valve reduces the connection time between the sampling and purge processes, can complete the connection switching between the gas outlet and the purge tubes 131 of different sampling tubes 110, ensuring the continuity and smoothness of the sampling and purge processes, and further improving the work efficiency. Through the reasonable scheduling of the multi-channel control valve, each sampling tube 110 can be fully utilized. During the sampling process, the sampling tube 110 in the non-sampling state can be purged and maintained in a timely manner. The multi-channel control valve can dynamically adjust the allocation of gas resources according to the actual needs of sampling and purge. When a sampling tube 110 needs more gas for sampling, it can give priority to ensuring its gas supply; during the purge process, it can also ensure that the purge tube 131 obtains sufficient gas flow to achieve an effective purge effect. This intelligent gas resource allocation method makes the system resources more reasonably utilized and improves the resource utilization efficiency. The precise control of the multi-channel control valve ensures the independence of the sampling and purge processes. When one sampling tube 110 is sampling, the gas outlet of the separation component 120 communicates with the purge tube 131 of another sampling tube 110, avoiding the mutual interference and cross-contamination between the sampling gas and the purge gas, ensuring the accuracy and reliability of the sampling data. This helps to improve the repeatability and comparability of the sampling data, providing a more accurate and reliable basis for subsequent data analysis and processing.

[0046] The cleaning component 130 further includes a discharge pipe, the end of which directly leads to the flue 400, forming a complete and efficient waste discharge system after cleaning. The added discharge pipe directly leading to the flue 400 enables the waste after cleaning to quickly and smoothly be discharged into the flue 400 through the discharge pipe without the need for intermediate processing, greatly simplifying the waste treatment process, reducing the workload of operators, and improving the overall work efficiency. The setting of the discharge pipe directly leading to the flue 400 makes the connection between the cleaning component 130 and the flue 400 closer, reducing the occupied space of intermediate pipes and equipment. The compact design is conducive to the rational layout of the entire system in a limited space, improving the space utilization rate, making the equipment installation more flexible, and being able to meet the requirements of different site conditions.

[0047] The detection component 200 can further include a sampling tube 110 cleanliness detection component 200, which integrates sensor technology and intelligent algorithms to detect the inner wall cleanliness of the sampling tube 110 in real time and accurately, capture the tiny particles, impurities and possible contaminants remaining on the inner wall of the sampling tube 110, and transmit the detection data to the control system in real time. The intelligent algorithm then quickly analyzes and processes these data to accurately determine whether the cleanliness state of the sampling tube 110 meets the preset cleanliness standard, thus providing a strong guarantee for the reliable operation of the entire detection system. The sampling tube 110 cleanliness detection component 200 can timely detect the cleanliness problems of the sampling tube 110, ensure that the sampling tube 110 is in a clean state before sampling, effectively avoid the detection errors caused by the contamination of the sampling tube 110, and improve the accuracy and reliability of the detection results. When the cleanliness of the sampling tube 110 does not meet the requirements, it may cause problems such as blockage of the sampling tube 110 and poor air flow, which will in turn affect the normal operation of the entire detection system. The sampling tube 110 cleanliness detection component 200 can detect these potential problems in advance and issue an alarm in time to remind the operator to take corresponding cleaning measures, avoiding system failures caused by problems with the sampling tube 110 and improving the operation stability and reliability of the system.

[0048] The device structure provided by this application realizes the fine division and accurate measurement of time periods. However, due to the fluctuations in process conditions, changes in fly ash composition, and complex interrelationships during the combustion process, traditional optimization methods are often difficult to meet the multi-dimensional and multi-objective optimization requirements. Most conventional optimization methods rely on a single objective function for optimization. This method usually cannot take into account all key parameters in multi-objective optimization problems, easily leading to the neglect of some parameters or unbalanced optimization. In addition, traditional optimization methods such as the gradient descent method and genetic algorithm often face problems such as long calculation time and local optimal solutions. Especially in multi-dimensional spaces, the optimization results are easily troubled by local optimal solutions. Therefore, there are obvious deficiencies in the optimization of dynamic and complex systems in the prior art.

[0049] Based on the above content, the present application further provides relevant control strategies. In the system, a process adjustment model is also integrated according to the structure of the device, and multi-dimensional parameters are selected for comprehensive consideration. The process parameters of the device include a, b, and c, and the measurement data of fly ash include x, y, and z. The three process parameters of the device and the three measurement data of fly ash with the highest relevance are selected and applied to the adjustment model. Define a as the first process parameter, b as the second process parameter, c as the third process parameter, x as the first fly ash data, y as the second fly ash data, and z as the third fly ash data, all of which are the change ratios relative to the standard value. For example, the process parameters of the device are selected as temperature, air flow rate, and oxygen content in the gas, and the fly ash measurement data are selected as carbon content, fly ash content, and particle size. Of course, in different test scenarios, more relevant data can be selected accordingly.

[0050] To solve this problem, the present application adopts an evolutionary optimization algorithm based on multi-objective optimization. By comprehensively considering the relationship between the device process parameters and fly ash data, multiple objective functions are optimized simultaneously to ensure the corresponding relationship between the process parameters and fly ash data, so as to be able to perform more accurate balance adjustment. The optimal solution is adaptively searched through the evolutionary process to avoid falling into local optima and finally achieve the global optimization goal. The specific implementation steps are as follows:

[0051] Define the objective function and initialization. The objective function is the relationship between the process parameters and fly ash data. Let the objective function be in the form of a weighted combination of multiple objectives, and each objective represents a different optimization goal. The formula is expressed as:

[0052]

[0053] In the calculation formula of the objective function , is the relationship between the process parameter and the carbon content of fly ash, aiming to minimize the error between the process parameter and the carbon content of fly ash, that is, to ensure that the gap between the carbon content of fly ash and the expected value is minimized under different process conditions.

[0054] In one embodiment, the relationship between the process parameter and the carbon content of fly ash is calculated by calculating the relative error between the actual measured value and the target value to ensure that the error between the target value and the actual carbon content of fly ash is minimized. The calculation method is expressed as:

[0055]

[0056] In the formula, is the carbon content of fly ash measured by the current process parameter ; is the target carbon content of fly ash, which is a reference value calculated based on the fly ash data and is usually calculated by historical data or theoretical models. For example, it is set to 0.1.

[0057] In the calculation formula of the said objective function , is the optimization objective of combustion efficiency, aiming to minimize energy consumption and improve combustion efficiency, reduce fuel consumption by optimizing process parameters, and ensure the stability of the combustion process at the same time. In one embodiment, the optimization objective of combustion efficiency reflects the optimization degree of combustion efficiency by calculating the gap between the maximum theoretical efficiency and the actual efficiency, considers the maximization of combustion efficiency, uses relative combustion efficiency to eliminate the influence brought by the change of process parameters, enables the combustion optimization to be adaptively adjusted under different process conditions, and avoids the fluctuation of combustion efficiency caused by the change of equipment characteristics or coal types. The calculation method is expressed as:

[0058]

[0059] In the formula, is the maximum value of the theoretical combustion efficiency, which is usually determined by factors such as combustion conditions, coal types, equipment types, etc. For example, it is set to 0.85; is the actual combustion efficiency under the current process parameters. For example, it is set to 0.63.

[0060] In the calculation formula of the said objective function , is the stability objective of the system, aiming to ensure the stable operation of the equipment process, and ensuring the system operation stability can reduce the failure rate and maintenance cost. In one embodiment, the stability objective of the system is expressed by minimizing the fluctuation of the equipment process to ensure the long-term stability of the equipment during the combustion process. The calculation method is expressed as:

[0061]

[0062] In the formula, are the process parameter values at the th moment respectively. Define as the first process parameter value, as the second process parameter value, as the third process parameter value; are the process parameter values at the th moment respectively. Define as the fourth process parameter value, as the fifth process parameter value, as the sixth process parameter value; is the monitoring time period. In this embodiment, the stability objective of the system evaluates the stability of the system by calculating the fluctuation amplitude of the process parameters. If the process parameters change little within multiple time steps, it indicates that the system is relatively stable, otherwise it indicates that the system is unstable.

[0063] In the said objective function In the calculation formula of is the weight of the objective function, reflecting the importance of carbon content in fly ash, combustion efficiency, and system stability in the entire optimization process, and is defined as is the weight of the first objective function, is the weight of the second objective function, is the weight of the third objective function, satisfying . In a specific embodiment, according to different production objectives (such as energy conservation, improving combustion efficiency, or reducing system fluctuations), the weight coefficients can be adjusted. For example, set to 0.3, to 0.5, to 0.2.

[0064] Based on the above model, the parameter evolutionary optimization is carried out. First, the population is initialized. According to the initial values of the process parameters and the initial measured values of the fly ash data , the initial population is generated. Each individual is represented as a candidate solution, which contains different process parameters and fly ash data. The fitness of each individual in the initial population is evaluated by the objective function . The roulette wheel selection mechanism is adopted to select individuals from the current population according to the fitness, and individuals with higher fitness are preferentially selected for reproduction. The selected individuals are subjected to a crossover operation to generate new individuals. The purpose of the crossover operation is to generate a new solution space, thereby expanding the search range. The crossover method is a linear combination of process parameters and fly ash data;

[0065] Since there are interdependent relationships among process parameters (for example, the relationship between temperature and flow rate has a direct impact on the carbon content in fly ash), in traditional evolutionary algorithms, the crossover operation usually generates offspring individuals by linearly combining the process parameters of the parent individuals, but this method cannot fully consider the complex non-linear relationships among the carbon content in fly ash, combustion efficiency, and system stability;

[0066] To improve the effectiveness of the crossover operation, this application adopts a crossover operation based on the dependence relationship of process parameters, and enhances the mutual dependence among parameters during the crossover process through a weighted crossover strategy. Specifically, the dependence relationship among process parameters is determined through correlation analysis of historical data. Assuming that the process parameter has a strong relationship with and , higher crossover weights can be assigned to them. Set the dependence relationship matrix as:

[0067]

[0068] In the formula, represents the parameter The degree of dependence between itself (parameter ), and the value range is [0,1]. Similarly, represents the degree of dependence between parameter and parameter . represents the degree of dependence between parameter and parameter , and so on. In a specific embodiment, the degree of dependence is calculated by performing a correlation analysis on historical process data to calculate the degree of dependence between process parameters. Specifically, the Pearson correlation coefficient is used to quantify the linear relationship between two variables. For example, The calculation method of

[0069]

[0070] In the formula, is the value of the first process parameter for the th measurement (corresponding to the first sample), is the value of the second process parameter for the th measurement (corresponding to the second sample), is the mean value of the first process parameter values of all samples, is the mean value of the second process parameter values of all samples, is the number of samples (i.e., the total number of measurements). Please compare Figure 3 and Figure 4 . Through the visualization of the three-dimensional parameter space trajectory, compare the differences in the process parameter exploration capabilities between the traditional optimization method and the algorithm of this application, and verify the improvement effect of the improved crossover strategy on the global search efficiency. The experimental results show that the parameter search trajectory of the traditional method presents a linear aggregation feature and is limited to a narrow local area, while the parameter distribution of this technology shows a directional spatial diffusion characteristic and achieves a more comprehensive spatial coverage in all three process parameter dimensions, indicating the core advantage of this technology's algorithm in breaking through the local optimum limit. The multi-dimensional collaborative search ability stems from the weighted crossover mechanism guided by the dependence relationship matrix, which enables the parameter optimization process to fully consider the non-linear coupling characteristics between process parameters.

[0071] Furthermore, based on the above dependence relationship, weighted crossover is used to generate the process parameters of new individuals. The crossover operation is expressed as:

[0072]

[0073]

[0074]

[0075] In the formula, is a randomly generated crossover coefficient, e.g., set to 0.3;

[0076] and are the process parameters of the parent individuals, i.e., the process parameters of the parent individuals in the previous iteration; set as the process parameter of the first parent individual, as the process parameter of the second parent individual, as the process parameter of the third parent individual, as the process parameter of the fourth parent individual, as the process parameter of the fifth parent individual, as the process parameter of the sixth parent individual;

[0077] is the process parameter after the first crossover operation, is the process parameter after the second crossover operation, is the process parameter after the third crossover operation.

[0078] Then, for the new individuals generated after crossover, a mutation operation is performed to randomly adjust some process parameters or fly ash data in the individuals to jump out of the local optimal solution, which is expressed as:

[0079]

[0080]

[0081]

[0082] In the formula, is the mutation amplitude of the process parameter, set as the mutation amplitude of the first process parameter, as the mutation amplitude of the second process parameter, as the mutation amplitude of the third process parameter; is the process parameter after the first mutation operation, is the process parameter after the second mutation operation, is the process parameter after the third mutation operation; rand is a random number, e.g., the value range is .

[0083] Calculate the fitness of the new individuals, and select the new generation of individuals according to the fitness, and retain the individuals with higher fitness to enter the next generation. In one embodiment, the fitness is calculated by a fitness function, and the calculation method of the fitness function is expressed as:

[0084]

[0085] In the formula, is the fitness function.

[0086] In this embodiment, individuals with high fitness values ​​have smaller objective function values, that is, they perform better in terms of fly ash carbon content, combustion efficiency and system stability. When the preset maximum number of iterations is reached or the fitness is no longer significantly improved, the algorithm is stopped and the optimal solution is output. For example, the maximum number of iterations is 1000. Figure 5 The dynamic scatter plot is used to analyze the essential differences in the multi-objective optimization effects. By analyzing the distribution of the dual-objective optimization results of fly ash carbon content error and combustion efficiency loss, as well as the performance in multi-objective trade-offs, the experimental results show that the solution set of the traditional method is in a disordered scattered state and lacks a clear optimization direction. The solution set of this technology forms a continuous and clearly bounded Pareto frontier, and the parameter optimization degree and the objective function value show a regular distribution, which verifies the effectiveness of the multi-objective weighted combination function and the evolutionary optimization framework, indicating that the algorithm can systematically coordinate the contradictory relationships between the various objectives and maximize the combustion efficiency while ensuring the fly ash quality control.

[0087] See also Figures 6 to 8 By comparing the process stability performance in long-term operation through multi-axis timing diagrams, the experiment simulates the dynamic change process of parameters in continuous production scenarios. Due to the lack of stability constraints in traditional methods, the three key process parameters all show periodic and large fluctuations. However, this technology significantly reduces the change amplitude of each parameter and the fluctuation frequency tends to be flat through the reverse constraint of the stability objective function. The smooth parameter adjustment feature is derived from the algorithm's cumulative penalty mechanism for the historical change of parameters, which reflects the design advantages of this technology in controlling parameter mutations and maintaining smooth system operation, and effectively solves the system oscillation problem caused by the pursuit of single-target extreme values ​​by traditional optimization methods.

[0088] In addition, the present application further provides a method for time-sharing measurement of the carbon content in flue fly ash, including using the aforementioned time-sharing measurement device for the carbon content in flue fly ash, wherein a regulating valve is arranged at the inlet end of the sampling tube, and a sampling tube in the sampler is opened by the regulating valve, and the other sampling tubes are closed, and the gas passing through the sampling tube enters the separation component, the fly ash is separated from the gas and the fly ash is collected, and the collected fly ash is spectrally analyzed to obtain spectral characteristic information of the fly ash; the carbon content of the fly ash is calculated based on the spectral characteristic information and the corresponding relationship model between the spectral characteristic and the carbon content, wherein, when one sampling tube is collecting and detecting, the other sampling tubes are cleaned.

[0089] Using the aforementioned on-line measuring device for carbon content in flue gas fly ash as a measuring tool, a regulating valve is arranged at the inlet end of the sampling pipe. The action of the regulating valve is controlled by an intelligent control system, enabling it to open one of the sampling pipes in a specified sampler according to a preset program, while ensuring that other sampling pipes are in a closed state, guaranteeing the independence and accuracy of each measurement. After the used sampling pipe is opened, the gas in the flue enters the sampling pipe along a predetermined path. The gas enters the separation component, which adopts gas-solid separation technology to separate the fly ash from the gas, and collects the separated fly ash. Professional spectral analysis is performed on the collected fly ash to obtain detailed spectral characteristic information of the fly ash. According to the obtained spectral characteristic information, combined with the pre-established corresponding relationship model between spectral characteristics and carbon content, the corresponding relationship model is verified and optimized using a large amount of experimental data.

[0090] To achieve full-time coverage and minimize the time interval, when fly ash is collected and detected in one sampling pipe, cleaning operations are performed on other sampling pipes. Since the regulating valve is arranged at the opening position, the cleaning pipeline can be arranged close to the regulating valve. When the regulating valve is closed, the cleaning pipeline can introduce cleaning gas from the front end, effectively completing the cleaning of the entire sampling pipeline. During the measurement process, the unused pipelines are also cleaned simultaneously. By switching between sampling pipe detection and cleaning, the entire time period can be divided into independent and continuous intervals, and it can be ensured that collection, measurement, and cleaning of other pipelines that are not being detected are all carried out, preparing for the next detection. In this way, the time-sharing measurement method can be adopted. Through the switching control of the regulating valve, sequential sampling, detection, and cleaning of different sampling pipes are realized, and there is no mutual interference between multiple sampling pipes, enabling full-time detection with higher real-time performance. In a preferred technical solution, the gas outlet of the separation component can be connected to the gas inlet of the cleaning component, and a filtering component is provided to filter the gas of the separation component as the cleaning gas in the cleaning component, which can further improve the overall efficiency.

[0091] Furthermore, while closing one sampling pipe, another sampling pipe that has been cleaned is opened for sampling and detection. Using this method, it can be ensured that gas collection and detection in all time periods in the flue are carried out without missing time periods, thereby making real-time detection more accurate and intuitive.

[0092] As can be seen from the foregoing, the flue gas fly ash carbon content time-sharing measurement device of the present application realizes efficient, accurate, and real-time measurement of the carbon content in fly ash during the combustion process, further optimizes the combustion process parameters, and improves the combustion efficiency and system stability. The device mainly consists of three core parts: a sampler, a detection component, and a control module. The sampler part is equipped with more than two sampling tubes, and the openings of the sampling tubes are designed as conical structures to reduce the interference with the airflow distribution in the flue and improve the accuracy of fly ash collection. Each sampling tube is equipped with a regulating valve, and time-sharing sampling is realized through PLC logic control to ensure that at most only one sampling tube is in the open state within a period of time, thereby achieving fine coverage of the entire time period. A separation component is built into the sampling tube to separate fly ash from gas and collect fly ash samples. The cleaning component includes a purge tube and a discharge tube. The purge tube provides clean gas to independently clean different sampling tubes and separation components to ensure the cleanliness and efficiency of the sampling process; the discharge tube leads directly to the flue, simplifying the waste treatment process.

[0093] Another innovation of the present application lies in its control strategy. The system integrates a process adjustment model and comprehensively considers multi-dimensional equipment process parameters (such as temperature, airflow velocity, gas oxygen content, etc.) and fly ash measurement data (such as carbon content, fly ash content, particle size, etc.). Through an evolutionary optimization algorithm based on multi-objective optimization, multiple objective functions such as fly ash carbon content, combustion efficiency, and system stability are optimized simultaneously to ensure the corresponding relationship between process parameters and fly ash data and achieve global optimization. The algorithm adopts weighted crossover and mutation operations, fully considering the dependency relationship between process parameters, improving the effectiveness of the crossover operation, and avoiding falling into local optimal solutions. Experimental results show that the algorithm of the present application can systematically coordinate the contradictory relationships between various objectives, maximize the combustion efficiency while ensuring fly ash quality control, and maintain the stable operation of the system at the same time.

[0094] In summary, the flue gas fly ash carbon content time-sharing measurement device and its control strategy provided by the present application realize accurate and real-time measurement of the carbon content in flue gas fly ash through a unique sampler design, efficient detection components, intelligent control modules, and advanced optimization algorithms, further optimize the combustion process parameters, improve the combustion efficiency and system stability, and provide strong support for the refined analysis and management of the combustion process.

[0095] The above is only a specific implementation manner of the present application, and any improvement made on the premise of the present application's concept is regarded as the protection scope of the present application.

Claims

1. A time-sharing measurement device for flue fly ash carbon content, characterized in that: It includes a sampler, a detection component and a control module; The sampler includes more than two sampling tubes, a separation component, and a cleaning component. The openings of the sampling tubes are arranged at a plurality of different positions. Each of the sampling tubes includes a regulating valve, which controls the flow rate and opening and closing of the sampling tubes and ensures that at most only one sampling tube opening is open in a time period. Each sampling tube includes at least one separation component, which separates fly ash from gas and collects fly ash. The cleaning component includes a purge pipe, which provides clean gas to independently clean different sampling tubes and separation components. The detection component includes a spectral analysis unit and a data processing unit, wherein the spectral analysis unit performs spectral analysis on the collected fly ash to obtain spectral characteristic information of the fly ash; The data processing unit calculates the carbon content of the fly ash according to the spectral feature information and the corresponding relationship model between the spectral feature and the carbon content.

2. The time-sharing measuring device for flue fly ash carbon content according to claim 1 is characterized in that: The opening of the sampling tube is conical.

3. The time-sharing measurement device for flue fly ash carbon content according to claim 1 is characterized in that: The gas outlet of the separation component is connected to the gas inlet of the cleaning component.

4. The time-sharing measuring device for flue fly ash carbon content according to claim 3 is characterized in that: It also includes a multi-channel control valve. When one sampling tube is sampling, the multi-channel control valve controls the gas outlet of the separation component to be connected to the purge tube of another sampling tube.

5. The time-sharing measuring device for flue fly ash carbon content according to claim 1 is characterized in that: The control module also includes a data statistics unit, which includes a statistical model. The statistical model establishes a corresponding relationship between the process parameters of the equipment and the carbon content in the fly ash based on the obtained fly ash measurement data.

6. The time-sharing measuring device for flue fly ash carbon content according to claim 5 is characterized in that: The control module also includes an equipment process adjustment unit, which includes a process adjustment model, and automatically outputs an adjustment signal of the equipment process according to the acquired process parameters of the equipment and the fly ash measurement data.

7. The time-sharing measurement device for flue fly ash carbon content according to claim 6 is characterized in that: It also includes mutation operations on new individuals generated after crossover.

8. The time-sharing measurement device for flue fly ash carbon content according to claim 7 is characterized in that: It also includes calculating the fitness of new individuals, selecting a new generation of individuals based on the fitness, and retaining individuals with higher fitness to enter the next generation.

9. A time-sharing measurement method for flue fly ash carbon content, characterized in that: include: Using the flue fly ash carbon content time-sharing measurement device as described in any one of claims 1 to 8, the regulating valve is arranged at the inlet end of the sampling tube, and one sampling tube in the sampler is opened by the regulating valve, and the other sampling tubes are closed; The gas passing through the sampling tube enters the separation component to separate the fly ash from the gas and collect the fly ash; Performing spectral analysis on the collected fly ash to obtain spectral characteristic information of the fly ash; calculating the carbon content of the fly ash based on the spectral characteristic information and a corresponding relationship model between the spectral characteristic and the carbon content; When one sampling tube is collecting and testing, other sampling tubes are cleaned.

10. The method for measuring the carbon content of flue fly ash by time according to claim 9, characterized in that: While closing one sampling tube, open another sampling tube that has been cleaned for sampling and testing.

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