A coal blending online monitoring system and method based on LIBS technology

Abstract

The present application relates to the technical field of coal quality monitoring, and discloses a coal blending online monitoring system and method based on LIBS technology, which comprises a data acquisition module, a temperature conversion module, a balance correction module, an intensity calculation module, a proportion determination module and an instruction control module. The original spectrum data and flow rate value are collected, the intensity compensation of the original spectrum is carried out according to the flow rate to obtain compensated spectrum data, different characteristic spectral lines of excitation energy level are selected from the compensated spectrum data, and the plasma electron temperature is determined according to the spectral line intensity ratio to complete the thermodynamic balance correction and obtain temperature-flow rate double correction spectrum data. Then, the spectral line intensities corresponding to two kinds of coal are extracted and the geometric mean values are calculated as comprehensive intensities. The current coal blending proportion is obtained by combining the two sets of comprehensive intensity ratio and the double correction spectrum data. The deviation is obtained by comparing the current coal blending proportion with the preset target blending proportion, and finally the blending proportion control instruction is generated. The present application can improve the efficiency of coal blending online monitoring.

Description

Technical Field

[0001] This invention relates to the field of coal quality monitoring technology, and in particular to an online monitoring system and method for coal blending based on LIBS technology. Background Technology

[0002] Currently, in the field of online monitoring of coal blending, existing monitoring schemes based on LIBS technology are easily affected by changes in coal powder flow rate and fluctuations in plasma electron temperature in practical applications. These schemes do not specifically correct for spectral signal distortion caused by flow rate and temperature, resulting in intensity shifts in the acquired spectral data. This leads to significant errors in the identification of coal characteristic spectral lines and the calculation of spectral line intensity, making it impossible to accurately determine the real-time blending ratio of coal, and the monitoring accuracy falls short of industrial application standards.

[0003] The existing monitoring architecture can only realize the basic detection function of blending ratio. It has not established a closed-loop mechanism for deviation quantification, level classification and automatic control. When the actual blending ratio deviates from the preset target, the system cannot distinguish the magnitude of the deviation and output the corresponding adjustment command. It is difficult to quickly correct the coal blending status. At the same time, the monitoring, analysis and control links are not well connected, the overall system has weak real-time response capability and cannot adapt to the continuous and dynamic blending operation scenario in the coal transportation process. Therefore, how to improve the efficiency of online monitoring of coal blending has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides an online monitoring system and method for coal blending based on LIBS technology to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an online monitoring system for coal blending based on LIBS technology, characterized in that the system includes a data acquisition module, a temperature conversion module, a balance correction module, an intensity calculation module, a proportion determination module, and an instruction control module, wherein:

[0006] The data acquisition module is used to acquire the original spectral data and flow velocity value of the pulverized coal flow, and to perform intensity compensation on the original spectral data based on the flow velocity value to obtain the compensated spectral data of the pulverized coal flow.

[0007] The temperature conversion module is used to select characteristic spectral lines with different excitation energy levels from the compensated spectral data, and determine the plasma electron temperature of the pulverized coal flow based on the intensity ratio between the characteristic spectral lines.

[0008] The balance correction module is used to perform thermodynamic balance correction on the compensated spectral data based on the plasma electron temperature to obtain temperature-flow rate dual-corrected spectral data of the pulverized coal flow.

[0009] The intensity calculation module is used to extract the first spectral line intensity of the first coal type and the second spectral line intensity of the second coal type from the temperature-flow rate dual-corrected spectral data, and to take the geometric average of the first spectral line intensity and the second spectral line intensity as the first comprehensive intensity and the second comprehensive intensity of the coal flow, respectively.

[0010] The ratio determination module is used to obtain the current blending ratio of the pulverized coal flow based on the ratio between the first comprehensive strength and the second comprehensive strength, combined with the temperature-flow rate dual-corrected spectral data.

[0011] The instruction control module is used to compare and analyze the current blending ratio with the preset target blending ratio to obtain the ratio deviation of the pulverized coal flow, and generate the blending ratio control instruction of the pulverized coal flow.

[0012] In a preferred embodiment, when the data acquisition module acquires the raw spectral data and flow velocity value of the pulverized coal flow, and performs intensity compensation on the raw spectral data based on the flow velocity value to obtain the compensated spectral data of the pulverized coal flow, it is specifically used for:

[0013] At the monitoring window of the coal blending and conveying pipeline, laser pulses are emitted towards the flowing coal powder stream to collect the raw spectral data of the coal powder stream and simultaneously measure the flow velocity of the coal powder stream.

[0014] The flow rate value is aligned pulse-by-pulse with the spectral signal in the original spectral data to obtain the flow rate-spectral pairing record of the pulverized coal flow.

[0015] Based on the proportional relationship between the flow velocity value and the original spectral signal intensity value in the flow velocity-spectrum pairing record, the dynamic compensation coefficient of the original spectral data is determined;

[0016] The dynamic compensation coefficient is used as the weight of the original spectral signal intensity value to obtain the weighted spectral signal of the pulverized coal flow;

[0017] The weighted spectral signals are combined by median merging to obtain the compensated spectral data of the pulverized coal flow.

[0018] In a preferred embodiment, when the temperature conversion module selects characteristic spectral lines with different excitation energy levels from the compensated spectral data and determines the plasma electron temperature of the pulverized coal flow based on the intensity ratio between the characteristic spectral lines, it is specifically used for:

[0019] The peak intensity value of the spectral line and the excitation energy level value corresponding to the spectral line are extracted from the compensated spectral data to obtain the original data pair of the pulverized coal flow;

[0020] Arrange the original data pairs in ascending order of the excitation energy level values ​​to obtain the sorted spectral sequence of the pulverized coal flow;

[0021] The energy level difference and energy level intensity ratio between adjacent spectral lines in the sorted spectral line sequence are statistically analyzed to obtain the energy level difference sequence and intensity ratio sequence of the pulverized coal flow.

[0022] The natural logarithm of the intensity ratios in the intensity ratio sequence is taken to obtain the logarithmic ratio sequence of the pulverized coal flow;

[0023] The plasma electron temperature of the pulverized coal flow is calculated based on the energy level difference sequence and the logarithmic ratio sequence.

[0024] In a preferred embodiment, the formula for calculating the plasma electron temperature is:

[0025] ;

[0026] in, This indicates the plasma electron temperature. Represents the first energy level in the energy level difference sequence. One energy level difference, Represents the first in the logarithmic ratio sequence Log-ratio, This represents the preset Boltzmann constant. This represents the index variable for summation.

[0027] In a preferred embodiment, when the balance correction module performs thermodynamic balance correction on the compensated spectral data based on the plasma electron temperature to obtain temperature-flow velocity dual-corrected spectral data of the pulverized coal flow, it is specifically used for:

[0028] Wavelength decoupling is performed on the compensated spectral data to obtain the wavelength channels of the compensated spectral data, and the corresponding excitation energy level values ​​are associated with the wavelength channels.

[0029] Based on the plasma electron temperature, the temperature correction weight of the wavelength channel is determined. The value of the temperature correction weight is inversely correlated with the plasma electron temperature and positively correlated with the excitation energy level value of the wavelength channel.

[0030] The original intensity value of the wavelength channel is assigned the temperature correction weight to obtain the initial corrected intensity value of the wavelength channel;

[0031] The initial correction intensity value is normalized to obtain the standard spectral data of the pulverized coal flow.

[0032] The standard spectral data and the flow rate value are channel-cascaded and registered to obtain the flow rate label corresponding to the wavelength channel, and the spectral data with the flow rate label is used as the temperature-flow rate dual-corrected spectral data of the pulverized coal flow.

[0033] In a preferred embodiment, when the balance correction module determines the temperature correction weight of the wavelength channel based on the plasma electron temperature, it is specifically used for:

[0034] The excitation energy level values ​​of the wavelength channels are extracted from the compensated spectral data, and the wavelength channels are arranged in ascending order of the excitation energy level values ​​to obtain the channel sequence of the wavelength channels;

[0035] The reference correction value of the wavelength channel is obtained by performing differential analysis between the plasma electron temperature and the excitation energy level value of the first channel in the channel sequence.

[0036] Traverse the channel sequence and use the difference between the excitation energy level value of the current channel and the excitation energy level value of the previous channel as the step increment of the wavelength channel;

[0037] The temperature correction weight of the wavelength channel is obtained by combining the cumulative sum of the reference correction value and the step increment.

[0038] In a preferred embodiment, when the intensity determination module extracts the first spectral line intensity of the first coal type and the second spectral line intensity of the second coal type from the temperature-flow rate dual-corrected spectral data, it is specifically used for:

[0039] The temperature-flow rate dual-corrected spectral data is resolved into a multi-dimensional data record containing wavelength channels, normalized intensity, excitation energy level, and flow rate value.

[0040] Based on the standard characteristic wavelength position of sulfur element in the first coal type, the first candidate channel with the smallest deviation from the standard characteristic wavelength position of sulfur element is located in the multidimensional data record, and the normalized intensity value of the first candidate channel is aggregated into the sulfur element characteristic intensity of the first coal type.

[0041] Based on the standard characteristic wavelength position of calcium element in the second coal type, the multidimensional data records are screened and matched to obtain the second candidate channel of the second coal type.

[0042] The normalized intensity values ​​in the second candidate channel are weighted and fused to obtain the characteristic intensity of calcium element in the second coal type;

[0043] By integrating the iron element data from the multidimensional data records, the characteristic intensity of iron element in the pulverized coal flow is obtained.

[0044] The characteristic intensity of the iron element is correlated and paired with the characteristic intensity of the sulfur element and the characteristic intensity of the calcium element to obtain the first spectral line intensity set and the second spectral line intensity set of the pulverized coal flow;

[0045] The maximum value of the first spectral line intensity set is taken as the first spectral line intensity of the pulverized coal flow, and the maximum value of the second spectral line intensity set is taken as the second spectral line intensity of the pulverized coal flow.

[0046] In a preferred embodiment, when the ratio determination module determines the current blending ratio of the pulverized coal flow based on the ratio between the first and second comprehensive intensities and the temperature-flow-rate dual-corrected spectral data, it is specifically used for:

[0047] The ratio between the first comprehensive intensity and the second comprehensive intensity in the pulverized coal flow is taken as the initial intensity ratio of the pulverized coal flow;

[0048] The temperature-flow-rate dual-corrected spectral data are integrated to obtain the carbon and nitrogen intensity values ​​of the pulverized coal flow.

[0049] The median values ​​of the carbon and nitrogen elements are combined to obtain the carbon-representative intensity and nitrogen-representative intensity of the pulverized coal flow, and the ratio between the carbon-representative intensity and the nitrogen-representative intensity is taken as the carbon-nitrogen intensity ratio of the pulverized coal flow.

[0050] By mapping the initial intensity ratio to the carbon-nitrogen intensity ratio, the two-dimensional ratio coordinate points of the pulverized coal flow are obtained;

[0051] The distance between the two-dimensional ratio coordinate points and the standard coordinate points pre-stored in the blending ratio feature library is compared to obtain the current blending ratio of the pulverized coal flow.

[0052] In a preferred embodiment, when the instruction control module performs a comparative analysis of the current blending ratio with a preset target blending ratio to obtain the proportion deviation of the pulverized coal flow and generates a blending ratio control instruction for the pulverized coal flow, it is specifically used for:

[0053] The current blending ratio and the preset target blending ratio are mapped to the same numerical reference axis to obtain the first reference point and the second reference point of the pulverized coal flow, and the interval between the first reference point and the second reference point is used as the proportional deviation of the pulverized coal flow.

[0054] The blending state of the pulverized coal flow is determined based on the positional relationship between the first reference point and the second reference point. If the first reference point is located before the second reference point, the pulverized coal flow is determined to be under-blended; otherwise, the pulverized coal flow is determined to be over-blended.

[0055] The first and second characteristic intensities of the first and second coal types are extracted from the temperature-flow rate dual-corrected spectral data, and the first and second characteristic intensities are accumulated and merged to obtain the total coal flux characterization value of the coal powder flow.

[0056] Based on the total pulverized coal flux characterization value, the deviation tolerance range of the pulverized coal flow is determined, and the width of the deviation tolerance range is positively correlated with the total pulverized coal flux characterization value.

[0057] The proportional deviation is compared with the deviation tolerance range to obtain the deviation level of the proportional deviation.

[0058] The blending state and the deviation level are integrated and encoded to obtain the blending ratio control command for the pulverized coal flow.

[0059] To address the above problems, the present invention also provides an online monitoring method for coal blending based on LIBS technology, the method comprising:

[0060] A. Obtain the original spectral data and flow velocity value of the pulverized coal flow, and perform intensity compensation on the original spectral data based on the flow velocity value to obtain the compensated spectral data of the pulverized coal flow;

[0061] B. Select characteristic spectral lines with different excitation energy levels from the compensated spectral data, and determine the plasma electron temperature of the pulverized coal flow based on the intensity ratio between the characteristic spectral lines;

[0062] C. Based on the plasma electron temperature, thermodynamic equilibrium correction is performed on the compensated spectral data to obtain the temperature-flow rate dual-corrected spectral data of the pulverized coal flow;

[0063] D. Extract the first spectral line intensity of the first coal type and the second spectral line intensity of the second coal type from the temperature-flow rate dual-corrected spectral data, and take the geometric average of the first spectral line intensity and the second spectral line intensity as the first comprehensive intensity and the second comprehensive intensity of the pulverized coal flow, respectively.

[0064] E. Based on the ratio between the first comprehensive intensity and the second comprehensive intensity, and combined with the temperature-flow rate dual-corrected spectral data, the current blending ratio of the pulverized coal flow is obtained;

[0065] F. Compare and analyze the current blending ratio with the preset target blending ratio to obtain the ratio deviation of the pulverized coal flow, and generate a blending ratio control command for the pulverized coal flow.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] 1. This invention achieves dynamic compensation of flow velocity and correction of plasma electron temperature through coal powder flow spectral data, realizing dual-dimensional optimization of spectral data in terms of temperature and flow velocity. This effectively reduces signal interference caused by coal powder flow state and plasma parameters, improves the stability and accuracy of spectral data, and lays a solid data foundation for subsequent coal type characteristic analysis and blending ratio calculation.

[0068] 2. This invention accurately solves the real-time blending ratio of coal by extracting multi-element characteristic spectral lines, calculating comprehensive intensity, and using a two-dimensional ratio matching method. At the same time, it divides the control levels by the magnitude of blending deviation and generates corresponding control commands, which can complete the online monitoring and automated graded adjustment of coal blending status, effectively improving the overall operating efficiency of online monitoring and linkage control of coal blending. Attached Figure Description

[0069] Figure 1 A system architecture diagram of an online monitoring system for coal blending based on LIBS technology is provided in an embodiment of the present invention.

[0070] Figure 2 This is a schematic flowchart of an online monitoring method for coal blending based on LIBS technology, provided as an embodiment of the present invention.

[0071] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0074] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0075] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0076] In practice, the server-side equipment deployed in a LIBS-based online monitoring system for coal blending may consist of one or more devices. This LIBS-based online monitoring system for coal blending can be implemented as: a business instance, a virtual machine, and hardware devices. For example, this LIBS-based online monitoring system for coal blending can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this LIBS-based online monitoring system for coal blending can be understood as software deployed on a cloud node, used to provide a LIBS-based online monitoring system for coal blending to various user terminals. Alternatively, this LIBS-based online monitoring system for coal blending can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this LIBS-based online monitoring system for coal blending can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide a LIBS-based online monitoring system for coal blending to various user terminals.

[0077] In terms of implementation, the online monitoring system for coal blending based on LIBS technology and the user terminal are mutually compatible. That is, if the online monitoring system for coal blending based on LIBS technology is implemented as an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the online monitoring system for coal blending based on LIBS technology is implemented as a website, then the user terminal is implemented as a webpage; or if the online monitoring system for coal blending based on LIBS technology is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0078] like Figure 1 The figure shown is a system architecture diagram of an online monitoring system for coal blending based on LIBS technology provided in an embodiment of the present invention.

[0079] The LIBS-based online monitoring system 100 for coal blending described in this invention can be installed on a cloud server. In terms of implementation, it can function as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the LIBS-based online monitoring system for coal blending may include a data acquisition module, a temperature conversion module, a balance correction module, an intensity calculation module, a ratio determination module, and an instruction control module. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0080] In this embodiment of the invention, in a LIBS-based online monitoring system for coal blending, each of the above modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. The LIBS-based online monitoring system for coal blending provided by this embodiment of the invention allows for adjustments to the applicable scope of the LIBS-based online monitoring system architecture without modifying the program code. This is achieved by adding modules and directly calling them, enabling cluster-based horizontal expansion and flexibly expanding the LIBS-based online monitoring system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0081] The following describes, with reference to specific embodiments, each component and its specific workflow of an online monitoring system for coal blending based on LIBS technology:

[0082] The data acquisition module is used to acquire the original spectral data and flow velocity value of the pulverized coal flow, and to perform intensity compensation on the original spectral data based on the flow velocity value to obtain the compensated spectral data of the pulverized coal flow.

[0083] In this embodiment of the invention, when the data acquisition module acquires the original spectral data and flow velocity value of the pulverized coal flow, and performs intensity compensation on the original spectral data based on the flow velocity value to obtain the compensated spectral data of the pulverized coal flow, it is specifically used for:

[0084] At the monitoring window of the coal blending and conveying pipeline, laser pulses are emitted towards the flowing coal powder stream to collect the raw spectral data of the coal powder stream and simultaneously measure the flow velocity of the coal powder stream.

[0085] The flow rate value is aligned pulse-by-pulse with the spectral signal in the original spectral data to obtain the flow rate-spectral pairing record of the pulverized coal flow.

[0086] Based on the proportional relationship between the flow velocity value and the original spectral signal intensity value in the flow velocity-spectrum pairing record, the dynamic compensation coefficient of the original spectral data is determined;

[0087] The dynamic compensation coefficient is used as the weight of the original spectral signal intensity value to obtain the weighted spectral signal of the pulverized coal flow;

[0088] The weighted spectral signals are combined by median merging to obtain the compensated spectral data of the pulverized coal flow.

[0089] At the optically transparent monitoring window pre-drilled in the coal blending and conveying pipeline, a laser emitting device, a spectral acquisition device, and a flow velocity measuring device are fixedly installed. The laser emitting device emits high-energy laser pulses at a preset fixed frequency into the continuously flowing coal powder inside the pipeline. The laser pulses act on the surface of the coal powder, causing the coal powder particles to instantly vaporize and form plasma. The spectral acquisition device captures all the spectral signals generated by the plasma radiation in real time and stores them as raw spectral data. At the same time, the flow velocity measuring device detects the flow velocity of the coal powder in real time through the Doppler effect principle and records it as a flow velocity value. The time when the laser emitting device emits each laser pulse is completely consistent with the time when the spectral acquisition device acquires the corresponding spectral signal and the time when the flow velocity measuring device acquires the corresponding flow velocity value.

[0090] According to the emission sequence of the laser pulses, the spectral signal corresponding to each laser pulse and the flow velocity value collected at the same time are extracted sequentially. The spectral signal corresponding to each laser pulse is bound one by one with the flow velocity value collected at the time of laser pulse emission, forming a single record containing the spectral signal content of the laser pulse number and the corresponding flow velocity value. All single records are arranged and combined according to the order of laser pulse emission to obtain the flow velocity-spectrum pairing record of the coal powder flow.

[0091] Extract the flow velocity value and the intensity value of the corresponding spectral signal from each record in the flow velocity-spectrum pairing records. Statistically analyze the corresponding change pattern of flow velocity values ​​and corresponding spectral signal intensity values ​​in all records. Based on this change pattern, establish a correspondence table between flow velocity values ​​and spectral signal intensity values. For each flow velocity value in a record, determine the adjustment coefficient of the corresponding spectral signal intensity value of that record according to the above correspondence table. This adjustment coefficient is the dynamic compensation coefficient of the original spectral data. The value of the dynamic compensation coefficient decreases as the flow velocity value increases and increases as the original spectral signal intensity value increases.

[0092] The dynamic compensation coefficient corresponding to each velocity-spectrum pairing record is multiplied by the intensity value of each wavelength channel of the original spectral signal in that record to obtain the weighted intensity value of each wavelength channel of the original spectral signal in that record. The weighted intensity values ​​of all wavelength channels are combined in the order of the original wavelengths to form the weighted spectral signal segment corresponding to that record. The weighted spectral signal segments corresponding to all records are arranged in the order of laser pulse emission to obtain the weighted spectral signal of the pulverized coal flow.

[0093] Extract all intensity values ​​of each same wavelength channel in the weighted spectral signal from all laser pulse weighted spectral signal segments. Sort all intensity values ​​of each wavelength channel in ascending order of numerical value. Select the intensity value in the middle position of each wavelength channel after sorting as the final intensity value of that wavelength channel. Arrange and combine the final intensity values ​​of all wavelength channels in ascending order of wavelength to obtain the compensated spectral data of the pulverized coal flow.

[0094] The beneficial effects are that precise time matching between flow velocity and spectral signal is achieved through pulse-by-pulse alignment, and pulse-by-pulse dynamic intensity compensation is performed based on the inverse correlation between flow velocity and spectral intensity. This effectively eliminates the systematic interference of coal powder flow velocity fluctuations on spectral signal intensity. The influence of single-pulse abnormal spectral signals is removed by median merging, which improves the accuracy and repeatability of spectral data and provides reliable basic data for subsequent plasma electron temperature calculation, thermodynamic balance correction, and determination of coal blending ratio.

[0095] The temperature conversion module is used to select characteristic spectral lines with different excitation energy levels from the compensated spectral data, and determine the plasma electron temperature of the pulverized coal flow based on the intensity ratio between the characteristic spectral lines.

[0096] In this embodiment of the invention, when the temperature conversion module selects characteristic spectral lines with different excitation energy levels from the compensated spectral data and determines the plasma electron temperature of the pulverized coal flow based on the intensity ratio between the characteristic spectral lines, it is specifically used for:

[0097] The peak intensity value of the spectral line and the excitation energy level value corresponding to the spectral line are extracted from the compensated spectral data to obtain the original data pair of the pulverized coal flow;

[0098] Arrange the original data pairs in ascending order of the excitation energy level values ​​to obtain the sorted spectral sequence of the pulverized coal flow;

[0099] The energy level difference and energy level intensity ratio between adjacent spectral lines in the sorted spectral line sequence are statistically analyzed to obtain the energy level difference sequence and intensity ratio sequence of the pulverized coal flow.

[0100] The natural logarithm of the intensity ratios in the intensity ratio sequence is taken to obtain the logarithmic ratio sequence of the pulverized coal flow;

[0101] The plasma electron temperature of the pulverized coal flow is calculated based on the energy level difference sequence and the logarithmic ratio sequence.

[0102] The formula for calculating the plasma electron temperature is:

[0103] ;

[0104] in, This indicates the plasma electron temperature. Represents the first energy level in the energy level difference sequence. One energy level difference, Represents the first in the logarithmic ratio sequence Log-ratio, This represents the preset Boltzmann constant. This represents the index variable for summation.

[0105] When extracting characteristic spectral lines from compensated spectral data, spectral lines that meet all of the following conditions should be selected: no obvious self-absorption phenomenon, i.e., the deviation of the spectral line's full width at half maximum (FWHM) from the theoretical value is ≤10%; no interference from adjacent elemental spectral lines, i.e., the wavelength difference with other elemental characteristic spectral lines is ≥0.1 nm; the excitation energy level difference between adjacent spectral lines is ≥0.5 eV to ensure the sensitivity of temperature calculation; and the spectral line signal-to-noise ratio is ≥10 to ensure measurement accuracy. Carbon (C|247.86 nm), silicon (Si|288.16 nm), and iron (Fe|371.99 nm, Fe|438 nm) are preferred. Characteristic spectral lines of major elements in coal (35nm, etc.); each characteristic spectral line corresponds to a unique wavelength position. The maximum signal intensity value at the wavelength position corresponding to each characteristic spectral line is extracted as the peak intensity value of the spectral line. The excitation energy level value corresponding to each characteristic spectral line is retrieved from the pre-established standard element spectral database. The peak intensity value of each characteristic spectral line is bound to the excitation energy level value corresponding to the spectral line to form a single data pair. All single data pairs corresponding to the characteristic spectral lines are fully summarized to obtain the original data pair of the coal powder flow.

[0106] The excitation energy level value contained in each data pair in the original data pair is extracted. The excitation energy level value is used as the sole sorting criterion. All original data pairs are arranged in ascending order of excitation energy level value. During the arrangement process, the binding relationship between the peak intensity value and the excitation energy level value in each data pair is strictly maintained. After the arrangement is completed, a continuous data sequence is formed in ascending order of excitation energy level value. This sequence is the sorted spectral line sequence of the pulverized coal flow.

[0107] Starting with the first data pair in the sorted spectral line sequence, each pair of adjacent data pairs is selected as an independent calculation unit. For each calculation unit, the excitation energy level value of the previous data pair is subtracted from the excitation energy level value of the next data pair to obtain the energy level difference corresponding to the calculation unit. The peak intensity value of the next data pair is divided by the peak intensity value of the previous data pair to obtain the energy level intensity ratio corresponding to the calculation unit. The energy level differences obtained from all calculation units are arranged in the order of calculation to obtain the energy level difference sequence of the pulverized coal flow. The energy level intensity ratios obtained from all calculation units are arranged in the same order to obtain the intensity ratio sequence of the pulverized coal flow.

[0108] Extract the intensity ratio of each energy level in the intensity ratio sequence, perform a natural logarithm operation on each energy level intensity ratio to obtain the logarithmic result corresponding to each energy level intensity ratio, and arrange all the logarithmic results according to the order of their corresponding energy level intensity ratios in the intensity ratio sequence, keeping the order completely consistent with the intensity ratio sequence during the arrangement process, to obtain the logarithmic ratio sequence of the pulverized coal flow.

[0109] Each energy level difference in the energy level difference sequence is multiplied by the logarithmic result at the same position in the logarithmic ratio sequence to obtain the corresponding product result. All product results are summed to obtain the first accumulated value. Each logarithmic result in the logarithmic ratio sequence is multiplied by itself to obtain the square value corresponding to each logarithmic result. All square values ​​are summed to obtain the second accumulated value. The first accumulated value is divided by the second accumulated value, and the quotient is divided by the preset Boltzmann constant. The final calculation result is the plasma electron temperature of the pulverized coal flow.

[0110] Each energy level difference in the energy level difference sequence comes from the pre-acquired atomic energy level transition energy difference data corresponding to the characteristic spectral lines of coal types. Each logarithmic ratio in the logarithmic ratio sequence comes from the pre-calculated logarithmic ratio data of the intensity of the characteristic spectral lines of coal types. The Boltzmann constant is a preset fixed physical constant. The summation index variable is a counting variable that iterates through all elements in the energy level difference sequence and the logarithmic ratio sequence. The corresponding elements of the energy level difference sequence and the logarithmic ratio sequence are multiplied, and the results of all multiplication operations are summed.

[0111] The plasma electron temperature is calculated by squaring each element in the logarithmic ratio sequence, summing the results of all squaring operations, multiplying the Boltzmann constant by the sum of the squares of the elements in the logarithmic ratio sequence, and dividing the sum of the products of the energy level difference sequence and the corresponding elements in the logarithmic ratio sequence by the product of the Boltzmann constant and the sum of the squares of the elements in the logarithmic ratio sequence. This calculation method eliminates random errors of single spectral lines by weighting multiple sets of spectral data, thus achieving accurate quantification of the plasma electron temperature.

[0112] The calculated plasma electron temperature increases when the sum of the corresponding element-wise products of the energy level difference sequence and the logarithmic ratio sequence increases, while the calculated plasma electron temperature decreases when the sum of the squares of the elements in the logarithmic ratio sequence increases. The overall fluctuation of the spectral line logarithmic ratio directly affects the calculated electron temperature.

[0113] The beneficial effects are as follows: by binding the peak intensity values ​​of characteristic spectral lines with the corresponding excitation energy level values, a strict correspondence is established between the basic data for temperature calculation. The ascending order of excitation energy level values ​​ensures the physical continuity and comparability of the energy level differences between adjacent spectral lines. The plasma electron temperature is calculated based on the correlation between the intensity ratio of adjacent spectral lines and the energy level difference, which effectively offsets the systematic error caused by the overall spectral intensity fluctuation. The natural logarithmic transformation optimizes the numerical calculation process and reduces the influence of extreme values, significantly improving the calculation accuracy and stability of plasma electron temperature, and providing accurate and reliable temperature reference data for subsequent thermodynamic equilibrium correction.

[0114] The balance correction module is used to perform thermodynamic balance correction on the compensated spectral data based on the plasma electron temperature to obtain temperature-flow rate dual-corrected spectral data of the pulverized coal flow.

[0115] In this embodiment of the invention, when the balance correction module performs thermodynamic balance correction on the compensated spectral data based on the plasma electron temperature to obtain the temperature-flow velocity dual-corrected spectral data of the pulverized coal flow, it is specifically used for:

[0116] Wavelength decoupling is performed on the compensated spectral data to obtain the wavelength channels of the compensated spectral data, and the corresponding excitation energy level values ​​are associated with the wavelength channels.

[0117] Based on the plasma electron temperature, the temperature correction weight of the wavelength channel is determined. The value of the temperature correction weight is inversely correlated with the plasma electron temperature and positively correlated with the excitation energy level value of the wavelength channel.

[0118] The original intensity value of the wavelength channel is assigned the temperature correction weight to obtain the initial corrected intensity value of the wavelength channel;

[0119] The initial correction intensity value is normalized to obtain the standard spectral data of the pulverized coal flow.

[0120] The standard spectral data and the flow rate value are channel-cascaded and registered to obtain the flow rate label corresponding to the wavelength channel, and the spectral data with the flow rate label is used as the temperature-flow rate dual-corrected spectral data of the pulverized coal flow.

[0121] When the balance correction module determines the temperature correction weight of the wavelength channel based on the plasma electron temperature, it is specifically used for:

[0122] The excitation energy level values ​​of the wavelength channels are extracted from the compensated spectral data, and the wavelength channels are arranged in ascending order of the excitation energy level values ​​to obtain the channel sequence of the wavelength channels;

[0123] The reference correction value of the wavelength channel is obtained by performing differential analysis between the plasma electron temperature and the excitation energy level value of the first channel in the channel sequence.

[0124] Traverse the channel sequence and use the difference between the excitation energy level value of the current channel and the excitation energy level value of the previous channel as the step increment of the wavelength channel;

[0125] The temperature correction weight of the wavelength channel is obtained by combining the cumulative sum of the reference correction value and the step increment.

[0126] The compensated spectral data is divided into multiple independent discrete data units according to a preset fixed wavelength interval. Each discrete data unit corresponds to a unique wavelength range. The discrete data unit is the wavelength channel of the compensated spectral data. The excitation energy level value of the characteristic spectral line corresponding to each wavelength channel is retrieved from a pre-established standard element spectral database, and each excitation energy level value is bound to the corresponding wavelength channel one by one.

[0127] The excitation energy level value corresponding to each wavelength channel is extracted from the compensated spectral data. Using the excitation energy level value as the sole sorting criterion, all wavelength channels are arranged sequentially in ascending order of excitation energy level value. During the arrangement process, the binding relationship between each wavelength channel and its corresponding excitation energy level value is strictly maintained. After the arrangement is completed, a continuous channel sequence is formed in ascending order of excitation energy level value. This sequence is the channel sequence of the wavelength channels.

[0128] The difference between the plasma electron temperature and the excitation energy level of the first channel in the channel sequence is the reference correction value for the wavelength channel.

[0129] Starting from the second channel in the channel sequence, each channel is selected as the current channel in turn. The excitation energy level value of the current channel is subtracted from the excitation energy level value of the previous channel. The difference is the step increment of the wavelength channel corresponding to the current channel.

[0130] The reference correction value is sequentially added to the step increment corresponding to all channels to obtain the temperature correction weight corresponding to each wavelength channel. The value of the temperature correction weight decreases as the plasma electron temperature increases and increases as the excitation energy level value of the wavelength channel increases.

[0131] The initial corrected intensity value of each wavelength channel is calculated by multiplying the original intensity value of that wavelength channel by the temperature correction weight corresponding to that wavelength channel.

[0132] Extract the initial calibration intensity values ​​of all wavelength channels, find the maximum intensity value, divide the initial calibration intensity value of each wavelength channel by the maximum intensity value to obtain the normalized intensity value of each wavelength channel, and arrange the normalized intensity values ​​of all wavelength channels in ascending order of wavelength to obtain the standard spectral data of the pulverized coal flow.

[0133] Extract the flow velocity value collected at the same time as the compensated spectral data from the data acquisition module, and bind the flow velocity value to each wavelength channel in the standard spectral data one by one. The flow velocity value bound to each wavelength channel is the flow velocity mark corresponding to the wavelength channel. Arrange all wavelength channels with flow velocity marks in ascending order of wavelength to obtain the temperature-flow velocity dual-corrected spectral data of the pulverized coal flow.

[0134] The beneficial effects are as follows: wavelength decoupling of the wavelength channel enables refined segmentation of spectral data; temperature correction weight is determined based on the dual correlation of plasma electron temperature and excitation energy level value, accurately reflecting the differences in the influence of plasma temperature on spectral lines of different excitation energy levels; overall normalization process eliminates the influence of absolute intensity fluctuations in the spectrum; channel cascade registration enables precise correlation between temperature correction data and flow rate data channel by channel; and the final temperature-flow rate dual-corrected spectral data simultaneously eliminates the interference of flow rate fluctuations and plasma temperature non-equilibrium state on the spectral signal, providing high-precision basic data for subsequent extraction of coal type characteristic intensity and calculation of blending ratio.

[0135] The intensity calculation module is used to extract the first spectral line intensity of the first coal type and the second spectral line intensity of the second coal type from the temperature-flow rate dual-corrected spectral data, and to take the geometric average of the first spectral line intensity and the second spectral line intensity as the first comprehensive intensity and the second comprehensive intensity of the coal flow, respectively.

[0136] In this embodiment of the invention, when the intensity determination module extracts the first spectral line intensity of the first coal type and the second spectral line intensity of the second coal type from the temperature-flow rate dual-corrected spectral data, it is specifically used for:

[0137] The temperature-flow rate dual-corrected spectral data is resolved into a multi-dimensional data record containing wavelength channels, normalized intensity, excitation energy level, and flow rate value.

[0138] Based on the standard characteristic wavelength position of sulfur element in the first coal type, the first candidate channel with the smallest deviation from the standard characteristic wavelength position of sulfur element is located in the multidimensional data record, and the normalized intensity value of the first candidate channel is aggregated into the sulfur element characteristic intensity of the first coal type.

[0139] Based on the standard characteristic wavelength position of calcium element in the second coal type, the multidimensional data records are screened and matched to obtain the second candidate channel of the second coal type.

[0140] The normalized intensity values ​​in the second candidate channel are weighted and fused to obtain the characteristic intensity of calcium element in the second coal type;

[0141] By integrating the iron element data from the multidimensional data records, the characteristic intensity of iron element in the pulverized coal flow is obtained.

[0142] The characteristic intensity of the iron element is correlated and paired with the characteristic intensity of the sulfur element and the characteristic intensity of the calcium element to obtain the first spectral line intensity set and the second spectral line intensity set of the pulverized coal flow;

[0143] The maximum value of the first spectral line intensity set is taken as the first spectral line intensity of the pulverized coal flow, and the maximum value of the second spectral line intensity set is taken as the second spectral line intensity of the pulverized coal flow.

[0144] The normalized intensity excitation level and flow rate label information bound to each wavelength channel in the temperature-flow rate dual-calibration spectral data are completely extracted. A single data entry containing the above four dimensions of information is generated for each wavelength channel. All single data entries are arranged and combined in order of wavelength from smallest to largest to obtain the multidimensional data record corresponding to the temperature-flow rate dual-calibration spectral data.

[0145] The standard characteristic wavelength position of sulfur element corresponding to the first coal type is retrieved from the pre-established standard element spectral database. All wavelength channels in the multidimensional data record are traversed, and the difference between the center wavelength of each wavelength channel and the standard characteristic wavelength position of sulfur element is calculated. The wavelength channel with the smallest difference is selected as the first candidate channel, and the normalized intensity value corresponding to the first candidate channel is directly used as the characteristic intensity of sulfur element of the first coal type.

[0146] Retrieve all standard characteristic wavelength positions of calcium element corresponding to the second coal type from the pre-established standard element spectral database, traverse all wavelength channels in the multidimensional data record, and filter out all wavelength channels whose center wavelength falls within the preset fixed deviation range of each calcium element standard characteristic wavelength position. Summarize these filtered wavelength channels to obtain the second candidate channel of the second coal type.

[0147] A fixed weight value is pre-assigned to each standard characteristic wavelength position of calcium element. The weight value is positively correlated with the characteristic identification degree of the characteristic wavelength position in the second coal type. The normalized intensity value corresponding to each second candidate channel is multiplied by the fixed weight value corresponding to its standard characteristic wavelength position to obtain the weighted intensity value of each second candidate channel. The weighted intensity values ​​of all second candidate channels are accumulated, and the accumulated result is the characteristic intensity of calcium element in the second coal type.

[0148] Retrieve all standard characteristic wavelength positions of iron from a pre-established standard element spectral database, traverse all wavelength channels in the multidimensional data record, filter out all wavelength channels whose center wavelength falls within a preset fixed deviation range of each iron element standard characteristic wavelength position, extract the normalized intensity values ​​corresponding to these wavelength channels, and calculate the average value of these normalized intensity values. This average value is the characteristic intensity of iron in the pulverized coal flow.

[0149] The characteristic intensity of iron is correlated and paired with the characteristic intensity of sulfur of the first coal type to form a first set of data containing the characteristic intensity of iron and sulfur, which is the first spectral intensity set of the pulverized coal flow; the characteristic intensity of iron is correlated and paired with the characteristic intensity of calcium of the second coal type to form a second set of data containing the characteristic intensity of iron and calcium, which is the second spectral intensity set of the pulverized coal flow.

[0150] Extract the intensity values ​​corresponding to all elements in the first spectral line intensity set, find the maximum value among them, and the maximum value is the first spectral line intensity of the pulverized coal flow; extract the intensity values ​​corresponding to all elements in the second spectral line intensity set, find the maximum value among them, and the maximum value is the second spectral line intensity of the pulverized coal flow.

[0151] The complete numerical values ​​corresponding to the first and second spectral line intensities obtained from on-site detection, along with all valid collected data, are acquired sequentially. The mean value of the acquired first spectral line intensity-related data is calculated, and each pair of corresponding data is integrated to obtain the geometric mean result corresponding to the first spectral line intensity. This result is directly defined as the first comprehensive intensity of the pulverized coal flow. Similarly, the mean value of the acquired second spectral line intensity-related data is calculated, and each pair of corresponding data is integrated to obtain the geometric mean result corresponding to the second spectral line intensity. This result is also directly defined as the second comprehensive intensity of the pulverized coal flow.

[0152] Sulfur was selected as the characteristic element for the first coal type and calcium as the characteristic element for the second coal type, based on the differences in elemental composition between the two coal types. When the first coal type is high-sulfur coal (sulfur content ≥1%), the second coal type is low-sulfur coal (sulfur content ≤0.5%), and the second coal type is high-calcium coal (calcium content ≥5%), while the first coal type is low-calcium coal (calcium content ≤2%), the characteristic spectral line intensities of sulfur and calcium are significantly different, effectively distinguishing the two coal types. If the differences in sulfur and calcium content between the two coal types are not significant, other elements with significant differences, such as silicon and aluminum, can be substituted. Iron was selected as the internal standard element because its content in coal is relatively stable (usually 0.5%–3%), and its characteristic spectral lines are less affected by matrix effects, effectively correcting for overall plasma intensity fluctuations.

[0153] The beneficial effects are as follows: by parsing temperature-flow rate dual-calibrated spectral data into multi-dimensional data records, unified management and retrieval of spectral information and auxiliary parameters are achieved. For the first coal type, the single-channel precise positioning of sulfur element ensures the specificity of feature extraction. For the second coal type, the multi-channel weighted fusion of calcium element improves the stability and anti-interference ability of feature intensity. The introduction of iron element as an internal standard element effectively eliminates the influence of coal powder flow matrix effect on feature intensity. A standardized intensity comparison benchmark is established by the correlation pairing of internal standard and feature element. The selection of maximum value ensures the representativeness of feature intensity. Geometric mean calculation further reduces the error caused by single-channel random fluctuation. Finally, the first and second comprehensive intensities can accurately reflect the relative content of the two coal types in the coal powder flow, providing reliable feature data support for the accurate calculation of subsequent blending ratios.

[0154] The ratio determination module is used to obtain the current blending ratio of the pulverized coal flow based on the ratio between the first comprehensive strength and the second comprehensive strength, combined with the temperature-flow rate dual-corrected spectral data.

[0155] In this embodiment of the invention, when the ratio determination module performs the task of determining the current blending ratio of the pulverized coal flow based on the ratio between the first comprehensive intensity and the second comprehensive intensity, combined with the temperature-flow rate dual-corrected spectral data, it is specifically used for:

[0156] The ratio between the first comprehensive intensity and the second comprehensive intensity in the pulverized coal flow is taken as the initial intensity ratio of the pulverized coal flow;

[0157] The temperature-flow-rate dual-corrected spectral data are integrated to obtain the carbon and nitrogen intensity values ​​of the pulverized coal flow.

[0158] The median values ​​of the carbon and nitrogen elements are combined to obtain the carbon-representative intensity and nitrogen-representative intensity of the pulverized coal flow, and the ratio between the carbon-representative intensity and the nitrogen-representative intensity is taken as the carbon-nitrogen intensity ratio of the pulverized coal flow.

[0159] By mapping the initial intensity ratio to the carbon-nitrogen intensity ratio, the two-dimensional ratio coordinate points of the pulverized coal flow are obtained;

[0160] The distance between the two-dimensional ratio coordinate points and the standard coordinate points pre-stored in the blending ratio feature library is compared to obtain the current blending ratio of the pulverized coal flow.

[0161] The initial intensity ratio of the pulverized coal flow is obtained by dividing the first comprehensive intensity by the second comprehensive intensity.

[0162] Retrieve all standard characteristic wavelength ranges for carbon and nitrogen from a pre-established standard element spectral database. Extract all intensity values ​​of the regions without characteristic peaks in the temperature-flow rate dual-calibration spectral data. Calculate the average of these intensity values ​​as a preset baseline. Locate the peak regions corresponding to each standard characteristic wavelength range for carbon and nitrogen in the temperature-flow rate dual-calibration spectral data. Calculate the closed area between the spectral curve and the preset baseline within each peak region. Summarize the closed areas corresponding to all standard characteristic wavelength ranges for carbon to obtain the carbon intensity value of the pulverized coal flow. Summarize the closed areas corresponding to all standard characteristic wavelength ranges for nitrogen to obtain the nitrogen intensity value of the pulverized coal flow.

[0163] Extract all carbon intensity values, sort these intensity values ​​in ascending order, and select the intensity value in the middle position after sorting as the carbon representative intensity of the pulverized coal flow. Extract all nitrogen intensity values, sort these intensity values ​​in ascending order, and select the intensity value in the middle position after sorting as the nitrogen representative intensity of the pulverized coal flow. Divide the carbon representative intensity by the nitrogen representative intensity to obtain the carbon-nitrogen intensity ratio of the pulverized coal flow.

[0164] Using the initial intensity ratio as the abscissa value of the two-dimensional plane and the carbon-nitrogen intensity ratio as the ordinate value of the two-dimensional plane, the two values ​​are combined to form an independent point on the two-dimensional plane, which is the two-dimensional ratio coordinate point of the pulverized coal flow.

[0165] Traverse all standard coordinate points pre-stored in the blending ratio feature library. Each standard coordinate point corresponds to a unique standard blending ratio. Calculate the straight-line distance between the two-dimensional ratio coordinate point and each standard coordinate point. Find the straight-line distance with the smallest value among all straight-line distances. The standard blending ratio associated with the standard coordinate point corresponding to the smallest straight-line distance is the current blending ratio of the pulverized coal flow.

[0166] The beneficial effects are as follows: a characteristic element identification benchmark for coal blending is established by the ratio of the first comprehensive intensity to the second comprehensive intensity; an auxiliary identification dimension of coal organic composition is introduced by the carbon-nitrogen intensity ratio; the two-dimensional coordinate points formed by the combination of the two-dimensional ratios significantly improve the specificity and anti-interference ability of coal blending ratio identification; the element intensity value calculated by spectral peak integration makes full use of the overall information of the spectral signal; the median merging effectively removes the influence of abnormal signals of single characteristic wavelengths; and the distance comparison based on standard coordinate points realizes rapid and accurate matching of blending ratios, avoiding complex mathematical modeling processes, while ensuring the stability and reliability of the blending ratio calculation results, providing an accurate basis for the precise control of the blending ratio in the future.

[0167] The instruction control module is used to compare and analyze the current blending ratio with the preset target blending ratio to obtain the ratio deviation of the pulverized coal flow, and generate the blending ratio control instruction of the pulverized coal flow.

[0168] In this embodiment of the invention, when the instruction control module performs a comparative analysis of the current blending ratio with a preset target blending ratio to obtain the proportion deviation of the pulverized coal flow and generates a blending ratio control instruction for the pulverized coal flow, it is specifically used for:

[0169] The current blending ratio and the preset target blending ratio are mapped to the same numerical reference axis to obtain the first reference point and the second reference point of the pulverized coal flow, and the interval between the first reference point and the second reference point is used as the proportional deviation of the pulverized coal flow.

[0170] The blending state of the pulverized coal flow is determined based on the positional relationship between the first reference point and the second reference point. If the first reference point is located before the second reference point, the pulverized coal flow is determined to be under-blended; otherwise, the pulverized coal flow is determined to be over-blended.

[0171] The first and second characteristic intensities of the first and second coal types are extracted from the temperature-flow rate dual-corrected spectral data, and the first and second characteristic intensities are accumulated and merged to obtain the total coal flux characterization value of the coal powder flow.

[0172] Based on the total pulverized coal flux characterization value, the deviation tolerance range of the pulverized coal flow is determined, and the width of the deviation tolerance range is positively correlated with the total pulverized coal flux characterization value.

[0173] The proportional deviation is compared with the deviation tolerance range to obtain the deviation level of the proportional deviation.

[0174] The blending state and the deviation level are integrated and encoded to obtain the blending ratio control command for the pulverized coal flow.

[0175] A continuous linear numerical reference axis from 0 to 1 is established. The starting point of the numerical reference axis corresponds to 0% of the first coal type blending ratio, and the ending point of the numerical reference axis corresponds to 100% of the first coal type blending ratio. The current blending ratio is converted into a point on the numerical reference axis according to a linear ratio. This point is the first reference point of the pulverized coal flow. The preset target blending ratio is converted into another point on the numerical reference axis according to the same linear ratio. This point is the second reference point of the pulverized coal flow. The interval between the first reference point and the second reference point on the numerical reference axis is calculated. This interval is the proportional deviation of the pulverized coal flow.

[0176] On the numerical reference axis, the smaller the value, the further forward the position, and the larger the value, the further backward the position. Compare the values ​​of the first reference point and the second reference point on the numerical reference axis. If the value of the first reference point is less than the value of the second reference point, it is determined that the first reference point is before the second reference point, and the pulverized coal flow is in an under-blended state. If the value of the first reference point is greater than or equal to the value of the second reference point, it is determined that the first reference point is after the second reference point or coincides with the second reference point, and the pulverized coal flow is in an over-blended state.

[0177] The sulfur element characteristic intensity corresponding to the first coal type is extracted from the temperature-flow rate dual-corrected spectral data as the first characteristic intensity, and the calcium element characteristic intensity corresponding to the second coal type is extracted as the second characteristic intensity. The first characteristic intensity and the second characteristic intensity are numerically added together, and the sum is the total coal flux characterization value of the coal powder flow.

[0178] A one-to-one correspondence table between the total flux of pulverized coal and the width of the deviation tolerance interval is established in advance. The width of the deviation tolerance interval in the correspondence table increases linearly with the increase of the total flux of pulverized coal. Based on the calculated total flux of pulverized coal, the matching deviation tolerance interval width is found in the correspondence table. Taking the preset target blending ratio as the center and the found deviation tolerance interval width as half width, a continuous numerical interval is formed. This numerical interval is the deviation tolerance interval of the pulverized coal flow.

[0179] The proportional deviation is compared numerically with the lower and upper boundaries of the deviation tolerance interval. If the proportional deviation is greater than or equal to the lower boundary of the deviation tolerance interval and less than or equal to the upper boundary of the deviation tolerance interval, the proportional deviation is determined to be within the deviation tolerance interval and is marked as a fine-tuning deviation level. If the proportional deviation is less than the lower boundary of the deviation tolerance interval or greater than the upper boundary of the deviation tolerance interval, the proportional deviation is determined to be outside the deviation tolerance interval and is marked as a coarse-tuning deviation level.

[0180] The determined blending status and deviation level are combined to form four unique combination types. A corresponding blending ratio control command is generated according to each combination type. If the combination type is under-blended and the deviation level is fine-tuned, the generated command is to increase the feeder speed by a small amount. If the combination type is under-blended and the deviation level is coarse-tuned, the generated command is to increase the feeder speed by a large amount. If the combination type is over-blended and the deviation level is fine-tuned, the generated command is to decrease the feeder speed by a small amount. If the combination type is over-blended and the deviation level is coarse-tuned, the generated command is to decrease the feeder speed by a large amount.

[0181] The beneficial effects are as follows: by establishing a unified linear numerical reference axis, the current blending ratio and the target blending ratio are standardized and compared, ensuring the consistency and accuracy of the ratio deviation calculation; the blending state judgment logic based on the reference point position relationship is clear and unambiguous; the total coal flux characterization value obtained by accumulating the coal type characteristic intensity can accurately reflect the actual coal flux transported in the pipeline; the deviation tolerance interval width is dynamically adjusted based on the total flux to achieve adaptive deviation control under different transport loads, avoiding the problems of over-regulation under low load and under-regulation under high load; the graded deviation level classification realizes the fine matching of the control intensity; the control command generated by the integrated code directly corresponds to the coal feeder speed adjustment operation and can be executed without additional conversion, which significantly improves the response speed and control accuracy of coal blending control and ensures the stability of boiler combustion conditions.

[0182] Reference Figure 2 The diagram shown is a flowchart illustrating an online monitoring method for coal blending based on LIBS technology according to an embodiment of the present invention. In this embodiment, the online monitoring method for coal blending based on LIBS technology includes:

[0183] A. Obtain the original spectral data and flow velocity value of the pulverized coal flow, and perform intensity compensation on the original spectral data based on the flow velocity value to obtain the compensated spectral data of the pulverized coal flow;

[0184] B. Select characteristic spectral lines with different excitation energy levels from the compensated spectral data, and determine the plasma electron temperature of the pulverized coal flow based on the intensity ratio between the characteristic spectral lines;

[0185] C. Based on the plasma electron temperature, thermodynamic equilibrium correction is performed on the compensated spectral data to obtain the temperature-flow rate dual-corrected spectral data of the pulverized coal flow;

[0186] D. Extract the first spectral line intensity of the first coal type and the second spectral line intensity of the second coal type from the temperature-flow rate dual-corrected spectral data, and take the geometric average of the first spectral line intensity and the second spectral line intensity as the first comprehensive intensity and the second comprehensive intensity of the pulverized coal flow, respectively.

[0187] E. Based on the ratio between the first comprehensive intensity and the second comprehensive intensity, and combined with the temperature-flow rate dual-corrected spectral data, the current blending ratio of the pulverized coal flow is obtained;

[0188] F. Compare and analyze the current blending ratio with the preset target blending ratio to obtain the ratio deviation of the pulverized coal flow, and generate a blending ratio control command for the pulverized coal flow.

[0189] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0190] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An online monitoring system for coal blending based on LIBS technology, characterized in that, The system includes a data acquisition module, a temperature conversion module, a balance correction module, an intensity calculation module, a ratio determination module, and a command control module, wherein: The data acquisition module is used to acquire the original spectral data and flow velocity value of the pulverized coal flow, and to perform intensity compensation on the original spectral data based on the flow velocity value to obtain the compensated spectral data of the pulverized coal flow. The temperature conversion module is used to select characteristic spectral lines with different excitation energy levels from the compensated spectral data, and determine the plasma electron temperature of the pulverized coal flow based on the intensity ratio between the characteristic spectral lines. The balance correction module is used to perform thermodynamic balance correction on the compensated spectral data based on the plasma electron temperature to obtain temperature-flow rate dual-corrected spectral data of the pulverized coal flow. The intensity calculation module is used to extract the first spectral line intensity of the first coal type and the second spectral line intensity of the second coal type from the temperature-flow rate dual-corrected spectral data, and to take the geometric average of the first spectral line intensity and the second spectral line intensity as the first comprehensive intensity and the second comprehensive intensity of the coal flow, respectively. The ratio determination module is used to obtain the current blending ratio of the pulverized coal flow based on the ratio between the first comprehensive strength and the second comprehensive strength, combined with the temperature-flow rate dual-corrected spectral data. The instruction control module is used to compare and analyze the current blending ratio with the preset target blending ratio to obtain the ratio deviation of the pulverized coal flow, and generate the blending ratio control instruction of the pulverized coal flow.

2. The online monitoring system for coal blending based on LIBS technology as described in claim 1, characterized in that, When the data acquisition module acquires the raw spectral data and flow velocity value of the pulverized coal flow, and performs intensity compensation on the raw spectral data based on the flow velocity value to obtain the compensated spectral data of the pulverized coal flow, it is specifically used for: At the monitoring window of the coal blending and conveying pipeline, laser pulses are emitted towards the flowing coal powder stream to collect the raw spectral data of the coal powder stream and simultaneously measure the flow velocity of the coal powder stream. The flow rate value is aligned pulse-by-pulse with the spectral signal in the original spectral data to obtain the flow rate-spectral pairing record of the pulverized coal flow. Based on the proportional relationship between the flow velocity value and the original spectral signal intensity value in the flow velocity-spectrum pairing record, the dynamic compensation coefficient of the original spectral data is determined; The dynamic compensation coefficient is used as the weight of the original spectral signal intensity value to obtain the weighted spectral signal of the pulverized coal flow; The weighted spectral signals are combined by median merging to obtain the compensated spectral data of the pulverized coal flow.

3. The online monitoring system for coal blending based on LIBS technology as described in claim 1, characterized in that, When the temperature conversion module selects characteristic spectral lines with different excitation energy levels from the compensated spectral data and determines the plasma electron temperature of the pulverized coal flow based on the intensity ratio between the characteristic spectral lines, it is specifically used for: The peak intensity value of the spectral line and the excitation energy level value corresponding to the spectral line are extracted from the compensated spectral data to obtain the original data pair of the pulverized coal flow; Arrange the original data pairs in ascending order of the excitation energy level values ​​to obtain the sorted spectral sequence of the pulverized coal flow; The energy level difference and energy level intensity ratio between adjacent spectral lines in the sorted spectral line sequence are statistically analyzed to obtain the energy level difference sequence and intensity ratio sequence of the pulverized coal flow. The natural logarithm of the intensity ratios in the intensity ratio sequence is taken to obtain the logarithmic ratio sequence of the pulverized coal flow; The plasma electron temperature of the pulverized coal flow is calculated based on the energy level difference sequence and the logarithmic ratio sequence.

4. The online monitoring system for coal blending based on LIBS technology as described in claim 3, characterized in that, The formula for calculating the plasma electron temperature is: ； in, This indicates the plasma electron temperature. Represents the first energy level in the energy level difference sequence. One energy level difference, Represents the first in the logarithmic ratio sequence Log-ratio, This represents the preset Boltzmann constant. This represents the index variable for summation.

5. The online monitoring system for coal blending based on LIBS technology as described in claim 1, characterized in that, When the balance correction module performs thermodynamic balance correction on the compensated spectral data based on the plasma electron temperature to obtain the temperature-flow velocity dual-corrected spectral data of the pulverized coal flow, it is specifically used for: Wavelength decoupling is performed on the compensated spectral data to obtain the wavelength channels of the compensated spectral data, and the corresponding excitation energy level values ​​are associated with the wavelength channels. Based on the plasma electron temperature, the temperature correction weight of the wavelength channel is determined. The value of the temperature correction weight is inversely correlated with the plasma electron temperature and positively correlated with the excitation energy level value of the wavelength channel. The original intensity value of the wavelength channel is assigned the temperature correction weight to obtain the initial corrected intensity value of the wavelength channel; The initial correction intensity value is normalized to obtain the standard spectral data of the pulverized coal flow. The standard spectral data and the flow rate value are channel-cascaded and registered to obtain the flow rate label corresponding to the wavelength channel, and the spectral data with the flow rate label is used as the temperature-flow rate dual-corrected spectral data of the pulverized coal flow.

6. The online monitoring system for coal blending based on LIBS technology as described in claim 5, characterized in that, When the balance correction module determines the temperature correction weight of the wavelength channel based on the plasma electron temperature, it is specifically used for: The excitation energy level values ​​of the wavelength channels are extracted from the compensated spectral data, and the wavelength channels are arranged in ascending order of the excitation energy level values ​​to obtain the channel sequence of the wavelength channels; The reference correction value of the wavelength channel is obtained by performing differential analysis between the plasma electron temperature and the excitation energy level value of the first channel in the channel sequence. Traverse the channel sequence and use the difference between the excitation energy level value of the current channel and the excitation energy level value of the previous channel as the step increment of the wavelength channel; The temperature correction weight of the wavelength channel is obtained by combining the cumulative sum of the reference correction value and the step increment.

7. The online monitoring system for coal blending based on LIBS technology as described in claim 1, characterized in that, When the intensity calculation module extracts the first spectral line intensity of the first coal type and the second spectral line intensity of the second coal type from the temperature-flow rate dual-corrected spectral data, it is specifically used for: The temperature-flow rate dual-corrected spectral data is resolved into a multi-dimensional data record containing wavelength channels, normalized intensity, excitation energy level, and flow rate value. Based on the standard characteristic wavelength position of sulfur element in the first coal type, the first candidate channel with the smallest deviation from the standard characteristic wavelength position of sulfur element is located in the multidimensional data record, and the normalized intensity value of the first candidate channel is aggregated into the sulfur element characteristic intensity of the first coal type. Based on the standard characteristic wavelength position of calcium element in the second coal type, the multidimensional data records are screened and matched to obtain the second candidate channel of the second coal type. The normalized intensity values ​​in the second candidate channel are weighted and fused to obtain the characteristic intensity of calcium element in the second coal type; By integrating the iron element data from the multidimensional data records, the characteristic intensity of iron element in the pulverized coal flow is obtained. The characteristic intensity of the iron element is correlated and paired with the characteristic intensity of the sulfur element and the characteristic intensity of the calcium element to obtain the first spectral line intensity set and the second spectral line intensity set of the pulverized coal flow; The maximum value of the first spectral line intensity set is taken as the first spectral line intensity of the pulverized coal flow, and the maximum value of the second spectral line intensity set is taken as the second spectral line intensity of the pulverized coal flow.

8. The online monitoring system for coal blending based on LIBS technology as described in claim 1, characterized in that, When the ratio determination module determines the current blending ratio of the pulverized coal flow based on the ratio between the first and second comprehensive intensities and the temperature-flow-rate dual-corrected spectral data, it is specifically used for: The ratio between the first comprehensive intensity and the second comprehensive intensity in the pulverized coal flow is taken as the initial intensity ratio of the pulverized coal flow; The temperature-flow-rate dual-corrected spectral data are integrated to obtain the carbon and nitrogen intensity values ​​of the pulverized coal flow. The median values ​​of the carbon and nitrogen elements are combined to obtain the carbon-representative intensity and nitrogen-representative intensity of the pulverized coal flow, and the ratio between the carbon-representative intensity and the nitrogen-representative intensity is taken as the carbon-nitrogen intensity ratio of the pulverized coal flow. By mapping the initial intensity ratio to the carbon-nitrogen intensity ratio, the two-dimensional ratio coordinate points of the pulverized coal flow are obtained; The distance between the two-dimensional ratio coordinate points and the standard coordinate points pre-stored in the blending ratio feature library is compared to obtain the current blending ratio of the pulverized coal flow.

9. The online monitoring system for coal blending based on LIBS technology as described in claim 1, characterized in that, When the instruction control module performs a comparative analysis of the current blending ratio with the preset target blending ratio to obtain the ratio deviation of the pulverized coal flow and generates a blending ratio control instruction for the pulverized coal flow, it is specifically used for: The current blending ratio and the preset target blending ratio are mapped to the same numerical reference axis to obtain the first reference point and the second reference point of the pulverized coal flow, and the interval between the first reference point and the second reference point is used as the proportional deviation of the pulverized coal flow. The blending state of the pulverized coal flow is determined based on the positional relationship between the first reference point and the second reference point. If the first reference point is located before the second reference point, the pulverized coal flow is determined to be under-blended; otherwise, the pulverized coal flow is determined to be over-blended. The first and second characteristic intensities of the first and second coal types are extracted from the temperature-flow rate dual-corrected spectral data, and the first and second characteristic intensities are accumulated and merged to obtain the total coal flux characterization value of the coal powder flow. Based on the total pulverized coal flux characterization value, the deviation tolerance range of the pulverized coal flow is determined, and the width of the deviation tolerance range is positively correlated with the total pulverized coal flux characterization value. The proportional deviation is compared with the deviation tolerance range to obtain the deviation level of the proportional deviation. The blending state and the deviation level are integrated and encoded to obtain the blending ratio control command for the pulverized coal flow.

10. A method for online monitoring of coal blending based on LIBS technology, characterized in that, The method for using the LIBS-based online monitoring system for coal blending according to claim 1, wherein the method is as follows: A. Obtain the original spectral data and flow velocity value of the pulverized coal flow, and perform intensity compensation on the original spectral data based on the flow velocity value to obtain the compensated spectral data of the pulverized coal flow; B. Select characteristic spectral lines with different excitation energy levels from the compensated spectral data, and determine the plasma electron temperature of the pulverized coal flow based on the intensity ratio between the characteristic spectral lines; C. Based on the plasma electron temperature, thermodynamic equilibrium correction is performed on the compensated spectral data to obtain the temperature-flow rate dual-corrected spectral data of the pulverized coal flow; D. Extract the first spectral line intensity of the first coal type and the second spectral line intensity of the second coal type from the temperature-flow rate dual-corrected spectral data, and take the geometric average of the first spectral line intensity and the second spectral line intensity as the first comprehensive intensity and the second comprehensive intensity of the pulverized coal flow, respectively. E. Based on the ratio between the first comprehensive intensity and the second comprehensive intensity, and combined with the temperature-flow rate dual-corrected spectral data, the current blending ratio of the pulverized coal flow is obtained; F. Compare and analyze the current blending ratio with the preset target blending ratio to obtain the ratio deviation of the pulverized coal flow, and generate a blending ratio control command for the pulverized coal flow.

Images