A Dynamic Control Method and System for Ammonia-Coal Co-firing Based on Multispectral Fusion

Abstract

This invention discloses a dynamic control method and system for ammonia-coal co-firing based on multispectral fusion. The method involves setting up LIBS, PLIF, and TDLAS spectral units at the fuel inlet, reaction core region, and flue gas outlet of the ammonia-coal co-firing system, respectively. LIBS is used to analyze coal composition and fuel nitrogen content in real time at the front end, capturing initial reaction parameters. PLIF is used to monitor the transient spatial distribution of intermediate products in the combustion core region, revealing the microscopic evolution characteristics of nitrogen in the flame. TDLAS is used to accurately quantify the concentration of nitrogen-containing components on the flue gas side. Finally, a dynamic model is established by integrating multidimensional monitoring data, and the combustion organization parameters of the ammonia-coal co-firing system are optimized and adjusted in real time based on spectral feedback signals. This provides solid technical support for deeply revealing the combustion mechanism of ammonia-coal and promoting its large-scale industrial application.

Description

Technical Field

[0001] This invention relates to the field of combustion diagnostics technology, and in particular to a dynamic control method and system for ammonia-coal co-firing based on multispectral fusion. Background Technology

[0002] Green ammonia is an emerging clean energy carrier. Its preparation process involves using renewable energy to electrolyze water to produce hydrogen, which is then catalytically synthesized with nitrogen. Its carbon footprint over its entire life cycle is close to zero, making it considered an ideal zero-carbon fuel. The combustion of green ammonia only produces nitrogen and water vapor, with no emissions of carbonaceous pollutants such as CO2. Therefore, introducing green ammonia for co-firing in coal-fired power plants is seen as an effective strategy for controlling carbon emissions at their source. However, research on ammonia-coal co-firing technology is still in its early stages, with significant shortcomings in basic theory, key combustion technology development, and engineering application systems. This leads to difficulties in ignition, poor flame stability, and high levels of nitrogen oxides (NOx) in flue gas. x The core issues of high emission concentrations remain prominent. To optimize and adjust the combustion of power plant boilers, it is urgent to thoroughly clarify the gas-solid two-phase flow reaction characteristics in the transient high-temperature combustion field, such as the coal quality characteristics in the early stage of combustion, the component distribution field in the middle and later stages of combustion, and the change law of reactant concentration. This is of great significance for the rapid response and stable combustion of coal-fired boilers.

[0003] Combustion process diagnostics research is mainly divided into two categories: invasive and non-invasive diagnostics. Commonly used invasive diagnostic methods include thermocouple temperature measurement, hot-wire velocimetry, and online sampling analysis. These methods are characterized by simple principles, reliable measurements, and convenient operation, but they also disrupt the reaction process of the combustion field, and the measurement results do not reflect the true combustion process. Non-invasive diagnostics, based on flame spectroscopy diagnostic technology, has unique advantages such as non-contact and non-destructive testing, high sensitivity, real-time dynamic measurement, and the ability to simultaneously measure multiple components and parameters.

[0004] While non-invasive diagnostics offer significant advantages for flame diagnosis, ammonia-coal co-firing, while substantially reducing carbon dioxide emissions, also alters key processes within the combustion chamber, such as flame morphology, gas-solid reaction, pulverized coal ignition behavior, and the combustion and burnout of char, thus affecting the stable and efficient combustion of ammonia-coal. Furthermore, ammonia, as a nitrogen-containing fuel, produces NO during combustion. xThe problem of increased emissions is a significant concern. Therefore, a deep understanding of nitrogen conversion pathways and accurate acquisition of flame reaction characteristics are crucial for achieving clean combustion of ammonia-coal. Traditional spectroscopic monitoring technologies applicable to ammonia-coal co-firing scenarios have significant limitations: LIBS technology is mainly used for coal quality analysis and cannot detect intermediate products during combustion; PLIF technology can only achieve two-dimensional imaging of specific free radicals and lacks quantitative component analysis; TDLAS, while capable of accurately measuring specific gas concentrations, cannot acquire transient reaction information during combustion. The physical principles of these three spectroscopic technologies differ fundamentally: LIBS relies on atomic emission spectra, PLIF is based on molecular fluorescence excitation mechanisms, and TDLAS utilizes characteristic absorption spectra of molecules. This makes it difficult for any single technology to comprehensively cover the complete information chain from fuel characteristics and combustion process to products. This limitation of a single technology forms a significant technical barrier, severely restricting in-depth analysis of the mechanism of ammonia-coal co-firing and the optimization and upgrading of related engineering technologies. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a dynamic control method and system for ammonia-coal co-firing based on multispectral fusion, which mainly solves the problems in the background technology.

[0006] To address the aforementioned technical problems, the first aspect of this invention proposes a dynamic control method for ammonia-coal co-firing based on multispectral fusion, comprising the following steps:

[0007] The ammonia-coal co-firing system was operated according to the initial combustion organization parameters;

[0008] LIBS spectra of coal samples within the fuel inlet range of the ammonia-coal co-firing system are obtained, and the LIBS spectra are analyzed to obtain industrial analysis index results and elemental analysis index results.

[0009] The PLIF spectrum of the reaction core region of the ammonia-coal co-firing system was obtained, and the PLIF spectrum was analyzed to obtain a transient two-dimensional distribution image of the combustion intermediate products.

[0010] The TDLAS spectrum of the flue gas outlet of the ammonia-coal co-firing system is obtained, and the TDLAS spectrum is analyzed to obtain the exhaust gas concentration data.

[0011] A dynamic model based on the generation and conversion of ammonia-coal co-firing is established. The results of the industrial analysis indicators, the results of the elemental analysis indicators, the transient two-dimensional distribution image, and the exhaust gas concentration data are input into the dynamic model as spectral feedback signals. The optimal combustion organization parameters of the ammonia-coal co-firing system are obtained by solving the model under the constraints of pollutant control, combustion efficiency, operational stability, and economy.

[0012] In some implementations, the process of constructing the dynamic model includes:

[0013] The instantaneous nitrogen concentration is extracted from the results of the elemental analysis. The instantaneous nitrogen concentration is combined with the coal feed flow rate measured at the fuel inlet and the real-time ammonia injection rate fed back by the ammonia flow meter for data synchronization and vector weighting calculation to obtain the total instantaneous nitrogen flux.

[0014] The flame feature signals of the transient two-dimensional distribution image are extracted using a convolutional neural network;

[0015] Extract nitrogen oxide concentration data from the exhaust gas concentration data;

[0016] The instantaneous flux of total nitrogen and the flame characteristic signal are standardized to obtain standardized data;

[0017] A dynamic model based on a neural network is constructed according to the preset network parameters. The standardized data is used as the input features, and the optimal combustion organization parameters are used as the output variables. The optimal solution algorithm is used to optimize the network parameters so that the optimal combustion organization parameters meet the constraints.

[0018] The nitrogen oxide concentration data is used to verify whether the output of the dynamic model meets the requirements. If yes, the current model parameters are saved; otherwise, the network parameters are adjusted until the output of the dynamic model meets the requirements.

[0019] In some embodiments, the industrial analysis results include at least the unit calorific value, volatile matter, and ash content of the coal sample, and the elemental analysis results include at least the composition ratios of carbon, nitrogen, and sulfur in the coal sample.

[0020] In some embodiments, the combustion intermediates include at least amino functional groups, cyanide functional groups, and hydroxyl functional groups.

[0021] In some embodiments, the exhaust gas concentration data includes concentration values ​​of different types of nitrogen-containing compounds.

[0022] In some embodiments, the combustion organization parameters include at least the air equivalence ratio, staged air distribution ratio, and swirl angle of the ammonia-coal co-firing system.

[0023] A second aspect of this invention proposes a dynamic control system for ammonia-coal co-firing based on multispectral fusion, comprising:

[0024] The ammonia-coal co-firing system is used to operate based on the input combustion organization parameters;

[0025] The LIBS spectral unit is used to acquire the LIBS spectrum of coal samples in the fuel inlet range of the ammonia-coal co-firing system, and to analyze the LIBS spectrum to obtain industrial analysis index results and elemental analysis index results.

[0026] The PLIF spectral unit is used to acquire the PLIF spectrum of the reaction core region of the ammonia-coal co-firing system, and to analyze the PLIF spectrum to obtain a transient two-dimensional distribution image of the combustion intermediate products.

[0027] The TDLAS spectral unit is used to acquire the TDLAS spectrum of the flue gas outlet of the ammonia-coal co-firing system, and to analyze the TDLAS spectrum to obtain the exhaust gas concentration data.

[0028] The central control unit is used to establish a dynamic model based on the generation and conversion of ammonia-coal co-firing. The industrial analysis index results, the elemental analysis index results, the transient two-dimensional distribution image, and the exhaust gas concentration data are input into the dynamic model as spectral feedback signals. The combustion organization parameters of the ammonia-coal co-firing system are optimized and adjusted in real time under the constraints of pollutant control, combustion efficiency, operational stability, and economy.

[0029] In some embodiments, the ammonia-coal co-firing system includes a frame, a swirl ammonia-coal burner mounted on the frame, and a mixing device connected to the input end of the swirl ammonia-coal burner. The input end of the mixing device is connected to an ammonia cylinder, a methane cylinder, and a coal feeding unit. The coal feeding unit includes a coal feeder, a hopper, and a negative pressure device connected in sequence, and also includes an air compressor and a mass flow controller installed at the output end of the air compressor. The mass flow controller is connected to the gas input end of the negative pressure device, and the discharge end of the negative pressure device is connected to the mixing device.

[0030] In some embodiments, the LIBS spectral unit includes a computer, a spectrometer, a timing controller, and a pulsed laser connected in sequence. A dichroic mirror and a focusing lens 16 are arranged in sequence between the pulsed laser and the feed inlet of the funnel. A light-collecting fiber is arranged on the reflection path of the dichroic mirror and is connected to the spectrometer.

[0031] In some embodiments, the PLIF spectral unit includes a pump laser and a dye laser. The output end of the dye laser is provided with a first reflecting mirror. A cylindrical lens and a spherical lens are sequentially arranged along the reflection path of the first reflecting mirror. The focal point of the spherical lens is aligned with the flame nozzle range of the swirling ammonia-coal burner. The unit also includes a timing controller, which is connected to an ICCD camera and the pump laser, respectively.

[0032] In some embodiments, the TDLAS spectral unit includes a first lock-in amplifier, a first laser controller, and a first laser connected in sequence, as well as a second lock-in amplifier, a second laser controller, and a second laser connected in sequence. It also includes a heating furnace, a gas absorption cell disposed within the heating furnace, and a detector. The input end of the gas absorption cell is connected to the flue gas outlet of the swirling ammonia-coal burner. The first laser and the second laser are combined within the gas absorption cell range via a beam splitter to form a combined beam. The combined beam enters the input end of the detector via a second reflector. The data output ends of the first and second lock-in amplifiers are connected to a data acquisition card. A third reflector, a rotating reflector, and the beam splitter are used to switch between the combined beam and the calibration light emitted by the helium-neon laser.

[0033] The beneficial effects of this invention are as follows: LIBS, PLIF, and TDLAS spectral units are respectively set at the fuel inlet, reaction core region, and flue gas outlet of the ammonia-coal co-firing system. LIBS is used to analyze the coal composition and fuel nitrogen content in real time at the front end and capture the initial reaction parameters. PLIF is used to monitor the transient spatial distribution of intermediate products in the combustion core region and reveal the microscopic evolution characteristics of nitrogen in the flame. TDLAS is used to accurately quantify the concentration of nitrogen-containing components on the flue gas side. Finally, a dynamic model is established by integrating multi-dimensional monitoring data, and the combustion organization parameters of the ammonia-coal co-firing system are optimized and adjusted in real time based on spectral feedback signals. This provides solid technical support for in-depth understanding of the combustion mechanism of ammonia-coal and promoting its large-scale industrial application. Attached Figure Description

[0034] Figure 1 This is a schematic diagram illustrating the construction process of the dynamic model disclosed in Embodiment 1 of the present invention;

[0035] Figure 2 This is a schematic diagram of the dynamic control system for ammonia-coal co-firing based on multispectral fusion disclosed in Embodiment 2 of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the content of this invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to this invention are shown in the accompanying drawings, not all of them.

[0037] Example 1

[0038] This embodiment proposes a dynamic control method for ammonia-coal co-firing based on multispectral fusion, including the following steps:

[0039] Step 1: Run the ammonia-coal co-firing system according to the initial combustion organization parameters;

[0040] Step 2: Obtain the LIBS spectrum of the coal sample in the fuel inlet range of the ammonia-coal co-firing system, analyze the LIBS spectrum, and obtain the industrial analysis index results and elemental analysis index results.

[0041] Step 3: Obtain the PLIF spectrum of the reaction core region of the ammonia-coal co-firing system, analyze the PLIF spectrum to obtain a transient two-dimensional distribution image of the combustion intermediate products.

[0042] Step 4: Obtain the TDLAS spectrum of the flue gas outlet of the ammonia-coal co-firing system, analyze the TDLAS spectrum, and obtain the exhaust gas concentration data.

[0043] Step 5: Establish a dynamic model based on the generation and conversion of ammonia-coal co-firing. The results of industrial analysis indicators, elemental analysis indicators, transient two-dimensional distribution images, and exhaust gas concentration data are used as spectral feedback signals to input into the dynamic model. With pollutant control, combustion efficiency, operational stability, and economy as constraints, the optimal combustion organization parameters of the ammonia-coal co-firing system are solved to achieve real-time closed-loop optimization and adjustment of the combustion organization parameters of the ammonia-coal co-firing system.

[0044] The combustion process of fuel encompasses its physicochemical properties, combustion reaction kinetics, and flue gas emission characteristics. Among these, fuel characteristics and reaction mechanisms are key factors determining the NOx emission patterns in flue gas. The persistently high NOx emissions during ammonia-coal co-firing stem from a lack of crucial information regarding the nitrogen (N) content in the coal at the burner inlet and the combustion evolution process. Therefore, this proposal aims to employ a dynamic model to perform offline modeling of fuel input, the combustion process, and flue gas output, enabling automatic adjustment of combustion organization parameters. In one example, such as... Figure 1 As shown, the process of constructing a dynamic model includes:

[0045] Step 501: Extract the instantaneous nitrogen concentration from the elemental analysis results. Combine the instantaneous nitrogen concentration with the coal feed flow rate measured at the fuel inlet and the real-time ammonia injection rate fed back by the ammonia flow meter for data synchronization and vector weighting calculation to obtain the total instantaneous nitrogen flux.

[0046] Total instantaneous nitrogen flux F entering the furnace N The calculation method is shown in the following formula:

[0047] (1)

[0048] In the formula, F N The total nitrogen mass flux entering the burner is expressed in g / min, where m is the mass flow rate of the coal entering the furnace, in g / min, and ω is the mass flow rate of the coal entering the furnace.N,LIBS Nitrogen content in coal measured in real time by LIBS, in % (m). N M is the molar mass of nitrogen, with a value of 14 g / mol. NH3 The value of ammonia is 17 g / mol.

[0049] Step 502: Use a convolutional neural network to extract flame feature signals from the transient two-dimensional distribution image;

[0050] For the two-dimensional distribution images of nitrogen-containing functional groups such as NH, NH2, CN, and NO captured by PLIF, a convolutional neural network (CNN) is used to extract PLIF feature signals. The formula for the two-dimensional convolution operation is as follows:

[0051] (2)

[0052] In the formula, (I * K) is the feature map generated after convolution, I is the input image or the feature matrix of the previous layer, K is the convolution kernel (Kernel / Filter), whose parameters are learned and used to capture flame features; (i, j): the coordinate index of the pixel on the feature map; (m, n) is the offset index of the element inside the convolution kernel.

[0053] Nonlinear activation functions commonly used in flame detection can effectively separate bright areas in an image:

[0054] (3)

[0055] In the formula, x is the original output value of the convolutional layer (usually including negative values), f(x) is the output after activation; max(0, x) means taking the larger value between 0 and x. If x ≤ 0, the neuron is suppressed (output is 0), which helps to remove background interference.

[0056] Step 503: Extract nitrogen oxide concentration data from exhaust gas concentration data;

[0057] Step 504: The instantaneous flux of total nitrogen and the flame characteristic signal are standardized to obtain standardized data;

[0058] Step 505: Construct a dynamic model based on a neural network according to the preset network parameters, using standardized data as input features and the optimal combustion organization parameters as output variables. Use the optimal solution algorithm to optimize the network parameters so that the optimal combustion organization parameters meet the constraints.

[0059] Step 506: Use nitrogen oxide concentration data to verify whether the output of the dynamic model meets the requirements. If yes, save the current model parameters; otherwise, adjust the network parameters until the output of the dynamic model meets the requirements.

[0060] The above-mentioned industrial analysis results should include at least the unit calorific value, volatile matter and ash content of the coal sample, and the elemental analysis results should include at least the composition ratio of carbon, nitrogen and sulfur elements of the coal sample.

[0061] The combustion intermediate products include at least amino functional groups, cyanide functional groups, and hydroxyl functional groups; the exhaust gas concentration data includes the concentration values ​​of different types of nitrogen-containing compounds; the combustion organization parameters include at least the air equivalence ratio, staged air distribution ratio, and swirl angle of the ammonia-coal co-firing system.

[0062] Example 2

[0063] This embodiment proposes a dynamic control system for ammonia-coal co-firing based on multispectral fusion, such as... Figure 2 As shown, it includes:

[0064] The ammonia-coal co-firing system is used to operate based on the input combustion organization parameters;

[0065] The LIBS spectral unit is used to obtain the LIBS spectrum of coal samples in the fuel inlet range of the ammonia-coal co-firing system. The LIBS spectrum is analyzed to obtain industrial analysis index results and elemental analysis index results.

[0066] The PLIF spectral unit is used to acquire the PLIF spectrum of the reaction core region of the ammonia-coal co-firing system. The PLIF spectrum is analyzed to obtain a transient two-dimensional distribution image of the combustion intermediate products.

[0067] The TDLAS spectral unit is used to acquire the TDLAS spectrum of the flue gas outlet of the ammonia-coal co-firing system, and to analyze the TDLAS spectrum to obtain the exhaust gas concentration data.

[0068] The central control unit is used to establish a dynamic model based on the generation and conversion of ammonia-coal co-firing. It inputs industrial analysis index results, elemental analysis index results, transient two-dimensional distribution images, and exhaust gas concentration data as spectral feedback signals into the dynamic model. Under the constraints of pollutant control, combustion efficiency, operational stability, and economy, it optimizes and adjusts the combustion organization parameters of the ammonia-coal co-firing system in real time.

[0069] In one example, the aforementioned ammonia-coal co-firing system includes a frame 1, a swirl ammonia-coal burner 2 mounted on the frame 1, and a mixing device 3 connected to the input end of the swirl ammonia-coal burner 2. The input end of the mixing device 3 is connected to an ammonia cylinder 4, a methane cylinder 5, and a coal feeding unit. The coal feeding unit includes a coal feeder 6, a funnel 7, and a negative pressure device 8 connected in sequence. It also includes an air compressor 9 and a mass flow controller 10 installed at the output end of the air compressor 9. The mass flow controller 10 is connected to the gas input end of the negative pressure device 8, and the discharge end of the negative pressure device 8 is connected to the mixing device 3.

[0070] The experimental procedure for the ammonia-coal co-firing system is as follows: First, the mass flow controller 10 is zeroed to ensure the accuracy of gas control. After the mass flow controller 10 is zeroed, the pressure reducing valves of the air compressor, ammonia cylinder 4, and methane cylinder 5 are opened. Air receives pulverized coal particles from the coal feeder 6 through the negative pressure device 8, and then enters the mixing device 3 to mix with NH3 and CH4. After the pulverized coal particles and gas are mixed, they are transported through pipelines to the bottom of the burner for combustion. CH4 is mainly used as the ignition medium. The supply is stopped immediately after the ammonia-coal mixed fuel is stably ignited.

[0071] In a specific experimental case, the total input power of the swirl ammonia-coal burner 2 was kept constant at 2.28 kW, and the ammonia blending ratio was set to 0%, 10%, 20%, and 30% according to the calorific value. Yihua coal was selected as the test coal sample, and the coal feeding rate was set to 5 g / min, 4.5 g / min, 4 g / min, and 3.5 g / min, respectively. The coal feeder 6 adopted the HPFG-P01 vibrating screw feeder independently developed by Huapu Testing Co., Ltd., with a maximum coal feeding rate of 10 g / min. The feed plate of the coal feeder 6 has undergone surface hardening treatment and features stable and continuous feeding with small horizontal deviation. Users can manually adjust it through the controller according to actual needs.

[0072] During the experiment, the equivalence ratio was set to 0.75, and the total air flow rate was maintained at 46 L / min. The ammonia flow rate was correspondingly controlled at 0 L / min, 0.9 L / min, 1.8 L / min, and 2.7 L / min. The mass flow controller 10 used a Beijing Qixing Huachuang D07-60B flow meter, while the ammonia cylinder 4 and methane cylinder 5 were controlled by D07-9E flow meters respectively. The control accuracy of all flow meters was ±2% (full scale).

[0073] In one example, the LIBS spectral unit includes a computer 11, a spectrometer 12, a timing controller 13, and a pulsed laser 14 connected in sequence. A dichroic mirror 15 and a focusing lens 16 are arranged in sequence between the pulsed laser 14 and the feed inlet of the funnel 7. A light-collecting fiber is arranged on the reflection path of the dichroic mirror 15 and is connected to the spectrometer 12.

[0074] The measurement process of the LIBS spectral unit is as follows: The laser emitted by the pulsed laser 14 passes through the dichroic mirror 15 and is focused by the focusing lens 16 onto the continuous coal powder flow, exciting and generating plasma. The plasma radiation light is collected through a co-directional light-collecting mode. First, it is collected and collimated by the focusing lens 16, then reflected by the dichroic mirror 15 and enters the light-collecting fiber. The light-collecting fiber transmits the light signal to the spectrometer 12, and finally, the spectrometer 12 performs photoelectric conversion to obtain the LIBS spectrum of the coal sample. The timing controller 13 is connected to the spectrometer 12 and the pulsed laser 14 to control the light output of the pulsed laser 14 and the timing of spectral acquisition to ensure the quality of spectral acquisition. The acquired spectrum is input into the computer 11, and the integrated quantitative spectral analysis algorithm is used to perform real-time coal quality analysis, finally outputting industrial analysis (calorific value, volatile matter, ash content) and elemental analysis (elements C, N, S) results.

[0075] Optionally, in this scheme, the pulsed laser 14 is preferably an Nd:YAG laser, with typical parameters set as wavelength 1064nm, pulse width 7 ns, single pulse energy 80 mJ, and repetition frequency 5 Hz.

[0076] The feeding rate can be changed by setting the vibration frequency and amplitude of the coal feeder 6. In this example, the feeding rate is calibrated by the coal powder mass flow rate, which is preferably g / min.

[0077] Even better, the laser focusing position is located at the center of the pulverized coal stream, 3 mm above the upper end face of the funnel 7;

[0078] Even better, the spectral acquisition delay is controlled by the timing controller 13 to be 900 ns relative to the laser emission; the spectral integration time is 1.05 ms.

[0079] The preferred dichroic mirror 15 is a long-pass dichroic mirror 15, which can transmit light with a wavelength greater than 900 nm and has a light reflectivity greater than 90% in the 200-800 nm band.

[0080] In one example, the PLIF spectral unit described above includes a pump laser 17 and a dye laser 18. The output end of the dye laser 18 is provided with a first reflector 19. A cylindrical lens 20 and a spherical lens 21 are sequentially arranged on the reflection path of the first reflector 19. The focal point of the spherical lens 21 is aligned with the flame orifice range of the swirling ammonia-coal burner 2. The unit also includes a timing controller 13, which is connected to the ICCD camera 22 and the pump laser 17.

[0081] Specifically, the PLIF spectral unit is used to obtain intermediate products from ammonia-coal co-firing and consists of a pump laser 17 (Q-smart850, Quantel), a dye laser 18 (Q-Scan, Quantel), a sheet laser shaping system, a timing controller 13 (DG535 digital timing controller), an ICCD camera 22 (DH734, Andor), a spherical lens 21, a cylindrical lens 20, and a first reflecting mirror 19.

[0082] The PLIF spectral unit operates as follows: First, the pump laser 17 outputs a laser beam with a wavelength of 1064 nm. After being frequency-doubled by a frequency-doubled crystal, a laser beam with a wavelength of 532 nm is obtained. This laser beam is then guided into the dye laser 18 through a mirror reflection system, thereby converting it into a 566 nm output. Subsequently, the laser beam is frequency-doubled again by a BBO crystal to generate OH excitation light with a wavelength of 283.55 nm and a single pulse energy of approximately 5 mJ. After passing through a sheet beam shaping system consisting of a cylindrical beam expander, a collimating lens, and a focusing lens, the beam is adjusted into a uniform sheet beam with a height of approximately 10 cm and a thickness of approximately 0.5 mm, which is used to excite the fluorescence of OH free radicals in the flame. Then, an ICCD camera 22 equipped with an ultraviolet lens (AZURE-8528UV, Qianting Optoelectronics) is used to collect the OH fluorescence signal, with a gate width of 20 μs and a gain set to 400 times. Fluorescence signals were acquired by an ICCD camera 22 (DH734, Andor) equipped with an ultraviolet lens (AZURE-8528UV, Qianting Optoelectronics), with a gate width set to 20 μs and a gain of 400x. A bandpass filter with a center wavelength of 310 nm and a bandwidth of 10 nm was installed in front of the lens of the ICCD camera 22 to effectively filter out excitation light scattering and background stray light interference. Throughout the experiment, laser emission and camera exposure were precisely synchronized by a timing controller 13 (DG535) to achieve transient two-dimensional distribution imaging of OH radicals in the ammonia-coal combustion flame, thereby revealing the structural characteristics of the flame reaction zone and turbulent combustion behavior.

[0083] In one example, the aforementioned TDLAS spectral unit includes a first lock-in amplifier 23, a first laser controller 24, and a first laser 25 connected in sequence, and a second lock-in amplifier 26, a second laser controller 27, and a second laser 28 connected in sequence. It also includes a heating furnace 29, a gas absorption cell 30 disposed within the heating furnace 29, and a detector 31. The input end of the gas absorption cell 30 is connected to the flue gas outlet of the swirling ammonia-coal burner 2. The first laser 25 and the second laser 28 are combined within the gas absorption cell 30 via a beam splitter 36 to form a combined beam. The combined beam enters the input end of the detector 31 via a second reflector 32. The data output ends of the first lock-in amplifier 23 and the second lock-in amplifier 26 are connected to a data acquisition card 33. The combined beam and the calibration light emitted by the helium-neon laser 37 are switched by the third reflector 34, the rotating reflector 35, and the beam splitter 36.

[0084] Specifically, the TDLAS spectral unit employs frequency division multiplexing (FDM) technology to achieve simultaneous detection of NH3 and NO. The external modulation signal is generated by two lock-in amplifiers (HPLIA, Healthy Photon). Each lock-in amplifier integrates a signal generator, loading the two first lasers 25 and the second laser 28 (QCL lasers) with the following modulation signals: a 2Hz sawtooth wave superimposed with an 18kHz sine wave, and a 2Hz sawtooth wave superimposed with a 6kHz sine wave. This modulation signal modulates the laser drive current via the first laser controller 24 and the second laser controller 27, thereby generating a stable frequency scanning light source. Under these modulation conditions, both the second harmonic (2f) signals of NH3 and NO exhibit high signal-to-noise ratios. The two mid-infrared laser beams pass through a beam splitter 36 (BSW710, Thorlabs) made of zinc selenide crystal, and after beam combining, enter a multi-path gas absorption cell 30. To facilitate optical path calibration, the system uses a helium-neon laser 37 (DH-HN250, Daheng Optoelectronics) as an auxiliary indicator light source. The on / off state of the helium-neon laser and the mid-infrared beam is switched by a reflector mounted on a 90° rotating bracket to achieve rapid optical path alignment.

[0085] To simulate the high-temperature flue environment of an industrial flue while preventing gas condensation, a gas absorption cell 30 is placed within an electric heating furnace 29. The laser beam is reflected four times within the gas absorption cell 30 before exiting, with an effective optical path of 1.5 meters. The emitted laser beam is reflected by a mirror to a detector 31 (HgCdTe detector, HPPD-MA, Healthyphoto). The detector 31 converts the light intensity signal into an electrical signal, which is then input into two lock-in amplifiers for demodulation to obtain the 2f signals of NH3 and NO. These signals are subsequently acquired by a data acquisition card 33 (USB-6363, NI) and transmitted to a computer 11, where software processing calculates the actual concentration values.

[0086] In addition, to accurately monitor the gas temperature in the gas absorption cell 30, a type K thermocouple is inserted into the gas absorption cell 30 and sealed. The thermocouple is connected to a temperature recorder (UT3208, Uni-Trend) to record the internal temperature of the gas absorption cell 30 in real time, thereby ensuring measurement accuracy and the stability of gas absorption parameters.

[0087] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A dynamic control method for ammonia-coal co-firing based on multispectral fusion, characterized in that, Includes the following steps: The ammonia-coal co-firing system was operated according to the initial combustion organization parameters; LIBS spectra of coal samples within the fuel inlet range of the ammonia-coal co-firing system are obtained, and the LIBS spectra are analyzed to obtain industrial analysis index results and elemental analysis index results. The PLIF spectrum of the reaction core region of the ammonia-coal co-firing system was obtained, and the PLIF spectrum was analyzed to obtain a transient two-dimensional distribution image of the combustion intermediate products. The TDLAS spectrum of the flue gas outlet of the ammonia-coal co-firing system is obtained, and the TDLAS spectrum is analyzed to obtain the exhaust gas concentration data. A dynamic model based on the generation and conversion of ammonia-coal co-firing is established. The results of the industrial analysis indicators, the results of the elemental analysis indicators, the transient two-dimensional distribution image, and the exhaust gas concentration data are input into the dynamic model as spectral feedback signals. The optimal combustion organization parameters of the ammonia-coal co-firing system are obtained by solving the model under the constraints of pollutant control, combustion efficiency, operational stability, and economy.

2. The dynamic control method for ammonia-coal co-firing based on multispectral fusion as described in claim 1, characterized in that, The process of constructing the dynamic model includes: The instantaneous nitrogen concentration is extracted from the results of the elemental analysis. The instantaneous nitrogen concentration is combined with the coal feed flow rate measured at the fuel inlet and the real-time ammonia injection rate fed back by the ammonia flow meter for data synchronization and vector weighting calculation to obtain the total instantaneous nitrogen flux. The flame feature signals of the transient two-dimensional distribution image are extracted using a convolutional neural network; Extract nitrogen oxide concentration data from the exhaust gas concentration data; The instantaneous flux of total nitrogen and the flame characteristic signal are standardized to obtain standardized data; A dynamic model based on a neural network is constructed according to the preset network parameters. The standardized data is used as the input features, and the optimal combustion organization parameters are used as the output variables. The optimal solution algorithm is used to optimize the network parameters so that the optimal combustion organization parameters meet the constraints. The nitrogen oxide concentration data is used to verify whether the output of the dynamic model meets the requirements. If yes, the current model parameters are saved; otherwise, the network parameters are adjusted until the output of the dynamic model meets the requirements.

3. The dynamic control method for ammonia-coal co-firing based on multispectral fusion as described in claim 1, characterized in that, The industrial analysis results shall include at least the unit calorific value, volatile matter and ash content of the coal sample, and the elemental analysis results shall include at least the composition ratio of carbon, nitrogen and sulfur elements of the coal sample.

4. The dynamic control method for ammonia-coal co-firing based on multispectral fusion as described in claim 1, characterized in that, The combustion intermediates include at least amino functional groups, cyanide functional groups, and hydroxyl functional groups.

5. The dynamic control method for ammonia-coal co-firing based on multispectral fusion as described in claim 1, characterized in that, The combustion organization parameters include at least the air equivalence ratio, staged air distribution ratio, and swirl angle of the ammonia-coal co-firing system.

6. A dynamic control system for ammonia-coal co-firing based on multispectral fusion, characterized in that, include: The ammonia-coal co-firing system is used to operate based on the input combustion organization parameters; The LIBS spectral unit is used to acquire the LIBS spectrum of coal samples in the fuel inlet range of the ammonia-coal co-firing system, and to analyze the LIBS spectrum to obtain industrial analysis index results and elemental analysis index results. The PLIF spectral unit is used to acquire the PLIF spectrum of the reaction core region of the ammonia-coal co-firing system, and to analyze the PLIF spectrum to obtain a transient two-dimensional distribution image of the combustion intermediate products. The TDLAS spectral unit is used to acquire the TDLAS spectrum of the flue gas outlet of the ammonia-coal co-firing system, and to analyze the TDLAS spectrum to obtain the exhaust gas concentration data. The central control unit is used to establish a dynamic model based on the generation and conversion of ammonia-coal co-firing. The industrial analysis index results, the elemental analysis index results, the transient two-dimensional distribution image, and the exhaust gas concentration data are input into the dynamic model as spectral feedback signals. The combustion organization parameters of the ammonia-coal co-firing system are optimized and adjusted in real time under the constraints of pollutant control, combustion efficiency, operational stability, and economy.

7. The dynamic control system for ammonia-coal co-firing based on multispectral fusion as described in claim 6, characterized in that, The ammonia-coal co-firing system includes a frame, a swirl ammonia-coal burner mounted on the frame, and a mixing device connected to the input end of the swirl ammonia-coal burner. The input end of the mixing device is connected to an ammonia cylinder, a methane cylinder, and a coal feeding unit. The coal feeding unit includes a coal feeder, a hopper, and a negative pressure device connected in sequence, and also includes an air compressor and a mass flow controller installed at the output end of the air compressor. The mass flow controller is connected to the gas input end of the negative pressure device, and the discharge end of the negative pressure device is connected to the mixing device.

8. The dynamic control system for ammonia-coal co-firing based on multispectral fusion as described in claim 7, characterized in that, The LIBS spectral unit includes a computer, a spectrometer, a timing controller, and a pulsed laser connected in sequence. A dichroic mirror and a focusing lens 16 are arranged in sequence between the pulsed laser and the feed inlet of the funnel. A light-collecting fiber is arranged on the reflection path of the dichroic mirror and is connected to the spectrometer.

9. The dynamic control system for ammonia-coal co-firing based on multispectral fusion as described in claim 7, characterized in that, The PLIF spectral unit includes a pump laser and a dye laser. The output end of the dye laser is provided with a first reflecting mirror. A cylindrical lens and a spherical lens are arranged sequentially on the reflection path of the first reflecting mirror. The focal point of the spherical lens is aligned with the flame nozzle range of the swirling ammonia-coal burner. The unit also includes a timing controller, which is connected to an ICCD camera and the pump laser.

10. The dynamic control system for ammonia-coal co-firing based on multispectral fusion as described in claim 7, characterized in that, The TDLAS spectral unit includes a first lock-in amplifier, a first laser controller, and a first laser connected in sequence, as well as a second lock-in amplifier, a second laser controller, and a second laser connected in sequence. It also includes a heating furnace, a gas absorption cell located within the heating furnace, and a detector. The input end of the gas absorption cell is connected to the flue gas outlet of the swirling ammonia-coal burner. The first laser and the second laser are combined within the gas absorption cell range via a beam splitter to form a combined beam. The combined beam enters the input end of the detector via a second reflector. The data output ends of the first and second lock-in amplifiers are connected to a data acquisition card. A third reflector, a rotating reflector, and the beam splitter are used to switch between the combined beam and the calibration light emitted by the helium-neon laser.

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