Biomass and coal mixed combustion optimization method and system based on coal-fired boiler

Abstract

The invention discloses a biomass and coal mixed combustion optimization method and system based on a coal-fired boiler, and relates to the technical field of data processing.The biomass and coal mixed combustion optimization method comprises the steps that chemical structures of fire coal and biomass are determined, and a pyrolysis process mechanism is determined; interacting a hearth structure and combining a pyrolysis process mechanism to construct a hydrodynamic model; introducing a control element matrix-response element matrix; with the control element matrix-response element matrix as a reference, numerical simulation of the co-combustion process is carried out based on the hydrodynamic model, co-combustion simulation flow data is evaluated, and a co-combustion response coefficient is determined; and performing multi-round mixed combustion simulation and proofreading based on a mixed combustion response coefficient by controlling iteration of the element matrix, and selecting a target mixed combustion scheme. The technical problems that in the prior art, combustion is not uniform, pollutant emission is high, and efficiency is low in the fuel coal and biomass mixed combustion process are solved, and the technical effects that the combustion efficiency is improved, pollutant emission is reduced, and the stability of the combustion process is optimized are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a biomass and coal co - combustion optimization method and system based on a coal - fired boiler. Background Art

[0002] With the continuous growth of energy demand and the increasingly strict environmental protection requirements, the efficient utilization and emission reduction technologies of coal - fired boilers have become important topics in the modern energy industry. Traditional coal - fired boilers mostly adopt a single coal combustion method. However, with the increasing environmental protection pressure, single - coal combustion can no longer meet the requirements of green and low - carbon development. Especially the large amounts of carbon dioxide, nitrogen oxides, and sulfur oxides generated during coal combustion pose severe challenges to the environment and climate. To solve this problem, the co - combustion technology of biomass and coal has emerged, which can effectively reduce coal consumption and harmful gas emissions.

[0003] However, as the co - combustion ratio of biomass and coal continues to increase, the complexity and instability of the co - combustion process gradually emerge. How to reduce pollutant emissions while ensuring combustion efficiency has become a key technical problem in the development of the technology. Traditional co - combustion control methods often rely on empirical regulation, making it difficult to achieve real - time and accurate optimization control. Moreover, during the co - combustion process, problems such as uneven furnace temperature, incomplete combustion, and excessive emissions may occur, which cannot meet the requirements of modern industrial production for high efficiency and environmental protection. Summary of the Invention

[0004] This application provides a biomass and coal co - combustion optimization method and system based on a coal - fired boiler, which is used to solve the technical problems of uneven combustion, relatively high pollutant emissions, and low efficiency existing in the prior art during the co - combustion of coal and biomass.

[0005] In view of the above problems, this application provides a biomass and coal co - combustion optimization method and system based on a coal - fired boiler.

[0006] In the first aspect of this application, a biomass and coal co - combustion optimization method based on a coal - fired boiler is provided. The method includes:

[0007] Determine the chemical structures of coal and biomass, and use a TG-FTIR-MS combined analyzer to detect and determine the pyrolysis process mechanism; an interactive furnace structure, combined with the pyrolysis process mechanism, to construct a hydrodynamic model, wherein the furnace structure is provided with feed inlets at multiple positions from top to bottom along the furnace; introduce a control factor matrix - response factor matrix, wherein the control factor matrix is used to generate co-combustion parameter control conditions, and the response factor matrix is used as co-combustion evaluation conditions; based on the control factor matrix - response factor matrix, perform numerical simulation of the co-combustion process based on the hydrodynamic model and evaluate the co-combustion simulation flow data to determine the co-combustion response coefficient; through the iteration of the control factor matrix, conduct multiple rounds of co-combustion simulations and calibration based on the co-combustion response coefficient, and select a target co-combustion plan, wherein the target co-combustion plan is the optimal simulation plan.

[0008] In the second aspect of the present application, a biomass and coal co-combustion optimization system based on a coal-fired boiler is provided, and the system includes:

[0009] A chemical structure determination module, which is used to determine the chemical structures of coal and biomass, and use a TG-FTIR-MS combined analyzer to detect and determine the pyrolysis process mechanism; a model construction module, which is used to interact with the furnace structure, combined with the pyrolysis process mechanism, to construct a hydrodynamic model, wherein the furnace structure is provided with feed inlets at multiple positions from top to bottom along the furnace; a matrix introduction module, which is used to introduce a control factor matrix - response factor matrix, wherein the control factor matrix is used to generate co-combustion parameter control conditions, and the response factor matrix is used as co-combustion evaluation conditions; a co-combustion response coefficient determination module, which is used to perform numerical simulation of the co-combustion process based on the hydrodynamic model and evaluate the co-combustion simulation flow data based on the control factor matrix - response factor matrix to determine the co-combustion response coefficient; a target co-combustion plan determination module, which is used to conduct multiple rounds of co-combustion simulations and calibration based on the co-combustion response coefficient through the iteration of the control factor matrix, and select a target co-combustion plan, wherein the target co-combustion plan is the optimal simulation plan.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] This application determines the chemical structures of coal and biomass, and uses a TG-FTIR-MS combined analyzer to detect and determine the pyrolysis process mechanism. An interactive furnace structure is adopted. Combining with the pyrolysis process mechanism, a hydrodynamic model is constructed. Among them, the furnace structure is provided with feed inlets at multiple positions from top to bottom along the furnace. A control factor matrix-response factor matrix is introduced. Among them, the control factor matrix is used to generate the control conditions for co-combustion, and the response factor matrix is used as the co-combustion evaluation conditions. Based on the control factor matrix-response factor matrix, numerical simulation of the co-combustion process is carried out based on the hydrodynamic model, and the co-combustion simulation flow data is evaluated to determine the co-combustion response coefficient. Through the iteration of the control factor matrix, multiple rounds of co-combustion simulations and calibration based on the co-combustion response coefficient are carried out, and the target co-combustion plan is selected. Among them, the target co-combustion plan is the optimal simulation plan. The present invention solves the technical problems of uneven combustion, high pollutant emissions, and low efficiency existing in the process of co-combusting coal and biomass in the prior art. By adopting a method combining a hydrodynamic model, a control factor matrix, and a response factor matrix to optimize the co-combustion process, and carrying out multiple rounds of simulations and evaluations, the technical effects of improving combustion efficiency, reducing pollutant emissions, and optimizing the stability of the combustion process are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 It is a schematic flow chart of the biomass and coal co-combustion optimization method based on a coal-fired boiler provided by an embodiment of this application;

[0014] Figure 2 It is a schematic structural diagram of the biomass and coal co-combustion optimization system based on a coal-fired boiler provided by an embodiment of this application.

[0015] Description of the reference numerals: The chemical structure determination module 11, the model construction module 12, the matrix introduction module 13, the co-combustion response coefficient determination module 14, the target co-combustion plan determination module 15. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The present application provides a combustion optimization method and system for co - combustion of biomass and coal based on a coal - fired boiler, aiming to solve the technical problems of uneven combustion, high pollutant emissions, and low efficiency existing in the process of co - combustion of coal and biomass. By combining a hydrodynamic model, a control element matrix, and a response element matrix to optimize the co - combustion process, and conducting multiple rounds of simulation and evaluation, the technical effects of improving combustion efficiency, reducing pollutant emissions, and optimizing the stability of the combustion process are achieved.

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0018] It should be noted that any variations of the terms "including" and "having" are intended to cover non - exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0019] Embodiment 1, as Figure 1 shown, the present application provides a combustion optimization method for co - combustion of biomass and coal based on a coal - fired boiler, and the method includes:

[0020] Step S100: Determine the chemical structures of coal and biomass, and use a TG - FTIR - MS combined analyzer to detect and determine the pyrolysis process mechanism.

[0021] In the embodiments of the present application, to optimize the co - combustion process of coal and biomass, it is necessary to determine the chemical structures of coal and biomass. Specifically, first, an elemental analyzer is used to measure the contents of elements such as C, H, N, O, and S in coal and biomass to determine the basic chemical compositions of coal and biomass. Next, Fourier transform infrared spectroscopy (FTIR) is used to analyze the chemical functional groups in coal and biomass. Through FTIR technology, different functional groups in coal and biomass (such as hydroxyl groups, ether bonds, etc.) are identified. Subsequently, X - ray photoelectron spectroscopy (XPS) is used to analyze the existence forms of heteroatoms such as oxygen, nitrogen, and sulfur in coal and biomass, and to identify the chemical states of these heteroatoms in coal and biomass. In addition, X - ray diffraction (XRD) is used to analyze the types of minerals and their crystal structures in coal and biomass. Through XRD, the types of minerals in coal and biomass and their possible effects on the pyrolysis and combustion processes are revealed. Finally, pyrolysis gas chromatography - mass spectrometry (PY - GC - MS) technology is used to analyze the volatile components and decomposition products of coal and biomass. Through this step, the gases and volatiles released during the pyrolysis of coal and biomass are determined, which helps to reveal the material decomposition behavior during the pyrolysis process. Through these steps, the chemical structures of coal and biomass are finally determined.

[0022] After determining the chemical structures of coal and biomass, a TG - FTIR - MS combined analyzer is used to detect the pyrolysis process mechanism. First, thermogravimetric analysis (TG) is carried out. By heating the sample and recording its mass change, the release characteristics of volatile components during the pyrolysis of coal and biomass are studied. Then, Fourier transform infrared spectroscopy (FTIR) is carried out synchronously with the thermogravimetric analysis to monitor the types and concentrations of gases released during the pyrolysis process in real - time, and to reveal the chemical compositions of the volatile gases. Finally, mass spectrometry (MS) is used to further analyze the molecular compositions and concentrations of the volatile gases, which helps to accurately confirm the generation and changes of different gases during the pyrolysis process. Through TG - FTIR - MS combined analysis, the pyrolysis process mechanism of coal and biomass is finally determined.

[0023] Step S200: Interact with the furnace structure, and combine the pyrolysis process mechanism to construct a hydrodynamic model, wherein the furnace structure is provided with feed inlets at multiple positions from top to bottom along the furnace.

[0024] In the embodiments of the present application, first, by interacting with a preset database, the furnace structure design is obtained. The furnace structure is provided with feeding ports at multiple positions from top to bottom along the furnace as the fuel feeding positions, and the crushed biomass can be fed into the boiler through the feeding ports for co-combustion. Preferably, the optimal feeding positions are determined to ensure the quality of co-combustion. Further, the feeding ports can also adjust the input amount of air or gas, thereby optimizing the air flow distribution and temperature field during the combustion process and ensuring the uniformity and efficiency of the combustion reaction. Then, in combination with the pyrolysis process mechanism, the relevant characteristics in the pyrolysis process are first identified, and the conservation relations including energy conservation, momentum conservation, and mass conservation are determined. These conservation relations provide a theoretical basis for the energy and mass transfer during the combustion process. Subsequently, through the twin simulation of the furnace structure, a simulation space field is established, and the simulation space field is configured based on the conservation relations. Finally, by calling the co-combustion sample data for training and testing until the model converges, an accurate hydrodynamic model is finally constructed.

[0025] Further, in the method provided by the embodiments of the application, the constructing of the hydrodynamic model further includes:

[0026] Identifying the pyrolysis process mechanism and determining the conservation relations, where the conservation relations include the energy conservation relation, momentum conservation relation, and mass conservation relation before and after pyrolysis; performing twin simulation on the furnace structure to determine the simulation space field; configuring the simulation space field based on the conservation relations in combination with the pyrolysis process mechanism, and determining the hydrodynamic model by calling the co-combustion sample data for training and testing until convergence.

[0027] In the embodiments of the present application, it is first necessary to identify the pyrolysis process mechanism. This process is completed through a variety of experimental means and analytical techniques, such as thermogravimetric analysis (TG), Fourier transform infrared spectroscopy (FTIR), mass spectrometry (MS), etc. Through these techniques, it is possible to identify the release patterns of volatile components, the generation rules of decomposition products, and their reaction paths during the pyrolysis of coal and biomass. After identifying the pyrolysis process mechanism, it is analyzed to determine the conservation relationships. Energy conservation, momentum conservation, and mass conservation are the basic physical laws that must be followed during the combustion process. Specifically, energy conservation is described by a thermodynamic model. The thermodynamic model is based on the first law of thermodynamics (i.e., the law of conservation of energy), analyzes the heat input, output, and loss in the furnace, and ensures that the heat can be effectively transferred to the fuel and fully utilized during the combustion process. Through the energy balance equation, the heat loss of the furnace, the heat exchange between the fuel and the air, etc. can be calculated to ensure the reasonable distribution of heat. Momentum conservation is described by the Navier-Stokes equation to describe the flow state of the gas in the furnace. Through CFD (computational fluid dynamics) simulation, momentum conservation is used to calculate the velocity and pressure distribution of the gas flow to ensure the uniformity of the gas flow in the furnace and avoid local overheating or temperature fluctuations. Mass conservation is ensured by the mass balance equation to ensure that the mass of oxygen, fuel, and generated gas does not flow away during the combustion process, and helps to analyze the transformation and generation of substances during the combustion process. Through this process, the conservation relationships are determined.

[0028] Next, the furnace structure is modeled and analyzed through twin simulation. The twin simulation technology uses the computational fluid dynamics (CFD) method to construct a virtual model of the furnace and simulate the changes in the gas flow, temperature, and pressure fields. Through CFD simulation, the three-dimensional distribution of the gas flow, temperature, pressure, etc. in the furnace is established, that is, the simulation space field is simulated.

[0029] Based on the furnace structure simulation and the determination of the conservation relationships, next, the simulation space field is configured according to the pyrolysis process mechanism. By numerically solving the thermodynamic equations and fluid dynamics equations, the conservation relationships (energy, momentum, mass) are combined with the pyrolysis process mechanism to further optimize the gas flow path and temperature distribution in the furnace. Finally, the model is trained and tested by calling the co-combustion sample data. In this process, historical data or experimental data sets are collected from the historical database as the co-combustion sample data to be called, and the fluid dynamics model is trained using machine learning algorithms (such as the gradient descent method, etc.). The data set includes combustion experimental data under different co-combustion ratios, different particle sizes, and inlet configurations. These data are used to adjust and optimize the parameters of the model to ensure that the simulation results can more accurately reflect the actual combustion process. When the error of the model tends to be minimized and reaches the convergence state through multiple trainings and tests, an accurate fluid dynamics model is finally constructed.

[0030] Step S300: Introduce a control factor matrix - response factor matrix, where the control factor matrix is used to generate the co-combustion parameter control conditions, and the response factor matrix serves as the co-combustion evaluation conditions.

[0031] In the embodiment of the present application, a control factor matrix - response factor matrix is introduced.

[0032] Among them, the control factor matrix is used to generate the co-combustion parameter control conditions. When introducing the control factor matrix, for the intervention conditions of the mixed biomass, the first control factor is determined, which at least includes the particle size of the biomass, the feeding position, and the co-combustion ratio; then for the co-combustion control parameters, the second control factor is determined, which mainly includes combustion parameters, especially the parameter sequence that varies during the co-combustion period. By combining these control factors, the control factor matrix is constructed.

[0033] The response factor matrix serves as the co-combustion evaluation conditions and is used to evaluate the effect of the co-combustion process. The first response factor is determined based on the dynamic co-combustion process and mainly includes the alkali metal migration during the dynamic migration of particles and the dynamic deposition on the heating surface; while the second response factor is determined based on the static co-combustion results and covers indicators such as burnout products, energy consumption, and carbon emissions. According to these two response factors, the response factor matrix is constructed.

[0034] The control factor matrix is used to generate the co-combustion parameter control conditions, and the response factor matrix serves as the co-combustion evaluation conditions. Through the mutual influence mapping between the control factor matrix and the response factor matrix, the optimization adjustment of the co-combustion process can be realized, and the co-combustion effect can be given real-time feedback through the evaluation conditions, so as to achieve the best combustion efficiency, the lowest emissions, and stable combustion performance during the co-combustion process.

[0035] Furthermore, in the method provided by the application embodiment, when introducing the control factor matrix, it further includes:

[0036] For the intervention conditions of the mixed biomass, determine the first control factor, which at least includes the biomass particle size - feeding position - co-combustion ratio; for the co-combustion control parameters, determine the second control factor, which at least includes combustion parameters, and the combustion parameters are the parameter sequence that varies under the co-combustion period; according to the first control factor and the second control factor, construct the control factor matrix.

[0037] In the embodiments of the present application, first, the first control elements are determined, that is, the intervention conditions for the mixed biomass are set. Specifically, first, three control elements, namely the biomass particle size, the feeding position, and the co-combustion ratio, are directly determined. The determination of the biomass particle size is for optimizing the pyrolysis and combustion rates during the subsequent combustion process. The determination of the feeding position is to ensure the uniform distribution and effective mixing of the fuel in the furnace. The determination of the co-combustion ratio involves the appropriate ratio of coal and biomass, and these ratios will affect the energy output and emission characteristics of combustion. Through these steps, the first control elements, namely the biomass particle size, the feeding position, and the co-combustion ratio, are finally determined as the control basis during the co-combustion process.

[0038] Next, the second control elements are determined, that is, the co-combustion control parameters. The second control elements mainly include combustion parameters, especially the sequence of varying parameters under the co-combustion cycle. These parameters involve the gradual changes of dynamic variables such as temperature, oxygen concentration, and gas flow velocity during the co-combustion process. These parameters will change as the combustion process progresses, so it is necessary to determine their change rules in advance. Through these steps, the second control elements, that is, the sequence of varying parameters in the co-combustion cycle, are finally determined, providing a parameter basis for the precise regulation of the co-combustion process.

[0039] Finally, according to the determined first control elements and second control elements, a control element matrix is constructed. This matrix integrates all control elements, providing a clear and structured set of control parameters for the subsequent combustion process.

[0040] Furthermore, in the method provided by the embodiments of the application, when introducing the response element matrix, it further includes:

[0041] For the dynamic co-combustion process, the first response element is determined, which at least includes the migration of alkali metals under the dynamic migration of particles and the dynamic deposition on the heating surface; for the static co-combustion result, the second response element is determined, which at least includes the burnout products, energy consumption, and carbon emissions; according to the first response element and the second response element, the response element matrix is constructed; the mutual influence mapping between the control element matrix and the response element matrix is established.

[0042] In the embodiments of the present application, first, based on the dynamic co - combustion process, the first response elements are determined. This step is to set response elements according to the actual co - combustion requirements to evaluate the co - combustion effect. In this process, the first response elements at least include the alkali metal migration under particle dynamic migration and the dynamic deposition on the heating surface. Specifically, particle dynamic migration refers to the migration process of fuel particles (especially particles containing alkali metals) in the furnace during the combustion process. Alkali metal migration describes how alkali metal elements (such as sodium and potassium) are released from the fuel and move in different regions of the furnace, affecting the combustion efficiency and corrosion phenomenon. The dynamic deposition on the heating surface refers to the process of these alkali metals depositing on the heating surface of the furnace, which will affect the heat exchange efficiency of the furnace and the wear of the heating surface. Therefore, for these factors, technical experts set these two as the first response elements according to specific requirement criteria.

[0043] Next, based on the static co - combustion results, the second response elements are determined. The second response elements are mainly used to evaluate the conversion of solids and gases during the combustion process. The directly set ones include burnout products, energy consumption, and carbon emissions. Burnout products refer to the remaining solid substances after combustion, which is usually related to the complete combustion efficiency of the fuel; energy consumption refers to the energy consumed during the entire co - combustion process, and this element helps to evaluate the energy efficiency of combustion; carbon emissions are the carbon dioxide emissions during the combustion process, which is crucial for the evaluation of environmental impact. Technical experts set these as the second response elements according to the requirement criteria and the specific goals and environmental requirements of the combustion process in practical applications.

[0044] Subsequently, according to the first response elements and the second response elements, a response element matrix is constructed. This matrix combines the values of these response elements under different conditions and forms a multi - dimensional evaluation tool to provide specific evaluation criteria for the co - combustion process.

[0045] Finally, an interaction mapping between the control element matrix and the response element matrix is established. By comparing and analyzing each control factor in the control element matrix (such as biomass particle size, feeding position, co - combustion ratio, and sequence of gradient parameters) with the evaluation criteria in the response element matrix (such as alkali metal migration, burnout products, energy consumption, and carbon emissions), it is clear how each control element affects the evaluation indicators of the combustion reaction. Specifically, technical experts use methods such as statistical analysis and regression analysis through a series of experiments and data collection to determine how the changes in different control elements directly affect the values of the response elements. For example, the change in biomass particle size may affect the migration of alkali metals and the deposition on the heating surface, while the change in the co - combustion ratio will affect the burnout products and emissions. Through this analysis, a quantitative relationship model between the control elements and the response elements is established, providing a scientific basis for the subsequent optimization of the co - combustion process. Through this process, an interaction mapping between the control element matrix and the response element matrix is finally established.

[0046] Step S400: Based on the control element matrix-response element matrix, perform numerical simulation of the co-combustion process using the hydrodynamic model and evaluate the co-combustion simulation flow data to determine the co-combustion response coefficient.

[0047] In the embodiment of the present application, based on the control element matrix-response element matrix, a set of preset parameters is set by technical experts according to the control element matrix, and the numerical simulation of the co-combustion process is carried out using the hydrodynamic model. Specifically, the preset parameters (such as biomass particle size, feeding position, co-combustion ratio, etc.) set according to the control element matrix are input into the hydrodynamic model. The momentum transfer of the gas flow is described by the Navier-Stokes equation, combined with the mass conservation equation and the energy equation, to simulate the changes in temperature, pressure, gas concentration, particle trajectory, etc. Through CFD simulation, data such as the velocity field, temperature field, and oxygen concentration of the gas flow in the furnace are obtained, comprehensively describing the dynamic changes of the co-combustion process. These simulation results generate a set of co-combustion simulation flow data.

[0048] After completing the numerical simulation, the next step is to evaluate the simulation flow data according to the elements in the response element matrix. The response element matrix contains key evaluation indicators in the co-combustion process, such as alkali metal migration, burnout products, energy consumption, and carbon emissions. These evaluation elements help to quantify the combustion efficiency and environmental impact of the co-combustion process. By analyzing the simulation flow data, it is possible to evaluate how each control element affects the response element. For example, by analyzing the migration of alkali metals in the simulation data, the migration rate and deposition of alkali metals can be evaluated, so as to understand the possible risks of heat exchange efficiency and furnace heating surface corrosion in the co-combustion process; by analyzing the burnout products in the simulation results, the combustion efficiency can be evaluated, and the amount of burnout products directly reflects the combustion degree of the fuel; the energy consumption is evaluated by calculating the simulated temperature and energy transfer, and the carbon emissions are measured by analyzing the changes in gas concentration in the simulation data to measure the emissions of greenhouse gases such as carbon dioxide. Through these evaluations, the influence of the control elements in the co-combustion process on various indicators such as combustion efficiency, energy consumption, and emissions can be quantified. Finally, through the evaluation of the co-combustion simulation flow data, a set of co-combustion response coefficients is determined. Each co-combustion response coefficient is a numerical value that quantifies the influence degree of the change of the control element on the response element.

[0049] Step S500: Perform multiple rounds of co-combustion simulation and calibration based on the co-combustion response coefficient with the iteration of the control element matrix, and select the target co-combustion scheme, where the target co-combustion scheme is the optimal simulation scheme.

[0050] In the embodiment of the present application, in the process of selecting the target co - combustion scheme through multi - round co - combustion simulation based on the iteration of the control element matrix and calibration based on the co - combustion response coefficient, first, through the methods of directional adjustment and random adjustment, multi - round iterative simulation of the control element matrix is carried out. The directional adjustment is based on the co - combustion influence coefficient of the upper - level test, and combined with the mutual - influence mapping to determine the fuzzy control elements, and then adjust the upper - level control element matrix to determine the pre - control element matrix, and then carry out iterative simulation. Then, by obtaining the co - combustion response coefficients of multi - round iterative simulation, and through normalization processing and weighted calculation, the comprehensive optimization ranking is determined.

[0051] Based on these rankings, single - item optimization ranking is carried out, and finally, the target co - combustion scheme is selected according to the comprehensive optimization ranking and the single - item optimization ranking, where the target co - combustion scheme is the optimal simulation scheme.

[0052] Furthermore, in the method provided by the application embodiment, in the multi - round co - combustion simulation based on the iteration of the control element matrix, it further includes:

[0053] Carrying out iterative simulation management based on the directional adjustment of the upper - level control element matrix and the random adjustment of the control element matrix; among them, the directional adjustment based on the upper - level control element matrix includes: identifying the co - combustion influence coefficient of the upper - level test, taking the response optimization target as the guide, and combining the mutual - influence mapping to determine the fuzzy control elements; adjusting the upper - level control element matrix according to the fuzzy control elements to determine the pre - control element matrix; and carrying out iterative simulation with the pre - control element matrix.

[0054] In the embodiment of the present application, in the co - combustion optimization process, first, iterative simulation management based on the directional adjustment of the upper - level control element matrix and the random adjustment of the control element matrix is carried out. The directional adjustment provides guidance for the optimization target through the co - combustion influence coefficient of the upper - level test. The co - combustion influence coefficient is obtained through previous simulation tests, which quantifies the influence degree of different control elements on the co - combustion process. By analyzing these influence coefficients, the most critical control elements under the optimization target are identified, and the co - combustion effect is directionally adjusted in combination with the response optimization target.

[0055] Next, the fuzzy control elements are determined in combination with the mutual - influence mapping. The mutual - influence mapping is used to reveal the mutual relationship between different control elements and their common influence on the co - combustion effect. For example, the biomass particle size and the co - combustion ratio may have a common influence on energy efficiency and carbon emissions, and the mutual - influence mapping helps to identify these key control elements. Through this analysis, the fuzzy control elements that need to be adjusted under the guidance of the optimization target are determined.

[0056] Based on these fuzzy control elements, the upper - level control element matrix is adjusted. The adjusted matrix is called the pre - control element matrix, which contains the adjusted control elements and provides input data for the next - step simulation.

[0057] Finally, numerical simulation is carried out using the adjusted pre-control factor matrix, and the next round of iteration is entered. After each iteration, the control factor matrix is evaluated and further optimized according to the simulation results to ensure that the optimal co-combustion effect can be achieved finally. This iterative simulation management method based on directional adjustment and random adjustment optimizes the co-combustion process in multiple rounds of simulation and finally selects the best control factor configuration.

[0058] Furthermore, in the method provided by the application embodiment, when performing calibration based on the co-combustion response coefficient and selecting the target co-combustion scheme, it further includes:

[0059] Obtain N sets of co-combustion response coefficients from N iterative simulations, where each set of co-combustion response coefficients includes a coefficient matrix based on the response factor matrix; traverse the N sets of co-combustion response coefficients, and determine the comprehensive preference ranking based on N comprehensive coefficients through group coefficient weighting calculation under response factor normalization processing; traverse the N sets of co-combustion response coefficients, perform single coefficient calibration under response factor normalization processing, and determine the single item preference ranking; select the target co-combustion scheme according to the comprehensive preference ranking and the single item preference ranking.

[0060] In the embodiment of the present application, first, N sets of co-combustion response coefficients are obtained through multiple rounds of iterative simulation. Each set of response coefficients is based on various elements in the response factor matrix and reflects the influence of control factors on the co-combustion effect. Each set of co-combustion response coefficients includes various evaluation indexes (such as energy efficiency, alkali metal migration, burnout products, carbon emissions, etc.), and these coefficients quantify the influence of different control conditions on the co-combustion process. Next, response factor normalization processing is performed on these co-combustion response coefficients to ensure the comparability of data of each response factor. The normalization processing usually adopts the method of subtracting the minimum value of each response coefficient from it and then dividing by the difference between the maximum value and the minimum value to normalize all response coefficients to the range of [0, 1], eliminating the influence of different units and dimensions.

[0061] After the normalization processing is completed, group coefficient weighting calculation is carried out. The purpose of this step is to determine the weight of each response factor in the comprehensive ranking according to its relative importance. The weights are preset by technical experts according to the actual optimization goal. Some response factors (such as combustion efficiency, carbon emissions, etc.) may be more important for the optimization goal, so the weight values of these factors are higher. Through weighting calculation, each set of co-combustion response coefficients will be weighted and summed according to the weight value of each response factor to obtain a comprehensive coefficient. Then, all N sets of co-combustion response coefficients are sorted according to these comprehensive coefficients to form a comprehensive preference ranking.

[0062] However, during the optimization process, certain individual response factors may have relatively high weights, especially when these factors have a significant impact on the optimization goal. To balance overall optimization and the optimization of individual indicators, single-factor calibration is introduced. Single-factor calibration performs independent normalization and weighting calculations for each response factor to obtain the individual optimization rankings. Through the individual optimization rankings, the performance of each response factor (such as alkali metal migration, energy efficiency, carbon emissions, etc.) under different co-combustion scenarios can be evaluated, and further analysis can be carried out to identify which individual performances are the most prominent. For example, assuming that alkali metal migration has a great impact on combustion efficiency, if a certain set of scenarios performs well in terms of alkali metal migration, the individual optimization rankings will place this set of scenarios at the top.

[0063] Finally, by comparing the comprehensive optimization rankings and the individual optimization rankings, the target co-combustion scenario is selected. The comprehensive optimization rankings provide the overall performance of each set of co-combustion scenarios, while the individual optimization rankings show the independent performance of each response factor. By considering both simultaneously, it is ensured that an optimal scenario is selected, which not only performs well in the overall optimization goals (such as combustion efficiency, energy efficiency, carbon emissions, etc.) but also does not neglect the key individual performances. For example, if a certain scenario ranks first in the comprehensive ranking but performs poorly in terms of alkali metal migration or burnout products, another set of scenarios with more comprehensive performance is selected as the final target co-combustion scenario.

[0064] Furthermore, in the method provided by the application embodiment, after selecting the target co-combustion scenario, it further includes:

[0065] Connect the crusher, determine the pretreatment conditions according to the target co-combustion scenario, and control the crusher to perform the crushing control of biomass; determine the co-combustion control conditions according to the target co-combustion scenario, and control the coal-fired boiler to perform the co-combustion control of the entire cycle of coal and biomass co-combustion; monitor the flow data of the co-combustion control and add it to the temporary database, and perform periodic guidance management of the co-combustion feedback according to the temporary database.

[0066] In the embodiment of the present application, first, the pretreatment conditions are determined according to the target co-combustion scenario to provide the best raw material preparation for co-combustion. Specifically, the requirements for the particle size, co-combustion ratio, etc. of the biomass are clearly specified in the target co-combustion scenario. Therefore, the crusher is controlled to perform the crushing control of the biomass. According to the set range of the biomass particle size in the target scenario, by adjusting the working parameters of the crusher (such as rotation speed, feed rate, etc.), it is ensured that the biomass reaches the required particle size after crushing, so as to ensure its full mixing with the coal, improve the combustion efficiency and optimize the stability of the combustion process.

[0067] Next, determine the co-firing control conditions according to the target co-firing plan, which will directly affect the operation of the coal-fired boiler. The co-firing control conditions include key parameters such as co-firing ratio, combustion temperature, air supply, etc. These parameters will be precisely adjusted in the coal-fired boiler to ensure co-firing control throughout the entire co-firing cycle of coal and biomass. Specific operations include accurately controlling the mixing ratio of biomass and coal, temperature regulation, and air volume regulation according to the co-firing ratio set in the plan, to ensure that during the combustion process, the mixing of biomass and coal is sufficient and uniform, the combustion reaction is stable and efficient, while reducing carbon emissions and optimizing energy efficiency.

[0068] Subsequently, monitor the flow data of co-firing control. All co-firing control flow data (such as temperature, pressure, oxygen concentration, carbon emissions, etc.) are monitored in real-time through sensors and stored in a temporary database. The temporary database stores all the key data during the co-firing process, providing data support for subsequent analysis and optimization.

[0069] Finally, based on the real-time data in the temporary database, technicians conduct periodic guidance management on the co-firing process by providing feedback at regular intervals. By regularly analyzing the data stored in the database, technicians evaluate whether the various indicators in the co-firing process meet the expected goals. Data analysis may include evaluations such as co-firing efficiency, emission levels, temperature distribution, etc., to help identify potential problems in the co-firing process, such as incomplete combustion, excessive emissions, etc. On this basis, technicians adjust the control strategy according to the feedback results, such as optimizing the co-firing ratio, adjusting the air supply volume, regulating the combustion temperature, etc., to ensure that the co-firing process can continuously maintain the best operating state.

[0070] In the embodiment of the present application, in summary, the embodiment of the present application has at least the following technical effects:

[0071] This application determines the chemical structures of coal and biomass, and uses a TG-FTIR-MS combined analyzer to detect and determine the pyrolysis process mechanism; an interactive furnace structure, combined with the pyrolysis process mechanism, constructs a hydrodynamic model, wherein the furnace structure is provided with feed inlets at multiple positions from top to bottom along the furnace; introduces a control element matrix - response element matrix, wherein the control element matrix is used to generate co-combustion parameter control conditions, and the response element matrix serves as co-combustion evaluation conditions; based on the control element matrix - response element matrix, numerically simulates the co-combustion process based on the hydrodynamic model and evaluates the co-combustion simulation flow data to determine the co-combustion response coefficient; through the iteration of the control element matrix, conducts multiple rounds of co-combustion simulations and calibration based on the co-combustion response coefficient, and selects a target co-combustion plan, wherein the target co-combustion plan is the optimal simulation plan. The present invention solves the technical problems of uneven combustion, high pollutant emissions, and low efficiency existing in the prior art during the co-combustion of coal and biomass. By adopting a method combining a hydrodynamic model, a control element matrix, and a response element matrix to optimize the co-combustion process, and conducting multiple rounds of simulations and evaluations, it achieves the technical effects of improving combustion efficiency, reducing pollutant emissions, and optimizing the stability of the combustion process.

[0072] Embodiment 2, based on the same inventive concept as the method for optimizing the co-combustion of biomass and coal based on a coal-fired boiler in the foregoing embodiment, as Figure 2 shown, this application provides a system for optimizing the co-combustion of biomass and coal based on a coal-fired boiler. The system in the embodiments of this application and the method embodiments are based on the same inventive concept. Among them, the system includes:

[0073] A chemical structure determination module 11, used to determine the chemical structures of coal and biomass, and uses a TG-FTIR-MS combined analyzer to detect and determine the pyrolysis process mechanism; a model construction module 12, used to interact with the furnace structure, combine the pyrolysis process mechanism, and construct a hydrodynamic model, wherein the furnace structure is provided with feed inlets at multiple positions from top to bottom along the furnace; a matrix introduction module 13, used to introduce a control element matrix - response element matrix, wherein the control element matrix is used to generate co-combustion parameter control conditions, and the response element matrix serves as co-combustion evaluation conditions; a co-combustion response coefficient determination module 14, used to numerically simulate the co-combustion process based on the hydrodynamic model with the control element matrix - response element matrix as the reference and evaluate the co-combustion simulation flow data to determine the co-combustion response coefficient; a target co-combustion plan determination module 15, used to conduct multiple rounds of co-combustion simulations and calibration based on the co-combustion response coefficient through the iteration of the control element matrix, and select a target co-combustion plan, wherein the target co-combustion plan is the optimal simulation plan.

[0074] Further, the system is also used to implement the following functions:

[0075] Identify the mechanism of the pyrolysis process and determine the conservation relationships, where the conservation relationships include the energy conservation relationship, momentum conservation relationship, and mass conservation relationship before and after pyrolysis; perform twin simulation on the furnace structure to determine the simulation space field; based on the conservation relationships, configure the simulation space field in combination with the pyrolysis process mechanism, and determine the hydrodynamic model by calling the mixed combustion sample data for training and testing until convergence.

[0076] Further, the system is also used to implement the following functions:

[0077] Determine the first control element for the intervention conditions of the mixed biomass, which at least includes biomass particle size - feeding position - mixed combustion ratio; determine the second control element for the mixed combustion control parameters, which at least includes combustion parameters, and the combustion parameters are a sequence of varying parameters under the mixed combustion cycle; construct the control element matrix according to the first control element and the second control element.

[0078] Further, the system is also used to implement the following functions:

[0079] Determine the first response element in the dynamic mixed combustion process, which at least includes the migration of alkali metals under particle dynamic migration and dynamic deposition on the heating surface; determine the second response element based on the static mixed combustion result, which at least includes burnout products, energy consumption, and carbon emissions; construct the response element matrix according to the first response element and the second response element; establish the mutual influence mapping between the control element matrix and the response element matrix.

[0080] Further, the system is also used to implement the following functions:

[0081] Perform iterative simulation management with directional adjustment based on the upper control element matrix and random adjustment based on the control element matrix; among them, the directional adjustment based on the upper control element matrix includes: identifying the mixed combustion influence coefficient of the upper test, guiding by the response optimization target, determining the fuzzy control element in combination with the mutual influence mapping; adjusting the upper control element matrix according to the fuzzy control element to determine the pre-control element matrix; performing iterative simulation with the pre-control element matrix.

[0082] Further, the system is also used to implement the following functions:

[0083] Obtain N sets of co-combustion response coefficients for N iterations of simulation, where each set of co-combustion response coefficients includes a coefficient matrix based on the response factor matrix; traverse the N sets of co-combustion response coefficients, and determine the comprehensive optimal ranking based on N comprehensive coefficients through group coefficient weighting calculation under response factor normalization processing; traverse the N sets of co-combustion response coefficients, and perform single coefficient verification under response factor normalization processing to determine the single-item optimal ranking; select the target co-combustion scheme according to the comprehensive optimal ranking and the single-item optimal ranking.

[0084] Further, the system is also used to implement the following functions:

[0085] Connect to a crusher, determine the pre-treatment conditions according to the target co-combustion scheme, and control the crusher to perform crushing control of biomass; determine the co-combustion control conditions according to the target co-combustion scheme, and control the coal-fired boiler to perform co-combustion control for the entire co-combustion cycle of coal and biomass; monitor the flow data of co-combustion control and add it to a temporary database, and perform periodic guidance management of co-combustion feedback according to the temporary database.

[0086] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification has been made. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0088] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A biomass and coal co - combustion combustion optimization method based on a coal - fired boiler, characterized in that, The method includes: Determine the chemical structures of coal and biomass, and use a TG-FTIR-MS combined analyzer to detect and determine the pyrolysis process mechanism; Interact with the furnace structure, and construct a hydrodynamic model in combination with the pyrolysis process mechanism, wherein the furnace structure is provided with feeding ports at multiple positions from top to bottom along the furnace; Introduce a control factor matrix - response factor matrix, wherein the control factor matrix is used to generate co-combustion parameter control conditions, and the response factor matrix serves as co-combustion evaluation conditions; Based on the control factor matrix - response factor matrix, perform numerical simulation of the co-combustion process based on the hydrodynamic model and evaluate the co-combustion simulation flow data to determine the co-combustion response coefficient; Through iteration of the control factor matrix, perform multiple rounds of co-combustion simulation and calibration based on the co-combustion response coefficient, and select a target co-combustion plan, wherein the target co-combustion plan is the optimal simulation plan.

2. The biomass and coal co - firing combustion optimization method based on a coal - fired boiler according to claim 1, wherein, The construction of the hydrodynamic model includes: Identify the pyrolysis process mechanism and determine the conservation relationships, wherein the conservation relationships include the energy conservation relationship, momentum conservation relationship, and mass conservation relationship before and after pyrolysis; Perform twin simulation on the furnace structure to determine the simulation space field; Based on the conservation relationships, configure the simulation space field in combination with the pyrolysis process mechanism, and perform training and testing by calling co-combustion sample data until convergence to determine the hydrodynamic model.

3. The biomass and coal co - firing combustion optimization method based on a coal - fired boiler according to claim 1, wherein, Introducing the control factor matrix includes: For the intervention conditions of mixed biomass, determine the first control factor, which at least includes biomass particle size - feeding position - co-combustion ratio; For co-combustion control parameters, determine the second control factor, which at least includes combustion parameters, and the combustion parameters are a sequence of varying parameters under the co-combustion period; Construct the control factor matrix according to the first control factor and the second control factor.

4. The biomass and coal co-combustion combustion optimization method based on a coal-fired boiler according to claim 3, wherein, Introducing the response factor matrix includes: For the dynamic co-combustion process, determine the first response factor, which at least includes the migration of alkali metals under particle dynamic migration and dynamic deposition on the heating surface; For the static co-combustion result, determine the second response factor, which at least includes burnout products, energy consumption, and carbon emissions; Construct the response factor matrix according to the first response factor and the second response factor; Establish the mutual influence mapping between the control factor matrix and the response factor matrix.

5. The biomass and coal co-combustion combustion optimization method based on a coal-fired boiler according to claim 4, characterized in that Through iteration of the control factor matrix, perform multiple rounds of co-combustion simulation, including: Perform iterative simulation management with directional adjustment based on the upper control factor matrix and random adjustment based on the control factor matrix; Among them, the directional adjustment based on the upper control factor matrix includes: Identify the co-combustion influence coefficient of the upper test, and determine the fuzzy control factors in combination with the mutual influence mapping with the response optimization target as the guide; Adjust the upper control factor matrix according to the fuzzy control factors to determine the pre-control factor matrix; Perform iterative simulation with the pre-control factor matrix.

6. The biomass and coal co - firing combustion optimization method based on a coal - fired boiler according to claim 1, wherein, Perform calibration based on the co-combustion response coefficient and select the target co-combustion plan, including: Obtain N groups of co-combustion response coefficients from N iterative simulations, wherein each group of co-combustion response coefficients includes a coefficient matrix based on the response factor matrix; Traverse the N groups of co-combustion response coefficients, and determine the comprehensive optimal sorting based on N comprehensive coefficients through the calculation of group coefficient weighting under the normalization of response elements; Traverse the N groups of co-combustion response coefficients, conduct single coefficient verification under the normalization of response elements, and determine the single item optimal sorting; According to the comprehensive optimal sorting and the single item optimal sorting, select the target co-combustion scheme.

7. The biomass and coal co - firing combustion optimization method based on a coal - fired boiler according to claim 1, wherein After selecting the target co-combustion scheme, it includes: Connect the crusher, determine the pre-treatment conditions according to the target co-combustion scheme, and control the crusher to carry out the crushing control of biomass; Determine the co-combustion control conditions according to the target co-combustion scheme, and control the coal-fired boiler to carry out the co-combustion control of the whole cycle of coal and biomass co-combustion; Monitor the flow data of co-combustion control and add it to the temporary database, and conduct periodic guidance management of co-combustion feedback according to the temporary database.

8. A biomass and coal co - combustion combustion optimization system based on a coal - fired boiler, characterized in that, The system includes: A chemical structure determination module for determining the chemical structures of coal and biomass, and detecting and determining the pyrolysis process mechanism by using a TG-FTIR-MS combined analyzer; A model construction module for interacting with the furnace structure, and constructing a hydrodynamic model in combination with the pyrolysis process mechanism, wherein the furnace structure is provided with air inlets at multiple positions from top to bottom along the furnace; A matrix introduction module for introducing a control element matrix - response element matrix, wherein the control element matrix is used to generate co-combustion parameter control conditions, and the response element matrix is used as co-combustion evaluation conditions; A co-combustion response coefficient determination module for numerically simulating the co-combustion process and evaluating the co-combustion simulation flow data based on the hydrodynamic model with the control element matrix - response element matrix as the reference, and determining the co-combustion response coefficients; A target co-combustion scheme determination module for iterating the control element matrix, conducting multiple rounds of co-combustion simulation and verification based on the co-combustion response coefficients, and selecting the target co-combustion scheme, wherein the target co-combustion scheme is the optimal simulation scheme.

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