Boiler blending combustion biomass fuel coupling chemical looping combustion system

Abstract

The invention discloses a boiler blending combustion biomass fuel coupling chemical looping combustion system, and particularly relates to the technical field of fuel control, the system comprises an operation monitoring module, through deploying a multi-type sensor network at a key position, real-time collecting whole flow operation data, constructing a space-time monitoring data set covering the physical and chemical process of a combustion system, and determining a combustion state of the combustion system; the reliability of data acquisition is guaranteed through signal filtering and abnormal value correction; according to the method, a multi-dimensional cooperative control framework is constructed, the six-dimensional influence coefficients are fused into the comprehensive distribution index, the distribution strategy of the fuel between the main combustion area and the chemical looping reactor is dynamically optimized, traditional single control dimension is expanded to multi-dimensional cooperative control, and the adaptability of the system to complex fuel characteristics is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fuel control, more particularly, the present application relates to a boiler blending biomass fuel coupled with a chemical chain combustion system. BACKGROUND

[0002] With the advancement of global environmental protection strategy, the green low-carbon transformation of the power industry is imminent; the boiler blending biomass fuel is concerned because it can directly use the existing facilities and take advantage of the characteristics of biomass; at the same time, the chemical chain combustion technology as a new type of combustion method with inherent carbon capture capacity provides a new idea for low-cost emission reduction, how to deeply couple these two technologies to form a new intelligent, stable and efficient collaborative system has become an important research direction.

[0003] The traditional fuel control system includes a fuel main control module, a coal feeding and air supply adjusting module, and a secondary air distribution module; wherein the fuel main control module is responsible for calculating and distributing the total fuel quantity according to the unit load instruction, and stabilizing the energy output of the boiler at the set value; the coal feeding and air supply adjusting module controls the concentration and flow rate of the pulverized coal entering the furnace by adjusting the speed of the coal feeder and the amount of primary air, to ensure the stability of the coal conveying and the preliminary air distribution of the combustion; the secondary air distribution module controls the combustion atmosphere and temperature field in the furnace by adjusting the opening degree of each layer of secondary air damper, organizes the air power field in the furnace, and ensures the complete combustion of the fuel and the suppression of the production of nitrogen oxides.

[0004] However, in actual use, it still has some disadvantages, such as single control dimension, the traditional boiler control system is usually a single-input single-output decentralized control system, which cannot adapt to the strong mutual influence between variables, resulting in unstable system control performance; poor adaptability, the traditional boiler control system is usually adjusted for a certain specific design condition, and when the operating condition deviates from the design point, the control performance will decrease significantly; not environmentally friendly, the control target of the traditional boiler control system is limited to combustion efficiency and conventional pollutant control, which cannot respond to increasingly stringent carbon emission constraints.

[0005] Therefore, it is urgent to provide a boiler blending biomass fuel coupled with a chemical chain combustion system to solve the problems of single control dimension, poor adaptability and environmental protection of the existing fuel control system. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a boiler blending biomass fuel coupled with a chemical chain combustion system, which solves the problems raised in the above background art by the following scheme.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a boiler blending biomass fuel coupled with a chemical chain combustion system, comprising: The operation monitoring module collects real-time operation data of the entire process by deploying a network of multiple types of sensors at key locations, and builds a spatiotemporal monitoring dataset covering the physicochemical processes of the combustion system. The combustion operation data processing module establishes a carbon flow coupling calculation model based on the spatiotemporal monitoring dataset, and finally outputs the influence coefficients of combustion stability data, biomass co-firing adaptability data, and chemical loop carbon capture data. The system operates a data processing module, which establishes an energy flow coupling calculation model based on spatiotemporal monitoring datasets. Through multi-objective normalization processing, it finally generates the influence coefficients of oxygen carrier activity data, pollutant synergistic control data, and system energy efficiency data. The comprehensive analysis module adopts a multi-level comprehensive evaluation model based on the entropy weight method, which couples the influence coefficients of combustion stability data, biomass co-firing adaptability data, chemical chain carbon capture data, oxygen carrier activity data, pollutant synergistic control data, and system energy efficiency data into a comprehensive allocation index, providing a quantitative basis for decision-making. The intelligent fuel distribution module, based on core coordination indicators and combined with externally input load commands and carbon emission limit constraints, solves the optimal distribution ratio of biomass fuel between the main combustion zone and the chemical looping reaction zone, and generates corresponding transport path control commands. The reaction optimization module receives fuel allocation decision information and controls the exchange rate of solid materials between the two reactors by adjusting the conveying power of the oxygen carrier circulation system and the fluidization state of the reactor, and fine-tunes the reaction optimization strategy in real time. The suppression execution module intelligently adjusts the introduction position and mixing intensity of the two characteristic flue gases from the chemical chain system in the main boiler according to the comprehensive distribution index. It accurately introduces the reducing flue gas into the high-temperature reduction zone to establish a local low-nitrogen environment, while rationally distributing the high-temperature and low-oxygen flue gas to the main combustion zone to suppress the generation of thermal nitrogen oxides. The data interaction module transmits the corresponding comprehensive allocation index, impact coefficient, fuel allocation strategy, reaction optimization strategy, and suppression execution strategy before and after optimization to the user information terminal.

[0008] Preferably, the spatiotemporal monitoring dataset includes combustion operation data and system operation data; The combustion operation data includes parameters affecting combustion stability, biomass co-firing adaptability, and chemical chain carbon capture; the system operation data includes parameters affecting oxygen carrier activity, pollutant synergistic control, and system energy efficiency.

[0009] Preferably, the combustion stability data influencing parameters include furnace temperature, denoted as T; pressure oscillation power, denoted as f; and CO concentration fluctuation coefficient, denoted as... The parameters affecting biomass co-firing adaptability data include biomass moisture content (M); volatile matter index (V); total alkali metal content (K); unburned carbon content (UBC); and parameters affecting chemical looping carbon capture data include fuel reactor outlet. Volume concentration, denoted as The flue gas flow rate of the fuel reactor is denoted as Q; the carbon-containing gas concentration is denoted as C; and the input carbon is denoted as... The parameters affecting oxygen carrier activity data include the oxygen carrier circulation rate, denoted as V; and the enthalpy change of the reaction, denoted as... The activity decay function, denoted as The parameters affecting pollutant synergistic control data include the nitrogen oxide formation rate, denoted as R. Emission flux, denoted by F; reduction selectivity, denoted by S. Influence coefficient, denoted as The system energy efficiency data is affected by parameters including steam power generation, denoted as... Carbon capture energy-saving equivalent, denoted as Auxiliary power consumption, denoted as Lower heating value of fuel, denoted as LHV; fuel mass flow rate, denoted as m; system control loss coefficient, denoted as .

[0010] Preferably, the combustion stability data influence coefficient is obtained by normalizing and weighting three key parameters: furnace temperature field standard deviation, pressure oscillation energy, and CO concentration fluctuation. Temperature fluctuation has the highest weight, reflecting its dominant influence on stability. Pressure oscillation reflects combustion pulsation characteristics, and CO fluctuation characterizes combustion efficiency. The three together constitute a stability evaluation system, which is used to evaluate the anti-interference ability and operational stability of the combustion system in real time.

[0011] Preferably, the biomass co-firing adaptability data influence coefficient uses an exponential function to handle the negative impact of moisture, a linear function to handle the positive impact of volatile matter, and a reciprocal function to handle the negative impact of alkali metals. An unburned carbon correction term is introduced into the denominator to construct a quaternary evaluation model of moisture-volatile matter-alkali metals-burnout degree, which is used to quantify the system's compatibility with biomass of different characteristics.

[0012] Preferably, the influence coefficient of the chemical loop carbon capture data is based on the fuel reactor outlet. Based on flow rate, deducting incompletely oxidized products CO and The carbon loss caused by carbon loss is taken into account, and the impact of unburned carbon is considered. Finally, the net capture efficiency is obtained by comparing it with the total input carbon, which reflects the directional conversion efficiency of carbon in the chemical chain.

[0013] Preferably, the oxygen carrier activity data influence coefficient is based on the Arrhenius kinetic principle, taking the cycle rate and reaction enthalpy change as the driving force of activity, introducing an integral term of the cumulative effect of activity decay into the denominator, and finally multiplying by the time decay factor to construct a comprehensive activity evaluation model covering transport performance, reaction thermodynamics and time decay.

[0014] Preferably, the impact coefficient of the pollutant synergistic control data is based on the nitrogen oxide emission reduction rate, and is weakened by a logarithmic function. The negative impacts of emissions were investigated, and reduction selectivity was introduced as a process control correction factor to construct a multi-pollutant synergistic control evaluation system covering nitrogen oxides, sulfur oxides, and reaction selectivity.

[0015] Preferably, the system energy efficiency data influence coefficient extends the traditional definition of thermal efficiency to a generalized energy efficiency model that includes carbon capture energy-saving benefits and system control losses. The numerator integrates the main power generation, carbon capture energy saving and auxiliary energy consumption, the denominator considers the differences in conversion efficiency of different fuels, and finally introduces control loss correction to comprehensively reflect the system's comprehensive energy utilization level.

[0016] Preferably, the comprehensive allocation index reflects the synergistic and restrictive effects among the indicators through a multiplicative relationship. Combustion stability, biomass adaptability, and carbon capture efficiency, as the basic capabilities of the system, constitute the core driving factors in a power-law form. Oxygen carrier activity is characterized by its threshold effect on system performance through an exponential decay function. Pollutant control performance is weakened by a logarithmic function. System energy efficiency is reflected by its marginal contribution in a relative value form, ultimately forming a comprehensive evaluation system that can reflect the nonlinear coupling relationship among the indicators.

[0017] The technical effects and advantages of this invention are as follows: 1. This invention constructs a multi-dimensional collaborative control architecture, integrates six-dimensional influence coefficients into a comprehensive allocation index, dynamically optimizes the fuel allocation strategy between the main combustion zone and the chemical loop reactor, expands the traditional single control dimension to multi-dimensional collaborative regulation, and improves the system's adaptability to complex fuel characteristics. 2. This invention establishes a dynamic optimization mechanism for reaction depth and adopts a feedforward-feedback composite control strategy to automatically adjust the oxygen carrier circulation rate and reactor air distribution parameters, so that the chemical looping combustion process always maintains the optimal reaction state. This achieves deep synergy between the combustion process and the carbon capture process, forming an integrated internal control mechanism for combustion and carbon capture, and realizing a highly efficient and low-energy-consumption carbon capture process. 3. This invention utilizes intelligent scheduling of the characteristic flue gas generated by the chemical chain subsystem to precisely introduce reducing flue gas into the high-temperature zone of the main boiler to establish an in-situ denitrification environment. At the same time, it optimizes the combustion atmosphere in the main combustion zone using high-temperature, low-oxygen flue gas, forming a pollutant synergistic inhibition method based on the internal material cycle of the system. This significantly reduces the original concentration of nitrogen oxides and improves environmental protection. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] As attached Figure 1 The boiler co-firing biomass fuel coupled with chemical looping combustion system shown includes: The operation monitoring module collects real-time operation data throughout the entire process by deploying a network of multiple types of sensors at key locations, constructs a spatiotemporal monitoring dataset covering the physicochemical processes of the combustion system, and ensures the reliability of data acquisition through signal filtering and outlier correction.

[0021] In this embodiment, it should be specifically noted that the spatiotemporal monitoring dataset includes combustion operation data and system operation data.

[0022] In this embodiment, it should be specifically noted that the combustion operation data includes combustion stability data influence parameters, biomass co-firing adaptability data influence parameters, and chemical chain carbon capture data influence parameters.

[0023] In this embodiment, it should be specifically noted that the combustion stability data influencing parameters include furnace temperature, denoted as T; pressure oscillation power, denoted as f; and CO concentration fluctuation coefficient, denoted as... .

[0024] In this embodiment, it should be specifically explained that the CO concentration fluctuation coefficient is obtained by acquiring the instantaneous CO concentration value at a frequency of 100Hz using a tunable laser absorption spectrometer installed at the tail flue of the boiler, continuously acquiring 1000 data points for 10 seconds, and calculating the ratio of the standard deviation to the mean of the time series.

[0025] In this embodiment, it should be specifically noted that the biomass co-firing adaptability data influence parameters include biomass moisture content, denoted as M; volatile matter index, denoted as V; total alkali metal content, denoted as K; and unburned carbon content, denoted as UBC.

[0026] In this embodiment, it should be specifically noted that the biomass moisture content is obtained in real time by using an online near-infrared spectrometer on the biomass feeding belt, employing the absorbance ratio at characteristic wavelengths of 1450nm and 1940nm, combined with a pre-calibrated moisture calibration model; the volatile matter index is obtained by measuring the real and imaginary parts of the dielectric constant of the biomass particles using a microwave transmission analyzer integrated into the feeding system, performing pattern matching with a thermogravimetric analysis database, predicting its volatile matter release characteristics at high temperatures, and obtaining the volatile matter index; the total alkali metal content is obtained by irradiating the crushed biomass sample for 60 seconds using an energy-dispersive X-ray fluorescence analyzer, simultaneously detecting potassium. X-rays and sodium element The characteristic peak intensity of the X-rays was analyzed using the basic parameter method without standard samples. The concentration percentages of the two elements were added together to obtain the total alkali metal content. The unburned carbon content was obtained by extracting fly ash samples from the boiler tail flue using an isokinetic sampling system. The samples were burned to constant weight at 815℃ using the gravimetric method. The carbon content was calculated based on the mass loss before and after burning to obtain the unburned carbon content.

[0027] In this embodiment, it should be specifically noted that the chemical loop carbon capture data influence parameters include the fuel reactor outlet. Volume concentration, denoted as The flue gas flow rate of the fuel reactor is denoted as Q; the carbon-containing gas concentration is denoted as C; and the input carbon is denoted as... The unburned carbon content is denoted as UBC.

[0028] In this embodiment, it should be specifically noted that the system operation data includes parameters affecting oxygen carrier activity, parameters affecting pollutant synergistic control, and parameters affecting system energy efficiency.

[0029] In this embodiment, it should be specifically noted that the parameters affecting the oxygen carrier activity data include the oxygen carrier circulation rate, denoted as V; and the reaction enthalpy change, denoted as... The activity decay function, denoted as .

[0030] In this embodiment, it should be specifically noted that the pollutant synergistic control data influence parameters include the nitrogen oxide generation rate, denoted as R; Emission flux, denoted by F; reduction selectivity, denoted by S. Influence coefficient, denoted as .

[0031] In this embodiment, it should be specifically noted that the reduction selectivity is achieved by performing a full-component scan of the flue gas at the fuel reactor outlet using a Fourier transform infrared spectroscopy instrument to measure CO, , and At a concentration of [specific concentration], CO and [other substances] will be generated. The ratio of the amount of oxygen carrier lattice oxygen consumed to the total amount of lattice oxygen consumed is calculated. The influence coefficient was systematically studied using the controlled variable method in an experimental setup simulating the actual flue gas environment. The inhibitory effect of concentration on the reduction process of nitrogen oxides was investigated by configuring different concentrations of [agent / concentration] in a fixed-bed reactor. / Mixed gases, analyzed using chemical calculation software The competitive adsorption energy barrier on the oxygen carrier surface, combined with in-situ infrared spectroscopy observation of the sulfur species accumulation process, was fitted using the least squares method. Decrease curves of concentration and denitrification efficiency.

[0032] In this embodiment, it should be specifically noted that the system energy efficiency data influence parameters include steam power generation, denoted as... Carbon capture energy-saving equivalent, denoted as Auxiliary power consumption, denoted as Lower heating value of fuel, denoted as LHV; fuel mass flow rate, denoted as m; system control loss coefficient, denoted as .

[0033] In this embodiment, it should be specifically noted that the system control loss coefficient is obtained by collecting the core operating indicators of the control loop at a frequency of 1Hz through a performance monitoring agent deployed in the distributed control system.

[0034] The combustion operation data processing module establishes a carbon flow coupling calculation model based on spatiotemporal monitoring datasets, and finally outputs the influence coefficients of combustion stability data, biomass co-firing adaptability data, and chemical loop carbon capture data, forming a quantitative assessment of combustion process stability, fuel adaptability, and carbon capture performance, providing a direct decision-making basis for front-end combustion control.

[0035] In this embodiment, it should be specifically explained that the combustion stability data influence coefficient is obtained by normalizing and weighting three key parameters: furnace temperature field standard deviation, pressure oscillation energy, and CO concentration fluctuation. Temperature fluctuation has the highest weight, reflecting its dominant influence on stability. Pressure oscillation reflects combustion pulsation characteristics, and CO fluctuation characterizes combustion efficiency. The three together constitute a stability evaluation system, used to evaluate the combustion system's anti-interference capability and operational stability in real time. Specifically: , in This represents the standard deviation of the furnace temperature field. Indicates the maximum furnace temperature. Indicates the minimum furnace temperature; f represents the pressure oscillation power; Indicates the maximum pressure oscillation power. Indicates the minimum pressure oscillation power. Indicates the maximum allowable oscillation power of the system; Indicates the CO concentration fluctuation coefficient. This indicates the maximum allowable fluctuation range.

[0036] In this embodiment, it should be specifically noted that the biomass co-firing adaptability data influence coefficient uses an exponential function to handle the negative impact of moisture, a linear function to handle the positive impact of volatile matter, and a reciprocal function to handle the negative impact of alkali metals. An unburned carbon correction term is introduced into the denominator, constructing a quaternary evaluation model of moisture-volatile matter-alkali metals-burnout degree. This model is used to quantify the system's compatibility with biomass of different characteristics. Specifically: Where M represents the moisture content of biomass; V represents the volatile matter index. The index represents the volatile matter standardization index; K represents the total alkali metal content; UBC represents the unburned carbon content.

[0037] In this embodiment, it should be specifically noted that the influence coefficient of the chemical loop carbon capture data is based on the fuel reactor outlet. Based on flow rate, deducting incompletely oxidized products CO and The carbon loss incurred, and the impact of unburned carbon, are considered. The net capture efficiency is then calculated by comparing this with the total input carbon, reflecting the directional conversion efficiency of carbon in the chemical chain. Specifically: in Indicates fuel reactor outlet Volume concentration, where Q represents the flue gas flow rate of the fuel reactor. Indicates CO gas concentration. This represents the CO carbon equivalent coefficient, which is a constant. express Gas concentration, express The carbon equivalent coefficient is a constant. The input carbon is represented by UBC, which indicates the unburned carbon content.

[0038] The system's data processing module establishes an energy flow coupling calculation model based on spatiotemporal monitoring datasets. Through multi-objective normalization processing, it ultimately generates the influence coefficients of oxygen carrier activity data, pollutant synergistic control data, and system energy efficiency data, providing panoramic decision support for the long-term operation of the system.

[0039] In this embodiment, it should be specifically noted that the oxygen carrier activity data influence coefficient is based on the Arrhenius kinetic principle, taking the cycle rate and reaction enthalpy change as the driving forces of activity. An integral term for the cumulative effect of activity decay is introduced into the denominator, and finally multiplied by the time decay factor to construct a comprehensive activity evaluation model covering transport performance, reaction thermodynamics, and time decay. Specifically: Where V represents the oxygen carrier circulation rate. Indicates the enthalpy change of the reaction. This represents the activity decay function at time t. This represents the cumulative attenuation coefficient, which is a constant. This represents the time decay coefficient, which is a constant, and t represents time.

[0040] In this embodiment, it should be specifically noted that the pollutant synergistic control data influence coefficient is based on the nitrogen oxide emission reduction rate, and is weakened by a logarithmic function. The negative impacts of emissions were investigated, and reduction selectivity was introduced as a process control correction factor. A multi-pollutant synergistic control evaluation system covering nitrogen oxides, sulfur oxides, and reaction selectivity was constructed, specifically as follows: in Indicates the maximum formation rate of nitrogen oxides. Indicates the rate of formation of nitrogen oxides. Indicates the baseline nitrogen oxide formation rate; F represents Emission flux express The standardized coefficient is a constant. express Influence coefficient; S represents reduction selectivity, This indicates the optimal reduction selectivity.

[0041] In this embodiment, it should be specifically noted that the system energy efficiency data influence coefficient extends the traditional definition of thermal efficiency to a generalized energy efficiency model that includes carbon capture energy-saving benefits and system control losses. The numerator integrates the main power generation, carbon capture energy saving, and auxiliary energy consumption, while the denominator considers the differences in conversion efficiency of different fuels. Finally, a control loss correction is introduced to comprehensively reflect the system's overall energy utilization level. Specifically: The steam power generation capacity is denoted as... Carbon capture energy-saving equivalent, denoted as Auxiliary power consumption, denoted as ; This indicates the lower heating value of coal fuel; Indicates the mass flow rate of coal fuel. This indicates the lower heating value of biomass. Indicates biomass mass flow; This represents the system control loss coefficient.

[0042] The comprehensive analysis module employs a multi-level comprehensive evaluation model based on the entropy weight method. It couples the influence coefficients of combustion stability data, biomass co-firing adaptability data, chemical chain carbon capture data, oxygen carrier activity data, pollutant synergistic control data, and system energy efficiency data into a comprehensive allocation index, providing a quantitative basis for decision-making.

[0043] In this embodiment, it should be specifically noted that the comprehensive allocation index reflects the synergistic and restrictive effects among various indicators through a multiplicative relationship. Combustion stability, biomass adaptability, and carbon capture efficiency, as fundamental system capabilities, constitute core driving factors in a power-law manner. Oxygen carrier activity is characterized by its threshold effect on system performance through an exponential decay function. Pollutant control performance is mitigated by a logarithmic function to weaken its extreme value influence. System energy efficiency is expressed as a relative value to reflect its marginal contribution. Ultimately, a comprehensive evaluation system that reflects the nonlinear coupling relationship among various indicators is formed, specifically: , in , in This represents the influence coefficient of combustion stability data. This represents the influence coefficient of biomass co-firing adaptability data. Indicates the influence coefficient of chemical chain carbon capture data. This represents the influence coefficient of oxygen carrier activity data. Indicates the impact coefficient of pollutant synergistic control data. This represents the influence coefficient of system energy efficiency data. This represents the activity decay function at time t. Indicates the time decay coefficient. This represents the impact coefficient of the historical maximum system energy efficiency data. This represents the minimum operating boundary of historical indicators. This indicates the maximum operating boundary of historical indicators.

[0044] The intelligent fuel distribution module, based on core coordination indicators and combined with externally input load commands and carbon emission limit constraints, solves the optimal distribution ratio of biomass fuel between the main combustion zone and the chemical looping reaction zone, and generates corresponding transport path control commands.

[0045] In this embodiment, it is specifically noted that the intelligent fuel distribution module identifies the constraints on the current system operation based on the normalized index values. When the influence coefficient of combustion stability data is lower than 0.6, its weight in the comprehensive distribution index is automatically increased to 0.35; when the influence coefficient of chemical chain carbon capture data is lower than 0.75, its weight is increased to 0.25; when the influence coefficient of pollutant synergistic control data is abnormal, its influence factor is adjusted to less than 1 accordingly. Taking the maximization of the comprehensive distribution index as the objective function and the load demand, carbon emission limit, and equipment safe operating range as constraints, a constrained nonlinear optimization model is established. A sequential quadratic programming algorithm is used to solve the optimal distribution ratio of biomass fuel between the main combustion zone and the chemical chain reaction zone, ensuring that the synergistic optimization of various indicators within the system is achieved while meeting external demands. Based on the optimal distribution ratio, the mixing parameters and conveying rate of biomass and pulverized coal are set in the main combustion zone, and the feed rate of biomass particles, carrier gas flow rate, and preheating temperature are set in the chemical chain reaction zone.

[0046] The reaction optimization module receives fuel allocation decision information and controls the exchange rate of solid materials between the two reactors by adjusting the transport power of the oxygen carrier circulation system and the fluidization state of the reactor, thereby fine-tuning the reaction optimization strategy in real time.

[0047] In this embodiment, it is specifically noted that the reaction optimization module compares the comprehensive allocation index value obtained after optimizing the fuel allocation decision with multiple preset comprehensive allocation index values, calculates the minimum difference value among the differences between the comprehensive allocation index value and the multiple preset comprehensive allocation index values, and forms a reaction optimization strategy based on the preset comprehensive allocation index value corresponding to the calculated difference value.

[0048] In this embodiment, it should be specifically noted that the multiple preset comprehensive allocation index values ​​are the average values ​​of the comprehensive allocation index values ​​under different historical working modes.

[0049] The suppression execution module intelligently adjusts the introduction position and mixing intensity of the two characteristic flue gases from the chemical chain system in the main boiler according to the comprehensive distribution index. It accurately introduces the reducing flue gas into the high-temperature reduction zone to establish a local low-nitrogen environment, while rationally distributing the high-temperature and low-oxygen flue gas to the main combustion zone to suppress the generation of thermal nitrogen oxides.

[0050] In this embodiment, it should be specifically noted that the suppression execution module activates the corresponding control mode according to the numerical range of the comprehensive allocation index: when the index is higher than 0.8, the economic operation mode is adopted, and only the basic suppression measures are activated; when the index is between 0.6 and 0.8, the standard control mode is adopted, and the synergistic suppression function of the two flues is fully activated; when the index is lower than 0.6, the enhanced suppression mode is activated, and the reducing agent injection amount is increased by 20% on the basis of the standard mode.

[0051] The data interaction module transmits the corresponding comprehensive allocation index, impact coefficient, fuel allocation strategy, reaction optimization strategy, and suppression execution strategy before and after optimization to the user information terminal, providing reference data for making adjustment measures.

[0052] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A boiler system for co-firing biomass fuel coupled with chemical looping combustion, characterized in that, include: The operation monitoring module collects real-time operation data of the entire process by deploying a network of multiple types of sensors at key locations, and builds a spatiotemporal monitoring dataset covering the physicochemical processes of the combustion system. The combustion operation data processing module establishes a carbon flow coupling calculation model based on the spatiotemporal monitoring dataset, and finally outputs the influence coefficients of combustion stability data, biomass co-firing adaptability data, and chemical loop carbon capture data. The system operates a data processing module, which establishes an energy flow coupling calculation model based on spatiotemporal monitoring datasets. Through multi-objective normalization processing, it finally generates the influence coefficients of oxygen carrier activity data, pollutant synergistic control data, and system energy efficiency data. The comprehensive analysis module adopts a multi-level comprehensive evaluation model based on the entropy weight method, which couples the influence coefficients of combustion stability data, biomass co-firing adaptability data, chemical chain carbon capture data, oxygen carrier activity data, pollutant synergistic control data, and system energy efficiency data into a comprehensive allocation index, providing a quantitative basis for decision-making. The intelligent fuel distribution module, based on core coordination indicators and combined with externally input load commands and carbon emission limit constraints, solves the optimal distribution ratio of biomass fuel between the main combustion zone and the chemical looping reaction zone, and generates corresponding transport path control commands. The reaction optimization module receives fuel allocation decision information and controls the exchange rate of solid materials between the two reactors by adjusting the conveying power of the oxygen carrier circulation system and the fluidization state of the reactor, and fine-tunes the reaction optimization strategy in real time. The suppression execution module intelligently adjusts the introduction position and mixing intensity of the two characteristic flue gases from the chemical chain system in the main boiler according to the comprehensive distribution index. It accurately introduces the reducing flue gas into the high-temperature reduction zone to establish a local low-nitrogen environment, while rationally distributing the high-temperature and low-oxygen flue gas to the main combustion zone to suppress the generation of thermal nitrogen oxides. The data interaction module transmits the corresponding comprehensive allocation index, impact coefficient, fuel allocation strategy, reaction optimization strategy, and suppression execution strategy before and after optimization to the user information terminal.

2. The boiler co-firing biomass fuel coupled with chemical looping combustion system according to claim 1, characterized in that: The spatiotemporal monitoring dataset includes combustion operation data and system operation data; The combustion operation data includes parameters affecting combustion stability, biomass co-firing adaptability, and chemical chain carbon capture; the system operation data includes parameters affecting oxygen carrier activity, pollutant synergistic control, and system energy efficiency.

3. A boiler co-firing biomass fuel coupled with chemical looping combustion system according to claim 2, characterized in that: The combustion stability data influencing parameters include furnace temperature, denoted as T; pressure oscillation power, denoted as f; and CO concentration fluctuation coefficient, denoted as... ; The parameters affecting biomass co-firing adaptability data include biomass moisture content (M); volatile matter index (V); total alkali metal content (K); unburned carbon content (UBC); and parameters affecting chemical looping carbon capture data include fuel reactor outlet. Volume concentration, denoted as The flue gas flow rate of the fuel reactor is denoted as Q; the carbon-containing gas concentration is denoted as C; and the input carbon is denoted as... The parameters affecting oxygen carrier activity data include the oxygen carrier circulation rate, denoted as V; and the enthalpy change of the reaction, denoted as... The activity decay function, denoted as ; The parameters affecting pollutant synergistic control data include the nitrogen oxide formation rate, denoted as R; Emission flux, denoted by F; reduction selectivity, denoted by S. Influence coefficient, denoted as The system energy efficiency data includes parameters affecting steam power generation, denoted as... Carbon capture energy-saving equivalent, denoted as Auxiliary power consumption, denoted as The lower heating value of the fuel, denoted as LHV; the fuel mass flow rate, denoted as m; and the system control loss coefficient, denoted as... .

4. A boiler co-firing biomass fuel coupled with chemical looping combustion system according to claim 1, characterized in that: The combustion stability data influence coefficient is obtained by normalizing and weighting three key parameters: furnace temperature field standard deviation, pressure oscillation energy, and CO concentration fluctuation. Temperature fluctuation has the highest weight, reflecting its dominant influence on stability. Pressure oscillation reflects combustion pulsation characteristics, and CO fluctuation characterizes combustion efficiency. Together, these three parameters constitute a stability evaluation system, which is used to evaluate the anti-interference ability and operational stability of the combustion system in real time.

5. A boiler co-firing biomass fuel coupled with chemical looping combustion system according to claim 1, characterized in that: The biomass co-firing adaptability data influence coefficients are processed by an exponential function to handle the negative impact of moisture, a linear function to handle the positive impact of volatile matter, and a reciprocal function to handle the negative impact of alkali metals. An unburned carbon correction term is introduced into the denominator to construct a quaternary evaluation model of moisture-volatile matter-alkali metals-burnout degree, which is used to quantify the system's compatibility with biomass of different characteristics.

6. A boiler co-firing biomass fuel coupled with chemical looping combustion system according to claim 1, characterized in that: The influence coefficient of the chemical chain carbon capture data is based on the fuel reactor outlet. Based on flow rate, deducting incompletely oxidized products CO and The carbon loss caused by carbon loss is taken into account, and the impact of unburned carbon is considered. Finally, the net capture efficiency is obtained by comparing it with the total input carbon, which reflects the directional conversion efficiency of carbon in the chemical chain.

7. A boiler co-firing biomass fuel coupled with chemical looping combustion system according to claim 1, characterized in that: The influence coefficient of oxygen carrier activity data is based on the Arrhenius kinetic principle. The cycle rate and reaction enthalpy change are used as the driving force for activity. An integral term of the cumulative effect of activity decay is introduced into the denominator. Finally, it is multiplied by the time decay factor to construct a comprehensive activity evaluation model that covers transport performance, reaction thermodynamics and time decay.

8. A boiler co-firing biomass fuel coupled with chemical looping combustion system according to claim 1, characterized in that: The impact coefficient of the pollutant synergistic control data is based on the nitrogen oxide emission reduction rate, and is weakened by a logarithmic function. The negative impacts of emissions were investigated, and reduction selectivity was introduced as a process control correction factor to construct a multi-pollutant synergistic control evaluation system covering nitrogen oxides, sulfur oxides, and reaction selectivity.

9. A boiler co-firing biomass fuel coupled with chemical looping combustion system according to claim 1, characterized in that: The system energy efficiency data influence coefficient extends the traditional definition of thermal efficiency to a generalized energy efficiency model that includes energy-saving benefits from carbon capture and system losses. The numerator integrates the main power generation, energy saving from carbon capture, and auxiliary energy consumption, while the denominator considers the differences in conversion efficiency of different fuels. Finally, a control loss correction is introduced to comprehensively reflect the system's overall energy utilization level.

10. A boiler co-firing biomass fuel coupled with chemical looping combustion system according to claim 1, characterized in that: The comprehensive allocation index reflects the synergistic and restrictive effects among various indicators through a multiplicative relationship. Combustion stability, biomass adaptability, and carbon capture efficiency, as the basic capabilities of the system, constitute the core driving factors in a power-law form. Oxygen carrier activity is characterized by its threshold effect on system performance through an exponential decay function. Pollutant control performance is weakened by a logarithmic function. System energy efficiency is reflected by its marginal contribution in a relative value form. Finally, a comprehensive evaluation system that can reflect the nonlinear coupling relationship among various indicators is formed.

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