Biomass blending combustion alkali-chlorine regulation system and method for coal-fired boiler

Abstract

The application provides a coal-fired boiler biomass blending combustion alkali-chlorine regulation system and method, relates to the technical field of coal power generation, and an online monitoring unit is used to obtain online monitoring data, the online monitoring data including a measured value of alkali metal concentration, a measured value of HCl concentration and a heating surface wall temperature in the process of monitoring the boiler biomass blending combustion; a control unit is used to determine alkali metal release parameters under the current working condition according to the alkali metal content in the fuel, the furnace temperature field and the boiler load; predict the chlorine element parameters in the flue gas according to the chlorine element content in the fuel, the furnace temperature field and the flue gas composition; input the online monitoring data, the alkali metal release parameters and the chlorine element parameters into a model predictive control (MPC) optimizer, construct an optimization function based on a safety boundary constraint, and solve a control variable set; and the execution mechanism configured for each control variable in the control variable set is used to execute each control variable in the control variable set, so that the synergy of alkali-chlorine regulation is improved.

Description

Technical Field

[0001] This application relates to the field of coal-fired power generation technology, and in particular to a biomass caustic soda and chlorine regulation system and method for coal-fired boilers. Background Technology

[0002] Blending biomass (such as agricultural and forestry waste, urban sludge, and sawdust) into large coal-fired power boilers is an important technological pathway to achieve carbon emission reduction. However, biomass fuels differ significantly from coal in elemental composition; its alkali metal (potassium K, sodium Na) content is typically 3 to 10 times that of coal, and its chlorine (Cl) content can be as high as 5 to 20 times that of coal. Under high-temperature combustion conditions, the combined action of alkali metals and chlorine can produce a series of serious problems, mainly including: alkali metal compounds condensing on heating surfaces to form slag and fouling layers, leading to decreased heat transfer efficiency; chlorine initiating high-temperature chlorine corrosion, accelerating the failure of superheater and reheater tubes; and alkali metal chlorides forming low-melting-point eutectics, exacerbating ash deposition and accumulation problems. Therefore, effective control of alkali metals and chlorine during biomass blending is crucial to ensuring the safe, efficient, and long-term operation of the boiler. Summary of the Invention

[0003] In view of this, this application provides a system and method for regulating the alkali metal and chlorine content in biomass co-firing in coal-fired boilers, aiming to achieve synergistic regulation of alkali metals and chlorine elements during biomass co-firing.

[0004] In a first aspect, this application provides a biomass-caustic soda-chlorine blending control system for coal-fired boilers, the system comprising: An online monitoring unit is used to acquire online monitoring data, which includes measured values ​​of alkali metal concentration, measured values ​​of HCl concentration, and wall temperature of the heating surface during the biomass co-firing process in the boiler. The control unit is used to determine the alkali metal release parameters under the current operating conditions based on the alkali metal content in the fuel, the furnace temperature field, and the boiler load; predict the chlorine parameters in the flue gas based on the chlorine content in the fuel, the furnace temperature field, and the flue gas composition; and construct an optimization function based on the online monitoring data, alkali metal release parameters, and chlorine parameters, using the model predictive control (MPC) optimizer as input, with the objectives of minimizing the growth rate of the ash fouling coefficient and the high-temperature corrosion rate of the heated surface and maximizing the biomass co-firing ratio, and solve for the set of control variables. An actuator configured for each control variable in the control variable set is used to execute each control variable in the control variable set, which includes the biomass blending ratio, the first additive acceleration rate for fixing alkali metals, the second additive acceleration rate for fixing chlorine, and combustion parameters.

[0005] Optionally, the online monitoring data may also include fuel alkali metal content, chlorine content, furnace temperature field, flue gas composition, high-temperature corrosion rate, and ash pollution coefficient; the online monitoring unit includes a LIBS alkali metal sensor, a TDLAS-HCl analyzer, a pipe wall temperature thermocouple, a fuel online composition analyzer, a temperature measurement module, a flue gas analysis module, a high-temperature corrosion monitoring probe, and an ash pollution heat flow meter; The LIBS alkali metal sensor collects the gaseous potassium and sodium concentrations in flue gas in real time as the alkali metal concentration; the TDLAS-HCl analyzer collects the HCl concentration in flue gas in real time as the chlorine concentration; the tube wall temperature thermocouple collects the wall temperature of the heated surface; the online fuel composition analyzer monitors the online analysis data of the fuel, including alkali metal content and chlorine content; the temperature measurement module is used to obtain the temperature field distribution of the furnace cross section to determine the furnace temperature field; the flue gas analysis module is used to monitor and obtain the flue gas composition; the high-temperature corrosion monitoring probe is used to monitor the high-temperature corrosion rate; and the ash and dirt heat flow meter is used to compare the difference in heat flux density between the clean tube wall and the actual tube wall to determine the ash and dirt coefficient of the degree of deposition of reactive alkali metal compounds on the heated surface.

[0006] Optionally, the control unit includes an alkali metal release analysis module and a chlorine element release and conversion analysis module; The alkali metal release analysis module is used to calculate the alkali metal release parameters under the current operating conditions using the alkali metal content, furnace temperature field and boiler load as inputs, and to determine the predicted value of the alkali metal concentration through the alkali metal release kinetic model. The alkali metal release parameters include the alkali metal release rate and the total release amount. The chlorine release and conversion analysis module is used to predict chlorine parameters in flue gas by taking the chlorine content in fuel, furnace temperature field and flue gas composition as inputs, and through thermodynamic balance calculations and chemical reaction kinetic models. The chlorine parameters include the predicted value of HCl concentration, alkali metal chloride vapor partial pressure and chlorine corrosion risk index.

[0007] Optionally, the system includes an MPC optimizer; The MPC optimizer is used to take the online monitoring data, alkali metal release parameters, and chlorine parameters as inputs, and combine the upper limit constraints of the heated surface wall temperature, the upper limit constraints of CO concentration in flue gas, the upper limit constraints of HCl concentration in flue gas, the upper limit constraints of high-temperature corrosion rate, and the upper limit constraints of NOx emission concentration as safety boundary constraints to construct an optimization function with the objectives of minimizing the growth rate of the heated surface ash pollution coefficient and the high-temperature corrosion rate, and maximizing the biomass co-firing ratio. The set of control variables is solved using a particle swarm optimization algorithm or a sequential quadratic programming algorithm. The optimization function is expressed as follows: min J = w1×Fs + w2×Cr - w3×Br; Where w1, w2, and w3 are the configured weighting coefficients, Fs is the ash pollution coefficient growth rate, Cr is the high-temperature corrosion rate, and Br is the biomass co-firing ratio.

[0008] Optionally, the control unit further includes a feedforward compensation module; The feedforward compensation module is used to determine the feedforward compensation amount based on the changes in the online analysis data and the changes in the boiler load command, using a pre-calibrated feedforward coefficient matrix. The feedforward compensation amount includes a first component for the dosage of the first additive, a second component for the dosage of the second additive, and a third component for the combustion parameters. Each component is superimposed onto the corresponding control variables in the control variable set to obtain a composite variable set. If one or more control variables in the composite variable set exceed the corresponding allowable range, the control variables exceeding the allowable range are subjected to amplitude limiting processing to obtain the final control variable set.

[0009] Optionally, the fuel parameters include the furnace outlet excess air coefficient setting value, the burnout air ratio, and the furnace outlet flue gas temperature; The system includes a fuel blending actuator configured for the biomass blending ratio, a first feeding mechanism configured for the first additive injection acceleration rate, a second feeding mechanism configured for the second additive injection acceleration rate, a blower inlet guide vane or frequency conversion adjustment mechanism configured for the furnace outlet excess air coefficient setting value, a burnout air damper opening adjustment mechanism configured for the burnout air ratio, and a burner sway angle or flue gas recirculation damper adjustment mechanism configured for the furnace outlet flue gas temperature.

[0010] Optionally, the system may also include a human-computer interaction interface; The human-machine interface is used to display real-time data and trend curves fed back by the LIBS alkali metal sensor, real-time data and trend curves fed back by the TDLAS-HCl analyzer, predicted values ​​of alkali metal / chlorine release, the final set of control variables, system operating status, fault alarm information, and control mode configuration items. The control mode configuration options include fully automatic mode, semi-automatic mode, and manual mode. When fully automatic mode is selected, the control unit is further configured to generate control commands based on each control variable in the final control variable set, and send the control commands to the actuator corresponding to the control variable to drive the actuator. When semi-automatic mode is selected, the control unit is further configured to provide a recommended value based on each control variable in the final control variable set to the human-machine interface, and in response to the user's confirmation of the recommended value, send the control command corresponding to the recommended value to the actuator corresponding to the control variable to drive the actuator. When manual mode is selected, the control unit is further configured to generate control commands based on each control variable in the user-configured control variable set, and send the control commands to the actuator corresponding to the control variable to drive the actuator. The control unit is also used to automatically trigger a protection command and issue the fault alarm information when the wall temperature of the heated surface exceeds the upper limit constraint, the HCl concentration in the flue gas exceeds the upper limit constraint of the HCl concentration, or any monitoring element fails. The monitoring element is a device pre-selected from the online monitoring unit. The trigger protection command includes one or more of the following: a command to indicate a reduction in the biomass co-firing ratio and a command to activate the backup additive dosing pipeline.

[0011] Optionally, the system further includes a data processing unit, the output of the online monitoring unit is connected to the data processing unit, and the output of the data processing unit is connected to the control unit; The data processing unit is used to receive the online monitoring data acquired by the online monitoring unit, perform signal filtering, dimension conversion, timestamp synchronization and alignment and validity verification on various acquired signals in the online monitoring data, store the processed acquired signals and periodically package and upload them to the control unit based on the communication protocol.

[0012] Secondly, this application also provides a method for regulating the chlorine content of biomass in a coal-fired boiler by blending caustic soda with water, the method comprising: Acquire online monitoring data, which includes measured values ​​of alkali metal concentration, measured values ​​of HCl concentration, and wall temperature of the heating surface during the biomass co-firing process in the boiler. Based on the alkali metal content in the fuel, the furnace temperature field, and the boiler load, the alkali metal release parameters under the current operating conditions are determined; based on the chlorine content in the fuel, the furnace temperature field, and the flue gas composition, the chlorine parameters in the flue gas are predicted; using the online monitoring data, alkali metal release parameters, and chlorine parameters as inputs, the Model Predictive Control (MPC) optimizer constructs an optimization function based on safety boundary constraints, aiming to minimize the growth rate of the ash fouling coefficient of the heated surface and the high-temperature corrosion rate, and maximize the biomass co-firing ratio, and solves the set of control variables. The control command is determined according to each control variable in the control variable set, and the control command is sent to the actuator corresponding to the control variable to execute the control command. The control variable set includes the biomass blending ratio, the first additive acceleration rate for fixing alkali metals, the second additive acceleration rate for fixing chlorine elements, and combustion parameters.

[0013] Optionally, after solving for the set of control variables, the method further includes: Based on the changes in online analysis data and boiler load command, a feedforward compensation amount is determined using a pre-calibrated feedforward coefficient matrix. The feedforward compensation amount includes a first component for the dosage of the first additive, a second component for the dosage of the second additive, and a third component for the combustion parameters. The online analysis data includes alkali metal content and chlorine content. Each component is superimposed onto the corresponding control variable in the control variable set to obtain the composite variable set; If one or more control variables in the synthetic variable set exceed the corresponding allowable range, the control variables that exceed the allowable range are subjected to amplitude limiting to obtain the final control variable set; After executing the control command, the method further includes: Continue to acquire online monitoring data, compare the measured value of alkali metal concentration with the predicted value of alkali metal concentration determined according to the metal release parameters to determine the first control deviation, and compare the measured value of HCl concentration with the predicted value of HCl concentration in the chlorine element parameters to determine the second control deviation. If the first control deviation or the second control deviation exceeds the corresponding threshold, perform model parameter correction on the MPC optimizer.

[0014] Thirdly, this application provides an apparatus comprising a memory and a processor, the memory for storing instructions or code, and the processor for executing the instructions or code to cause the apparatus to perform a method for regulating the biomass co-firing with caustic soda and chlorine in a coal-fired boiler as described in any of the first aspects above.

[0015] Fourthly, this application provides a computer storage medium storing code, wherein when the code is executed, a device running the code implements the method for regulating the biomass co-firing with caustic soda and chlorine in a coal-fired boiler as described in any of the first aspects above.

[0016] This application provides a biomass-based caustic soda and chlorine blending control system and method for coal-fired boilers. Based on the above embodiments, for the system of this application, firstly, the online monitoring unit acts as the front-end sensing layer, collecting real-time raw monitoring data of alkali, chlorine, and heating surface wall temperature and transmitting it to the control unit; then, the control unit acts as the decision layer, completing the identification of operating parameters and multi-objective coupled optimization in two steps, outputting a set of control variables composed of multiple control variables to achieve subsequent multi-control variable coordinated regulation; the actuators act as the final execution layer, configuring corresponding actuators for each of the multiple control variables included in the control variable set. Each actuator receives the control commands corresponding to the control variables in the control variable set and simultaneously completes the adjustment of fuel blending, the acceleration rate of alkali metal and chlorine additives, and combustion parameters. Furthermore, the adjusted boiler operating conditions can be collected again by the online monitoring unit and transmitted back to the control unit to achieve continuous iterative closed-loop regulation. Thus, this application abandons the separate control logic of alkali and chlorine in the prior art, embedding the coupling prediction of alkali metal release and chlorine conversion into the same control unit, and relying on the MPC model predictive control framework to uniformly solve the multi-dimensional regulation parameters of fuel blending, additives, and combustion. Reduce the alkali-chlorine coupling antagonism in biomass co-firing scenarios and improve the timeliness of regulation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings used in the description of the embodiment or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of a biomass-caustic soda-chlorine regulation system for a coal-fired boiler provided in this application embodiment; Figure 2 A schematic flowchart illustrating a method for regulating the chlorine content of biomass in a coal-fired boiler by blending caustic soda with chlorine, provided as an embodiment of this application; Figure 3 A schematic diagram of the application process of a biomass co-firing method for chlorine regulation in a coal-fired boiler, provided in an embodiment of this application; Figure 4 This is a schematic diagram illustrating the principle of multi-parameter coupling optimization provided in an embodiment of this application. Detailed Implementation

[0019] In existing technologies, to reduce the alkali metal or chlorine content in fuel, one approach is to pre-treat the fuel offline, such as by water washing, acid washing, or baking. However, these methods cannot respond in real time to fluctuations in fuel quality during boiler operation, and the pre-treatment costs are high. Furthermore, pre-treatment only reduces the initial alkali and chlorine content of the fuel and cannot control the release and transformation of alkali metals caused by factors such as furnace atmosphere and temperature field distribution during combustion. If offline testing is used, there is a time lag between sampling, sending for testing, and issuing the test report, which can lead to biased control decisions, and the representativeness of the samples is insufficient.

[0020] Furthermore, existing technologies employ separate alkali metal control schemes and separate chlorine control schemes, such as the closed-loop optimization scheme for kaolin in high-alkali coal, and the corrosion early warning and coal blending optimization scheme based on chlorine monitoring. Both of these schemes share a common limitation: treating the alkali metal problem and the chlorine problem as two independent technical problems to be solved separately. However, in the context of biomass co-firing, this divide-and-conquer strategy is theoretically ineffective. The specific reasons include: (1) The alkali / chlorine mass ratio of coal is usually between 5 and 20 (high alkali and low chlorine), while the alkali / chlorine mass ratio of biomass (especially straw) is usually between 1 and 5 (high alkali and high chlorine). During biomass co-firing, the formation of alkali metal chlorides (KCl / NaCl) is not controlled by a single factor of alkali or chlorine, but by the nonlinear control of the alkali-chlorine molar ratio. If only alkali metals are controlled (e.g., by adding kaolin to fix alkali metal oxides), the remaining chlorine will still penetrate the protective layer of the heated surface in the form of HCl or Cl2, causing corrosion; if only chlorine is controlled (e.g., by adding limestone powder to capture HCl), the unfixed alkali metals will still condense on the heated surface in the form of gaseous KOH / NaOH, leading to slag formation. Therefore, if either alkali or chlorine is not effectively controlled, it will become a bottleneck in the system's failure. (2) There is an antagonistic effect between alkali metal control measures and chlorine control measures: Kaolin reacts with alkali metals and releases water of crystallization (Al2O3·2SiO2·2H2O → Al2O3·2SiO2 + 2H2O), increasing the partial pressure of water vapor in the flue gas, thereby accelerating the conversion of HCl gas into corrosive Cl2 (Deacon reaction: 4HCl + O2). (2Cl2 + 2H2O); Limestone powder (CaCO3) decomposes into CaO at high temperatures. CaO reacts with HCl to form CaCl2, but CaCl2 is also corrosive at high temperatures and lowers the melting point of fly ash. Therefore, the dosage of alkali metal additives and chlorine additives are not two independent variables—increasing the dosage of kaolin may reduce alkali metal slagging but exacerbate the risk of chlorine corrosion, while increasing the dosage of limestone powder may reduce HCl concentration but alter ash melting characteristics, thus affecting alkali metal deposition behavior. This antagonistic effect dictates that alkali-chlorine regulation must be optimized as a coupled system; separate optimization will inevitably lead to suboptimal or even deteriorated global performance.

[0021] Based on this, this application provides a system and method for regulating the alkali metal (K, Na) and chlorine elements in biomass co-firing in coal-fired boilers. It monitors key indicators of alkali metals (K, Na) and chlorine (Cl) during the biomass co-firing process in the boiler online. Based on a multi-parameter coupled MPC (Model Predictive Control) optimizer, it automatically and collaboratively regulates the fuel blending ratio, additive dosage, and combustion parameters to achieve closed-loop integrated and precise regulation of alkali metals and chlorine elements. This effectively alleviates slagging and fouling of heating surfaces and high-temperature chlorine corrosion, and improves the biomass co-firing ratio and the safety and economy of boiler operation.

[0022] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0023] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0024] Unless otherwise stated, the term "multiple" means two or more. In embodiments of this disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B. The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0026] See Figure 1 , Figure 1 This application provides a schematic diagram of the structure of a biomass-caustic soda-chlorine blending control system for a coal-fired boiler. The system includes: The online monitoring unit 11 is used to acquire online monitoring data, which includes the measured values ​​of alkali metal concentration, HCl concentration and heating surface wall temperature during the biomass co-firing process in the boiler. Optionally, the online monitoring unit can be installed in key locations such as boiler flue gas passages, heating surface areas, and fuel supply pipelines.

[0027] Optionally, the measured values ​​of the above-mentioned alkali metal concentrations can be obtained by using a laser-induced breakdown spectroscopy (LIBS) sensor to detect the real-time concentrations of gaseous K and Na in the flue gas. The LIBS sensor can be installed in the horizontal flue or the tail vertical flue of the boiler, before the inlet of the high-temperature superheater, and at the outlet of the economizer.

[0028] The LIBS sensor utilizes high-energy pulsed laser irradiation of flue gas or fly ash particles to generate laser-induced plasma. The plasma emission spectrum is collected by a spectrometer, and through characteristic spectral line identification and quantitative analysis, the concentrations of gaseous potassium (K, characteristic wavelengths 766.49 nm and 769.90 nm) and gaseous sodium (Na, characteristic wavelengths 588.99 nm and 589.59 nm) in the flue gas are obtained in real time. The detection range of the LIBS sensor is 0.1~1000 mg / Nm³, the detection cycle is adjustable from 1 to 10 seconds, and the relative detection error is ≤±10%. Two to three LIBS sensors are configured at each measuring point, and the median value is taken as the valid data for that point to improve measurement reliability.

[0029] Optionally, the measured value of HCl concentration can be obtained by using a tunable semiconductor laser absorption spectroscopy (TDLAS-HCl) analyzer to detect the real-time concentration of HCl in the flue gas; the TDLAS-HCl analyzer can be installed at the economizer outlet or the air preheater inlet flue.

[0030] The TDLAS-HCl analyzer utilizes the characteristic absorption lines of HCl molecules in the near-infrared band (such as the ν=0→2 overtone absorption band at 1742nm) to achieve highly sensitive online detection of HCl concentration through wavelength modulation spectroscopy. The analyzer has a detection range of 0~500mg / Nm³, a detection limit of 0.5mg / Nm³, and a response time ≤3 seconds.

[0031] Optionally, the wall temperature of the heated surface can be monitored in real time using pipe wall temperature thermocouples. For example, thermocouples are arranged in key pipe sections of the heated surface to monitor the metal wall temperature. When the wall temperature rises abnormally (e.g., exceeding the design temperature by more than 30°C), it indicates that there may be serious ash accumulation and slag buildup.

[0032] Control unit 12 is used to determine the alkali metal release parameters under the current operating conditions based on the alkali metal content in the fuel, the furnace temperature field, and the boiler load; predict the chlorine parameters in the flue gas based on the chlorine content in the fuel, the furnace temperature field, and the flue gas composition; and construct an optimization function based on the online monitoring data, alkali metal release parameters, and chlorine parameters through the model predictive control (MPC) optimizer, with the objectives of minimizing the growth rate of the ash fouling coefficient of the heated surface and the high-temperature corrosion rate, and maximizing the biomass co-firing ratio, and solve for the set of control variables. And, an execution mechanism configured for each control variable in the control variable set, for executing each control variable in the control variable set.

[0033] The set of control variables mentioned above includes the biomass blending ratio, the first additive addition acceleration rate with fixed alkali metals, the second additive addition acceleration rate with fixed chlorine, and combustion parameters.

[0034] Optionally, the first additive for fixing alkali metals can be kaolin, and the second additive for fixing chlorine can be limestone powder.

[0035] Optionally, the online monitoring unit may also include an online fuel composition analyzer, which is used to monitor online analysis data of the fuel, including the alkali metal content and chlorine content mentioned above.

[0036] For example, the online component analyzer can be configured with a near-infrared (NIR) online component analyzer or an X-ray fluorescence (XRF) online elemental analyzer on the conveyor belts of biomass and coal respectively, to acquire online analysis data in real time. In addition to the K content, Na content, and Cl content in the corresponding fuel, the online analysis data can also include parameters such as sulfur content, ash content, moisture content, and calorific value. The analysis cycle is 3 to 5 minutes.

[0037] Optionally, the online monitoring unit may further include a temperature measurement module, which is used to acquire the temperature field distribution of the furnace cross-section to determine the aforementioned furnace temperature field. For example, a furnace acoustic temperature measurement system or an infrared radiation temperature measurement system can be used to acquire the temperature field distribution of the furnace cross-section, with a temperature measurement range of 600~1800℃ and a spatial resolution ≤1m.

[0038] Optionally, the online monitoring unit may also include a flue gas analysis module for monitoring and acquiring the aforementioned flue gas components. For example, a Fourier transform infrared spectroscopy (FTIR) multi-component flue gas analyzer can be configured at the air preheater outlet flue, capable of simultaneously detecting multiple flue gas components such as HCl, SO2, and H2O, and can be cross-calibrated with the aforementioned TDLAS-HCl analyzer. Alternatively, a power plant CEMS (Continuous Emission Monitoring System) can be used to acquire real-time data on conventional parameters such as O2, CO, NOx, and SO2.

[0039] Optionally, the online monitoring unit may also include a high-temperature corrosion monitoring probe, which is used to monitor the aforementioned high-temperature corrosion rate. This high-temperature corrosion monitoring probe employs an online high-temperature corrosion rate monitoring sensor based on resistance probes or electrochemical noise principles, and is installed between the tube panels of the high-temperature superheater to measure the corrosion rate of the metallic material in real time (unit: μm / year), with a detection range of 0.01~50 μm / year. Optionally, when the corrosion rate exceeds a preset threshold (e.g., 1.0 μm / year), an early warning message is issued.

[0040] Optionally, the online monitoring unit may also include a fouling heat flux meter to compare the heat flux density difference between clean and actual tube walls, determining a fouling coefficient that reflects the degree of alkali metal compound deposition on the heated surface. For example, heat flux density sensors are installed on the tube walls of each stage of the superheater and reheater. By comparing the heat flux density difference between clean and actual tube walls, the fouling coefficient is calculated online, indirectly reflecting the degree of alkali metal compound deposition on the heated surface. The calculation cycle for the fouling coefficient is 1-5 minutes.

[0041] Based on the above embodiments, the online monitoring unit, control unit, and actuators of this application form a complete closed-loop link of perception, analysis, decision-making, and execution feedback. First, the online monitoring unit, as the front-end perception layer, collects raw monitoring data on alkali chlorine and heating surface wall temperature in real time and transmits it to the control unit. Then, the control unit, as the decision-making layer, completes the identification of operating parameters and multi-objective coupled optimization in two steps, outputting a set of control variables composed of various control variables to achieve subsequent multi-control variable coordinated regulation. The actuators, as the final execution layer, are configured one-to-one with the various control variables included in the control variable set. Each actuator receives the control commands corresponding to the control variables in the control variable set and simultaneously completes the fuel blending, coordinated regulation of the dosing rate of the two additives (alkali metal and chlorine), and adjustment of combustion parameters. Furthermore, the adjusted boiler operating conditions can be collected again by the online monitoring unit and transmitted back to the control unit to achieve continuous iterative closed-loop regulation. Thus, this application abandons the existing technology's separate control logic for alkali and chlorine, embedding the coupled prediction of alkali metal release and chlorine element conversion into the same control unit. It relies on the MPC model predictive control framework to uniformly solve for multi-dimensional regulation parameters related to fuel blending, additives, and combustion. This reduces alkali-chlorine coupling antagonism in high-alkali, high-chlorine biomass co-firing scenarios and improves the timeliness of regulation.

[0042] Based on the above embodiments, this application may further include a data processing unit 13 connecting the online monitoring unit 11 and the control unit 12. The data processing unit may be an intermediate data processing platform built on a PLC or DCS, and the data processing unit may have the following functional modules: The input module 131 can be specifically divided into an analog input submodule and a digital input submodule, which are used to acquire the online monitoring data sent by the online monitoring module, such as the output signals of all monitoring components included in the online monitoring module, including the LIBS sensor, TDLAS analyzer, thermocouple, ash heat flow meter, high temperature corrosion monitoring probe, etc.

[0043] The analog signal acquisition accuracy is no less than 0.1%FS, and the sampling period is configurable from 100ms to 1s.

[0044] The preprocessing module 132 is used to perform signal filtering, dimension conversion, time synchronization alignment, and validity verification on the acquired online monitoring data to obtain the processed online monitoring data.

[0045] Optionally, the signal filtering described above can combine median filtering and Kalman filtering to remove abnormal jumps in sensor data and random noise. Furthermore, for the spectral data of the LIBS sensor, a wavelet transform denoising algorithm is used to improve the signal-to-noise ratio. The dimensional conversion described above can convert electrical signals into physical quantities (such as concentration mg / Nm³, temperature ℃, flow rate t / h, etc.) based on the calibration curves of each sensor. The time alignment synchronization described above can align time deviations caused by different sampling periods and transmission delays of different sensors, ensuring that all data correspond to the same time slice. The validity verification described above can determine the validity of each sensor's data through methods such as threshold checking, rate of change checking, and redundancy comparison, marking invalid data as "unusable" and excluding it in subsequent calculations.

[0046] The data communication module 133 is used to package and upload the online monitoring data processed by the preprocessing module 132 to the intelligent control unit via OPC UA or Modbus TCP protocol at intervals of 1 to 5 seconds. Simultaneously, the PLC (Programmable Logic Controller) or DCS (Distributed Control System) locally stores at least 30 days of historical data for trend analysis and accident tracing.

[0047] Based on the above embodiments, the online monitoring data output by the online monitoring unit of this application is connected to the data processing unit via a signal cable. The data processing unit transmits the processed online monitoring data to the control unit via an industrial Ethernet or fieldbus (such as PROFIBUS-DP, Modbus TCP). The control unit calculates and outputs multiple control variables, including those for adjusting the biomass co-firing of the boiler, forming a control variable set. Based on the values ​​of each control variable in the control variable set, the control unit generates control commands and sends them to the corresponding actuators. After the actuators operate according to the received control commands, the operating status of the coal-fired boiler will be monitored again by the online monitoring unit in a new cycle and a new round of adjustments will be executed, forming a closed-loop feedback.

[0048] Based on this, in the biomass co-firing process of coal-fired boilers, alkali metal control and chlorine control constitute an inseparable coupled system. Their intrinsic connection is reflected in the following technical facts: Alkali metal vapors (K, Na) in flue gas react with HCl in the gas phase to generate alkali metal chlorides (KCl, NaCl). This reaction pathway makes simply reducing the HCl concentration in the flue gas (e.g., limestone powder dechlorination) ineffective in blocking the formation of alkali metal chlorides; the condensation and deposition behavior of gaseous KCl and NaCl on the heated surface is nonlinearly affected by the alkali metal / chlorine molar ratio, and the deposition of one component alters the nucleation-growth kinetics of the other; the sulfate products (K2SO4, Na2SO4) generated by the reaction of kaolin with alkali metals, and the low-melting-point eutectic formed with fly ash (melting temperature can be as low as 550~650℃), exhibit varying adhesion states on the heated surface with wall temperature. When the wall temperature is below the eutectic melting temperature, an adhesion layer forms; when the wall temperature is above the melting temperature, it transforms into a molten medium. Its protective or corrosion-promoting effect depends on the relative relationship between the wall temperature and the melting temperature. The aforementioned coupling relationship implies that the optimization problems of the ash pollution coefficient Fs and the chlorine corrosion rate Cr cannot be decomposed into two independent sub-problems. If only alkali metal deposition is controlled (such as an optimized kaolin addition scheme), the corrosion risk caused by chlorine is not suppressed; if only the chlorine concentration is controlled (such as a blending scheme based on a chlorine concentration threshold), the heat transfer deterioration caused by alkali metal slagging cannot be alleviated. To address this, this invention uses alkali metal concentration, chlorine concentration, and heated surface wall temperature as coupling variables, introduces a joint alkali-chlorine constraint term into the MPC optimization framework, and places the minimization of Fs and the minimization of Cr in a unified optimization problem for collaborative solution, replacing the decentralized control architecture in the prior art where the alkali metal control loop and the chlorine control loop operate independently.

[0049] Based on the above embodiments, the control unit 12 described above is the core of this application, and can use an industrial control computer (IPC) or an embedded controller as the hardware platform. The control unit 12 can have the following functional modules: The alkali metal release analysis module 121 is used to calculate the alkali metal release parameters under the current operating conditions by taking the alkali metal content, furnace temperature field and boiler load as inputs and using the alkali metal release kinetic model. The alkali metal release parameters include the alkali metal release rate and the total release amount.

[0050] The above alkali metal release kinetic model can be expressed as: R_K = A · exp(-Ea / RT) · f(α) · C_K In the above figures, R_K represents the alkali metal release rate (mg / (kg·s)); A is the pre-exponential factor; Ea is the apparent activation energy (kJ / mol); R is the gas constant; T is the local temperature of the furnace, which can be determined by monitoring the furnace temperature field through the temperature measurement module; f(α) is the reaction mechanism function; and C_K is the initial concentration of alkali metals in the fuel (mg / kg), which can be determined in real time online by an online component analyzer. The pre-exponential factor A, apparent activation energy Ea, and reaction mechanism function f(α) are pre-calibrated using laboratory thermogravimetric-mass spectrometry (TG-MS) and tubular furnace experiments, and a dedicated parameter library is established for different biomass types (straw, sawdust, sludge, etc.).

[0051] Understandably, based on the above alkali metal release rate and total release amount, combined with the current boiler cavity volume of biomass co-firing, the current predicted value of alkali metal concentration is determined (which can be divided into predicted K concentration and predicted Na element).

[0052] The chlorine release and conversion analysis module 122 is used to predict chlorine parameters in flue gas by taking the chlorine content in fuel, furnace temperature field and flue gas composition (such as H2O, SO2, etc.) as inputs, through thermodynamic balance calculation and chemical reaction kinetic model. The chlorine parameters include the predicted value of HCl concentration, the vapor partial pressure of alkali metal chloride and the chlorine corrosion risk index.

[0053] Optionally, the above thermodynamic balance calculation can be performed using a multidimensional lookup table database generated offline by FactSage software. This module receives data from online component analysis of the fuel, such as chlorine content, and uses the furnace temperature field and flue gas components (e.g., H2O, SO2) as indexes to query the pre-built multidimensional lookup table database. It can directly return the preliminary prediction results obtained from the query. Since the boiler state is constantly changing, the input parameter indexes also change accordingly. Therefore, this query process can be executed cyclically at a certain frequency to ensure that the prediction results keep up with the actual operating state of the boiler.

[0054] Based on this preliminary prediction, and further combined with a chemical reaction kinetic model that focuses on reaction rate and time factors (e.g., describing how long it takes for chlorine in fuel to convert to HCl and be released into the gas phase at a specific temperature), the dynamic reaction formation results calculated by the chemical reaction kinetic model can be combined with the preliminary prediction results of thermodynamic equilibrium. For example, FactSage can be used to calculate the theoretical maximum formation amounts of HCl, Cl2, KCl, and NaCl at a certain temperature. Then, the chemical reaction kinetic model can be used to calculate a conversion rate or the degree of near-equilibrium based on factors such as actual residence time. This allows for the correction of the predicted values ​​of each compound in the thermodynamic equilibrium calculation, determining the formation amounts and concentration distributions of HCl, Cl2, and alkali metal chlorides (KCl, NaCl) in the flue gas. Finally, the predicted values ​​of HCl concentration and alkali metal chloride vapor partial pressure in the flue gas can be output.

[0055] In addition, the above-mentioned chlorine corrosion risk index takes into account multiple factors such as HCl concentration, KCl / NaCl vapor partial pressure, and heated surface wall temperature to determine the risk index reflecting the occurrence of high-temperature chlorine corrosion under the current operating conditions, providing clear early warning signals for operators or automatic control systems.

[0056] The MPC optimizer 123 is used to take the online monitoring data, alkali metal release parameters, and chlorine parameters as inputs, and combine the upper limit constraints of the heated surface wall temperature, the upper limit constraints of CO concentration in flue gas, the upper limit constraints of HCl concentration in flue gas, the upper limit constraints of high-temperature corrosion rate, and the upper limit constraints of NOx emission concentration as safety boundary constraints to construct an optimization function with the objectives of minimizing the growth rate of the heated surface ash pollution coefficient and the high-temperature corrosion rate, and maximizing the biomass co-firing ratio. The set of control variables is solved using a particle swarm optimization algorithm or a sequential quadratic programming algorithm.

[0057] For details, please refer to Figure 4 The diagram shows a principle of multi-parameter coupled optimization, where the input variables of the MPC optimizer may include fuel K / Na / Cl content, furnace temperature field, boiler load, flue gas composition (H2O / SO2 / O2 / CO / NOx / HCl), heated surface wall temperature, ash pollution coefficient, corrosion rate, etc.

[0058] The optimization function in MPC is expressed as: min J = w1×Fs + w2×Cr - w3×Br; Where Fs is the ash fouling coefficient growth rate, Cr is the high-temperature corrosion rate, Br is the biomass co-firing ratio, and w1, w2, and w3 are the configured weighting coefficients, where w1 can be 1, w2 can be 1, and w3 can be 0.5. Thus, the primary optimization objective is to minimize the slagging and fouling rate of the heating surface and the high-temperature corrosion rate, while the secondary optimization objective is to maximize the biomass co-firing ratio (maximizing carbon emission reduction benefits), with the boiler operation safety boundary (heating surface wall temperature not exceeding limits, flue gas temperature not exceeding limits, CO emissions not exceeding standards, etc.) as constraints.

[0059] In one example, the constraints configured in the MPC optimizer may include: The wall temperature of the heated surface metal T_wall ≤ T_max (the maximum allowable wall temperature in the design, such as 580℃); The CO concentration in the flue gas is ≤ 200 mg / Nm³ (to prevent incomplete chemical combustion). The concentration of HCl in flue gas is ≤ 30 mg / Nm³ (environmental protection emission and corrosion prevention requirements). High-temperature corrosion rate ≤ Cr_limit (preset minimum high-temperature corrosion rate); NOx emission concentration ≤ 50mg / Nm³ (ultra-low emission requirement).

[0060] The aforementioned fuel parameters include the furnace outlet excess air coefficient setpoint and the burnout air ratio. Specifically, the aforementioned set of control variables can be represented as [u1, u2, u3, u4, u5], where each control variable has the following meaning: u1 is the set value for the biomass co-firing ratio (range 5%~30%, in 1% increments). u2 is the dosing rate of the first additive (kaolin) (range 0~500kg / h, step 5kg / h). u3 is the addition acceleration rate of the second additive (limestone powder) (range 0~300kg / h, step 5kg / h). u4 is the set value for the excess air coefficient at the furnace outlet (range 1.10~1.30, step 0.01). u5 is the burnout air (OFA) ratio (range 15%~35%, in 1% increments).

[0061] Optionally, the set of control variables in this application may also include the flue gas temperature u6 at the furnace outlet. Therefore, the set of control variables may also be expressed as [u1, u2, u3, u4, u5, u6].

[0062] Optionally, the above-mentioned improved particle swarm optimization algorithm PSO or sequential quadratic programming algorithm SQP can solve for the optimal set of control variables [u1,u2,u3,u4,u5,u6] within each control cycle (the control cycle is 5 to 15 minutes and can be configured as needed).

[0063] The feedforward compensation module 124 is used to determine the feedforward compensation amount based on the changes in online fuel analysis data and boiler load command, using a pre-calibrated feedforward coefficient matrix. The feedforward compensation amount includes a first component for the dosage of the first additive, a second component for the dosage of the second additive, and a third component for the combustion parameters. Each component is superimposed onto the corresponding control variables in the control variable set to obtain a composite variable set. If one or more control variables in the composite variable set exceed the corresponding allowable range, the control variables exceeding the allowable range are subjected to amplitude limiting processing to obtain the final control variable set.

[0064] This application addresses the time delay (typically 3-15 minutes, depending on boiler capacity and flue gas velocity) between the release of alkali metals and chlorine and their detection by incorporating a feedforward compensation channel into the MPC optimizer. The feedforward signals originate from online fuel analysis data (changes in K, Na, and Cl content) and boiler load command changes. This allows for pre-adjustment of additive dosage and combustion parameters before changes in alkali metal and chlorine release are detected by online monitoring sensors (LIBS and TDLAS), thereby shortening the system response time.

[0065] Furthermore, online monitoring data (LIBS and TDLAS) can be used as feedback signals to correct the residuals of the feedforward compensation through PID or adaptive controllers, thus forming a feedforward-feedback composite control structure.

[0066] Based on the above embodiments, this application further includes an execution mechanism configured for each control variable in the control variable set, used to execute each control variable in the control variable set. Specifically, this configuration mechanism includes: The system includes a fuel blending actuator 141 configured for the biomass blending ratio setting value u1, a first feeding mechanism 142 configured for the first additive injection acceleration rate u2, a second feeding mechanism 143 configured for the second additive injection acceleration rate u3, a blower inlet guide vane or frequency conversion adjustment mechanism 144 configured for the furnace outlet excess air coefficient setting value u4, and a burnout air damper opening adjustment mechanism 145 configured for the burnout air ratio u5.

[0067] Optionally, it may also include a burner tilt angle or flue gas recirculation damper adjustment mechanism 146 configured for the flue gas temperature u6 at the furnace outlet.

[0068] In one example, the aforementioned fuel blending actuator 141 consists of a biomass feeder frequency converter and a coal feeder frequency converter. The biomass blending ratio setpoint u1 output by the control unit 12 is calculated and converted into the target speed setpoint for each feeder, forming a corresponding control command. This command drives the feeder motors via the frequency converter, thereby achieving precise adjustment of the biomass and coal feed amounts.

[0069] Optionally, the feed rate control accuracy is ±1.0%FS, and the response time is ≤10 seconds. A metering device (e.g., using an electronic weighing sensor for feed rate measurement) is installed on the fuel conveyor belt to measure the instantaneous feed rate (unit: t / h) of biomass and coal in real time, with an accuracy class of 0.5. The metering results are fed back to the online monitoring unit; therefore, the online monitoring data can also include the feed rates of both the biomass feeder and the coal feeder via frequency conversion speed control.

[0070] In one example, the first feeding mechanism 142 can consist of a kaolin storage silo, a variable frequency screw feeder, a Roots blower (or compressed air source), and a pneumatic conveying pipeline. The first additive addition acceleration rate u2 output by the control unit 12 forms a corresponding control command, which is sent to the variable frequency screw feeder in the first feeding mechanism 142 to adjust the speed of the screw feeder, thereby achieving stepless adjustment of the dosage of kaolin as the first additive. Similarly, the second feeding mechanism 143 can consist of a limestone powder storage silo, a variable frequency screw feeder, a Roots blower (or compressed air source), and a pneumatic conveying pipeline. The second additive addition acceleration rate u2 output by the control unit 12 forms a corresponding control command, which is sent to the variable frequency screw feeder in the second feeding mechanism 143 to adjust the speed of the screw feeder.

[0071] This application can adjust combustion parameters through the existing control loop of the DCS system. Specifically, the aforementioned blower inlet guide vane or frequency converter adjustment mechanism 144 receives a control command generated by the control unit 12 based on the furnace outlet excess air coefficient setpoint u4, adjusts the total air volume, and ultimately adjusts the furnace outlet excess air coefficient. Simultaneously, the burnout air damper opening adjustment mechanism 145 responds to the control command generated by the control unit based on u5, adjusts the burnout air (OFA) damper opening to adjust the actual burnout air ratio. Furthermore, this application may also include adaptive adjustment of the furnace outlet flue gas temperature u6 through the adjustment mechanism corresponding to the burner sway angle or flue gas recirculation damper.

[0072] Optionally, the response time of the aforementioned actuators for adjusting combustion parameters shall not exceed 30 seconds to ensure that the combustion index can quickly follow the changes in control commands.

[0073] Based on the embodiments of the above system, this application can also configure a human-computer interaction interface, which can be implemented as follows: This human-machine interface is used for real-time data (measured values ​​of gaseous K and Na concentrations) from the LIBS alkali metal sensor and the corresponding fitted trend curves, real-time data (measured values ​​of flue gas HCl concentration) from the TDLAS-HCl analyzer and the corresponding fitted trend curves, predicted values ​​of alkali metal / chlorine release, the final set of control variables [u1,u2,u3,u4,u5,u6] output by the aforementioned feedforward compensation module 124, the assessment of the heated surface status, the system operating status, fault alarm information, and control mode configuration items.

[0074] The above-mentioned alkali metal / chlorine element release prediction values ​​may include the alkali metal (K, Na) concentration prediction values ​​determined by the alkali metal release analysis module 121 and the HCl concentration prediction values ​​determined by the chlorine element release and conversion analysis module 122.

[0075] The above assessment of the condition of the heated surface may include the ash and dirt coefficient (reflecting the degree of deposition of alkali metal compounds on the heated surface) determined by the ash and dirt heat flow meter and the corrosion risk index in the above chlorine element parameters.

[0076] The above system operating status can be the key parameters of boiler operation displayed by the user configuration, such as the wall temperature of the heating surface monitored by the thermocouple, the K, Na, Cl, S, ash, moisture, calorific value of the fuel monitored and output by the online fuel analyzer, the feed rate output by the electronic weighing sensor, the furnace temperature field, and the O2, CO, NOx, SO2, etc. monitored and output by the CEMS system.

[0077] In one example, the control mode configuration options include fully automatic mode, semi-automatic mode, and manual mode. When fully automatic mode is selected, the control unit is further configured to generate control commands based on each control variable in the final control variable set, and send the control commands to the actuator corresponding to the control variable to drive the actuator. When semi-automatic mode is selected, the control unit is further configured to provide a recommended value based on each control variable in the final control variable set to the human-machine interface, and in response to the user's confirmation of the recommended value, send the control command corresponding to the recommended value to the actuator corresponding to the control variable to drive the actuator. When manual mode is selected, the control unit is further configured to generate control commands according to each control variable in the user-configured control variable set, and send the control commands to the actuator corresponding to the control variable to drive the actuator. In one example, the control unit is further configured to automatically trigger a protection command and issue the fault alarm information (including issuing an audible and visual alarm) when the heated surface wall temperature exceeds the upper limit constraint, the HCl concentration in the flue gas exceeds the upper limit constraint of the HCl concentration, or any monitoring element fails. The monitoring element is a device pre-selected from the online monitoring unit. The trigger protection command includes one or more of the following: a command to indicate a reduction in the biomass co-firing ratio and a command to activate the backup additive dosing pipeline.

[0078] The above describes some specific implementations of a biomass-caustic soda-chlorine co-regulation system for a coal-fired boiler, as provided in this application. Based on this, this application also provides a corresponding method. The method provided in this application will be described below from the perspective of the execution flow.

[0079] See Figure 2 The diagram shows a process flow chart of a method for regulating the chlorine content of biomass in a coal-fired boiler by blending caustic soda with biomass. The method includes: S201. Obtain online monitoring data, which includes the measured values ​​of alkali metal concentration, HCl concentration, and heating surface wall temperature during the biomass co-firing process in the boiler. S202. Based on the alkali metal content in the fuel, the furnace temperature field, and the boiler load, determine the alkali metal release parameters under the current operating conditions; based on the chlorine content in the fuel, the furnace temperature field, and the flue gas composition, predict the chlorine parameters in the flue gas; using the online monitoring data, alkali metal release parameters, and chlorine parameters as inputs, construct an optimization function based on safety boundary constraints, with the objectives of minimizing the growth rate of the ash and fouling coefficient of the heated surface and the high-temperature corrosion rate, and maximizing the biomass co-firing ratio, and solve for the set of control variables. S203. Determine the corresponding control command based on each control variable in the control variable set, and send the control command to the actuator corresponding to the control variable to execute the control command. The control variable set includes the biomass blending ratio, the first additive acceleration rate for fixing alkali metals, the second additive acceleration rate for fixing chlorine elements, and combustion parameters.

[0080] Based on the above method embodiments, this application forms a complete closed-loop chain of perception, analysis, decision-making, and execution feedback. It abandons the separate control logic of alkali and chlorine in existing technologies, embedding the coupled prediction of alkali metal release and chlorine element conversion into the same control unit. It relies on the MPC model predictive control framework to uniformly solve for multi-dimensional regulation parameters of fuel blending, additives, and combustion. This reduces alkali-chlorine coupling antagonism in high-alkali, high-chlorine biomass co-firing scenarios and improves the timeliness of regulation.

[0081] Based on the above Figure 2This application also provides a specific application embodiment, for details see below. Figure 3 The diagram shows an application flow chart of a biomass co-firing caustic soda chlorine regulation method for coal-fired boilers. The method includes: S1. Data Acquisition: Online monitoring data is acquired at a preset sampling period (1~5 seconds). The online monitoring data may include: LIBS sensor output signal (measured values ​​of gaseous K and Na concentrations), TDLAS-HCl analyzer output signal (measured value of HCl concentration in flue gas), high-temperature corrosion probe output signal (high-temperature corrosion rate), ash and grease heat flow meter output signal (ash and grease coefficient), pipe wall temperature thermocouple output signal (heated surface wall temperature), NIR fuel online analyzer output signal (K, Na, Cl, S, ash, moisture, and calorific value in fuel), electronic weighing sensor (e.g., belt scale) output signal (feed rate), furnace acoustic temperature measurement system output signal (furnace temperature field distribution), and CEMS system output signal (O2, CO, NOx, SO2).

[0082] S2. Data preprocessing and validity assessment: Perform signal filtering, dimension conversion, timestamp synchronization and alignment, and validity verification on various acquired signals in the online monitoring data.

[0083] The data collected in step S1 is processed by filtering and denoising, dimension conversion, time alignment, and validity verification. Optionally, invalid data points can be filled by extrapolating data from the previous valid period or replacing them with redundant data from adjacent sensors.

[0084] S3. Alkali metal release identification: Using alkali metal (K, Na) content, furnace temperature field and boiler load as inputs, the alkali metal release parameters under the current operating conditions are calculated and the alkali metal concentration is determined through the alkali metal release kinetic model. The alkali metal release parameters include the alkali metal release rate and the total release amount.

[0085] S4. Chlorine element prediction: Using the chlorine content in the fuel, the furnace temperature field, and the flue gas composition (H2O, SO2) as inputs, the chlorine parameters in the flue gas are predicted through thermodynamic balance calculations and chemical reaction kinetic models. The chlorine parameters include the predicted HCl concentration, the partial pressure of alkali metal chloride vapor, and the chlorine corrosion risk index.

[0086] S5. Multi-parameter coupled optimization solution: Using the online monitoring data, alkali metal release parameters, and chlorine parameters as inputs, the upper limit constraints of the heated surface wall temperature, the upper limit constraints of CO concentration in flue gas, the upper limit constraints of HCl concentration in flue gas, the upper limit constraints of high-temperature corrosion rate, and the upper limit constraints of NOx emission concentration are combined as safety boundary constraints to construct an optimization function with the objectives of minimizing the growth rate of the heated surface ash pollution coefficient and the high-temperature corrosion rate, and maximizing the biomass co-firing ratio. The set of control variables is solved using an improved particle swarm optimization algorithm or a sequential quadratic programming algorithm.

[0087] The above optimization function is expressed as: min J = w1×Fs + w2×Cr - w3×Br; Where w1, w2, and w3 are the configured weighting coefficients, Fs is the ash pollution coefficient growth rate, Cr is the high-temperature corrosion rate, and Br is the biomass co-firing ratio.

[0088] Using the calculation results of steps S2 to S4 above and the actual values ​​of each control variable in the current control variable set as input, the above optimization function is constructed. Considering safety boundary constraints such as heated surface wall temperature, CO emission, HCl emission and NOx emission, the optimal combination of control variables [u1, u2, u3, u4, u5] is solved within 5 to 15 minutes using an improved PSO algorithm (e.g., population size 50 to 100, maximum number of iterations 100 to 200).

[0089] S6. Feedforward Compensation Calculation: Based on the changes in the online analysis data (K, Na, Cl) and the changes in the boiler load command, the feedforward compensation amount is determined based on the pre-calibrated feedforward coefficient matrix. The feedforward compensation amount includes a first component Δu2_ff for the dosage of the first additive, a second component Δu3_ff for the dosage of the second additive, and a third component for the combustion parameters.

[0090] Understandably, when the combustion parameters include u4 and u5, the third component includes Δu4_ff and Δu5_ff.

[0091] S7. Control Variable Synthesis and Limiting Output: Each component is superimposed onto the corresponding control variables in the control variable set to obtain a synthesized variable set; if one or more control variables in the synthesized variable set exceed the corresponding allowable range, the control variables exceeding the allowable range are limited to obtain the final control variable set.

[0092] When the above composite variable sets are superimposed, they can be represented as: u i _final = u i _opt + Δu i_ff (i=2,3,4,5).

[0093] S8. Determine the corresponding control command based on each control variable in the final control variable set, and send the control command to the actuator corresponding to the control variable to execute the control command.

[0094] The final set of control variables is represented as [u1,u2,u3,u4,u5] or [u1,u2,u3,u4,u5,u6] as described in the above system embodiment.

[0095] S9. Feedback Closed-Loop Verification and Model Adaptive Update: Continue to acquire online monitoring data, compare the measured value of alkali metal concentration with the predicted value of alkali metal concentration determined according to the metal release parameters to determine the first control deviation, and compare the measured value of HCl concentration with the predicted value of HCl concentration in the chlorine element parameters to determine the second control deviation. If the first control deviation or the second control deviation exceeds the corresponding threshold (e.g., 20%), perform model parameter correction on the MPC optimizer and return to step S1 to enter the next cycle; of course, if neither the first control deviation nor the second control deviation exceeds the corresponding threshold (e.g., 20%), directly return to step S1 to enter the next cycle.

[0096] The measured values ​​of the alkali metal concentrations mentioned above include the measured values ​​of K and Na concentrations, obtained through real-time monitoring using a LIBS alkali metal sensor. The measured value of the chlorine element concentration mentioned above refers to the HCl concentration obtained through actual monitoring using a TDLAS analyzer.

[0097] Optionally, since alkali metals are divided into K and Na, the model parameters of the MPC optimizer can be corrected if the control deviation between any measured value of K concentration and Na concentration and their corresponding predicted value exceeds the threshold.

[0098] Based on the above system and method embodiments, it can be seen that this application has the following beneficial effects: 1. It has achieved fully online closed-loop automatic control of alkali metals and chlorine elements.

[0099] Existing technologies rely on offline sampling and testing (with a detection lag of 4-12 hours) and manual adjustment based on experience, resulting in poor timeliness and accuracy in regulation. This invention constructs an online monitoring system covering the entire process from fuel composition to combustion release, flue gas monitoring, and heating surface condition. This shortens the detection cycle for alkali metals (LIBS) and chlorine (TDLAS-HCl) to 1-10 seconds. Through a multi-parameter coupling optimization algorithm using MPC, closed-loop automatic regulation can be achieved at the 5-15 minute level, improving the response speed by 2-3 orders of magnitude compared to offline manual regulation. In fully automated operation mode, the frequency of operator intervention can be reduced from 10-20 times per shift to 0-2 times per shift.

[0100] 2. Significantly reduced the rate of slagging and fouling on the heated surface and the rate of high-temperature corrosion.

[0101] Existing fixed-ratio additive dosing schemes may result in excessive (wasteful) or insufficient (protective failure) additives under unsuitable operating conditions. This invention, by real-time monitoring of alkali metal concentration and heating surface condition, dynamically optimizes additive dosage and combustion parameters, improving the reaction efficiency between kaolin and alkali metals and the HCl capture efficiency of limestone powder; reducing the average annual growth rate of the heating surface ash fouling coefficient; and decreasing the corrosion rate of high-temperature superheater tubes, thus extending the expected service life of the heating surfaces.

[0102] 3. It increased the proportion of biomass co-firing, thereby enhancing carbon emission reduction benefits.

[0103] In existing technologies, due to concerns about slagging, fouling, and high-temperature corrosion, the biomass co-firing ratio is typically limited to a conservative range of 5% to 15%. This invention, by precisely controlling the influence of alkali metals and chlorine, can increase the biomass co-firing ratio to 15% to 30% while ensuring safe boiler operation. Taking a 1000MW ultra-supercritical coal-fired unit as an example, increasing the co-firing ratio from 10% to 25% can increase CO2 emission reduction by approximately 460,000 tons annually, demonstrating significant carbon reduction benefits.

[0104] 4. Multi-dimensional synergistic optimization of fuel blending, additive addition, and combustion parameters has been achieved.

[0105] In existing technologies, fuel blending, additive addition, and combustion parameter adjustment are handled independently by different professionals, lacking coordination and easily leading to a vicious cycle of "high blending ratio → increased slagging → forced load reduction." This invention utilizes the MPC multi-parameter coupled optimization algorithm to collaboratively solve the control variables of the above three dimensions within a unified optimization framework. This ensures boiler safety while maximizing the carbon emission reduction benefits of biomass blending, achieving a comprehensive optimization of safety and economy.

[0106] 5. Improved the automation and intelligence level of boiler operation.

[0107] This invention provides three operating modes: fully automatic, semi-automatic, and manual, to meet the needs of different operating scenarios. In fully automatic mode, the system can complete the entire process of integrated alkali-chlorine control without manual intervention, freeing operators from the heavy workload of sampling and testing, data analysis, and parameter adjustment. Simultaneously, the system can automatically display and record operating data and the control process, generating a complete operating log to provide data support for operation optimization, accident analysis, and equipment lifespan management. The human-machine interface can intuitively display alkali metal concentration trends, heating surface slagging index (ash pollution coefficient), and chlorine corrosion risk index in data or graphical form, enabling operators to quickly grasp the boiler's alkali-chlorine safety status.

[0108] This application also provides corresponding devices and computer storage media for implementing the solutions provided in this application.

[0109] The device includes a memory and a processor. The memory is used to store instructions or code, and the processor is used to execute the instructions or code so that the device performs a method for regulating the biomass blending with caustic soda and chlorine in a coal-fired boiler, as described in any embodiment of this application.

[0110] The computer storage medium stores code. When the code is executed, the device running the code implements a method for regulating the biomass co-firing with caustic soda and chlorine in a coal-fired boiler, as described in any embodiment of this application.

[0111] In the embodiments of this application, the terms "first" and "second" (if they exist) are used only as name identifiers and do not represent the order of first and second.

[0112] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus a general-purpose hardware platform. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network communication device such as a router) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0113] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0114] The above description is merely an exemplary implementation of this application and is not intended to limit the scope of protection of this application.

Claims

1. A biomass-caustic soda-chlorine co-regulation system for coal-fired boilers, characterized in that, The system includes: An online monitoring unit is used to acquire online monitoring data, which includes measured values ​​of alkali metal concentration, measured values ​​of HCl concentration, and wall temperature of the heating surface during the biomass co-firing process in the boiler. The control unit is used to determine the alkali metal release parameters under the current operating conditions based on the alkali metal content in the fuel, the furnace temperature field, and the boiler load; predict the chlorine parameters in the flue gas based on the chlorine content in the fuel, the furnace temperature field, and the flue gas composition; and construct an optimization function based on the online monitoring data, alkali metal release parameters, and chlorine parameters, using the model predictive control (MPC) optimizer as input, with the objectives of minimizing the growth rate of the ash fouling coefficient and the high-temperature corrosion rate of the heated surface and maximizing the biomass co-firing ratio, and solve for the set of control variables. An actuator configured for each control variable in the control variable set is used to execute each control variable in the control variable set, which includes the biomass blending ratio, the first additive acceleration rate for fixing alkali metals, the second additive acceleration rate for fixing chlorine, and combustion parameters.

2. The system according to claim 1, characterized in that, The online monitoring data also includes fuel alkali metal content, chlorine content, furnace temperature field, flue gas composition, high-temperature corrosion rate, and ash pollution coefficient; the online monitoring unit includes a LIBS alkali metal sensor, a TDLAS-HCl analyzer, a pipe wall temperature thermocouple, a fuel online composition analyzer, a temperature measurement module, a flue gas analysis module, a high-temperature corrosion monitoring probe, and an ash pollution heat flow meter; The LIBS alkali metal sensor collects the gaseous potassium and sodium concentrations in flue gas in real time as the alkali metal concentration; the TDLAS-HCl analyzer collects the HCl concentration in flue gas in real time as the chlorine concentration; the tube wall temperature thermocouple collects the wall temperature of the heated surface; the online fuel composition analyzer monitors the online analysis data of the fuel, including alkali metal content and chlorine content; the temperature measurement module is used to obtain the temperature field distribution of the furnace cross section to determine the furnace temperature field; the flue gas analysis module is used to monitor and obtain the flue gas composition; the high-temperature corrosion monitoring probe is used to monitor the high-temperature corrosion rate; and the ash and dirt heat flow meter is used to compare the difference in heat flux density between the clean tube wall and the actual tube wall to determine the ash and dirt coefficient of the degree of deposition of reactive alkali metal compounds on the heated surface.

3. The system according to claim 2, characterized in that, The control unit includes an alkali metal release analysis module and a chlorine element release and conversion analysis module; The alkali metal release analysis module is used to calculate the alkali metal release parameters under the current operating conditions using the alkali metal content, furnace temperature field and boiler load as inputs, and to determine the predicted value of the alkali metal concentration through the alkali metal release kinetic model. The alkali metal release parameters include the alkali metal release rate and the total release amount. The chlorine release and conversion analysis module is used to predict chlorine parameters in flue gas by taking the chlorine content in fuel, furnace temperature field and flue gas composition as inputs, and through thermodynamic balance calculations and chemical reaction kinetic models. The chlorine parameters include the predicted value of HCl concentration, alkali metal chloride vapor partial pressure and chlorine corrosion risk index.

4. The system according to claim 2, characterized in that, The system includes an MPC optimizer; The MPC optimizer is used to take the online monitoring data, alkali metal release parameters, and chlorine parameters as inputs, and combine the upper limit constraints of the heated surface wall temperature, the upper limit constraints of CO concentration in flue gas, the upper limit constraints of HCl concentration in flue gas, the upper limit constraints of high-temperature corrosion rate, and the upper limit constraints of NOx emission concentration as safety boundary constraints to construct an optimization function with the objectives of minimizing the growth rate of the heated surface ash pollution coefficient and the high-temperature corrosion rate, and maximizing the biomass co-firing ratio. The set of control variables is solved using a particle swarm optimization algorithm or a sequential quadratic programming algorithm. The optimization function is expressed as follows: min J = w1×Fs + w2×Cr - w3×Br; Where w1, w2, and w3 are the configured weighting coefficients, Fs is the ash pollution coefficient growth rate, Cr is the high-temperature corrosion rate, and Br is the biomass co-firing ratio.

5. The system according to claim 4, characterized in that, The control unit also includes a feedforward compensation module; The feedforward compensation module is used to determine the feedforward compensation amount based on the changes in the online analysis data and the changes in the boiler load command, and on a pre-calibrated feedforward coefficient matrix. The feedforward compensation amount includes a first component for the dosage of the first additive, a second component for the dosage of the second additive, and a third component for the combustion parameters. Each component is superimposed onto the corresponding control variable in the control variable set to obtain the composite variable set; if one or more control variables in the composite variable set exceed the corresponding allowable range, the control variables exceeding the allowable range are subjected to amplitude limiting to obtain the final control variable set.

6. The system according to claim 5, characterized in that, The fuel parameters include the furnace outlet excess air coefficient setpoint, the burnout air ratio, and the furnace outlet flue gas temperature. The system includes a fuel blending actuator configured for the biomass blending ratio, a first feeding mechanism configured for the first additive injection acceleration rate, a second feeding mechanism configured for the second additive injection acceleration rate, a blower inlet guide vane or frequency conversion adjustment mechanism configured for the furnace outlet excess air coefficient setting value, a burnout air damper opening adjustment mechanism configured for the burnout air ratio, and a burner sway angle or flue gas recirculation damper adjustment mechanism configured for the furnace outlet flue gas temperature.

7. The system according to claim 6, characterized in that, The system also includes a human-computer interaction interface; The human-machine interface is used to display real-time data and trend curves fed back by the LIBS alkali metal sensor, real-time data and trend curves fed back by the TDLAS-HCl analyzer, predicted values ​​of alkali metal / chlorine release, the final set of control variables, system operating status, fault alarm information, and control mode configuration items. The control mode configuration options include fully automatic mode, semi-automatic mode, and manual mode. When fully automatic mode is selected, the control unit is further configured to generate control commands based on each control variable in the final control variable set, and send the control commands to the actuator corresponding to the control variable to drive the actuator. When semi-automatic mode is selected, the control unit is further configured to provide a recommended value based on each control variable in the final control variable set to the human-machine interface, and in response to the user's confirmation of the recommended value, send the control command corresponding to the recommended value to the actuator corresponding to the control variable to drive the actuator. When manual mode is selected, the control unit is further configured to generate control commands based on each control variable in the user-configured control variable set, and send the control commands to the actuator corresponding to the control variable to drive the actuator. The control unit is also used to automatically trigger a protection command and issue the fault alarm information when the wall temperature of the heated surface exceeds the upper limit constraint, the HCl concentration in the flue gas exceeds the upper limit constraint of the HCl concentration, or any monitoring element fails. The monitoring element is a device pre-selected from the online monitoring unit. The trigger protection command includes one or more of the following: a command to indicate a reduction in the biomass co-firing ratio and a command to activate the backup additive dosing pipeline.

8. The system according to claim 2, characterized in that, The system also includes a data processing unit, the output of the online monitoring unit is connected to the data processing unit, and the output of the data processing unit is connected to the control unit; The data processing unit is used to receive the online monitoring data acquired by the online monitoring unit, perform signal filtering, dimension conversion, timestamp synchronization and alignment and validity verification on various acquired signals in the online monitoring data, store the processed acquired signals and periodically package and upload them to the control unit based on the communication protocol.

9. A method for regulating the chlorine content of biomass in a coal-fired boiler by blending caustic soda with chlorine, characterized in that, The method includes: Acquire online monitoring data, which includes measured values ​​of alkali metal concentration, measured values ​​of HCl concentration, and wall temperature of the heating surface during the biomass co-firing process in the boiler. Based on the alkali metal content in the fuel, the furnace temperature field, and the boiler load, the alkali metal release parameters under the current operating conditions are determined; based on the chlorine content in the fuel, the furnace temperature field, and the flue gas composition, the chlorine parameters in the flue gas are predicted; using the online monitoring data, alkali metal release parameters, and chlorine parameters as inputs, the Model Predictive Control (MPC) optimizer constructs an optimization function based on safety boundary constraints, aiming to minimize the growth rate of the ash fouling coefficient of the heated surface and the high-temperature corrosion rate, and maximize the biomass co-firing ratio, and solves the set of control variables. The control command is determined according to each control variable in the control variable set, and the control command is sent to the actuator corresponding to the control variable to execute the control command. The control variable set includes the biomass blending ratio, the first additive acceleration rate for fixing alkali metals, the second additive acceleration rate for fixing chlorine elements, and combustion parameters.

10. The method according to claim 9, characterized in that, After solving for the set of control variables, the process also includes: Based on the changes in online analysis data and boiler load command, a feedforward compensation amount is determined using a pre-calibrated feedforward coefficient matrix. The feedforward compensation amount includes a first component for the dosage of the first additive, a second component for the dosage of the second additive, and a third component for the combustion parameters. The online analysis data includes alkali metal content and chlorine content. Each component is superimposed onto the corresponding control variable in the control variable set to obtain the composite variable set; If one or more control variables in the synthetic variable set exceed the corresponding allowable range, the control variables that exceed the allowable range are subjected to amplitude limiting to obtain the final control variable set; After executing the control command, the method further includes: Continue to acquire online monitoring data, compare the measured value of alkali metal concentration with the predicted value of alkali metal concentration determined according to the metal release parameters to determine the first control deviation, and compare the measured value of HCl concentration with the predicted value of HCl concentration in the chlorine element parameters to determine the second control deviation. If the first control deviation or the second control deviation exceeds the corresponding threshold, perform model parameter correction on the MPC optimizer.

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