Application of intelligent control software ARC on circulating fluidized bed boiler and control method

Abstract

The invention provides application of intelligent control software ARC on a circulating fluidized bed boiler and a control method. A main pipe coordination control and combustion variable control optimization system of the circulating fluidized bed boiler is constructed. By collecting data such as main steam pressure and flow, system disturbance is distinguished, load weights of multiple boilers are dynamically distributed, and load optimization regulation and control are achieved; meanwhile, parameters such as bed temperature, smoke oxygen and hearth pressure difference are collected, expert rules and a nonlinear control technology are combined, feeding, air volume and deslagging are adjusted in a coordinated mode, and combustion efficiency and thermal load stability are improved. An ARC system and a simulation training platform are integrated, a process simulation model is constructed based on actual control logic, starting and stopping, steady-state and abnormal working conditions are simulated, and an operation training scene is generated. Through docking a process industry AI large model, fusing operation and answer data in an operation procedure and a training process, intelligently analyzing operation deviation and a knowledge short board, automatically generating a personalized feedback and learning plan, and realizing closed-loop optimization of operation experience and knowledge inheritance.

Description

Technical Field

[0001] This invention relates to the field of circulating fluidized bed boilers, and in particular to the application and control method of intelligent control software ARC in circulating fluidized bed boilers. Background Technology

[0002] A circulating fluidized bed boiler is a device that uses coal as fuel and generates steam by burning coal in a combustion chamber to release heat energy and heat water to produce steam for power. Its components include a combustion chamber, a coal feeder, an under-bed ignition system, a steam-cooled vortex separator, a water cooling system, a superheater system, a flue gas system, and a flue. Its working principle is to achieve energy conversion through the combustion process of coal in the combustion chamber.

[0003] Currently, the operation of circulating fluidized bed boilers mainly relies on manual valve adjustment by personnel. This operation method has obvious drawbacks: on the one hand, manual adjustment is delayed and personnel are prone to deviations during observation, resulting in inaccurate operation and affecting the boiler's thermal efficiency; on the other hand, when operators are fatigued, the risk of operational errors increases, posing safety hazards and making it difficult to meet the boiler's optimal production needs.

[0004] Meanwhile, with increasingly tight energy demand, energy conservation and emission reduction have become important issues that industrial enterprises must face. Furthermore, with the development of social civilization and increasingly fierce market competition, chemical enterprises are constantly increasing their demand for reducing the workload in the production process and improving product quality. Traditional manual operation and control modes can no longer meet the current industry development requirements for boiler operating efficiency, safety and intelligence. Summary of the Invention

[0005] The main objective of this invention is to provide an application and control method of the intelligent control software ARC in circulating fluidized bed boilers. This application constructs a main pipe coordinated control optimization system and a boiler combustion variable control optimization system through the intelligent control software ARC, and links them with a simulation training platform and a process industry AI large model. This solves the problems in the background technology of circulating fluidized bed boilers that rely on manual valve adjustment, such as delays, inaccurate operation, affected thermal efficiency, safety hazards when personnel are fatigued, and inability to meet optimal production. At the same time, it is difficult to achieve energy saving and emission reduction, reduce workload, improve product quality and knowledge transfer.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an application and control method of intelligent control software ARC in a circulating fluidized bed boiler, the method comprising: S1. Based on the function block library and graphical configuration tool of the intelligent control software ARC, construct a main tube coordinated control optimization system and a boiler combustion variable control optimization system adapted to the circulating fluidized bed boiler, and complete the control module configuration and parameter initial setting. S2. Through the main pipe coordination control optimization system, real-time data of main steam header pressure and main steam flow of circulating fluidized bed boiler are collected, internal and external disturbances of boiler system are distinguished online, and load adjustment weight of each boiler is dynamically allocated according to the load balancing capacity and load margin of parallel boilers, and load setpoint adjustment command is output. S3. Through the boiler combustion variable control optimization system, the boiler bed temperature, furnace pressure difference, flue gas oxygen content and steam drum pressure data are collected. Based on expert rule control and nonlinear control technology, the feed rate, primary air volume, secondary air volume and ash discharge rate are coordinated and controlled to achieve boiler combustion optimization and heat load stability. S4, associated with the intelligent control software ARC and simulation training platform, relies on ARC control logic to build a process simulation model of circulating fluidized bed boiler, simulates the start-up and shutdown of the unit, steady-state operation and abnormal conditions, and generates training scenarios; S5. By connecting the intelligent control software ARC with the process industry AI big model, it collects boiler operation rules and regulations, operating procedures and operation data and answer data during simulation training. The AI ​​big model analyzes operation deviations and knowledge gaps, generates targeted training feedback and learning plans, and realizes knowledge transfer.

[0007] In the preferred embodiment, the intelligent control software ARC is a dedicated control solution support tool for special equipment, including calcium carbide furnaces, boilers, rotary kilns, and precalciner units. Its modular configuration process supports independent switching of the control sections of the main pipe coordinated control optimization system and the boiler combustion variable control optimization system, and the configuration parameters can be adjusted in real time through online operation monitoring functions.

[0008] In the preferred scheme, the online differentiation of internal and external disturbances in the boiler system in step S2 is specifically determined by the following logic: when the boiler main steam pressure changes... Changes in main steam flow rate satisfy and If the symbols are the same, it is determined to be an internal disturbance; when and When the signs are opposite, it is determined to be an external disturbance, where It is a time variable.

[0009] In the preferred scheme, the dynamic allocation of parallel boiler load adjustment weights in step S2 includes the following calculation process: S21. Calculate the overall load adjustment of the parallel boiler. The formula is ,in This is the proportionality coefficient. Main steam header pressure setpoint The actual pressure value of the main steam header; S22, Dynamic Load Weight The calculation formula is as follows: ,in Pre-set initial weights manually. For the first Boiler load margin This represents the maximum load margin for all pressure-regulating boilers. For the first The current efficiency of the boiler The average efficiency of all pressure-regulating boilers. , , The weighting coefficients and ; S23, No. Boiler load adjustment ,according to Adjust the corresponding boiler load setting value.

[0010] In the preferred scheme, the main steam header coordination control optimization system in step S2 uses a nonlinear SPID control algorithm to achieve main steam header pressure control, and the control output formula is: ; in, To control the output, For pressure deviation, For proportional gain, The integral time constant is... The differential time constant is For nonlinear correction functions, when hour ,when hour , For correction factor, This is the deviation threshold.

[0011] In the preferred scheme, step S3 involves multi-variable coordinated control of boiler combustion, with bed temperature as the primary factor. The core parameter is used in the heat load calculation model. ,in For heat load, Main steam flow rate, For the steam drum pressure, , For coefficients; basis for air volume adjustment Pressure difference with furnace Calculate the primary air volume Secondary air volume ,in This is the furnace pressure differential setpoint. Set the oxygen content value. This represents the actual oxygen content. , This is the air volume coefficient.

[0012] In the preferred embodiment, the formula for adjusting the feed rate in step S3 is: ,in For feed rate, The feed coefficient is... For combustion efficiency, The lower heating value of the fuel; bed temperature correction is achieved through primary air volume compensation, when hour, Reduce primary air volume; when hour, Increase the primary air volume This is the bed temperature correction factor.

[0013] In the preferred embodiment, step S4 involves constructing a boiler process simulation model based on ARC control logic. This includes embedding the disturbance differentiation logic of the main pipe coordinated control optimization system, the load allocation algorithm, and the air-fuel ratio logic and parameter adjustment rules of the boiler combustion variable control optimization system into the simulation model. Combined with the circulating fluidized bed boiler mechanism, the dynamic changes in main steam pressure, bed temperature, and flue gas oxygen content under different loads are simulated.

[0014] In the preferred scheme, step S5 involves analyzing operational deviations using the AI ​​large model, employing a hierarchical knowledge association algorithm, and the similarity calculation formula is as follows: ,in To assess overall similarity, To train on the text similarity between answer keys and standard answers, To measure the similarity between the simulated operation and the standard operation sequence, , Weights are used; the edit distance between the sequence of operation steps and the standard sequence is compared. ,when This was determined to be an operational deviation. This is the distance threshold.

[0015] In the preferred scheme, step S5 involves the AI ​​large model generating a personalized learning plan based on skill deficiency scoring. Study time ,in For full marks, For time coefficient; Recommended practice questions are based on deviation type matching, and the degree of matching is... , The degree of matching between the question and the deviation type. To ensure the match between the difficulty level of the questions and the students' skill level, , For the matching coefficient, select The highest-level questions generate a practice list.

[0016] This invention provides an application and control method of the intelligent control software ARC in a circulating fluidized bed boiler. The application and control method of the intelligent control software ARC in a circulating fluidized bed boiler can quickly configure and construct a control model that meets actual needs with the help of the software's built-in function blocks, realize online differentiation of internal and external disturbances of the boiler system and dynamic allocation of parallel boiler load adjustment weights, and ensure stable main steam header pressure and rapid response to load command changes.

[0017] By using multivariate coordinated control, boiler combustion is optimized, and the relationships between air-fuel ratio and primary and secondary air ratio are balanced to ensure the boiler operates smoothly and automatically, so that the flue gas indicators meet emission standards. Combined with the simulation training platform, a process simulation model is built, which can simulate various operating conditions of the equipment and support operator training, equipment knowledge and emergency drills.

[0018] Leveraging AI large-scale models for process industries, it can collect and learn from multi-source corpora and operational data, identify operational deviations and knowledge gaps in training, and generate targeted feedback and learning plans to facilitate knowledge accumulation and experience transfer. Overall, the application can reduce energy consumption in boiler production, increase output and product quality, reduce employee workload, improve training and knowledge transfer efficiency, and maintain the safe operation of equipment, better meeting the needs of industrial enterprises in energy conservation and emission reduction, production optimization, and personnel capacity building. Attached Figure Description

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the main pipe coordination principle of the present invention; Figure 2 This is a multivariable control block diagram of the boiler combustion system of the present invention; Figure 3 This is a schematic diagram of the combustion optimization principle of the present invention; Figure 4 This is a schematic diagram illustrating the integration of the simulation model and AI-enabled features of this invention; Figure 5 This is a flowchart of the control software application and control method of the present invention; Figure 6 This is the monitoring system page for the circulating fluidized bed boiler optimization system of this invention. Detailed Implementation

[0020] Example 1 like Figure 1-6 As shown, an intelligent control software ARC is applied to a circulating fluidized bed boiler, and a control method thereof is described. The method includes: S1. Based on the function block library and graphical configuration tool of the intelligent control software ARC, construct a main tube coordinated control optimization system and a boiler combustion variable control optimization system adapted to the circulating fluidized bed boiler, and complete the control module configuration and parameter initial setting. S2. Through the main pipe coordination control optimization system, real-time data of main steam header pressure and main steam flow of circulating fluidized bed boiler are collected, internal and external disturbances of boiler system are distinguished online, and load adjustment weight of each boiler is dynamically allocated according to the load balancing capacity and load margin of parallel boilers, and load setpoint adjustment command is output. S3. Through the boiler combustion variable control optimization system, the boiler bed temperature, furnace pressure difference, flue gas oxygen content and steam drum pressure data are collected. Based on expert rule control and nonlinear control technology, the feed rate, primary air volume, secondary air volume and ash discharge rate are coordinated and controlled to achieve boiler combustion optimization and heat load stability. S4, associated with the intelligent control software ARC and simulation training platform, relies on ARC control logic to build a process simulation model of circulating fluidized bed boiler, simulates the start-up and shutdown of the unit, steady-state operation and abnormal conditions, and generates training scenarios; S5. By connecting the intelligent control software ARC with the process industry AI big model, it collects boiler operation rules and regulations, operating procedures and operation data and answer data during simulation training. The AI ​​big model analyzes operation deviations and knowledge gaps, generates targeted training feedback and learning plans, and realizes knowledge transfer.

[0021] First, based on the function block library and graphical configuration tool of the intelligent control software ARC, a master tube coordinated control optimization system and a boiler combustion variable control optimization system adapted to the circulating fluidized bed boiler are built. At the same time, the configuration of the control modules and the initial setting of various parameters are completed, laying the foundation for subsequent boiler control.

[0022] Next, the main steam header coordination and optimization system is activated. This system collects real-time data on the pressure and flow rate of the main steam header of the circulating fluidized bed boiler. Through data analysis, it distinguishes between internal and external disturbances in the boiler system online. Combined with information such as the load balancing capacity and load margin of parallel boilers, it dynamically calculates and allocates the load adjustment weight of each boiler, and finally outputs the load setpoint adjustment command to ensure the stability of the main steam header pressure.

[0023] Then, the boiler combustion variable control optimization system is run. This system collects key data such as boiler bed temperature, furnace pressure difference, flue gas oxygen content, and steam drum pressure. Using expert rule control and nonlinear control technology, it coordinates and controls the feed rate, primary air volume, secondary air volume, and ash discharge rate to optimize the boiler combustion process and stabilize the heat load. Next, the intelligent control software ARC is linked with the simulation training platform. A circulating fluidized bed boiler process simulation model is built using ARC's control logic as the core. This model simulates boiler start-up and shutdown, steady-state operation, and abnormal operating conditions, generating various training scenarios for operator training.

[0024] Finally, by connecting the intelligent control software ARC with the process industry AI big model, the AI ​​big model will collect the boiler operation rules and regulations, operating procedures, as well as the operation data and answer data of trainees during simulation training. After analyzing this data, it will identify operation deviations and knowledge gaps, and then generate targeted training feedback and learning plans to realize the inheritance of boiler operation knowledge.

[0025] This method, leveraging the function block library and graphical configuration tools of the intelligent control software ARC, enables the rapid and flexible construction of a control optimization system adapted to circulating fluidized bed boilers. It eliminates the need for extensive customized development, reducing the complexity and cost of system setup. Simultaneously, the completed control module configuration and initial parameter settings provide a reliable prerequisite for subsequent precise control. The main steam header coordination control optimization system, by real-time acquisition of main steam header pressure and flow data and online differentiation of internal and external disturbances, avoids the delays and errors associated with manual disturbance judgment. Combined with the actual conditions of parallel boilers, it dynamically allocates load adjustment weights, ensuring that the main steam header pressure can respond quickly and accurately to load command changes, maintaining overall stability during the parallel operation of multiple boilers, and reducing energy waste or equipment overload caused by uneven load distribution. The boiler combustion variable control optimization system, by collecting multi-dimensional boiler operating data and employing professional control technology to coordinate and control key aspects such as feeding, air distribution, and ash removal, effectively optimizes the combustion process, ensures stable boiler heat load, and ensures that flue gas indicators meet emission standards, improving boiler combustion efficiency and reducing pollutant emissions, thus meeting industry requirements for energy conservation and emission reduction. The integration of the intelligent control software ARC with the simulation training platform creates a process simulation model that can simulate various operating conditions. This provides operators with a safe and efficient training environment, avoiding the safety risks and production impacts that might arise from training on actual boilers. It also helps operators quickly familiarize themselves with boiler operation procedures and emergency response methods. By connecting to a large-scale AI model for the process industry, training data can be systematically analyzed to accurately identify operator operational deviations and knowledge gaps. The resulting targeted training feedback and learning plans help operators efficiently improve their operational skills, achieving effective transfer of boiler operation knowledge and experience. This ensures a stable level of operation even with personnel turnover, further enhancing the safety and reliability of boiler operation.

[0026] In the preferred embodiment, the intelligent control software ARC is a dedicated control solution support tool for special equipment, including calcium carbide furnaces, boilers, rotary kilns, and precalciner units. Its modular configuration process supports independent switching of the control sections of the main pipe coordinated control optimization system and the boiler combustion variable control optimization system, and the configuration parameters can be adjusted in real time through online operation monitoring functions.

[0027] The usage of the intelligent control software ARC involves first defining the scope of its applicable specialized equipment, including calcium carbide furnaces, boilers, rotary kilns, and precalciner units. When applying it to circulating fluidized bed boilers, ARC's modular configuration features are utilized. When constructing the control system for a circulating fluidized bed boiler, ARC is used to configure the main control coordination optimization system and the boiler combustion variable control optimization system. During configuration, each control segment in the two optimization systems can be independently switched on and off according to the actual operating needs of the boiler. For example, when a control segment does not need to participate in the current control or requires maintenance, it can be deactivated without affecting the normal operation of other control segments and the entire control system. Simultaneously, during boiler operation, ARC's online monitoring function allows real-time viewing of the control system's operating status. Configuration parameters are adjusted according to changes in operating conditions (such as boiler load adjustments, fuel type changes, etc.), such as adjusting load distribution parameters in the main control coordination optimization system or air-fuel ratio parameters in the boiler combustion variable control optimization system, ensuring that parameters always adapt to current operating requirements.

[0028] In terms of applicability, ARC can provide control solutions for various specialized devices such as calcium carbide furnaces, boilers, rotary kilns, and precalciner units. This eliminates the need to develop separate control tools for different devices, reducing enterprise investment costs in control tools and improving the tool's versatility and cost-effectiveness. Regarding control flexibility, modular configuration supports independent switching of control segments between two control optimization systems, avoiding the shutdown of the entire control system due to a problem with a single control segment. This reduces the impact of control adjustments on continuous boiler operation. Simultaneously, operators can flexibly combine control segments according to actual needs to form the most suitable control scheme for the current operating conditions, improving the control's specificity and flexibility. In terms of operational adaptability, the online operation monitoring function supports real-time adjustment of configuration parameters, enabling rapid response to changes in boiler operating conditions and timely optimization of control parameters. This avoids problems such as decreased control accuracy and reduced combustion efficiency caused by parameter lag, ensuring that the circulating fluidized bed boiler is always in a stable and efficient operating state. This helps improve boiler thermal efficiency, reduce energy waste, and lower the risk of equipment failure due to parameter incompatibility, extending equipment lifespan.

[0029] In the preferred scheme, the online differentiation of internal and external disturbances in the boiler system in step S2 is specifically determined by the following logic: when the boiler main steam pressure changes... Changes in main steam flow rate satisfy and If the symbols are the same, it is determined to be an internal disturbance; when and When the signs are opposite, it is determined to be an external disturbance, where It is a time variable.

[0030] In step S2, the method for online differentiation of internal and external disturbances in the boiler system is as follows: first, the parameter definitions involved in the judgment logic are clarified, wherein... It represents the change in the main steam pressure of the boiler, that is, the difference between the actual value and the initial value of the main steam header pressure within a certain period of time, and is used to reflect the fluctuation of the main steam pressure. It represents the change in the main steam flow rate of the boiler, that is, the difference between the actual value and the initial value of the main steam flow rate within the same time period, and is used to reflect the fluctuation of the main steam flow rate. Represents a time variable, i.e., calculation and The specific time period corresponds to the time dimension for determining the rate of change of pressure and flow. In practical applications, the main steam header pressure and main steam flow data of the circulating fluidized bed boiler are collected in real time through the main header coordination control optimization system. Based on the collected continuous data, the pressure and flow rate within different time periods are calculated. and Then calculate separately and The ratio ( )as well as and The ratio ( Then compare the signs of these two ratios: if and The same sign indicates that the disturbance in the boiler system is an internal disturbance. Internal disturbances mainly originate from changes in the boiler combustion rate, such as fluctuations in coal feed leading to changes in combustion intensity, which in turn cause the main steam pressure and flow rate to change in the same direction. and If the signs are opposite, it indicates that the disturbance is an external disturbance. External disturbances mainly come from the adjustment of the steam turbine's electric and thermal load or changes in the operating status of adjacent boilers. For example, the steam turbine increases its steam consumption, leading to an increase in the main steam flow rate, while the main steam pressure decreases due to insufficient steam supply, resulting in a situation where the two change in opposite directions.

[0031] The formula and parameter definitions of this judgment logic play a crucial supporting role in this application. and It directly reflects the fluctuations in the core operating parameters of the boiler's main steam system and is the basis for judging the existence of disturbances; As a time variable, it ensures and It can accurately characterize the rate of parameter change, avoiding judgment errors caused by inconsistent time dimensions; through comparison and The symbol transforms abstract disturbance types into quantifiable and automatically identifiable indicators, providing a clear basis for subsequent load adjustments in the main steam header coordination control optimization system. Its technical advantages lie in achieving rapid and accurate disturbance type identification. Compared to traditional manual observation and judgment, this method relies on real-time data calculation, enabling disturbance type determination within a short time after parameter fluctuations occur, avoiding the delays and subjective errors of manual judgment. Simultaneously, it clearly distinguishes between internal and external disturbances, making subsequent load adjustments more targeted. For example, internal disturbances can be balanced by adjusting boiler combustion-related parameters (such as coal feed and air volume), while external disturbances can be addressed by coordinating the load distribution of parallel boilers, ensuring stable main steam header pressure.

[0032] Accurate disturbance type identification can reduce control deviations caused by disturbance misjudgment, avoid large fluctuations in the pressure and flow of the main steam header, ensure the stable operation of downstream steam-using equipment (such as steam turbines), and reduce the risk of equipment failure caused by parameter fluctuations. The rapid disturbance identification response speed can shorten the impact time of disturbances on the boiler system and reduce energy waste. For example, when internal disturbances cause a decrease in combustion efficiency, combustion parameters can be adjusted in time to restore efficiency. In addition, this method can be completed automatically without manual intervention, reducing the workload of operators, improving the automation level of the boiler control system, and providing a strong guarantee for the stable and efficient operation of circulating fluidized bed boilers.

[0033] In the preferred scheme, the dynamic allocation of parallel boiler load adjustment weights in step S2 includes the following calculation process: S21. Calculate the overall load adjustment of the parallel boiler. The formula is ,in This is the proportionality coefficient. Main steam header pressure setpoint The actual pressure value of the main steam header; S22, Dynamic Load Weight The calculation formula is as follows: ,in Pre-set initial weights manually. For the first Boiler load margin This represents the maximum load margin for all pressure-regulating boilers. For the first The current efficiency of the boiler The average efficiency of all pressure-regulating boilers. , , The weighting coefficients and ; S23, No. Boiler load adjustment ,according to Adjust the corresponding boiler load setting value.

[0034] In the preferred scheme, the main steam header coordination control optimization system in step S2 uses a nonlinear SPID control algorithm to achieve main steam header pressure control, and the control output formula is: ; in, To control the output, For pressure deviation, For proportional gain, The integral time constant is... The differential time constant is For nonlinear correction functions, when hour ,when hour , For correction factor, This is the deviation threshold.

[0035] The method of using nonlinear SPID control in the parallel boiler load adjustment weight dynamic allocation and main pipe coordination control optimization system in step S2 is as follows: First, perform the calculation of parallel boiler load adjustment weight dynamic allocation. The first step is to calculate the total load adjustment amount of the parallel boiler. The definitions of each parameter in the formula need to be clearly defined, among which This is a proportionality coefficient used to adjust the overall load adjustment response based on the magnitude of the main steam header pressure deviation. The main steam header pressure setpoint, i.e., the standard value of the main steam header pressure that is expected to be maintained during production. The actual value of the main steam header pressure, i.e., the main steam header pressure data collected in real time by sensors, will be... and The difference multiplied by You can get This value reflects the total load adjustment required to bring the main steam header pressure back to the setpoint. The second step is to calculate the dynamic load weight. ,in The initial weights are preset manually, based on the boiler's daily operating experience or design parameters. The basic weight for load allocation of boilers in Taiwan For the first The boiler load margin, i.e., the first The difference between the current actual load and the maximum rated load of the boiler reflects the additional load capacity that the boiler can handle. This refers to the maximum load margin of all pressure-regulating boilers, i.e., all boilers participating in the coordinated pressure regulation of the main boiler header. The maximum value is used to normalize the load margin of each boiler. For the first The current efficiency of the boiler, i.e., the first The ratio of the effective energy output of the boiler to the total energy consumed by the fuel. The average efficiency of all pressure-regulating boilers, i.e., all boilers involved in pressure regulation. The arithmetic mean, , , The weighting coefficients are satisfied. , respectively used for adjustment , , exist The percentage of influence in the calculation will and The product of and The product of and Adding the products of the products, we can get the first product. Dynamic load weight of boiler The third step is to calculate the... Boiler load adjustment ,Will and Multiply by this to obtain the specific load adjustment amount that the boiler needs to undertake, and then according to... Adjust the load setting value of the corresponding boiler to achieve precise load distribution.

[0036] The main steam header coordination control optimization system uses a nonlinear SPID control algorithm to achieve main steam header pressure control and calculates the control output. When doing so, it is necessary to clarify the meaning of each parameter. To control the output, which is the control signal calculated by the system based on the pressure deviation to adjust the boiler's operating status, such as the command signal to adjust the coal feed rate and air volume, As a time variable, this ensures that the control output dynamically responds to pressure changes over time. For pressure deviation and In parallel load adjustment , Consistency in definition is the core basis for triggering control actions. The proportional gain is used to amplify the effect of pressure deviation on the control output and accelerate the initial control response. The integral time constant is used to eliminate static pressure deviation by adjusting the integral time constant. The integral accumulation gradually corrects deviations that proportional control cannot completely eliminate. The differential time constant is used to predict the trend of pressure deviation changes, based on... The rate of change is adjusted in advance to control output, avoiding control lag. This is a nonlinear correction function used to dynamically optimize the control output characteristics based on the magnitude of the pressure deviation. ( When the deviation threshold is defined as the set allowable fluctuation range of pressure deviation, At this time, the control output maintains the normal SPID control characteristics to avoid over-control under small deviations that could cause system fluctuations; when hour, ( (This is a correction coefficient used to adjust the strength of nonlinear correction). In this case, increasing the control output amplification factor accelerates the correction speed for large deviations. and The product of then multiplied by The control output can then be obtained. It is used to precisely control the pressure of the main steam header.

[0037] The formulas and parameter definitions have a significant impact on this application, particularly in parallel load adjustment. By quantifying the relationship between pressure deviation and overall load adjustment, a unified adjustment benchmark is provided for load allocation, avoiding blind load allocation due to lack of quantitative basis; Taking into account initial experience weights, load margins, and operating efficiency, the load allocation is ensured to conform to basic operating experience, fully utilize the carrying capacity of boilers with high load margins, and prioritize high-efficiency boilers to bear more loads, thereby improving overall operating economy. This allows for precise allocation of the overall adjustment to individual boilers, achieving "on-demand allocation." In nonlinear SPID control, It is the core calculation term of classic SPID control, realizing the proportional, integral, and derivative triple adjustment of pressure deviation. The introduction of this technology breaks through the limitations of traditional linear SPID, dynamically adjusting the control characteristics according to the magnitude of the deviation. The calculation converts the deviation signal into executable control commands, providing a direct operational basis for pressure control.

[0038] Its technical benefits are multifaceted. The dynamic weighting of parallel boiler load adjustment, through a three-step quantitative calculation, transforms load allocation from "empirical estimation" to "precise calculation," avoiding uneven load distribution that could lead to overloaded operation of some boilers or idle operation of others. This ensures precise matching between the overall load and the main steam header pressure demand when multiple boilers operate collaboratively. Simultaneously, the dynamic weighting... Follow , Real-time changes; when the load margin of a boiler increases or its efficiency improves, its The corresponding increase allows it to automatically handle more load, improving the flexibility and adaptability of load distribution. In the nonlinear SPID control algorithm, the proportional term accelerates the initial response, enabling rapid initiation of control actions when pressure deviations occur; the integral term eliminates static deviations, ensuring that the main steam header pressure remains stable during long-term operation. To avoid the continuous accumulation of deviations; the differential term predicts the deviation trend, suppressing the expansion of deviations in advance and reducing control overshoot; nonlinear correction function. It maintains stable control with small deviations to avoid system oscillations, and enhances control strength with large deviations to accelerate deviation correction. Compared with traditional linear SPID, it can control the main steam header pressure more accurately and quickly, reducing the pressure fluctuation amplitude and fluctuation duration.

[0039] In terms of beneficial effects, the precision and dynamism of parallel load adjustment can improve the economic efficiency of operating multiple boilers, prioritizing high-efficiency boilers to bear more load, reducing overall fuel consumption, and avoiding equipment damage caused by boiler overload, thus extending boiler lifespan. The flexibility of load distribution allows the system to quickly adapt to changes in operating conditions; for example, when a boiler needs maintenance, other high-load capacity boilers can automatically take over its load, ensuring a stable main steam supply. The precise control of the main steam header pressure by nonlinear SPID control ensures that downstream steam-using equipment (such as steam turbines) receives steam with stable parameters, avoiding equipment malfunctions or product quality problems caused by steam pressure fluctuations. The rapid deviation correction capability reduces the time when pressure deviates from the set value, lowering the risk of production interruptions due to pressure anomalies. Smooth control with small deviations reduces frequent adjustments to boiler operating status, lowering energy consumption and wear of auxiliary equipment such as fans and coal feeders, further improving overall operating efficiency and providing strong support for the safe, stable, and economical operation of circulating fluidized bed boilers.

[0040] In the preferred scheme, step S3 involves multi-variable coordinated control of boiler combustion, with bed temperature as the primary factor. The core parameter is used in the heat load calculation model. ,in For heat load, Main steam flow rate, For the steam drum pressure, , For coefficients; basis for air volume adjustment Pressure difference with furnace Calculate the primary air volume Secondary air volume ,in This is the furnace pressure differential setpoint. Set the oxygen content value. This represents the actual oxygen content. , This is the air volume coefficient.

[0041] The method for using multivariate coordinated control of boiler combustion in step S3 is as follows: first, clarify the bed temperature as the controlling factor. Bed temperature is a core control parameter. The heat storage capacity of the "heat regenerator" inside the circulating fluidized bed boiler is a key indicator for judging the stability of the combustion state. This is then addressed through a heat load calculation model. Calculate boiler heat load The parameters are defined as follows: Heat load represents the effective heat released by the boiler per unit time and is a core indicator for measuring the boiler's energy supply capacity. The main steam flow rate is the amount of steam generated and output by the boiler per unit time, which directly reflects the actual situation of the boiler's external energy supply. The steam drum pressure is the pressure of the steam inside the boiler steam drum, and its rate of change can indirectly reflect the fluctuation trend of the boiler's heat load. , For coefficients, Used to regulate the main steam flow. For heat load Influence weight, Used to regulate the rate of change of steam drum pressure. For heat load The influence weights of the two factors are determined through on-site commissioning to ensure that the calculated heat load results are consistent with the actual power supply status of the boiler. During the calculation, real-time main steam flow is first collected. And steam drum pressure Obtained through differential operations Then and The product of and By adding the products, the current boiler heat load can be obtained. .

[0042] Subsequently, based on the heat load Pressure difference with furnace Calculate primary air volume and secondary air volume .in, The furnace pressure difference, i.e. the pressure difference at different locations in the boiler furnace, reflects the material concentration distribution in the furnace and is an important parameter for judging the fluidization state in the furnace. The furnace differential pressure setpoint is a standard furnace differential pressure value preset according to the boiler operating conditions to ensure stable fluidization of materials in the furnace. The oxygen content setpoint, i.e. the target value of oxygen content in boiler flue gas, is used to ensure complete combustion of fuel and avoid excessive air supply that would increase energy consumption. This refers to the actual oxygen content, which is the actual oxygen content in the boiler flue gas collected in real time by the flue gas analyzer. , This is the air volume coefficient. Used to regulate heat load Ratio of furnace pressure difference For primary air volume The impact, Used to regulate heat load ratio of oxygen content For secondary air volume The impact of these factors was determined through on-site testing to ensure that the airflow adjustment could meet combustion requirements. The primary airflow was calculated. At that time, Heat load , Multiplying these three values ​​yields the primary air volume that needs adjustment. The primary air volume is mainly used to ensure stable fluidization of materials inside the furnace and to provide basic oxygen for fuel combustion; the secondary air volume is then calculated. At that time, Heat load , Multiplying these three values ​​yields the secondary air volume that needs adjustment. The secondary air volume is primarily used to supplement the oxygen required for fuel combustion and optimize combustion efficiency. Finally, based on the calculated... and Adjust the corresponding damper opening, taking into account the bed temperature. Real-time feedback enables coordinated control of combustion across multiple variables.

[0043] The formulas and parameter definitions are crucial to the application, including the heat load calculation model. This overcomes the limitation of solely relying on main steam flow rate to determine heat load by introducing the steam drum pressure change rate. This allows for faster and more accurate capture of dynamic changes in boiler heat load, especially when the main steam flow rate has not yet changed significantly but the drum pressure has already begun to fluctuate. It can predict heat load trends in advance, providing response time for subsequent airflow adjustments and preventing significant fluctuations in combustion status. Primary airflow formula. By linking heat load demand with the fluidization state of materials inside the furnace, it is ensured that the primary air volume can both meet the fluidization requirements of the material under the current heat load and also... Feedback adjustment maintains the furnace pressure differential near the set value, preventing poor fluidization due to excessively high material concentration or decreased heat transfer efficiency due to excessively low concentration; secondary air volume formula. Linking heat load demand with the oxygen supply for fuel combustion, through The feedback adjusts the secondary air volume in real time, ensuring that the oxygen content in the flue gas remains close to the set value under different heat loads. This ensures that the fuel is fully combusted to improve thermal efficiency, while also preventing excessive air supply from carrying away heat from the furnace and increasing energy consumption.

[0044] Its technical effectiveness is reflected in the precise and dynamic coordinated control of boiler combustion: based on bed temperature With heat load as the core, Precise calculations enable real-time monitoring of the core combustion state of the boiler, avoiding deviations in airflow and feed rate adjustments due to inaccurate heat load assessments. The linkage control between primary airflow and furnace pressure differential ensures the material inside the furnace remains in a stable fluidized state, providing a favorable environment for uniform fuel combustion and reducing risks such as localized coking and flameout. The linkage control between secondary airflow and flue gas oxygen content dynamically adjusts the air supply according to the actual oxygen demand for fuel combustion, avoiding fuel waste and increased pollutant emissions due to "under-oxygen combustion," or increased energy consumption due to "over-oxygen combustion." Furthermore, the entire control process is based on real-time collected operational data and quantitative formula calculations, avoiding the subjectivity and lag errors caused by traditional manual adjustments relying on experience, making combustion control more scientific and stable.

[0045] Firstly, it significantly improves boiler combustion efficiency by ensuring complete fuel combustion through precise airflow adjustment, reducing waste of unburned fuel, and lowering fuel consumption per unit of steam output, meeting industry requirements for energy conservation and emission reduction. Secondly, it stabilizes boiler operation, preventing significant changes in bed temperature, drum pressure, and main steam parameters caused by combustion fluctuations, ensuring stable operation of downstream steam-using equipment (such as steam turbines), and reducing the risk of equipment failure and downtime due to parameter fluctuations. Thirdly, it reduces pollutant emissions by optimizing the combustion process and oxygen content control, reducing the generation of pollutants such as carbon monoxide and nitrogen oxides, meeting environmental emission standards. Finally, it reduces the workload of operators, eliminating the need for frequent manual observation, judgment, and adjustment of combustion parameters. Automated multi-variable coordinated control achieves stable combustion status maintenance, improving operational safety and convenience.

[0046] In the preferred embodiment, the formula for adjusting the feed rate in step S3 is: ,in For feed rate, The feed coefficient is... For combustion efficiency, The lower heating value of the fuel; bed temperature correction is achieved through primary air volume compensation, when hour, Reduce primary air volume; when hour, Increase the primary air volume This is the bed temperature correction factor.

[0047] The method for adjusting the feed rate and correcting the bed temperature in step S3 is as follows: First, calculate and adjust the feed rate according to the formula. Determine the required feed rate for the boiler The parameters are defined as follows: The feed rate, which is the amount of fuel delivered to the boiler combustion chamber per unit time, directly determines the scale of the boiler's fuel supply. This is the feed coefficient, used to correct for the impact of factors such as fuel loss during fuel transportation and deviations in the actual calorific value of the fuel on the feed rate. It is determined through on-site commissioning to ensure the calculated feed rate is accurate. It can precisely match combustion requirements; The boiler heat load is consistent with the definition of heat load in the previous multivariate coordinated control of combustion. It represents the effective heat that the boiler needs to release per unit time and is the core basis for calculating the feed rate. Combustion efficiency, which is the ratio of the effective heat released by the fuel during boiler combustion to the total heat contained in the fuel itself, reflects the efficiency of fuel utilization and needs to be calculated by real-time monitoring of parameters such as flue gas temperature and unburned carbon content. The lower heating value (HHC) of a fuel is the heat released when a unit mass of fuel is completely burned and the combustion products cool to ambient temperature and water vapor condenses into water. It is a key parameter of the fuel's energy characteristics and needs to be determined based on industrial analysis data of the fuel used. Calculations first require obtaining the current boiler heat load. Real-time combustion efficiency and the lower heating value of the fuel used ,Will Divide by and The product of the two factors, multiplied by the feed coefficient. This will give you the required feed rate under the current working conditions. Subsequently, according to Adjusting the operating parameters of the coal feeder enables precise control of fuel supply.

[0048] Next, bed temperature correction is performed by adjusting the primary air volume to control the bed temperature. For precise control, the meaning of the relevant parameters needs to be clearly understood: Bed temperature, or the temperature of the material in the furnace bed of a circulating fluidized bed boiler, is a core indicator reflecting the combustion state and heat balance inside the furnace. The bed temperature setpoint is a standard bed temperature value that is preset based on the boiler operating conditions (such as load demand and fuel type) to ensure stable combustion and optimal efficiency. This is the primary air volume compensation amount, which is the additional primary air volume that needs to be adjusted based on the bed temperature deviation. This is the bed temperature correction coefficient, used to adjust the impact of bed temperature deviation on primary air volume compensation. It is determined through field testing to ensure that primary air volume adjustments can quickly and smoothly correct bed temperature deviations. When the bed temperature is monitored in real time... Greater than the bed temperature set value If this occurs, it indicates excessive heat inside the furnace, requiring a reduction in primary air volume to decrease heat dissipation from the bed material or to decrease fuel combustion intensity. In this case, according to the formula... Calculate the amount of primary air volume compensation that needs to be reduced. and in accordance with Reduce the primary ventilation system intensity; when Less than If the furnace is not hot enough, it indicates that the primary air volume needs to be increased to improve the fluidization of the bed material or the fuel combustion efficiency. At this time, according to the formula... Calculate the required increase in primary air volume compensation. (The negative sign in the formula indicates that the primary air volume needs to be increased in the positive direction), and according to By increasing the primary air supply speed and making the above dynamic adjustments, the bed temperature is kept stable near the set value.

[0049] The formulas and parameter definitions play a crucial and clear role in this application, including the feed rate formula. A quantitative correlation between heat load demand and fuel supply was established by introducing combustion efficiency. and lower heating value of fuel This avoids the problems of "overfeeding" or "underfeeding" caused by traditional fixed-ratio feeding—when combustion efficiency... As the feed rate decreases, the formula will automatically increase the feed rate. To compensate for efficiency losses and ensure heat load Stable; when the fuel has a low heating value The formula can also be adjusted when fluctuations occur. Ensuring the total heat input per unit time meets the heat load requirements provides a basic fuel guarantee for stable combustion. The bed temperature correction formula establishes a direct correlation between bed temperature deviation and primary air volume adjustment. Primary air volume not only affects the fluidization state of materials in the furnace but also indirectly affects the bed temperature by altering the heat exchange efficiency between the bed material and flue gas, and the mixing degree of fuel and oxygen. Dynamic calculations transform bed temperature control from "experience-based adjustment" to "quantitative correction," ensuring that bed temperature deviations can be quickly and accurately eliminated, thus avoiding the impact of large fluctuations in bed temperature on combustion stability and equipment safety.

[0050] Its technical effects are reflected in two aspects: First, it achieves precise matching of fuel supply by quantitatively calculating the feed rate formula to ensure accurate fuel supply. Able to adapt to heat load in real time Combustion efficiency and fuel calorific value The changes in feed rate not only avoid fuel waste, increased flue gas temperature, and increased pollutant emissions caused by excessive feeding, but also prevent insufficient heat load and reduced boiler output caused by insufficient feeding, ensuring that the boiler can obtain optimal fuel supply under different operating conditions; secondly, it achieves stable control of bed temperature. Through dynamic adjustment of primary air volume compensation, it can quickly respond to bed temperature deviations. When the bed temperature is too high, it can promptly reduce the primary air volume to suppress excess heat, and when the bed temperature is too low, it can promptly increase the primary air volume to supplement heat, avoiding the bed temperature from being continuously too high or too low, thereby ensuring stable combustion in the furnace (stable bed temperature can prevent incomplete fuel combustion or local coking), while ensuring stable heat exchange efficiency between the bed material and the heating surface, providing a core guarantee for the stability of the boiler's heat load output.

[0051] In terms of beneficial effects, firstly, it significantly improves the economic efficiency of boiler operation. Precise feed rate control reduces fuel waste and lowers the fuel consumption cost per unit of steam output. Stable bed temperature ensures that combustion efficiency remains at a high level, further reducing energy loss and meeting industry requirements for energy conservation and emission reduction. Secondly, it enhances the stability and safety of boiler operation, avoiding heat load fluctuations caused by improper feed rate and ensuring the stable operation of downstream steam-using equipment. It also prevents malfunctions such as coking and equipment overheating caused by abnormal bed temperature, extending the service life of core boiler components and reducing equipment maintenance costs and downtime risks. Finally, it reduces the workload of operators, eliminating the need for frequent manual monitoring of parameters such as heat load, combustion efficiency, and bed temperature, and manual adjustment of feed rate and primary air volume. Automated formula calculations and dynamic adjustments achieve precise control of feed rate and bed temperature, improving operational convenience and control reliability.

[0052] In the preferred embodiment, step S4 involves constructing a boiler process simulation model based on ARC control logic. This includes embedding the disturbance differentiation logic of the main pipe coordinated control optimization system, the load allocation algorithm, and the air-fuel ratio logic and parameter adjustment rules of the boiler combustion variable control optimization system into the simulation model. Combined with the circulating fluidized bed boiler mechanism, the dynamic changes in main steam pressure, bed temperature, and flue gas oxygen content under different loads are simulated.

[0053] The method for constructing the boiler process simulation model based on ARC control logic in step S4 is as follows: First, the core control logic is extracted from the control system of the intelligent control software ARC. Specifically, this includes the disturbance differentiation logic and load allocation algorithm in the main pipe coordinated control optimization system, and the air-fuel ratio logic and parameter adjustment rules in the boiler combustion variable control optimization system. These logics and algorithms are the core of ARC software's precise boiler control and the key basis for the simulation model to reproduce the real control process. Next, the extracted disturbance differentiation logic, load allocation algorithm, air-fuel ratio logic, and parameter adjustment rules are embedded one by one into the control module of the simulation model to ensure that the control logic of the simulation model is completely consistent with the control logic of the ARC software during actual boiler operation, avoiding distortion of simulation results due to logical differences. Subsequently, combining the inherent mechanisms of the circulating fluidized bed boiler—including the in-furnace fuel combustion reaction mechanism, material fluidization and heat and mass transfer mechanism, and steam-water system heat exchange mechanism—these mechanism models are integrated with the embedded ARC control logic to construct a complete simulation model framework that combines control logic and physical mechanisms. Finally, different load conditions (such as low load, rated load, high load, etc.) are set in the simulation model. The model is used to simulate the dynamic changes of main steam pressure, bed temperature, and flue gas oxygen content over time under each load condition. For example, the model simulates the downward trend of main steam pressure, the rise of bed temperature, and the fluctuation of flue gas oxygen content when the load increases sharply. At the same time, the change curves and response times of each parameter are recorded to form a simulation scenario that can be used for training or debugging.

[0054] In the preferred scheme, step S5 involves analyzing operational deviations using the AI ​​large model, employing a hierarchical knowledge association algorithm, and the similarity calculation formula is as follows: ,in To assess overall similarity, To train on the text similarity between answer keys and standard answers, To measure the similarity between the simulated operation and the standard operation sequence, , Weights are used; the edit distance between the sequence of operation steps and the standard sequence is compared. ,when This was determined to be an operational deviation. This is the distance threshold.

[0055] In the preferred scheme, step S5 involves the AI ​​large model generating a personalized learning plan based on skill deficiency scoring. Study time ,in For full marks, For time coefficient; Recommended practice questions are based on deviation type matching, and the degree of matching is... , The degree of matching between the question and the deviation type. To ensure the match between the difficulty level of the questions and the students' skill level, , For the matching coefficient, select The highest-level questions generate a practice list.

[0056] The method for using the AI ​​large-scale model to analyze operational bias and generate personalized learning plans in step S5 is as follows: First, operational bias analysis is performed. The AI ​​large-scale model uses a hierarchical knowledge association algorithm, and the similarity calculation formula is used to... The training process comprehensively evaluates the trainees' performance, with each parameter defined as follows: $Sim$ is the overall similarity, used to measure the degree to which the trainees' answers and operational results conform to the standard requirements. The higher the $Sim$ value, the closer the trainees' performance is to the standard. To train trainees on the text similarity between their answers and the standard answers, the semantic overlap and logical consistency of their answers to boiler operation-related theoretical and procedural questions were compared with the standard answers to reflect their mastery of theoretical knowledge. The similarity between the simulated operation and the standard operation sequence is determined by comparing the sequence of steps, timing of operation, and parameter settings of the trainees' operations such as boiler start-up and shutdown, load adjustment, and parameter correction on the simulation training platform with the standard operation sequence, which reflects the trainees' actual operation skill level. , The weights are used to adjust the weights respectively. and The weighting of the overall similarity (Sim) calculation can be flexibly adjusted according to the training focus; for example, it can be increased when emphasizing practical application. The value of is determined by comparing the edit distance between the student's sequence of operation steps and the standard sequence. Determine if there is any operational deviation. Edit distance is the minimum number of steps required to transform a student's sequence of operations into a standard sequence of operations through insertion, deletion, and replacement. A higher value indicates a more severe operational deviation; The distance threshold is a pre-set critical value for determining whether operational deviations exist. It is determined by statistically analyzing a large amount of standard operational data. When the calculated value... If the student's operation is found to be flawed, the specific steps and type of the flaw are recorded.

[0057] In the preferred approach, when the AI ​​model generates a personalized learning plan, it first calculates a skill deficiency score based on the student's operational deviation records and the comprehensive similarity Sim. , Used to quantitatively assess the extent to which trainees have insufficient mastery of each skill module. The lower the value, the more significant the weakness in that skill module. Then, based on the learning time formula... Calculate the learning time required for each skill module, among which A perfect score is awarded when the skill module has been fully mastered and meets the required standard. This is a time coefficient used to adjust the baseline learning time based on the company's training cycle and the trainees' skill level, and is calibrated based on feedback from actual training results. When recommending practice questions, the matching degree formula is used based on the type of operational deviation by the trainees (such as load adjustment deviation, air volume ratio deviation). Calculate the degree of match between the questions and the students' needs, where For matching degree, A higher value indicates that the questions better match the students' learning needs; The matching degree between the question and the deviation type, that is, the degree of overlap between the skill points involved in the question and the skill points in which the student has deviations; This refers to the match between the difficulty level of the questions and the students' skill level, that is, the degree to which the difficulty level of the questions matches the students' current skill level. , Adjust the matching coefficients separately. and Total matching degree The impact, for example, can increase the benefit for students with weak foundations. Prioritize recommending questions of suitable difficulty. Finally, select... The highest-scoring questions are used to generate a practice list, which is then combined with the learning time for each skill module. This forms a complete personalized learning plan.

[0058] Similarity formula It breaks through the limitations of traditional single evaluation theories or practices, and uses weighting... , This approach integrates theoretical and practical performance to ensure that assessment results fully reflect the trainees' true abilities; edit distance With threshold The combination of these methods transforms the abstract concept of "operational deviation" into quantifiable indicators, avoiding the subjectivity and ambiguity of manual judgment and ensuring the accuracy and consistency of deviation identification. (Learning time formula) Scoring based on skill gaps With full marks The difference quantifies the degree of weakness, and then combines it with the time coefficient. Generate scientifically sound learning durations to avoid a "one-size-fits-all" approach to training time settings, ensuring a precise match between learning time and the need to address weaknesses; matching formula. By selecting questions through dual matching dimensions (deviation type and difficulty suitability), we ensure that the recommended exercises can not only address the learners' skill gaps in a targeted manner, but also match the learners' learning ability, thus avoiding low training efficiency caused by questions that are too difficult or too easy.

[0059] Example 2 Further explanation in conjunction with Example 1, such as Figure 1-6 The structure shown illustrates the application and control method of the intelligent control software ARC in a circulating fluidized bed boiler provided in this embodiment of the invention. The core of this method revolves around a main pipe coordinated control optimization system and a boiler combustion variable control optimization system. Combining the intelligent control software ARC, the InPlant APC software platform, the InPlant OTS software platform, and the TPT large model, it achieves full-process control and optimization of the boiler unit from four aspects: main pipe coordinated control, combustion variable control, simulation model construction, and AI-enabled training. Specifically, as follows: (I) Basic Software Platform for Intelligent Control ARC Intelligent Control Software: ARC is a dedicated control solution support tool for specialized equipment (including calcium carbide furnaces, boilers, rotary kilns, and precalciner units), featuring graphical design capabilities. It employs a modular configuration approach for advanced control scheme design, providing a modular design environment for diverse industry solutions, supporting standardized solution implementation, and improving implementation efficiency. Simultaneously, the ARC software platform possesses basic functions such as a function block library, graphical configuration, template library, simulation operation, and online operation monitoring, enabling the rapid construction of control models that meet the actual needs of circulating fluidized bed boilers.

[0060] InPlant APC Software Platform: This platform is a modular, standardized, and open data platform composed of modules such as a core real-time database, application components, and configuration components. It can realize the acquisition, processing, storage, and application of industrial production process data, support the monitoring, advanced control and optimization of production processes, and process statistical management, and provide data support for advanced control systems. It is the basic data platform for realizing advanced control, soft measurement, process calculation, and process simulation optimization.

[0061] InPlant OTS Software Platform: This platform is a modular, standardized, and open simulation and training platform, consisting of a mechanism model kernel, a 3D simulation outer kernel, training management components, and value-added applications. It can realize dynamic simulation, 3D visualization, and interaction of industrial production processes, and supports multiple application scenarios such as operator training, equipment cognition, and accident emergency drills. It is the basic support for realizing digital twins and virtual-real integrated smart factories.

[0062] TPT Large Model: This model is an artificial intelligence large model that is specialized for time-series data and professional corpora in the process industry. It can collect and learn from corpus information such as rules and regulations, operating procedures, and equipment maintenance manuals, providing robust semantic understanding and data association capabilities for knowledge accumulation and experience transfer.

[0063] (II) Optimization of main pipeline coordination control This section achieves precise control of the main steam header pressure through the header coordination control module. The core objective is to coordinate the operation of parallel boilers, ensuring that the main steam header pressure reaches its optimal state, and to respond quickly, accurately, and stably to changes in the thermal and electrical load commands on the turbine side. The specific implementation process is as follows: Online differentiation of internal and external disturbances: Internal disturbances refer to fluctuations in the boiler combustion rate, characterized by the boiler main steam pressure and main steam flow reacting in the same direction; external disturbances refer to disturbances from the turbine's electrical and thermal load or neighboring boilers, characterized by the boiler main steam pressure and main steam flow reacting in opposite directions. By collecting main steam pressure and main steam flow data in real time, and based on the correlation between their changing directions, accurate online differentiation of internal and external disturbances can be achieved, providing a clear directional basis for subsequent load adjustments. The logical framework of this section can be found in Figure 1, where the internal and external disturbance differentiation module marks the main steam pressure and main steam flow collection points and the disturbance judgment logic for their unidirectional / inverse reactions, intuitively presenting the implementation path of disturbance differentiation.

[0064] Dynamic allocation of load adjustment weights for parallel boilers: Boilers operating in parallel on the main pipe can choose between "constant pressure operation" (with fixed load) or "pressure regulating operation" (main pipe coordination) based on their own operating conditions and load balancing capabilities. The MCC module tracks and controls the main pipe pressure in real time, calculates the overall load adjustment of parallel boilers, and, combined with the manually set initial weights, dynamically calculates the load weight and specific load adjustment of each boiler participating in "pressure regulating operation," based on the number of boilers, the load margin of each boiler, and its load tracking capability. This allows for the adjustment of the load setpoints of each boiler, ensuring reasonable load allocation. The process of load weight calculation and setpoint output in this section can be seen in Figure 1. Figure 1 shows the dynamic weight optimization allocator, which clearly illustrates the complete logic of load allocation by marking the initial weight input, load margin and load adjustment capability parameter acquisition paths, and load setpoint output paths.

[0065] Composition and anti-interference measures of the main control module: Module Composition: The main steam header coordination module consists of two parts: a main steam header pressure controller and a dynamic weight optimization allocator. The main steam header pressure controller uses nonlinear SPID technology to control the main steam header pressure and calculates the overall load adjustment demand of the parallel-operating boilers based on the current pressure tracking effect. The dynamic weight optimization allocator fully considers the adjustability, load adjustment capacity, load margin, current efficiency, and manually preset weights of the currently operating boilers, dynamically allocating the load-bearing weight of each adjustable boiler according to the load adjustment direction. This ensures accurate output of load demand while maximizing the overall efficiency of the boiler group. The technical logic of the main steam header pressure controller in this section can be found in Figure 2. Figure 2 The input interfaces for the main steam header pressure setpoint and actual value are labeled, along with the functions of the pressure deviation calculation unit, proportional-integral-derivative operation unit, and nonlinear correction function module, intuitively demonstrating the implementation process of nonlinear SPID technology.

[0066] Anti-disturbance measures: In order to overcome the heat storage inertia of the main pipe operation mode and enable the boiler control to follow the turbine demand as soon as possible (i.e., overcome the external disturbances of the main pipe pressure control), the energy signal of the peak-shaving unit on the turbine side is introduced into the boiler side to participate in the control; in addition, the internal heat disturbance of the non-pressure regulating boiler is treated as load disturbance feedforward for comprehensive intelligent processing, thereby quickly overcoming the internal disturbances on the boiler side and ensuring the stability of the main pipe pressure.

[0067] (III) Optimization of Boiler Combustion Variation Control This section utilizes APC control of the boiler combustion system to achieve multi-variable coordinated control of the combustion and steam-water systems. The core of this control scheme is an advanced control approach developed based on boiler process mechanisms, boiler combustion industry expert experience, and efficient operational experience. The specific implementation process is as follows: Core control strategies and module functions: Control Strategy: The heat flow released from the furnace bed of a circulating fluidized bed boiler is proportional to the amount of fuel contained in the bed material. Based on this, a basic combustion control strategy is proposed: "controlling the heat flow released from the furnace bed by the ratio of primary air and secondary air, and controlling and stabilizing the amount of fuel in the bed material by the feed rate".

[0068] Module Functions: The combustion system comprises two core modules: main pipe coordination and automatic combustion. The main pipe coordination module, taking into full account the dynamic characteristics, load adjustment capabilities, and load margins of each boiler, dynamically allocates the pressure-regulating load of multiple parallel boilers, ensuring stable control of the main steam header pressure. The automatic combustion module achieves multi-variable coordinated control of boiler combustion and the steam-water system, fully balancing relationships such as air-fuel ratio and primary / secondary air ratio to achieve fully automatic and stable boiler operation. The correlation between the acquisition and control output of various parameters in this section can be seen in Appendix 3. Appendix 3 marks the acquisition points for key parameters such as main steam pressure, main steam flow rate, steam drum pressure, bed temperature, furnace pressure difference, flue gas oxygen content, and bed pressure, as well as the control paths for coal feed regulation, primary air regulation, secondary air regulation, induced draft air regulation, and ash discharge regulation, clearly demonstrating the logic of multi-variable coordinated control.

[0069] Multivariable integrated control model operation logic: The main feature of the multivariable integrated control model is that it is based on the bed temperature signal, which reflects the heat storage in the "heat regenerator" of the circulating fluidized bed furnace, and dynamically adjusts according to the following logic within the constraints of the rated design parameters of each operating parameter: The air volume is adjusted according to the current heat load (characterized by the differential function of steam flow and steam drum pressure) and the furnace pressure difference to adjust the material concentration, thereby quickly stabilizing the changes in boiler heat load; The load is stabilized and the bed temperature is adjusted by rapidly adjusting the air-fuel ratio to maintain the stability of the heat stored in the furnace. To ensure fuel economy, the optimal oxygen content is used. Adjust the furnace negative pressure using air volume feedforward and furnace pressure signals; Simultaneously, through automatic adjustment of ash discharge, the bed pressure is stabilized at the corresponding optimal setpoint under different loads. The operational logic of this part can be understood in conjunction with Figure 3, which intuitively presents how the model achieves combustion optimization through multi-parameter collaboration by showing the linkage relationship of each control path.

[0070] Load and key parameter adjustment details: Load Regulation: The boiler's heat signal is calculated from the differential combination of the main steam pressure and the steam drum pressure, serving as a representation of the boiler load. Boiler load adjustment is divided into two modes: pressure regulation mode and quantitative mode. In pressure regulation mode, the boiler participates in the main pipe pressure regulation, and the boiler heat setpoint is corrected by the main pipe coordination control module. In quantitative mode, the boiler operates at a fixed load, and the boiler heat value can be manually set. The adjustment of the boiler load loop is achieved through three control methods: feeding, primary air, and secondary air. The feeding is calculated based on the load adjustment requirements; the primary air adjustment amount is calculated through an appropriate air-to-material ratio; and the secondary air is adjusted through the primary and secondary air ratio. Thus, the coordinated control of these three aspects achieves the tracking and adjustment of the boiler load.

[0071] Oxygen content control: The system calculates the optimal oxygen content setting in real time for different boiler load conditions (this value can also be set manually). If the air-fuel ratio is inappropriate, the actual oxygen content will differ from the set value. Precise tracking of oxygen content can be achieved by adjusting the secondary air output.

[0072] Bed temperature control: The bed temperature can be automatically adjusted according to the load or manually set. Precise tracking is achieved primarily through primary air adjustment, while bed temperature correction and coordination are incorporated based on the feed and primary / secondary air ratios. This balances the relationship between bed temperature adjustment and boiler load and flue gas oxygen content adjustments, ensuring the bed temperature remains stable within a reasonable range. The specific paths for load regulation, oxygen content control, and bed temperature correction in this section can be found in the combustion optimization principle diagram on the left side of Figure 4. Figure 4 on the left side illustrates the heat signal calculation path, air-to-feed ratio adjustment logic, oxygen content control path, and bed temperature correction logic, providing visual support for parameter adjustment details.

[0073] (iv) Construction of simulation model for fluidized bed boiler A simulation model of a circulating fluidized bed boiler process was built using the InPlant OTS software platform. This model is used to simulate plant start-up and shutdown, steady-state operation, and abnormal operating conditions, supporting operator training and skills assessment. During model construction, the mechanisms of fuel combustion, material fluidization, heat and mass transfer in the circulating fluidized bed boiler were fully integrated. Simultaneously, the disturbance differentiation logic and load distribution algorithm of the main pipe coordinated control optimization system, as well as the air-fuel ratio logic and parameter adjustment rules of the boiler combustion variable control optimization system, were embedded to ensure that the simulation model can accurately reproduce the actual boiler's operating state and control logic, providing realistic and reliable scenario support for training. The relationship between the simulation model and other systems in this section can be seen in the simulation model and AI-enabled relationship diagram on the right side of Figure 4. The right side of Figure 4 indicates the data source and functional positioning of the simulation model, demonstrating the model's role in the training system.

[0074] (V) Teaching analysis based on large models and AI empowerment The objective of this section is to analyze the simulation operation and question-answering data of trainees during circulating fluidized bed boiler training using the TPT (Train the Trainer) large-scale model to identify operational deviations and knowledge gaps, extract key skill gaps and common error patterns, and provide targeted feedback based on standardized operating procedures. This supports training effectiveness evaluation and knowledge transfer, and improves the operational accuracy and responsiveness of operators in actual production. The specific implementation process is as follows: AI model data sources: Data sources include three levels: Pre-training phase: Using industry-standard corpora, chemical engineering corpora, and relevant academic papers, a knowledge base for the process industry is formed. On-site operation phase: Collect structured and semi-structured data, including regulations, operating procedures, equipment maintenance manuals, inspection and maintenance records, and industry standards, to supplement and improve the knowledge system for operation and maintenance; Simulation training phase: Acquire limited data, including four types of information: candidate answers, candidate operations, correct answers, and correct operations. This data can be further refined into a step-by-step comparison of answers and operation steps during the training process analysis.

[0075] The model is compatible with the aforementioned multiple data sources during data acquisition and processing, enabling unified collection and correlation analysis of knowledge and operational information from different formats and sources. The data source paths in this section can be referenced in the simulation model and AI-enabled correlation diagram on the right side of Figure 4. Figure 4 clearly shows the acquisition paths of multi-source data (pre-training corpus, on-site operation and maintenance data, and simulation training data), clearly presenting the data sources and classifications.

[0076] AI Model Data Processing: The collected multi-source data is categorized and organized, establishing a hierarchical knowledge storage and retrieval mechanism. Knowledge from different sources and categories is stored separately according to topic, applicable scenario, and content attributes for precise retrieval based on specific functional requirements. During the retrieval process, a priority- and permission-based content scheduling mechanism is introduced. Based on the user's role and the importance of the current task, the appropriate information source is dynamically selected. This ensures that when generating training feedback or suggestions, high-priority knowledge and data within the user's permission scope are prioritized (e.g., when dealing with emissions-related content, more stringent internal company emission standards are prioritized over industry-standard standards). This avoids low-priority information interfering with results or outputting content beyond the user's permission scope, improving the relevance and reliability of the analysis results. The knowledge storage and scheduling logic in this section can be seen in the simulation model and AI-enabled relationship diagram on the right side of Figure 4. Figure 4 shows the hierarchical knowledge storage module structure, illustrating the hierarchical mechanism of data processing.

[0077] AI Model Application: After processing the training data, the system generates analysis results including operational deviation types, skill gaps, and common error patterns. Based on these results, personalized feedback, learning plans, and targeted practice questions are output. These results can be used for immediate feedback during the training phase, the development of subsequent retraining plans, and the standardized application of knowledge and operational procedures, ultimately achieving effective knowledge transfer and steady improvement in operator skills. The output path of the analysis results in this section can be seen in the simulation model and AI-enabled relationship diagram on the right side of Figure 4. Figure 4 on the right side illustrates the operational deviation and skill gap analysis stages and the output path of personalized feedback (feedback, learning plans, practice questions), intuitively presenting the application process of the AI ​​model.

[0078] In actual deployment, the first step is to prepare the necessary software and modules. The core software is the intelligent control software ARC (including function block library and graphical configuration tool), and the supporting software includes the InPlant APC data platform (core real-time database, application components, configuration components), the InPlant OTS simulation training platform (mechanism model kernel, 3D simulation outer kernel, training management components), and the process industry AI large model. Then, the deployment is started. The first step is to build a main pipe coordinated control optimization system and a boiler combustion variable control optimization system adapted to the circulating fluidized bed boiler based on the function block library and graphical configuration tool of the intelligent control software ARC. The control module configuration and initial parameter settings are completed. At the same time, the InPlant APC data platform is used to configure the acquisition, processing and storage functions of operating data such as main steam header pressure, main steam flow, and bed temperature.

[0079] The second step is to start the main steam header coordination control optimization system. Relying on the InPlant APC data platform, the system collects real-time data on the main steam header pressure and main steam flow. After distinguishing between internal and external disturbances online, the system dynamically allocates load adjustment weights based on the load balancing capacity and load margin of the parallel boilers, and outputs load setpoint adjustment commands.

[0080] The third step is to run the boiler combustion variable control optimization system. The InPlant APC data platform is used to collect data on bed temperature, furnace pressure difference, flue gas oxygen content, and steam drum pressure. Based on expert rule control and nonlinear control technology, the feed rate, primary air volume, secondary air volume, and ash discharge rate are coordinated and controlled to achieve combustion optimization and heat load stability.

[0081] The fourth step involves linking the intelligent control software ARC with the InPlant OTS simulation training platform, embedding ARC's main pipe coordination and combustion control logic into the simulation platform's mechanism model kernel, simulating the device's start-up and shutdown, steady-state operation, and abnormal operating conditions, and generating training scenarios.

[0082] The fifth step involves connecting the intelligent control software ARC to the process industry AI big model, collecting operation data, answer data, and boiler operation rules and procedures from the InPlant OTS simulation training platform. The AI ​​big model then analyzes operational deviations and knowledge gaps, generating targeted training feedback and learning plans to complete the entire process deployment and use.

[0083] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. An application and control method of intelligent control software ARC in circulating fluidized bed boilers, characterized by: The method comprises: S1, based on the function block library of intelligent control software ARC and the graphical configuration tool, construct the mother pipe coordinated control optimization system and the boiler combustion variable control optimization system suitable for the circulating fluidized bed boiler, complete the control module configuration and parameter initial setting; S2, through the mother pipe coordinated control optimization system, collect the real-time data of the circulating fluidized bed boiler main steam mother pipe pressure and main steam flow, online distinguish the internal and external disturbances of the boiler system, and according to the load balance ability and load margin of the parallel boiler, dynamically allocate the load adjustment weight of each boiler, and output the load set value adjustment instruction; S3, through the boiler combustion variable control optimization system, collect the boiler bed temperature, furnace pressure difference, smoke oxygen content and drum pressure data, based on expert rule control and nonlinear control technology, coordinate the control of feeding amount, primary air quantity, secondary air quantity and deslagging amount, realize the optimization of boiler combustion and heat load stability; S4, associate the intelligent control software ARC with the simulation training platform, rely on the ARC control logic to build the circulating fluidized bed boiler process simulation model, simulate the device start-stop, steady-state operation and abnormal working condition, and generate the training scene; S5, through the intelligent control software ARC, connect the process industrial AI large model, collect the operation rules and regulations, operation procedures and operation data and answer data in the simulation training process, analyze the operation deviation and knowledge short board by the AI large model, generate the targeted training feedback and learning plan, and realize the knowledge inheritance.

2. The application of the intelligent control software ARC and the control method thereof in a circulating fluidized bed boiler according to claim 1, characterized in that: The intelligent control software ARC is a special control solution supporting tool for special devices, including calcium carbide furnace, boiler, rotary kiln and decomposition furnace device. The modular configuration process supports the control section independent switching of the mother pipe coordinated control optimization system and the boiler combustion variable control optimization system, and can realize real-time adjustment of configuration parameters through online operation monitoring function.

3. The application of the intelligent control software ARC and the control method in the circulating fluidized bed boiler according to claim 1, characterized in that: In step S2, the internal and external disturbances in the boiler system are distinguished online, and the specific judgment logic is as follows: when the change amount of the main steam pressure of the boiler satisfies and the change amount of the main steam flow satisfies , the same sign, it is determined that the disturbance is internal; When With the sign is opposite, it is determined as an external disturbance, wherein is the time variable.

4. The application of the intelligent control software ARC and the control method thereof in a circulating fluidized bed boiler according to claim 1, characterized in that: In step S2, the parallel boiler load adjustment weight is dynamically allocated, and the calculation process comprises: S21, calculating the total load adjustment amount of the parallel boilers , the formula is wherein is a proportional coefficient, is a set value of the main steam header pressure, is an actual value of the main steam header pressure; S22, dynamic load weight The calculation is as follows Wherein is the initial weight preset by man, is the initial weight of the first boiler load margin, is the maximum load margin of all the pressure regulating boilers, is the initial weight of the first boiler current efficiency, is the average efficiency of all the pressure regulating boilers, , , is the weight coefficient and ; S23、th Boiler load adjustment amount , according to Adjust the corresponding boiler load set value.

5. The application and control method of the intelligent control software ARC in circulating fluidized bed boiler according to claim 1, characterized in that: In step S2, the mother pipe coordinated control optimization system adopts nonlinear SPID control algorithm to realize main steam mother pipe pressure control, and the control output formula is: ； in, To control the output, For pressure deviation, For proportional gain, The integral time constant is... The differential time constant is For nonlinear correction functions, when hour ,when hour , For correction factor, This is the deviation threshold.

6. The application and control method of the intelligent control software ARC in circulating fluidized bed boiler according to claim 1, characterized in that: The boiler combustion multivariable coordinated control in step S3 takes bed temperature as the core parameter, and a heat load calculation model is wherein is the heat load, is the main steam flow, is the drum pressure, , is a coefficient; the air volume adjustment is based on and the furnace pressure difference calculation, primary air volume , and secondary air volume wherein is the furnace pressure difference set value, is the oxygen content set value, is the oxygen content actual value, , is the air volume coefficient.

7. The application and control method of the intelligent control software ARC in circulating fluidized bed boiler according to claim 1, characterized in that: The feed quantity adjustment formula in step S3 is wherein is the feed quantity, is the feed coefficient, is the combustion efficiency, is the low heat value of the fuel; the bed temperature correction is compensated by the primary air quantity, when , , the primary air quantity is reduced; when , , the primary air quantity is increased, is the bed temperature correction coefficient.

8. The application and control method of the intelligent control software ARC in circulating fluidized bed boiler according to claim 1, characterized in that: In step S4, the boiler process simulation model is built based on the ARC control logic, which comprises: embedding the disturbance differentiation logic, load distribution algorithm of the mother pipe coordinated control optimization system and the wind material ratio logic, parameter adjustment rule of the boiler combustion variable control optimization system into the simulation model, combining the circulating fluidized bed boiler mechanism, simulating the dynamic change process of main steam pressure, bed temperature and smoke oxygen content under different loads.

9. The application and control method of the intelligent control software ARC in circulating fluidized bed boiler according to claim 1, characterized in that: In step S5, the AI large model analyzes the operation deviation, adopts a hierarchical knowledge correlation algorithm, and the similarity calculation formula is , wherein is the comprehensive similarity, is the text similarity of the training answer and the standard answer, is the similarity of the simulation operation and the standard operation sequence, , is the weight; by comparing the edit distance of the operation step sequence and the standard sequence , when , it is determined that the operation deviation exists, is the distance threshold.

10. The application of the intelligent control software ARC and the control method in a circulating fluidized bed boiler according to claim 1, characterized in that: In step S5, the AI large model generates a personalized learning plan based on the skill short board score , learning duration , wherein is the full score, is the time coefficient; The recommended exercise questions are matched according to the bias type, the matching degree , The matching degree of the question and the bias type, The matching degree of the question difficulty and the student level, 、 The matching coefficient, select The highest question to generate the exercise list.

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