Method and system for controlling pressure difference of carbonization silicon smelting furnace gas hood to prevent external air from being introduced

Abstract

The application relates to a differential pressure control method and system for preventing external air from being introduced into a gas collection hood of a silicon carbide smelting furnace, and relates to the technical field of environmental protection equipment manufacturing. The method comprises the following steps: collecting pressure difference data and air extraction amount data inside and outside the gas collection hood in a minute level sampling cycle, establishing and estimating slow-varying parameters of a grey-box steady-state pressure difference model; in a second level sampling cycle, the slow-varying parameters are used to linearize the grey-box steady-state pressure difference model to construct a prediction model, the control precision of the system is improved, pre-compensation of fast and slow disturbances is realized, the micro-negative pressure state in the gas collection hood is maintained, the process gas collection efficiency is optimized, and the external air is prevented from entering the gas collection hood cavity; when a sudden change of the disturbance is detected, the slow-varying parameters are used to adjust the reference air extraction amount, the reference air extraction amount is superimposed with an optimal air extraction amount increment, the gas collection hood is ensured to maintain a stable micro-negative pressure under different working conditions, the gas collection efficiency is improved, the external air interference is reduced, and the process gas collection effect of the full-closed gas collection hood is ensured.

Description

Technical Field

[0001] This application relates to the field of environmental protection equipment manufacturing technology, specifically to a method and system for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air. Background Technology

[0002] Silicon carbide is a crucial foundational material for the development of the third-generation semiconductor industry, widely used in metallurgy, military, building materials, aerospace, and many other fields. It is a key industry encouraged by the National New Materials Industry Development Guidelines. Statistics show that my country's silicon carbide production is approximately 3 million tons, accounting for 80% of the world's total output. Gansu Province alone has 75 silicon carbide enterprises, representing about 50% of the national total. As an important industrial material, silicon carbide is widely used in semiconductors, abrasives, and other fields due to its excellent properties such as high hardness, high thermal conductivity, high temperature resistance, and chemical stability. Currently, statistics indicate that fugitive emissions from silicon carbide smelting are extremely serious. Smelting furnaces typically operate in open or semi-open environments, reducing raw materials such as quartz sand and coke at temperatures as high as 2200°C. This not only consumes a lot of energy but also produces large amounts of harmful gases such as NOx, SO2, H2S, and CO, with approximately 90% of the exhaust gas being emitted fugitively. Meanwhile, safe recycling is extremely difficult. Producing 10,000 tons of SiC emits approximately 14,000 tons of CO and 20,000 tons of CO2. Besides NOx, SO2, H2S, and CO, the exhaust gas from silicon carbide smelting contains pollutants such as CO, which accounts for over 98% of the emissions. This carbon emission is enormous, highly flammable, and explosive, making safe recycling extremely challenging and posing a serious threat to the environment and public health. There is an urgent need to address the severe environmental pollution and safety hazards posed by fugitive emissions, and to resolve the issue of coordinated pollution reduction and carbon reduction in exhaust gas emissions.

[0003] The latest mainstream solution for silicon carbide is to construct large-scale enclosed gas collection hoods that completely cover the production equipment. A negative pressure extraction system collects harmful gases generated inside the hood and transports them to a purification treatment unit for harmless disposal. In this process, because the fully enclosed hood can be tens or even hundreds of meters long and range in width and height from about 10 meters, there is a complex nonlinear relationship between the pressure difference inside and outside and the extraction volume. The formation of the pressure difference is not only directly related to the flow rate of the extraction system, but is also influenced by a variety of factors such as the temperature and composition of the gas inside the hood, buoyancy effects, and leaks in the hood structure.

[0004] Currently, silicon carbide smelting furnaces use large gas collection hoods to cover the production equipment. However, these hoods cannot achieve absolute sealing, allowing outside air to easily seep in due to poor pressure differential control, affecting the smelting process and causing pollution. In existing technologies, fully enclosed gas collection hood extraction systems can cause excessively high localized negative pressure around the hood's extraction port. Combined with the significant changes in gas density caused by drastic temperature variations in different areas of the furnace, this leads to nonlinear changes in buoyancy flux. This strong nonlinearity makes it difficult to establish accurate mathematical models using traditional control methods based on linear system theory, resulting in poor control performance and an inability to meet high-precision control requirements. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for controlling the differential pressure of a gas collecting hood for preventing the introduction of external air into a silicon carbide smelting furnace. The main purpose is to solve the technical problems that the current control methods are difficult to establish accurate mathematical models due to the complex nonlinear relationship between the differential pressure inside the hood and the air volume, resulting in poor control effect and failure to meet the high-precision and balanced differential pressure control requirements of a fully enclosed gas collecting hood. In addition, existing control methods are often designed only for a single time scale, making it difficult to effectively suppress both fast and slow disturbances at the same time, which leads to performance drift or instability of the system during long-term operation.

[0006] According to a first aspect of this application, a method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air is provided, the method comprising:

[0007] With a sampling period of minutes, data on the pressure difference between the inside and outside of the gas collection hood and data on the air volume used to adjust the pressure inside the gas collection hood are collected. Based on the pressure difference data and the air volume data, a steady-state pressure difference model of the ash box is established (describing the nonlinear relationship between the pressure difference between the inside and outside of the gas collection hood and the air volume). The slowly varying parameters of the steady-state pressure difference model of the ash box (including the estimated value of the buoyancy term and the estimated value of the equivalent leakage area) are updated and estimated online.

[0008] With a sampling period of seconds, at each sampling moment, the slowly varying parameters are used to linearize the steady-state pressure difference model of the gray box in order to construct a prediction model;

[0009] Based on the prediction model, solve the optimization objective function that satisfies the target constraints and output the optimal ventilation volume increment;

[0010] When a sudden change in disturbance is detected, the reference exhaust volume is adjusted through the feedforward channel using the slowly varying parameters. The adjusted reference exhaust volume is then superimposed with the incremental optimal exhaust volume to adjust the internal and external pressure difference based on the superimposed exhaust volume.

[0011] According to a second aspect of this application, a differential pressure control system for preventing the introduction of external air into a silicon carbide smelting furnace gas collecting hood is provided. The system includes: a slow-loop estimation module, a fast-loop MPC module, a feedforward injection module, and an actuator.

[0012] The slow-loop estimation module is used to collect the pressure difference data inside and outside the gas collection hood and the air volume data used to adjust the pressure inside the gas collection hood at a sampling period of minutes. Based on the pressure difference data and the air volume data, a steady-state pressure difference model of the ash box is established (describing the nonlinear relationship between the pressure difference inside and outside the gas collection hood and the air volume). The module also updates and estimates the slow-varying parameters of the steady-state pressure difference model of the ash box online (including the estimated value of the buoyancy term and the estimated value of the equivalent leakage area).

[0013] The fast-loop MPC module is connected to the slow-loop estimation module. The fast-loop MPC module is used to receive slowly varying parameters from the slow-loop estimation module at each sampling time with a sampling period of seconds. The slow-variable parameters are used to linearize the gray box steady-state pressure difference model to construct a prediction model. Based on the prediction model, the optimization objective function that satisfies the target constraint is solved, and the optimal ventilation volume increment is output.

[0014] The feedforward injection module is connected to the slow-loop estimation module and the fast-loop MPC module respectively. The feedforward injection module is used to receive the slowly varying parameters from the slow-loop estimation module and the optimal exhaust volume increment from the fast-loop MPC module. When a sudden change in disturbance is detected, the reference exhaust volume is adjusted through the feedforward channel using the slowly varying parameters, and the adjusted reference exhaust volume is superimposed with the optimal exhaust volume increment to generate an exhaust volume control command based on the superimposed exhaust volume.

[0015] The actuator is connected to the feedforward injection module. The actuator is used to receive the air volume control command from the feedforward injection module and adjust the air volume according to the air volume control command to adjust the internal and external pressure difference.

[0016] According to a third aspect of this application, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the aforementioned method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, as described in the first aspect.

[0017] According to a fourth aspect of this application, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the aforementioned method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, as described in the first aspect.

[0018] The method and system for controlling the pressure difference of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, provided in this application, differs from existing technologies in that it collects internal and external pressure difference data and exhaust volume data for adjusting the pressure inside the gas collecting hood using a sampling period of minutes. Based on the internal and external pressure difference data and exhaust volume data, a steady-state pressure difference model of the ash box is established, and the slowly varying parameters of the ash box steady-state pressure difference model are updated and estimated online. The slowly varying parameters include the estimated value of the buoyancy term and the estimated value of the equivalent leakage area. Using a sampling period of seconds, the steady-state pressure difference model of the ash box is linearized using the slowly varying parameters at each sampling time to construct a predictive model. Based on the predictive model, an optimization objective function that satisfies the target constraints is solved, and the optimal exhaust volume increment is output. When a sudden change in disturbance is detected, the reference exhaust volume is adjusted through a feedforward channel using the slowly varying parameters. The adjusted reference exhaust volume is superimposed with the optimal exhaust volume increment to adjust the internal and external pressure difference of the gas collecting hood based on the superimposed exhaust volume.

[0019] By applying the scheme of this application, sampling data on the internal and external pressure difference and air volume of the gas collection hood are collected at minute-level intervals to establish a gray box steady-state pressure difference model. This model incorporates some physical mechanisms of the system and is not a completely black box model, thus better capturing the complex relationship between pressure difference and air volume. Online updates and estimations of the model's slowly varying parameters, including buoyancy term estimates and leakage equivalent area estimates, enable the model to adapt to dynamic changes in the system, thereby more accurately describing the nonlinear relationship between pressure difference and air volume.

[0020] Using a sampling period on the order of seconds, the steady-state pressure difference model of the gray box is linearized at each sampling moment using slowly varying parameters. In this way, the complex nonlinear model is transformed into a linear predictive model suitable for optimization control. Based on the linearized model, a predictive model is constructed, which can more effectively predict and control pressure difference changes, improve the control accuracy of nonlinear systems, and achieve pre-compensation for disturbances. It can also achieve pre-compensation for both fast and slow disturbances (preventing the pressure from exceeding the backflow threshold of external air) to maintain the pressure difference inside and outside the gas collection hood within a safe range to prevent the introduction of external air.

[0021] This application employs a dual-timescale design. The slow loop, with a sampling period of minutes, handles slowly varying disturbances in the system. The fast loop, with a sampling period of seconds, uses the slowly varying parameters provided by the slow loop to linearize the model at each sampling moment, constructing a predictive model and solving the objective function to output the optimal exhaust volume increment. When a sudden change in disturbance is detected, the reference exhaust volume is adjusted using the slowly varying parameters through a feedforward channel, and the adjusted reference exhaust volume is superimposed with the optimal exhaust volume increment output by the fast loop. This feedforward compensation mechanism can quickly respond to disturbance changes, achieving pre-compensation for disturbances, while effectively suppressing both fast and slow disturbance compensation (preventing pressure from exceeding the external air backflow threshold) to maintain the pressure difference inside and outside the gas collection hood within a safe range to prevent the introduction of external air. The adjusted reference exhaust volume is superimposed with the optimal exhaust volume increment to optimize the atmosphere, heat, and gas distribution in the smelting process, maintain a slight negative pressure state inside the gas collection hood, and drive the process gas to flow out of the gas collection hood through the exhaust system. This ensures that the gas collection hood maintains a stable slight negative pressure under different operating conditions, improves gas collection efficiency, reduces external air interference, and guarantees the process gas collection effect of the fully enclosed gas collection hood. At the same time, the internal and external pressure difference generated by the slight negative pressure can reduce the infiltration of external air through the leakage channel and prevent the process gas from being diluted. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A schematic flowchart illustrating a method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, provided in an embodiment of this application.

[0025] Figure 2 A schematic diagram of a differential pressure control system for preventing the introduction of external air into a gas collecting hood of a silicon carbide smelting furnace, provided in an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the structure of a gas collection hood provided in an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the structure of a water tank provided in an embodiment of this application. Detailed Implementation

[0028] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0029] The following describes, with reference to the accompanying drawings, a method and system for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air.

[0030] To address the challenges posed by the complex nonlinear relationship between pressure difference and air volume within the hood, which makes it difficult to establish accurate mathematical models using traditional control methods, resulting in poor control performance and inability to meet high-precision control requirements, and the fact that existing control methods are often designed for a single time scale and cannot effectively suppress both fast and slow disturbances simultaneously, leading to performance drift or instability during long-term operation, this application provides a method for controlling the pressure difference of a silicon carbide smelting furnace's gas collecting hood to prevent the introduction of external air. Figure 1 As shown, the method includes:

[0031] Step 101: Collect the pressure difference data inside and outside the gas collection hood and the air volume data used to adjust the pressure inside the gas collection hood at a sampling period of minutes. Based on the pressure difference data inside and outside the gas collection hood and the air volume data, establish a steady-state pressure difference model of the ash box, and update and estimate the slowly varying parameters of the steady-state pressure difference model of the ash box online. The slowly varying parameters include the estimated value of the buoyancy term, the estimated value of the equivalent leakage area, and the estimated value of the dynamic gain.

[0032] Among them, the steady-state pressure difference model of the ash box is used to describe the nonlinear relationship between the pressure difference inside and outside the gas collection hood and the air volume;

[0033] "Exhaust ventilation" is also known as "flue gas circulation micro-negative pressure protection". It refers to the protective measure of transporting the flue gas in the flue gas collection hood back into the hood through the flue gas circulation system, maintaining a micro-negative pressure state inside the hood and preventing the introduction of outside air.

[0034] The equivalent leakage area can be an equivalent model parameter used to quantify the gas leakage characteristics between the gas collection hood and the external environment (rather than the actual area directly corresponding to the physical sealing structure). The larger the value, the stronger the gas exchange capacity between the gas collection hood and the outside environment, and the higher the risk of outside air seeping into the gas collection hood through the leakage channel. This application estimates this slowly varying equivalent parameter online and dynamically adjusts the exhaust volume to maintain a slightly negative pressure state inside the gas collection hood (such as a safe range of -10Pa to -5Pa), thereby quantifying and compensating for the impact of leakage on the pressure difference at the model level and preventing outside air from seeping in due to pressure difference imbalance caused by changes in the equivalent leakage area.

[0035] In this embodiment, to address the dynamic changes in slowly varying parameters such as buoyancy flux and equivalent leakage area in a large gas collection hood system, a slow-loop estimation module is designed. Its core function is to estimate the slowly varying parameters of the system online with a sampling period of minutes, which may include the estimated value of the buoyancy term. Estimated equivalent area of ​​leakage And establish a steady-state pressure difference model for the gray box.

[0036] In this embodiment, considering the periodicity of furnace temperature changes and raw material input during silicon carbide production, the buoyancy flux and leakage equivalent area exhibit slow-varying characteristics. The slow-loop estimation module synchronously collects the internal and external pressure difference data of the gas collection hood every 60 seconds. (Unit: Pa) and exhaust volume data (Unit: m³ / h), and the ambient temperature at the time of sampling can be recorded. Temperature inside the cover and ambient air density (Unit: kg / m³, which can be calculated using the ideal gas law).

[0037] As one possible approach, median filtering can be applied to the internal and external pressure difference data and the ventilation volume data to eliminate abnormal data points caused by sensor noise or transient disturbances.

[0038] Based on the principles of fluid mechanics and thermodynamics, a steady-state pressure difference model for the ash box can be established, considering the effects of temperature buoyancy and flow resistance. The expression for the steady-state pressure difference model of the ash box can be:

[0039]

[0040] In the formula, Indicates the pressure difference between the inside and outside of the gas collection hood ( When there is a slight negative pressure inside the cover , Indicates the pressure inside the cover. (Indicating external pressure) Indicates ambient air density ( ); Represents gravitational acceleration ( ); Indicates the effective height of the cover ( ); Indicates the temperature difference between the inside and outside of the gas collection hood ( , Indicates the temperature inside the cover. (Indicates ambient temperature); Indicates the exhaust volume ( ); Indicates the effective flow area ( , , Indicates the area of ​​the opening in the cover. (Indicates the equivalent area of ​​the leak).

[0041] The effective flow area can be calculated based on the leakage area of ​​the enclosure itself and the opening area of ​​the enclosure, according to the principle of parallel impedance. The calculation formula is as follows:

[0042]

[0043] In the formula, Indicates the effective flow area. This represents the estimated equivalent area of ​​the leak. This represents the area of ​​the opening in the cover, where, (Corresponds to φ2000mm exhaust duct).

[0044] To facilitate online estimation of slowly varying parameters, the above gray box steady-state pressure difference model can be rewritten in parameter estimation form:

[0045]

[0046] In the formula, This represents the pressure difference data between the inside and outside of the gas collection hood. This represents the estimated value of the buoyancy term. This indicates the ventilation volume data. Indicates at the sampling time The dynamic gain parameter, This indicates a measure to prevent small quantities from having a denominator of zero. .

[0047] in,

[0048] In the formula, Indicates at the sampling time The dynamic gain parameter, Indicates ambient air density. This indicates the effective flow area.

[0049] For the embodiments of this application, a forgetting factor can be used. The recursive least squares method, with an update cycle of 60 seconds, can update and estimate the slow changes in buoyancy flux and leakage area of ​​the gray box steady-state pressure difference model online according to the following recursive formula, where the forgetting factor... .

[0050] The recursive formula is:

[0051]

[0052] In the formula, This represents the estimated value of the slowly varying parameter. This represents the estimated value of the buoyancy term. This represents the estimated equivalent area of ​​the leak.

[0053] Step 102: Using a sampling period of seconds, at each sampling time, linearize the gray box steady-state pressure difference model using slowly varying parameters to construct a prediction model.

[0054] In this embodiment of the application, a fast-loop MPC module is designed to achieve high-precision and rapid control of the large gas collection hood system. This module uses a sampling period of seconds, linearizes the steady-state pressure difference model of the ash box at each sampling moment, constructs a predictive model, and executes optimized control.

[0055] Specifically, the fast loop MPC module performs optimization once every 1 second for each sampling period, at each sampling moment. First, the slowly varying parameters (including buoyancy term estimates and leakage equivalent area estimates) provided by the slow-loop estimation module can be used to linearize the gray box steady-state pressure difference model. The linearization process involves applying a first-order Taylor expansion method to the gray box steady-state pressure difference model at each sampling time, making a local linear approximation of the nonlinear model (i.e., the gray box steady-state pressure difference model) near the current operating point. The expression for the linearized model can be:

[0056]

[0057] In the formula, Indicates pressure difference deviation. Indicates at the sampling time The linearized gain scalar, This indicates the change in ventilation volume. Indicates distractor items;

[0058] Among them, considering the measurement noise and modeling error of large enclosures, interference terms The upper limit can be set to .

[0059] The linearized gain scalar of the linearized model can be calculated using the formula for calculating the linearized gain scalar, where the formula for calculating the linearized gain scalar is:

[0060]

[0061] In the formula, Indicates at the sampling time The linearized gain scalar, Indicates at the sampling time The dynamic gain estimate, Indicates at the sampling time Reference exhaust volume.

[0062] The calculated linearized gain scalar can be used to construct a prediction model, which can be used to predict the change of pressure difference over a future period (i.e., within the prediction time domain), providing a basis for model predictive control (MPC) optimization.

[0063] Step 103: Based on the prediction model, solve the optimization objective function that satisfies the target constraints and output the optimal ventilation volume increment.

[0064] In the embodiments of this application, after constructing the prediction model, a fast-loop control step is further executed to achieve high-precision and rapid control of the large gas collection hood system.

[0065] Specifically, the prediction time domain is set. Seconds, controlling the time domain Seconds. That is, at each second-level sampling time, based on the prediction model at the current time, the pressure difference change in the next 10 seconds is predicted, and the system performance is optimized by adjusting the control input (i.e., the increase in ventilation volume) in the next 3 seconds.

[0066] Based on the prediction model, the optimization objective function that satisfies the target constraints can be solved, and the optimal ventilation volume increment can be output.

[0067] The objective function expression can be:

[0068]

[0069] In the formula, This represents the sequence of incremental ventilation volumes within the control time domain. This indicates the summation in the time domain for prediction ( step), Indicates control time-domain summation ( step), This represents the weighting coefficient of the differential pressure tracking term. Indicates differential pressure tracking item, This represents the increase in ventilation volume at time k. The weighting coefficient represents the increase in ventilation volume. The weighting coefficient represents the absolute value of the air volume increment at time k.

[0070] Considering the extremely high safety requirements for carbon monoxide, prioritizing the accuracy of differential pressure tracking is crucial. Therefore, the penalty weight for differential pressure deviation can be set to... .

[0071] To balance the smoothness of the exhaust volume with the response speed and avoid frequent and large adjustments to the exhaust volume, the weighting coefficient for the increase in exhaust volume can be set to... .

[0072] To slightly penalize drastic changes in ventilation volume, prevent system oscillations, and allow necessary rapid adjustments, the weighting coefficient of the absolute value term of the ventilation volume increment at time k can be set to... .

[0073] Solving the objective function requires satisfying the following objective constraints, which may include differential pressure safety constraints, ventilation volume physical constraints, and terminal contraction constraints.

[0074] To maintain a slight negative pressure within the gas collection hood to optimize gas collection efficiency and prevent outside air from entering the hood cavity, the pressure difference safety constraint expression can be:

[0075]

[0076] In the formula, The minimum permissible negative pressure, such as -10 Pa; The maximum permissible negative pressure, such as -5 Pa, This indicates the maximum permissible error coefficient (e.g., 2Pa). Indicates time Pressure difference deviation ( ), This indicates that a slight negative pressure is set (e.g., 5 Pa, usually taken as...). );

[0077] When the pressure difference is ≤0 Pa, outside air may seep into the gas collection hood (at this time, the internal pressure is lower than or equal to the outside pressure, and air will be forced in); when the pressure difference is >ΔPmax, it will cause gas leakage inside the hood (the leakage and energy consumption need to be balanced); when the pressure difference is <ΔPmin, it will cause excessive pumping energy consumption. Therefore, the pressure difference safety constraint needs to limit the pressure difference to a slightly negative pressure range. to Within a slightly negative pressure range, it prevents outside air from entering and avoids energy waste.

[0078] Considering the physical limitations of the exhaust equipment, the increase in exhaust volume is constrained. The physical constraint expression for the exhaust volume can be:

[0079]

[0080] In the formula, This indicates the minimum exhaust volume (which can be set according to actual conditions, such as...). ), This indicates the maximum exhaust volume (which can be set according to actual conditions, such as...). ), Indicates at the sampling time Reference exhaust volume, This represents the increase in ventilation volume at time k;

[0081] To ensure that the differential pressure deviation of the predicted time-domain terminal is within the allowable range, the terminal contraction constraint expression can be:

[0082]

[0083] In the formula, This represents the terminal value of the differential pressure deviation. This represents the terminal constraint coefficient (which can be set according to the actual situation, such as 0.8). This represents the maximum permissible error coefficient (which can be set according to actual conditions, such as 2Pa).

[0084] The aforementioned quadratic programming problem can be solved using the interior-point method, with a computation time of less than 10 milliseconds. Through rapid solution, the optimal exhaust volume increment sequence satisfying all objective constraints is obtained. The fast-loop MPC module can output the optimal exhaust volume increment at the current moment, which can be used to adjust the exhaust volume, achieving precise and rapid control of the pressure difference in the gas collection hood.

[0085] Step 104: When a sudden change in disturbance is detected, the reference exhaust volume is adjusted through the feedforward channel using the slowly varying parameters. The adjusted reference exhaust volume is then superimposed with the optimal exhaust volume increment to adjust the internal and external pressure difference of the gas collection hood based on the superimposed exhaust volume.

[0086] In this embodiment of the application, in order to further improve the control performance and stability of the large gas collection hood system, a feedforward injection module and a gain scheduling mechanism are designed and combined with a fast loop MPC module to achieve disturbance pre-compensation and adaptive control.

[0087] Specifically, the feedforward injection module continuously monitors the rate of change of the buoyancy term estimate and the rate of change of the leakage equivalent area estimate provided by the slow-loop estimation module. When the rate of change of the buoyancy term estimate exceeds a preset rate of change threshold (which can be set according to actual conditions, such as...), the module will initiate a response. When the buoyancy effect changes rapidly due to changes in feeding or furnace temperature, and / or when the rate of change of the estimated equivalent leakage area is less than the preset rate of change threshold (e.g., slow but large change in leakage characteristics due to improved sealing of the enclosure), it is determined that a sudden change in disturbance has been detected.

[0088] When a sudden disturbance is detected (such as a rapid drop in pressure inside the hood due to a sudden opening of the furnace door, or a sudden change in pressure outside the hood due to strong winds), if the exhaust volume is not adjusted in time, the pressure difference between the inside and outside of the hood may deviate from the safe micro-negative pressure range (e.g., pressure difference ≥ 0 Pa), causing outside air to infiltrate. At this time, the feedforward injection module quickly adjusts the reference exhaust volume (based on the reference exhaust volume adjustment formula) through the feedforward channel and superimposes it with the optimal exhaust volume increment output by the fast-loop MPC to maintain the micro-negative pressure state inside the hood, preventing outside air from being introduced into the hood due to sudden pressure changes. For example, when the furnace door opens, the pressure inside the hood drops instantly. The feedforward module immediately increases the reference exhaust volume, causing the exhaust fan to increase its speed and the exhaust volume, maintaining the micro-negative pressure inside the hood between -10 Pa and 5 Pa, preventing outside air from infiltrating due to insufficient pressure difference.

[0089] The formula for adjusting the reference exhaust volume can be:

[0090]

[0091] In the formula, Indicates at the sampling time Reference exhaust volume (m³ / s, the minimum exhaust volume required to maintain a slight negative pressure). This indicates that a slight negative pressure is set (Pa, such as 5Pa); This represents the estimated value of the buoyancy term (Pa; a negative number indicates that the temperature inside the enclosure is higher than the outside temperature, such as -20Pa). Indicates at the sampling time The dynamic gain estimate (Pa·s) 2 / m 6 A positive number reflects the degree of influence of the exhaust volume on the pressure difference.

[0092] When a sudden disturbance is detected, in addition to updating the steady-state reference suction volume as usual, a gain scheduling mechanism can be activated to update the gain adjustment factor using an update formula, thereby adjusting controller parameters (such as the MPC weight matrix, prediction time domain, and control time domain). For example, based on the magnitude and direction of the buoyancy flux change, the weight parameters in the fast-loop MPC control can be adjusted to ensure the stability of the system under different operating conditions. Through the gain scheduling mechanism, the feedforward channel can more accurately adjust the reference suction volume according to changes in disturbance.

[0093] The update formula can be:

[0094]

[0095] In the formula, Indicates the gain adjustment factor. This indicates the learning rate (which can be set according to the actual situation, such as 0.3). Indicates at the sampling time The dynamic gain estimate, This represents the reference dynamic gain.

[0096] At each second-level sampling moment, the reference exhaust volume adjusted by the feedforward channel is superimposed with the optimal exhaust volume increment obtained in step 103, and the superimposed exhaust volume is used as the control input signal for the actuator. The control signal is output through a 4-20mA analog signal, corresponding to an exhaust volume of 5-50 m³ / s. The actuator can adjust its rotation speed according to the received control signal, thereby regulating the exhaust volume and achieving precise control of the pressure difference inside and outside the gas collection hood.

[0097] The actuator can be a variable frequency speed-regulating centrifugal fan with a rated power of 200kW and a speed range of 10-100Hz. The variable frequency speed-regulating centrifugal fan has a fast response capability, with a response time of approximately 2-3 seconds, meeting the 1-second sampling requirement of the fast loop.

[0098] Two 10,000 m³ / h variable frequency fans are used, one in operation and one on standby, with automatic control to ensure a slight negative pressure inside the fume hood. The fume hood has four air outlets on each side, totaling eight, to extract gas from inside. Each outlet is equipped with an individual flow regulating valve and a slight negative pressure sensor to ensure uniform distribution of the slight negative pressure inside the fume hood. The ventilation ducts are made of 304 stainless steel, and the flexible connections are made of silicone fiber cloth.

[0099] like Figure 2 As shown, an embodiment of this application provides a differential pressure control system for preventing external air from entering a silicon carbide smelting furnace gas collecting hood. This system can be applied to the aforementioned differential pressure control method for preventing external air from entering a silicon carbide smelting furnace gas collecting hood. The differential pressure control system for preventing external air from entering a silicon carbide smelting furnace gas collecting hood may include: a slow-loop estimation module, a fast-loop MPC module, a feedforward injection module, and an actuator. Each module is connected via an industrial Ethernet and adopts a distributed control architecture.

[0100] like Figure 3 As shown, the gas collection hood consists of a rigid frame, silicone fiber cloth, a telescopic mechanism, and a water seal. The rigid frame is made of 304 stainless steel and is a multi-stage gantry-type main support structure. Each stage of the gantry has diagonal support rods to ensure overall stability, and steel cables are used to support the coated fiber cloth. The coated fiber cloth is made of silicone fiber cloth, with a temperature resistance of 150℃, and is placed on the outside of the frame to isolate the smelting furnace from the external environment. The fabric itself is waterproof and airtight, has high strength, can be folded multiple times, and is resistant to acid corrosion. The fiber cloth is fixed to the frame with flange clips and is further supported by steel cables. The telescopic mechanism uses an electrically driven wheel structure with tracks on the ground. The bottom of the gas collection hood is connected to a fiber cloth skirt, which is immersed in a surrounding water tank to form a water seal. The water tank is approximately 20cm wide and 30cm deep to ensure the overall airtightness of the gas collection hood. Figure 4As shown, the water seal trough is initially designed as a trough dug down around the smelting furnace (about 1m from the furnace body) and poured with cement (concrete) (requiring no water seepage into the ground). The trough has a net width of 20cm, a depth of 30cm, and a total length of about 140m.

[0101] The slow-loop estimation module is used to collect the pressure difference data inside and outside the gas collection hood and the air volume data used to adjust the pressure inside the gas collection hood at a sampling period of minutes. Based on the pressure difference data and air volume data, a steady-state pressure difference model of the ash box is established, and the slow-variable parameters of the steady-state pressure difference model of the ash box are updated and estimated online. The slow-variable parameters include the buoyancy term estimate and the leakage equivalent area estimate.

[0102] The fast-loop MPC module is connected to the slow-loop estimation module. The fast-loop MPC module is used to receive slowly varying parameters from the slow-loop estimation module at each sampling time with a sampling period of seconds. It uses the slowly varying parameters to linearize the gray box steady-state pressure difference model to build a prediction model. Based on the prediction model, it solves the optimization objective function that satisfies the target constraint and outputs the optimal ventilation volume increment.

[0103] The feedforward injection module is connected to the slow-loop estimation module and the fast-loop MPC module respectively. The feedforward injection module is used to receive the slowly varying parameters from the slow-loop estimation module and the optimal exhaust volume increment from the fast-loop MPC module. When a sudden change in disturbance is detected, the reference exhaust volume is adjusted through the feedforward channel using the slowly varying parameters. The adjusted reference exhaust volume is then superimposed with the optimal exhaust volume increment, and an exhaust volume control command is generated based on the superimposed exhaust volume.

[0104] The actuator is connected to the feedforward injection module. The actuator is used to receive the air volume control command from the feedforward injection module and adjust the air volume of the gas collection hood according to the air volume control command to adjust the pressure difference between the inside and outside of the gas collection hood.

[0105] In a specific application scenario, let's take a silicon carbide production line as an example. Silicon carbide production uses the Atcheson electric furnace process, where raw materials such as quartz sand and coke are reacted at a high temperature of 2200°C in an open-air environment to produce silicon carbide, while simultaneously generating a large amount of carbon monoxide gas. To prevent carbon monoxide from spreading and polluting the environment, a large gas collection hood is installed above the production area. The hood's dimensions are length × width × height = 50m × 30m × 3m, with a total volume of approximately 4500m³.

[0106] The flue gas hood may include: a main body, an exhaust duct, a differential pressure sensor, and an exhaust fan. The main body can be a steel frame structure covered with flame-retardant and corrosion-resistant material, forming a closed space approximately 8-12 meters from the electric furnace body. The exhaust duct (the core extraction channel of the 'flue gas circulation micro-negative pressure protection' system) can be installed at the top of the hood, with a diameter of φ2000mm, connected to a variable frequency speed-controlled exhaust fan (used to circulate and extract the flue gas from the hood back into the hood, maintaining a micro-negative pressure state). The differential pressure sensor can be installed on the side wall of the hood to measure the pressure difference between the inside and outside of the hood. .

[0107] The system's operation flow can be as follows:

[0108] Step S1: System initialization, settings , , ;

[0109] Step S2: The slow loop estimation module is updated every 60 seconds. and calculate ;

[0110] Step S3: The feedforward injection module can update based on the latest estimate. and ;

[0111] Step S4: The fast-loop MPC module can solve the QP problem and obtain the optimal control increment. ;

[0112] Step S5: The actuator can output... ;

[0113] Step S6: Return to step S2 and repeat the process.

[0114] After six months of field operation and verification, the system can improve the differential pressure control accuracy of the enclosure to within ±3Pa, reduce the carbon monoxide leakage rate by more than 95%, and save about 15% of the energy consumption of air extraction compared with traditional PID control.

[0115] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented through hardware. Compared with the prior art, the technical solution in this application, by applying the solution of this application, collects the internal and external pressure difference data and exhaust volume data of the gas collection hood with a sampling period of minutes, and establishes a gray box steady-state pressure difference model. This model incorporates some physical mechanisms of the system and is not a completely black-box model, thus better capturing the complex relationship between pressure difference and exhaust volume. Online updating and estimation of the model's slowly varying parameters, including buoyancy term estimates and leakage equivalent area estimates, enables the model to adapt to the dynamic changes of the system, thereby more accurately describing the nonlinear relationship between pressure difference and exhaust volume.

[0116] Using a sampling period on the order of seconds, the steady-state pressure difference model of the gray box is linearized at each sampling moment using slowly varying parameters. In this way, the complex nonlinear model is transformed into a linear predictive model suitable for optimization control. Based on the linearized model, a predictive model is constructed, which can more effectively predict and control pressure difference changes, improve the control accuracy of nonlinear systems, and meet the requirements of high-precision control.

[0117] This application employs a dual-timescale design. The slow loop, with a sampling period of minutes, handles slowly varying disturbances in the system. The fast loop, with a sampling period of seconds, linearizes the model using the slowly varying parameters provided by the slow loop at each sampling time, constructs a predictive model, solves the objective function, and outputs the optimal ventilation volume increment. When a sudden change in disturbance is detected, the reference ventilation volume is adjusted using the slowly varying parameters through a feedforward channel, and the adjusted reference ventilation volume is superimposed with the optimal ventilation volume increment output by the fast loop. This feedforward compensation mechanism can quickly respond to disturbance changes, achieve pre-compensation for disturbances, and effectively suppress both fast and slow disturbances, ensuring the stability and robustness of the system under different operating conditions.

[0118] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0119] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, characterized in that, include: With a sampling period of minutes, data on the pressure difference between the inside and outside of the gas collection hood and data on the air volume used to adjust the pressure inside the gas collection hood are collected. Based on the pressure difference data and the air volume data, a steady-state pressure difference model of the ash box is established, and the slowly varying parameters of the steady-state pressure difference model of the ash box are updated and estimated online. The steady-state pressure difference model of the ash box is used to describe the nonlinear relationship between the pressure difference between the inside and outside of the gas collection hood and the air volume. The slowly varying parameters include the buoyancy term estimate, the leakage equivalent area estimate, and the dynamic gain estimate. With a sampling period of seconds, at each sampling moment, the slowly varying parameters are used to linearize the steady-state pressure difference model of the gray box in order to construct a prediction model; Based on the prediction model, solve the optimization objective function that satisfies the target constraints and output the optimal ventilation volume increment; When a sudden change in disturbance is detected, the reference exhaust volume is adjusted through the feedforward channel using the slowly varying parameters. The adjusted reference exhaust volume is then superimposed with the incremental optimal exhaust volume to adjust the pressure difference between the inside and outside of the gas collection hood based on the superimposed exhaust volume.

2. The method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, as described in claim 1, is characterized in that... The expression for the steady-state pressure difference model of the gray box is: In the formula, This indicates the pressure difference between the inside and outside of the gas collection hood. When there is a slight negative pressure inside the cover , Indicates the pressure inside the cover. This indicates external pressure. Indicates ambient air density, unit: , The acceleration due to gravity is expressed as: , Indicates the effective height of the enclosure, unit: , This indicates the temperature difference between the inside and outside of the gas collection hood. , Indicates the temperature inside the cover. Indicates ambient temperature. This indicates the exhaust volume, in units of: , Indicates the effective flow area, unit: , , Indicates the area of ​​the opening in the cover. This indicates the equivalent area of ​​the leak.

3. The method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, as described in claim 2, is characterized in that... The effective flow area is calculated based on the leakage area of ​​the enclosure itself and the opening area of ​​the enclosure, according to the principle of parallel impedance.

4. The method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, as described in claim 1, is characterized in that... The online updating and estimation of the slowly varying parameters of the gray box steady-state pressure difference model includes: The slowly varying parameters of the gray box steady-state pressure difference model are updated and estimated online using the recursive least squares method with a forgetting factor, according to the following recursive formula; The recursive formula is: In the formula, This represents the estimated value of the slowly varying parameter. This represents the estimated value of the buoyancy term. This represents the estimated equivalent area of ​​the leak.

5. The method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, as described in claim 1, is characterized in that... The linearization of the steady-state pressure difference model of the gray box to construct a prediction model includes: At each sampling time, a first-order Taylor expansion is performed on the gray box steady-state pressure difference model to obtain a linearized model, and the linearized gain scalar of the linearized model is calculated using the linearized gain scalar calculation formula; The prediction model is constructed using the linearized gain scalar. The expression for the linearized model is as follows: In the formula, Indicates pressure difference deviation. Indicates at the sampling time The linearized gain scalar, in Pa·s / m³. This indicates the change in ventilation volume. Indicates distractor items; The formula for calculating the linearized gain scalar is as follows: In the formula, Indicates at the sampling time The linearized gain scalar, Indicates at the sampling time The dynamic gain estimate, Indicates at the sampling time Reference exhaust volume.

6. The method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, as described in claim 1, is characterized in that... The target constraints include differential pressure safety constraints, ventilation volume physical constraints, and terminal contraction constraints. The expression for the differential pressure safety constraint is as follows: In the formula, This indicates the minimum permissible negative pressure. Indicates the maximum permissible micro-negative pressure. This represents the pressure difference deviation at time k. , This indicates that a slight negative pressure has been set. This represents the maximum permissible error coefficient; The physical constraint on the exhaust volume is: In the formula, This indicates the minimum ventilation volume. This indicates the maximum exhaust volume. Indicates at the sampling time Reference exhaust volume, This represents the increase in ventilation volume at time k; The terminal contraction constraint is: In the formula, This represents the terminal value of the differential pressure deviation. Indicates the terminal constraint coefficient. This represents the maximum permissible error coefficient.

7. The method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, as described in claim 1, is characterized in that... When a sudden change in disturbance is detected, the reference exhaust volume is adjusted via a feedforward channel using the slowly varying parameters, including: Monitor the rate of change of the estimated buoyancy term and the rate of change of the estimated equivalent leakage area; When the rate of change of the estimated value of the buoyancy term is greater than the preset rate of change threshold, and when the rate of change of the estimated value of the equivalent leakage area is greater than the preset rate of change threshold, the reference exhaust volume is adjusted based on the reference exhaust volume adjustment formula. The formula for adjusting the reference exhaust volume is as follows: In the formula, Indicates at the sampling time Reference exhaust volume, This indicates that a slight negative pressure has been set. Indicates at the sampling time The dynamic gain estimate, This represents the estimated value of the buoyancy term.

8. The method for controlling the differential pressure of a silicon carbide smelting furnace gas collecting hood to prevent the introduction of external air, as described in claim 7, is characterized in that... The method further includes: The gain scheduling mechanism is activated, and the gain adjustment factor is updated using the update formula to adjust the controller parameters; The update formula is as follows: In the formula, Indicates the gain adjustment factor. Indicates the learning rate, Indicates at the sampling time The dynamic gain estimate, This represents the reference dynamic gain.

9. A differential pressure control system for preventing the introduction of external air into a silicon carbide smelting furnace gas collecting hood, characterized in that, include: Slow loop estimation module, fast loop MPC module, feedforward injection module, and actuator; The slow-loop estimation module is used to collect the pressure difference data inside and outside the gas collection hood and the air volume data used to adjust the pressure inside the gas collection hood at a sampling period of minutes. Based on the pressure difference data and the air volume, a steady-state pressure difference model of the ash box is established, and the slow-varying parameters of the steady-state pressure difference model of the ash box are updated and estimated online. The steady-state pressure difference model of the ash box is used to describe the nonlinear relationship between the pressure difference inside and outside the gas collection hood and the air volume. The slow-varying parameters include the buoyancy term estimate, the leakage equivalent area estimate, and the dynamic gain estimate. The fast-loop MPC module is connected to the slow-loop estimation module. The fast-loop MPC module is used to receive slowly varying parameters from the slow-loop estimation module at each sampling time with a sampling period of seconds. The slow-variable parameters are used to linearize the gray box steady-state pressure difference model to construct a prediction model. Based on the prediction model, the optimization objective function that satisfies the target constraint is solved, and the optimal ventilation volume increment is output. The feedforward injection module is connected to the slow-loop estimation module and the fast-loop MPC module respectively. The feedforward injection module is used to receive the slowly varying parameters from the slow-loop estimation module and the optimal exhaust volume increment from the fast-loop MPC module. When a sudden change in disturbance is detected, the reference exhaust volume is adjusted through the feedforward channel using the slowly varying parameters, and the adjusted reference exhaust volume is superimposed with the optimal exhaust volume increment to generate an exhaust volume control command based on the superimposed exhaust volume. The actuator is connected to the feedforward injection module. The actuator is used to receive the air volume control command from the feedforward injection module and adjust the air volume of the gas collection hood according to the air volume control command to adjust the pressure difference between the inside and outside of the gas collection hood.

10. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the differential pressure control method for preventing the introduction of external air into the gas collecting hood of a silicon carbide smelting furnace as described in any one of claims 1 to 8.

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