A nitrogen replacement compressed air supply dynamic scheduling and safety switching control method for a steel sintering process

Abstract

This invention relates to a dynamic scheduling and safe switching control method for nitrogen substitution compressed air supply in steel sintering processes, belonging to the field of energy medium optimization and automation control in the steel industry. It includes: S1 collecting real-time operational data from key gas-consuming points in the sintering area and nitrogen pipeline supply capacity data to establish a gas demand prediction model; S2 the controller dynamically generates a gas supply scheduling strategy based on predicted demand, including peak pre-pressurization, economic decline, and segmented switching logic; S3 executing a three-layer interlocked safety switching control based on pressure, quality, and equipment during the gas supply process; and S4 statistically analyzing and adaptively correcting the operational data through an operational evaluation module. This invention upgrades nitrogen substitution gas supply from static substitution to dynamic scheduling based on operating conditions, effectively solving problems such as powder caking, frequent unblocking, and corrosion of precision instruments caused by water carryover in compressed air during the sintering process. While ensuring safe and stable gas supply, it significantly reduces energy consumption and operation and maintenance costs.

Description

Technical Field

[0001] This invention belongs to the field of energy medium optimization and automation control technology in the iron and steel industry, specifically relating to a dynamic scheduling and safe switching control method for nitrogen-substituted compressed air supply in the iron and steel sintering process. Background Technology

[0002] The sintering process in steel enterprises is a crucial link in the production process, and its normal operation depends on a stable and reliable power medium. Currently, many devices in the sintering area, such as ash conveying systems, pneumatic instruments, and dust removal systems, commonly use compressed air as their power source and air supply.

[0003] However, traditional compressed air systems generally suffer from the following problems in actual operation: (1) Water and scale buildup: Condensate is generated during the compression process of compressed air. Although it is initially dried, the humidity is still high. In the sintering and ash conveying processes, the moisture combines with the powder and easily forms caking on the inner wall of the pipe, leading to pipe blockage. To solve this problem, frequent cleaning operations are necessary. For example, in the prior art, the cleaning cycle was less than once a month, which seriously disrupted the normal production rhythm.

[0004] (2) Insufficient end pressure: The gas consumption points in the sintering area have periodic and peak-valley characteristics, while the compressed air network is often supplied by small, decentralized piston air compressors, which have limited adjustment capabilities. During peak gas consumption periods, insufficient pressure often occurs at remote gas consumption points, affecting equipment efficiency and even leading to process failures.

[0005] (3) Corrosion and damage: Residual moisture and impurities in compressed air have a corrosive effect on precision pneumatic instruments, which significantly shortens the service life of the instruments and increases maintenance costs and equipment failure rate.

[0006] To address the aforementioned issues, existing engineering practices have proposed using medium-pressure nitrogen to partially replace compressed air, as exemplified by Chinese patent CN113919825A. While this patent focuses on work permit management in the power industry, it indirectly reflects the idea of ​​energy medium substitution. This type of engineering modification utilizes the drying, sufficient flow, and stable pressure characteristics of nitrogen to fundamentally solve the problems of water carryover and insufficient pressure in compressed air, and has achieved some success.

[0007] However, most existing alternatives remain at the level of static engineering modifications, that is, supplying gas at a fixed pressure and flow rate after pipeline laying. This approach fails to fully consider the inherent dynamic load characteristics of the sintering process. Because the gas consumption points in sintering experience periodic and intermittent fluctuations, improper switching control may lead to new problems: for example, on the nitrogen supply side, insufficient preparation for peak loads may cause instantaneous pressure drops; in cases where multiple branch lines are put into operation in phases or the pipeline network is complex, an unreasonable valve opening and closing sequence may cause localized pressure surges or incorrect gas supply, affecting production stability. Furthermore, existing solutions lack a comprehensive safety switching and fault fallback mechanism; if nitrogen supply or quality becomes abnormal, a seamless switchback to the original compressed air system is not possible, posing a significant production safety risk.

[0008] Therefore, how to provide a nitrogen-substituted compressed air method that can dynamically schedule according to the load characteristics of the sintering process and has a complete safety interlock and control logic, so as to maximize energy saving and operational efficiency while ensuring gas supply safety and stability, has become an urgent technical problem to be solved in this field. Summary of the Invention

[0009] In view of this, the present invention aims to solve the technical problems of the existing static alternative scheme of using nitrogen to replace compressed air for gas supply in the steel sintering process, namely: it cannot adapt to the periodic and peak-valley dynamic load characteristics of the sintering process, which easily leads to local pressure fluctuations, insufficient gas supply or mis-supply during gas supply switching; at the same time, it lacks a sound safety switching and fault rollback mechanism, and once the nitrogen supply or quality is abnormal, there is a significant risk to production safety and affects the stable and smooth operation of production.

[0010] To address the aforementioned technical problems, this invention provides a method for dynamic scheduling and safe switching control of nitrogen-substituted compressed air supply in steel sintering processes. This method includes the following steps: S1. Data Acquisition and Modeling: Collect real-time operational data of key gas consumption points in the sintering area and supply capacity data of the nitrogen pipeline system, and establish a regional gas demand forecasting model based on the collected historical data.

[0011] S2. Dynamic scheduling strategy generation: The controller dynamically generates a gas supply scheduling strategy based on the predicted demand generated by the gas demand prediction model and the nitrogen pipeline supply capacity data.

[0012] S3. Safety switching interlock control: During the process of nitrogen replacing compressed air supply, safety switching control is implemented based on a three-layer interlock mechanism of pressure interlock, quality interlock, and equipment interlock.

[0013] S4. Operational Evaluation and Adaptive Correction: The system is equipped with an operational evaluation module, which statistically analyzes operational data according to a preset cycle, and adaptively corrects the control parameters in the gas demand prediction model and the dynamic scheduling strategy based on the evaluation results.

[0014] Preferably, the real-time operating data includes gas supply pressure, gas supply flow rate, ash conveying system operating cycle signal, and key valve opening status; the nitrogen pipeline network system supply capacity data includes nitrogen main pipeline pressure, flow rate, and pressure stabilizing device status.

[0015] Preferably, the gas demand prediction model is constructed using an algorithm based on time series analysis or a neural network algorithm. Its inputs include historical gas flow rate, current production plan and sintering machine operating status, and its output is a predicted gas demand curve for a future period of time. It can also identify the peak and valley characteristics of gas consumption points and periodic load changes.

[0016] Preferably, the dynamic gas supply scheduling strategy includes: a peak pre-pressurization strategy, which increases the gas supply pressure or opens the backup bypass in advance before the predicted gas consumption peak arrives; an economic decline strategy, which lowers the gas supply pressure to a preset economic pressure range after the gas consumption peak ends; and a segmented switching logic, which generates and executes a preset valve opening and closing sequence for complex pipeline networks.

[0017] Preferably, the pressure interlock monitors the pressure at the end-point air consumption point in real time. When the pressure value is lower than the first preset threshold, a backoff command is automatically triggered to switch the air supply source back to the original compressed air system or enter the supply guarantee mode. The pressure interlock is also equipped with multiple pressure thresholds. When the pressure drops to different thresholds, different levels of backoff or alarm operations are triggered. The quality interlock monitors key quality indicators such as moisture content and oil content of the nitrogen source in real time. When the indicators are abnormal, the nitrogen supply is automatically cut off and the system switches back to the original compressed air system. When the equipment interlock detects a fault in a critical gas supply device or insufficient gas supply to a critical instrument, it automatically executes a priority protection strategy to ensure the safety of the gas supply to the core equipment.

[0018] Preferably, the operational data statistically analyzed by the operation evaluation module includes the decrease in compressed air consumption, power savings, frequency of ash conveying pipeline unblocking, and failure rate of precision instruments.

[0019] Preferably, the present invention further includes a human-machine interface for real-time display of the gas supply network topology, key operating parameters, scheduling strategy execution status, and historical data statistical reports; the human-machine interface provides selection of automatic mode, semi-automatic mode, and manual mode. In the manual mode, the three-layer interlocking mechanism remains active, and when any of the pressure interlock, quality interlock, or equipment interlock conditions are triggered, the system forcibly intervenes to perform a rollback or priority protection operation.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention establishes a gas demand prediction model for the sintering area and generates a dynamic scheduling scheme that includes strategies such as peak pre-pressurization and economic decline. This realizes the upgrade from static substitution to dynamic scheduling based on working conditions, effectively copes with the periodic load impact of the sintering process, and significantly reduces compressed air consumption and nitrogen consumption while ensuring stable gas supply, thus achieving energy saving and consumption reduction.

[0021] (2) The present invention sets up a three-layer interlocking mechanism of pressure, quality and equipment. When there is abnormal fluctuation in gas supply or gas source quality problem, the system can automatically and safely revert to the original compressed air system or execute the supply guarantee mode, avoiding production accidents caused by interruption or quality deterioration of a single medium gas supply, ensuring the continuous and stable operation of the sintering production line, and effectively protecting precision instruments from corrosion damage.

[0022] (3) This invention continuously tracks and analyzes key indicators such as power saving and failure rate through the operation evaluation module, and adaptively corrects the model and parameters, so that the system can continuously optimize the control strategy as the production conditions change, ensure long-term stable operation and continuously release energy-saving benefits, and provide a replicable advanced control method and management tool for steel enterprises to promote the substitution of energy media.

[0023] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0024] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for dynamic scheduling and safe switching control of nitrogen-substituted compressed air supply in steel sintering processes according to the present invention. Figure 2 This is a schematic diagram illustrating the generation logic of the dynamic scheduling strategy in this invention; Figure 3 This is a schematic diagram illustrating the implementation logic of the three-layer safety interlocking mechanism in this invention. Detailed Implementation

[0025] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0026] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0027] Example 1 like Figure 1 As shown in the figure, this embodiment provides a dynamic scheduling and safe switching control method for nitrogen substitution compressed air supply in the steel sintering process. This method was implemented in the sintering area of ​​a steel plant, aiming to solve problems such as water carryover and insufficient terminal pressure in the original compressed air system, and to achieve optimized control of nitrogen substitution supply.

[0028] I. Data Acquisition and Modeling This embodiment first constructs a data acquisition and modeling module. This module interacts with the industrial control system through a sensor network deployed on-site to achieve real-time acquisition of key parameters.

[0029] Specifically, sensors are installed at key gas consumption points in the target area, including but not limited to: Pressure transmitters are installed at the inlet of the nitrogen main pipe, the inlet of each branch pipe, and at the front end of terminal equipment such as sintering machine igniters, ash conveying system injectors, and pneumatic valve positioners.

[0030] Flow meter: Installed at the main nitrogen outlet and each major branch line to measure the instantaneous and cumulative flow of gas in real time.

[0031] Status signal acquisition device: Acquires operating status signals of key equipment from the DCS system or the equipment's own controller, such as the start-up and shutdown cycle of the ash conveying system, the opening feedback of key valves (0-100%), and the running speed of the sintering machine trolley.

[0032] At the same time, data on the supply capacity of the nitrogen pipeline system are collected, including the pressure and flow rate of the main nitrogen pipeline, as well as the current operating status of the pressure stabilizing device.

[0033] All collected data is transmitted to the central controller in real time and stored and preprocessed according to time series. After the data accumulates to a certain amount, the system uses machine learning algorithms to build a regional gas demand forecasting model.

[0034] In a preferred embodiment, the model is built upon a Long Short-Term Memory (LSTM) network. The model's input feature vector includes: gas flow rate sequences from past N time points, key equipment status sequences, and production plan parameters for the next M time points. The model's output is a predicted gas demand curve for a future period and can automatically identify the periodic peak and trough characteristics of gas consumption points. For example, by analyzing historical data, the model can accurately identify a peak gas consumption pattern in the ash conveying process that occurs every 2 hours, lasts for 30 minutes, and has a peak flow rate of 5000 Nm³ / h.

[0035] As an alternative to or supplement to the aforementioned neural network model, the gas demand forecasting model can also be constructed using time series analysis-based algorithms, such as the Autoregressive Integral Moving Average (ARIMA) model or the Seasonally Differential Autoregressive Moving Average (SARIMA) model. When gas consumption patterns are highly periodic and relatively stable, such linear models have the advantages of low computational cost and strong interpretability. In another implementation, models can be used in combination; for example, the SARIMA model can be used to predict base loads, while a neural network model can be used to predict stochastic shock loads.

[0036] To more accurately capture subtle changes in the process, the collected data can further include environmental parameters such as ambient temperature and humidity. Verification has shown a positive correlation between ambient humidity and the severity of water carryover issues in the compressed air system. While the impact of environmental parameters on gas supply quality is negligible after nitrogen replacement, they can still serve as auxiliary factors for assessing operating conditions.

[0037] II. Generation of Dynamic Scheduling Strategies Based on the output of the aforementioned prediction model and combined with the real-time monitoring of the nitrogen pipeline supply capacity, the controller dynamically generates a gas supply scheduling strategy.

[0038] like Figure 2 As shown, the scheduling strategy in this embodiment includes three core parts: Peak pre-pressurization strategy: When the model predicts that the gas consumption peak will arrive in the near future (e.g., 15-30 minutes), the controller issues an instruction in advance to adjust the actuators of the nitrogen supply system.

[0039] Example: The model predicts that the peak ash conveying period will begin in 30 minutes, with the terminal demand flow rate rapidly increasing from 2000 Nm³ / h to 5000 Nm³ / h. The controller calculates that if the current gas supply pressure of 1.2 MPa is maintained, the pressure drop at the end of the pipeline will exceed 0.1 MPa due to pressure drop along the pipeline and the sudden increase in flow rate, potentially falling below the safety threshold. Therefore, the controller initiates a pre-pressurization procedure 15 minutes in advance: First, it instructs the pressure regulating valve at the nitrogen main pipe to increase the opening, gradually increasing the gas supply pressure from 1.2 MPa to 1.4 MPa; simultaneously, based on the pipeline pressure drop model, it predicts a potential bottleneck in a specific branch and opens the auxiliary gas supply bypass valve on that branch by 10% in advance. This is equivalent to storing additional potential energy in the pipeline to smoothly cope with the upcoming flow surge, ensuring that the terminal pressure remains above 0.7 MPa.

[0040] In another scenario, if the nitrogen main pressure is close to the upper limit, and the predicted load will still exceed the current gas supply capacity, the controller can also execute a pre-pressurization strategy, that is, instruct the backup liquid nitrogen vaporization system to start in advance to supplement the pipeline with gas as a buffer during peak periods.

[0041] Economic pressure reduction strategy: When the peak gas consumption period is predicted to end, or when the actual monitored flow rate begins to decline, the controller implements a pressure reduction strategy. For example, after the peak ash transportation period ends, the flow rate stabilizes and drops to 2000 Nm³ / h. The controller issues a command to slowly reduce the gas supply pressure from 1.4 MPa to the preset economic pressure range of 1.1 MPa. This pressure value is the optimal energy-saving pressure point calibrated through extensive operational data, ensuring normal operation for all off-peak users. By reducing the pressure to the economic range, nitrogen consumption per unit time can be effectively reduced, achieving energy savings.

[0042] Segmented switching logic: For complex pipeline networks with phased commissioning or multiple branches, the system executes a preset valve opening and closing sequence, rather than simply opening or closing valves simultaneously. For example, if three new nitrogen branches (A, B, and C) are connected to the target area, requiring the gradual replacement of the existing compressed air branches, the system pre-stores a switching sequence of opening first, then closing, and step-by-step replacement. Step 1: Instruct the nitrogen branch line A valve to slowly open to 10% opening, and at the same time start monitoring the pressure fluctuation at the end of the branch line.

[0043] Step 2: After the terminal pressure stabilizes, instruct the nitrogen branch line A valve to continue opening to 50%, and finally to 100% full opening.

[0044] Step 3: After confirming that the nitrogen supply line A is stable, instruct the valve of the original compressed air line A to start closing. The closing process is also done in steps (100%→50%→0%), and pressure changes are monitored throughout the process.

[0045] Step 4: Repeat the above steps to replace branches B and C in turn.

[0046] For tree-like pipe networks, the core of the segmented switching logic is from near to far, first ensuring a stable gas supply to the main lines closest to the gas source, and then gradually opening the terminal branches. For ring-like pipe networks, the switching logic focuses more on maintaining the pressure differential balance of the ring network. It usually adopts a strategy of simultaneous opening and gradual closing, that is, while opening the nitrogen ring network valve, simultaneously and slowly closing the compressed air ring network valve, and using a PID controller to adjust the valve opening in real time to maintain the ring network pressure differential within the set range.

[0047] III. Safe switching interlocking control like Figure 3 As shown, during the dynamic scheduling process, the system executes safety switching control based on a three-layer interlocking mechanism in real time to ensure the gas supply safety of the sintering production line under any abnormal circumstances.

[0048] Pressure interlock: This is the most basic and also the most important interlock.

[0049] Implementation: The controller continuously receives pressure sensor signals from key gas consumption points at the end. The system has preset multiple pressure thresholds.

[0050] First threshold (early warning): For example, if the terminal pressure drops to 0.75 MPa, the system will only issue a yellow warning to alert the operator and record the event for subsequent analysis.

[0051] Second threshold (backoff): For example, the terminal pressure drops to 0.7 MPa. The controller determines that the gas supply capacity is severely insufficient. The system automatically triggers a backoff command and performs the following operations: ① Quickly open the shut-off valve of the original compressed air system in the corresponding branch; ②Simultaneously close the shut-off valve of the nitrogen branch; ③ Send a red alarm for pressure retraction to the central control room, displaying the retraction location and reason. The entire process must be completed within milliseconds to achieve seamless switching and avoid impacting the process.

[0052] The pressure interlock also supports tiered rollback. If only the pressure in an individual branch drops, only that branch is switched; if the pressure in the main pipe drops, the entire area is switched. If the pressure still cannot be restored after rollback, the system can further enter a supply backup mode, for example, by instructing the standby air compressor to load urgently, or by activating the liquid nitrogen vaporization system to replenish the nitrogen pipeline.

[0053] Quality Interlock: This interlock is designed for abnormal gas source quality.

[0054] Implementation: Install online gas quality monitoring instruments, such as dew point meters to monitor moisture and oil sensors to monitor oil content, at the nitrogen main pipe and key branch inlets. When the dew point is detected to be higher than a preset value (e.g., -40℃) or the oil content exceeds a threshold, the system determines that the gas source quality is unqualified, which may cause corrosion to precision instruments or lead to process abnormalities. The controller immediately triggers a quality interlock, cutting off the nitrogen supply to that branch and switching back to the original compressed air system.

[0055] To address the risk of detector failure, logical interlocks can also be implemented. For example, if the system detects that a nitrogen valve in a branch is open, but the pressure and flow changes downstream of that branch are significantly different from expectations, and the opening of the upstream pressure regulating valve is abnormal, the system can comprehensively determine that there may be a leak or abnormal gas supply, and can also trigger quality interlocks or pressure interlocks.

[0056] Equipment Interlock: This interlock focuses on the gas supply safety of the critical equipment itself.

[0057] Implementation: The system sets minimum gas flow or pressure thresholds for each critical piece of equipment, such as the sintering machine ignition furnace, main exhaust fan, and important instruments. When the gas flow rate of a branch containing a critical piece of equipment is detected to be lower than the minimum required for its safe operation, the controller determines that the gas supply is insufficient. For example, the combustion control valve of the sintering machine ignition furnace is extremely sensitive to the gas supply pressure. When the flow sensor of that branch reports an excessively low flow rate, the controller determines that it may be due to a valve malfunction in the upstream branch or insufficient pressure in the main pipeline. At this time, the system automatically executes a priority protection strategy: ① Immediately close the gas supply valves of non-critical equipment on that branch to centrally ensure the gas supply to critical equipment; ② Simultaneously issue an audible and visual alarm to prompt operators to manually intervene or check the faulty equipment. Only after the gas supply to critical equipment has stabilized is the gas supply to other equipment gradually restored.

[0058] IV. Operational Evaluation and Adaptive Correction The system has a built-in operation evaluation module that automatically generates operation evaluation reports at preset intervals (such as weekly, monthly, or quarterly) and adaptively adjusts scheduling parameters.

[0059] Evaluation metrics: The report compiles and displays core operational data, including but not limited to: Energy consumption indicators: year-on-year decrease in compressed air consumption, electricity savings, nitrogen consumption and cost.

[0060] Operation and maintenance indicators: frequency of clearing blockages in ash conveying pipelines and failure rate of precision instruments.

[0061] Gas supply indicators: nitrogen supply stability rate, switching success rate.

[0062] Adaptive correction: Based on the above evaluation results, the system uses machine learning algorithms to fine-tune the key parameters in the control model and strategy.

[0063] For example, after one month of operation, the evaluation report showed that although the frequency of unblocking was significantly reduced, nitrogen consumption was slightly higher than expected. System analysis revealed that the economic pressure range setting (1.1 MPa) in the economic downturn strategy might be too high, leaving room for optimization. The system initiated a self-optimization program, attempting to lower the economic pressure range to 1.05 MPa over the next month, while continuously monitoring the impact on end-user gas stability. If the evaluation report after one month shows further improvement in energy saving, while the unblocking frequency and instrument failure rate do not worsen, the system will permanently adopt the new pressure setting. Through this continuous evaluation-adjustment-re-evaluation closed loop, the system can adaptively optimize as production conditions change, maintaining optimal operating conditions in the long term.

[0064] Adaptive adjustments are not only made to scheduling parameters, but also iteratively applied to the prediction model itself. The system can periodically retrain the LSTM model using newly generated operational data, making its predictions of load changes more accurate.

[0065] Through the coordinated operation of the four components described above, this embodiment successfully switched the gas supply medium in the sintering area from compressed air to nitrogen, achieving an upgrade from static replacement to dynamic scheduling based on operating conditions. Simultaneously, a comprehensive safety interlock mechanism was established. After the system stabilized, it significantly improved production efficiency, reduced energy consumption and maintenance costs, and achieved the expected technical results.

[0066] Example 2 This embodiment further expands the safety switching control based on embodiment 1, adding a manual priority mode and intelligent switching suggestion function.

[0067] On the system's user interface (human-computer interaction unit), operators can select "automatic," "semi-automatic," or "manual" modes.

[0068] Automatic mode: The gas supply switching and scheduling are performed automatically by the system based on the prediction model and interlocking logic.

[0069] Semi-automatic mode: The system provides scheduling suggestions based on the predictive model, such as "It is recommended to perform pre-boost in 30 minutes." The system will only execute the corresponding operation after the operator clicks to confirm. If the operator does not confirm within the specified time, the system will execute automatically.

[0070] Manual mode: The system completely deactivates automatic control of the gas supply valves; all switching operations are manually initiated by the operator through the human-machine interface. However, all three-layer safety interlocks remain active. For example, even in manual mode, when the terminal pressure drops to the backoff threshold, the pressure interlock will still forcibly intervene, switching the gas source back to compressed air to ensure production safety.

[0071] In addition, the system has added an intelligent switching suggestion function. When a potential gas supply fluctuation is predicted in the future, or when the assessment module finds room for optimization in the current scheduling strategy, the system will push suggestion information to the operators, such as: "According to the forecast, the gas load will increase significantly from 9:00 to 11:00 this Friday. It is recommended to 'plan nitrogen supply in advance' or 'Based on last month's data, it is recommended to adjust the economic pressure range to 1.05-1.15 MPa, which is expected to save another 3% of energy.'"

[0072] Example 3 This embodiment provides a scheduling and control method applicable to the coordinated supply of multiple users and multiple media (such as nitrogen, compressed air, and steam), which can be considered a further extension of the present invention.

[0073] This method adds the collection of supply capacity and user demand data for other media during the data acquisition phase. In the dynamic scheduling strategy generation phase, the controller not only considers nitrogen substitution for compressed air but also incorporates the energy cost and supply margin of different media. For example, for certain processes that can use either nitrogen or steam, the system compares the current nitrogen and steam costs in real time. If nitrogen is abundant or steam is scarce, nitrogen is prioritized; otherwise, steam is prioritized. This multi-media collaborative scheduling elevates the substitution of a single energy medium to the level of comprehensive energy utilization at the enterprise level, further exploring potential for cost reduction and efficiency improvement.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic scheduling and safe switching control of nitrogen-substituted compressed air supply in steel sintering processes, characterized in that, Includes the following steps: S1. Data Acquisition and Modeling: Collect real-time operational data of key gas consumption points in the sintering area and supply capacity data of the nitrogen pipeline system, and establish a regional gas demand forecasting model based on the collected historical data. S2. Dynamic scheduling strategy generation: The controller dynamically generates a gas supply scheduling strategy based on the predicted demand generated by the gas demand prediction model and the nitrogen pipeline supply capacity data. S3. Safety switching interlock control: During the process of nitrogen replacing compressed air supply, safety switching control based on a three-layer interlock mechanism of pressure interlock, quality interlock and equipment interlock is implemented. S4. Operational Evaluation and Adaptive Correction: The system is equipped with an operational evaluation module, which statistically analyzes operational data according to a preset cycle, and adaptively corrects the control parameters in the gas demand prediction model and the dynamic scheduling strategy based on the evaluation results.

2. The method according to claim 1, characterized in that, In S1, the real-time operating data includes: gas supply pressure, gas supply flow rate, ash conveying system operating cycle signal, and key valve opening status; the nitrogen pipeline network system supply capacity data includes: nitrogen main pipeline pressure, flow rate, and pressure stabilizing device status.

3. The method according to claim 1, characterized in that, In S1, the gas demand prediction model is constructed using an algorithm based on time series analysis or a neural network algorithm. Its inputs include historical gas flow rate, current production plan and sintering machine operating status. Its output is a predicted gas demand curve for a future period of time, and it can identify the peak and valley characteristics of gas consumption points and periodic load changes.

4. The method according to claim 1, characterized in that, In step S2, the dynamic gas supply scheduling strategy includes: Peak pre-pressurization strategy: When the peak gas consumption is predicted to arrive, the nitrogen supply pressure is increased in advance or the backup bypass is opened to reserve energy to cope with the peak impact. The economic slowdown strategy involves reducing gas supply pressure to a preset economic pressure range after the peak gas consumption period ends, in order to reduce energy consumption. The segmented switching logic generates and executes a preset valve opening and closing sequence for complex pipeline networks that are put into operation in phases or have multiple branches, ensuring that the pressure at the end gas consumption point does not fall below the preset safety threshold during the switching process.

5. The method according to claim 1, characterized in that, In S3, the pressure interlock includes: real-time monitoring of the pressure at the end-point air consumption point; when the pressure value is lower than the first preset threshold, an automatic backoff command is triggered to switch the air supply source back to the original compressed air system or enter the supply guarantee mode; the pressure interlock also has multiple pressure thresholds, and when the pressure drops to different thresholds, different levels of backoff or alarm operations are triggered.

6. The method according to claim 1, characterized in that, In S3, the quality interlock includes: real-time detection of key quality indicators of the nitrogen gas source; when the key quality indicators are abnormal, the nitrogen gas supply is automatically cut off and the system is switched back to the original compressed air system; the key quality indicators include moisture content and oil content.

7. The method according to claim 1, characterized in that, In S3, the equipment interlocking includes: when a fault is detected in a critical gas supply device or when the gas supply to the branch where a critical instrument is located is insufficient, an automatic priority protection strategy is executed to ensure the gas supply safety of the core equipment.

8. The method according to claim 1, characterized in that, In S4, the operational data statistically analyzed by the operation evaluation module includes: the decrease in compressed air consumption, power saving, the frequency of ash conveying pipeline unblocking, and the failure rate of precision instruments.

9. The method according to claim 1, characterized in that, It also includes a human-machine interface for real-time display of gas supply network topology, key operating parameters, scheduling strategy execution status, and historical data statistical reports; the human-machine interface provides selection of automatic mode, semi-automatic mode, and manual mode.

10. The method according to claim 9, characterized in that, In the manual mode, the three-layer interlocking mechanism remains active. When any of the pressure interlock, quality interlock, or equipment interlock conditions are triggered, the system will forcibly intervene to perform a rollback or priority protection operation.

Images