Gas ratio control method for stable and standard reaching of SO2 in combustion exhaust smoke of heating furnace

Abstract

The invention discloses a coal gas proportion control method for enabling SO2 in combustion exhaust smoke of a heating furnace to stably reach the standard, and belongs to the field of coal gas proportion control. According to the method, a closed-loop control system integrating real-time monitoring, model prediction and accurate regulation and control is constructed. The method comprises the following steps: firstly, collecting parameters such as coal gas components, flow and flue gas SO2 concentration in real time through a high-precision sensor; then, based on a system identification technology, establishing a dynamic multivariable prediction model among the coal gas ratio, the air volume, the exhaust smoke SO2 concentration and the heat value; then adopting a model predictive control strategy, solving an optimal coal gas proportion instruction meeting SO2 emission constraints through rolling optimization, and dynamically adjusting the LDG proportion; meanwhile, air-fuel ratio optimization suggestions and a system stability protection mechanism are integrated in the method, and when it is monitored that the system is abnormal or the SO2 concentration exceeds the standard, early warning can be conducted quickly, and safe operation of equipment is guaranteed preferentially. According to the invention, accurate and rapid response control on the SO2 concentration of the discharged smoke is realized, and the emission standard reaching stability is obviously improved.

Description

Technical Field

[0001] This invention relates to gas ratio control, and more particularly to a method for controlling the gas ratio to ensure stable SO2 emission from a heating furnace. Background Technology

[0002] The steel industry is my country's most important raw material industry and also the industry with the highest energy consumption. It is a key area for environmental protection. With the advancement of China's modernization, my country has moved from the goal of high-speed development to high-quality development, and put forward the development concept of supporting high-quality development with a high-quality ecological environment. In response to the development requirements, the energy industry has put forward stricter standards for SO2 emissions from end-of-pipe flue gas.

[0003] Currently, sulfur control in industrial heating furnaces can be achieved through source desulfurization of blast furnace gas and coke oven gas, or by changing the gas ratio to control the SO2 concentration in the flue gas emissions. However, while source desulfurization can solve the SO2 emission problem at its source, its installation is limited by cost and plant facilities, and the two main types of desulfurization methods—dry desulfurization and wet desulfurization—each have their shortcomings at different levels. Changing the gas ratio generally involves manual input based on experience or the use of traditional gas ratio control systems, which has the following drawbacks:

[0004] Insufficient precision: Traditional control systems often rely on empirical parameters or simple mathematical models, making it difficult to accurately control the gas ratio. This results in large fluctuations in SO2 emission concentration during combustion, making it difficult to consistently meet national emission standards.

[0005] Response lag: When faced with changes in the quality of raw coal gas or load fluctuations, traditional systems have a long response time to adjust the coal gas ratio, making it difficult to adapt to changes quickly and thus affecting emission stability.

[0006] High energy consumption: Due to insufficient control precision, it is often necessary to increase the excess air coefficient to ensure complete combustion, which not only increases heat loss, but may also lead to an increase in the generation of other pollutants such as NOx. Summary of the Invention

[0007] Purpose of the invention: The purpose of this invention is to provide a method for controlling the gas ratio to ensure stable SO2 emissions during the combustion process of a heating furnace. The method aims to achieve precise control of the gas ratio by introducing intelligent algorithms and a real-time monitoring system, thereby ensuring that SO2 emissions during the combustion process of the heating furnace are stable and meet the standards.

[0008] Technical solution: A method for controlling the proportion of gas used to ensure stable SO2 emissions from a heating furnace, specifically including:

[0009] Real-time monitoring system: High-precision sensors are installed at key locations such as the gas inlet and flue gas outlet of the heating furnace to monitor parameters such as gas composition, flow rate, temperature, and SO2 concentration in the flue gas in real time. At the same time, these parameters will be used as model data input to determine the current system status and calculate decision quantities.

[0010] Intelligent Algorithm Model: Based on big data and machine learning technologies, a dynamic prediction model is established between the gas ratio and SO2 emission concentration. This model comprehensively considers various influencing factors such as raw gas quality, furnace load, flow rate, and ambient temperature. It adopts an MPC control strategy, establishes corresponding constraint optimization functions, and calculates and provides current instructions based on the current system state and the convergence of future output to the target value in each control cycle, continuously optimizing the gas ratio.

[0011] Precision control system: Based on the prediction and optimization results of the intelligent algorithm model, the system judges the state of the regulating valve through parameters such as gas flow rate, and adjusts the gas ratio according to the valve state to avoid the lag between the controller output and the actuator execution.

[0012] Early warning and protection mechanism: When the model detects that the SO2 concentration in the flue gas is about to exceed the set value, the system will automatically activate the early warning and start taking emergency measures, such as adjusting the gas ratio. At the same time, the system stability status will be monitored in real time. If an abnormal system status is detected, the model will alarm and issue instructions to no longer prioritize sulfur control and instead ensure system stability.

[0013] Optimization suggestion model: The model analyzes the relationship between air-fuel ratio and flue gas SO2 concentration, and provides a suitable air-fuel ratio suggestion value by judging the current state of the heating furnace.

[0014] Beneficial effects:

[0015] (1) Rapid response capability: The system has powerful data processing and analysis capabilities, and can quickly respond to changes in the quality of raw gas or fluctuations in the load of the heating furnace, adjust the gas ratio in a timely manner, and maintain emission stability.

[0016] (2) Improve control accuracy: Through real-time monitoring system and intelligent algorithm model, the precise control of coal gas ratio is realized, significantly reducing the fluctuation range of SO2 emission concentration and ensuring stable compliance.

[0017] (3) Optimization suggestion model: Based on the operating status of the heating furnace, provide appropriate air-fuel ratio suggestion values ​​as a supplement.

[0018] (4) Protection and early warning measures: Monitor the system status at all times, and quickly adjust the instructions when unstable factors occur to ensure system stability.

[0019] (5) Improved operating efficiency: Intelligent control reduces the need for manual intervention, improves the operating efficiency and stability of the heating furnace, and reduces the risk of production stoppage due to excessive emissions.

[0020] (6) Energy conservation and emission reduction: By optimizing the gas ratio, reducing the excess air coefficient, reducing heat loss, and reducing the generation of other pollutants such as NOx, the goal of energy conservation and emission reduction can be achieved. Attached Figure Description

[0021] Figure 1 This is a flowchart of correlation analysis;

[0022] Figure 2 This is a block diagram of SO2 emission control technology for heating furnaces based on MPC;

[0023] Figure 3 It is a control strategy flowchart;

[0024] Figure 4 This is a schematic diagram showing the recommended air-fuel ratio for heating furnaces;

[0025] Figure 5 This is a diagram showing the control effect of the controller output and the SO2 content in the flue gas from the heating furnace. Detailed Implementation

[0026] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] Example

[0028] Correlation analysis of SO2 content in flue gas from heating furnaces and gas ratio: By analyzing the relationship between the gas ratio of each heating furnace and the SO2 content in flue gas, the data were processed and the correlation coefficient was calculated to provide support for subsequent control of SO2 content in flue gas. The technical framework is attached. Figure 1 As shown.

[0029] Design of a Multi-Objective Controller for Heating Furnace Calorific Value and Flue Gas SO2 Content Based on MPC: Based on the correlation analysis between gas ratio and flue gas SO2, MPC is used to achieve multi-objective optimization control of calorific value and SO2 in the hottest region of the heating furnace, reducing SO2 emissions and stabilizing the calorific value of the mixed gas. The future values ​​of the controlled variables are predicted using the identified model and the current measured values ​​(including metric and soft-sensor values) of the relevant controlled variables (calorific value, O2) and control variables (gas ratio, etc.). Based on the predicted values, an objective function and corresponding constraints are established, an optimization model is constructed, and the required gas mixing ratio is obtained by solving the problem. The technical block diagram is attached. Figure 2 As shown.

[0030] A multivariate model of calorific value, SO2 content, and the proportions of BFG, COG, and LDG gas is fundamental for predictive control. Therefore, in this study, the proportions of BFG, COG, and LDG gas and the air volume are selected as control variables (MV) in the thick-plate mixing and pressurization station system of the gas station. Calorific value and SO2 content are the controlled variables (CV), and the user's mixed gas consumption is the disturbance variable (DV). Based on the gas proportions, air volume, and real-time estimated calorific value and SO2 data of the converter gas reaching the thick-plate side, the user constructs a multivariate mathematical model of the three gas proportions, air volume, calorific value, and SO2 using system identification technology. The model structure is as follows:

[0031] (1)

[0032] (2)

[0033] (3)

[0034] (4)

[0035] (5)

[0036] (6)

[0037] (7)

[0038] (8)

[0039] (9)

[0040] In the formula, G represents the identification model gain matrix between gas ratio, air volume and calorific value, and SO2, H represents the disturbance model vector, θ is the system model parameter matrix, and ψ is the system variable data vector.

[0041] The least squares method is used to predict the parameters of the high-order unbiased model. :

[0042] (10)

[0043] The high-order model obtained initially was reduced in order using asymptotic theory, thereby obtaining a model with good control performance for calorific value, SO2 content, BFG, COG, LDG gas ratios, and air volume identification. :

[0044] (11)

[0045] (12)

[0046] Based on the identified mathematical model, an optimization model is constructed and solved by setting an objective function and SO2 concentration constraints, and rolling optimization is achieved.

[0047] (14)

[0048] (15)

[0049] (16)

[0050] (17)

[0051] In the formula These represent the calorific value and the current SO2 value, respectively. This represents the ideal closed-loop response trajectory for calorific value and SO2. This indicates that an upper limit has been set for SO2 emissions. Let P represent the steady-state values ​​of the control and controlled variables in steady-state optimization, M be the prediction step size, M be the control step size, Q be the weight diagonal matrix of the controlled variables, R be the weight diagonal matrix of the control variables, and S be the weight diagonal matrix of the control variable increments. Indicating the controlled variables calorific value and SO2 at closed-loop steady state, , and , These represent the upper and lower limit vectors of the control variable constraints and the upper and lower limit vectors of the control variable increment constraints, respectively.

[0052] Control Strategy Flow Description: First, the model starts. When switching to automatic control mode, a smooth transition is required to prevent the switch from impacting the system or causing instability in the existing system, ensuring stable system integration. After integration, the model monitors the SO2 content in the flue gas. When the SO2 content exceeds the limit, the LDG (Low-Density Gas) distribution ratio is calculated based on the established optimization model. This ratio is then adjusted by considering the number of furnaces exceeding the limit, LDG flow rate, valve opening, and other factors. When the model identifies or predicts that the SO2 content in the flue gas (either condition is met) is decreasing and stabilizing, a relative deviation function between the current SO2 content and the SO2 boundary value is established based on the set SO2 boundary and LDG callback reference value. The model then gradually reverts the LDG ratio from the current setpoint back to the reference value based on the error function. set_min The callback formula is as follows:

[0053] (18)

[0054] Where f(e) is the relative deviation function between the current SO2 content in the heating furnace and the set lower limit boundary value of SO2, and its value range is [0,1]. The larger the deviation e at the current moment, the larger f(e) is, and the faster the LDG callback speed is, eventually returning to the LDG. set_min Similarly, the value of f(e) takes into account factors such as flow rate and valve opening.

[0055] Throughout the entire LDG ratio calculation and distribution process, the model constantly monitors the system status, prioritizing system stability. If the model detects any instability factors affecting the system, it will immediately exit the sulfur control task calculation logic, and the issued commands will focus on system stability. By adjusting the LDG ratio, the SO2 value of the heater flue gas is controlled within the target range. The control flowchart is attached. Figure 3 As shown.

[0056] Monitoring and optimization of air-fuel ratio operation in heating furnaces: Based on historical data of each heating furnace and SO2 content in flue gas, a boundary curve of the corresponding relationship between the two is fitted, as shown in the attached figure. Figure 4 As shown, the minimum air-fuel ratio that ensures the SO2 emissions do not exceed the given maximum SO2 value is determined based on this boundary curve, and this value is output as a recommended air-fuel ratio.

[0057] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for controlling the proportion of gas used to ensure stable SO2 emissions from a heating furnace, characterized in that, Includes the following steps: S1. Real-time monitoring of parameters at the gas inlet and flue gas outlet of the heating furnace, including gas composition, flow rate, temperature, and SO2 concentration in the flue gas; S2. Based on monitoring parameters, a dynamic prediction model between coal gas ratio and SO2 emission concentration is established through system identification technology. S3. A model predictive control (MPC) strategy is adopted to construct an optimization function and to continuously optimize the gas ratio based on the current system state. S4. Based on the optimization results, adjust the gas ratio to control the SO2 emission concentration to remain within the acceptable range.

2. The method for controlling the SO2 emission ratio of a heating furnace to ensure stable combustion in a compliant gas mixture, as described in claim 1, is characterized in that... The dynamic prediction model is a multivariate mathematical model, and its model structure includes: The proportions and air volume of blast furnace gas (BFG), coke oven gas (COG), and converter gas (LDG) are used as control variables; Calorific value and SO2 content were used as the controlled variables; The user's mixed gas consumption is used as the disturbance variable; The model expression is: Where G is the gain matrix and H is the perturbation model vector.

3. The method for controlling the SO2 emission ratio of a heating furnace to ensure stable combustion in a compliant gas mixture, as described in claim 1, is characterized in that... The parameters of the dynamic prediction model are estimated using the least squares method, and the higher-order model is reduced in order using asymptotic theory to obtain a simplified model with effective control.

4. The method for controlling the SO2 emission ratio of a heating furnace to ensure stable combustion in a compliant gas ratio, as described in claim 1, is characterized in that... The optimization function of the MPC strategy is: Where Y is the current value of the controlled variable, Y ref For the ideal trajectory, U is the control variable, P is the prediction step size, M is the control step size, and Q, R, and S are weight matrices. The optimization process must meet the constraints, including the upper limit of SO2 concentration and the upper and lower limits of the control variables and their increments.

5. The method for controlling the SO2 emission ratio of a heating furnace to ensure stable combustion in a compliant gas mixture, as described in claim 1, is characterized in that... It also includes an early warning mechanism: when the SO2 concentration in the flue gas is about to exceed the set value, the system will automatically activate the early warning and prioritize adjusting the gas ratio for emergency control.

6. The method for controlling the SO2 emission ratio of a heating furnace to ensure stable combustion in a compliant gas mixture, as described in claim 1, is characterized in that... It also includes a system stability protection mechanism: when an abnormal system state is detected, the control command prioritizes system stability and suspends the optimization calculation with sulfur control as the main task.

7. The method for controlling the proportion of gas used to ensure stable SO2 emissions from a heating furnace as described in claim 1, characterized in that, The adjustment of the gas ratio includes dynamic adjustment and reversal of the LDG gas ratio; the reversal formula is: Where f(e) is a function based on the relative deviation between SO2 content and boundary value, with a value range of [0,1]. The larger the deviation, the faster the callback speed.

8. The method for controlling the proportion of gas used to ensure stable SO2 emissions from a heating furnace according to claim 1, characterized in that, It also includes an air-fuel ratio optimization step: based on historical data, fit the relationship curve between flue gas SO2 concentration and air-fuel ratio to determine the minimum recommended air-fuel ratio value to ensure that SO2 emissions do not exceed the maximum value.

9. The method for controlling the proportion of gas used to ensure stable SO2 emissions from a heating furnace according to claim 1, characterized in that, The real-time monitoring parameters include measurement values ​​and soft measurement values ​​obtained through high-precision sensors, which are used for model input and state judgment.

10. The method for controlling the proportion of gas used to ensure stable SO2 emissions from a heating furnace according to claim 1, characterized in that, The method employs a non-disruptive switching mechanism during control mode switching to prevent system impact and ensure stable access.

Images