A dynamic control method for recovering CO concentration of converter gas

Abstract

The application discloses a kind of converter gas start-stop recovery CO concentration dynamic regulation method, belong to the field of energy optimization of steel enterprise.This method includes the following steps: step S101, construct prediction model and gas regulation model;Step S102, call data, use prediction model to predict gas generation amount, gas consumption in future blowing period;Step S103, according to blowing plan, determine blowing furnace and call corresponding CO characteristic curve for analysis;Step S104, according to gas regulation model, calculate the gas tank position difference ΔQy in certain tank full furnace;Step S105, gas tank position difference ΔQy is combined to obtain constraint condition with tank capacity;Step S106, use nonlinear programming optimization algorithm to contact CO characteristic curve to recover gas calorific value as optimization goal, combined with constraint condition to obtain the best recovery start-stop CO concentration time point.

Description

Technical Field

[0001] This invention belongs to the field of energy optimization in steel enterprises, and more specifically, relates to the technology for dynamic control of CO concentration during the start and stop recovery of converter gas. Background Technology

[0002] Converter gas (LDG) is the main energy product of the converter process and an important secondary energy source within steel enterprises. The recovery of converter gas directly affects the energy consumption level of the process and the overall energy dispatch balance. Therefore, improving the quality of converter gas recovery can not only effectively reduce the production cost of the steelmaking process and lay the foundation for achieving "negative energy" steelmaking, but also greatly reduce the total amount of pollutants emitted by steel plants, achieving clean production. Currently, the converter gas recovery and utilization effect of most steel plants in China is not ideal. During the smelting process, there is a situation where the amount of converter gas generated is large but the user cannot consume it sufficiently. Limited gas holder space leads to top-burst venting, especially in the steelmaking process where multiple furnace runs overlap (two or three furnace runs). The converter gas recovery method is rigid, resulting in gas being collected only when there is capacity in the gas holder and released when there is no capacity, causing the release of high-calorific-value converter gas.

[0003] Currently, most steel companies in my country rely on on-site dispatchers to regulate converter gas, depending entirely on workers' experience to determine control strategies. Analysis of the current status of converter gas regulation in steel companies reveals the following specific problems:

[0004] 1) Human experience control: In most steel enterprises, converter gas control is mainly carried out by dispatchers based on their experience and familiarity with the system. However, personal experience is difficult to meet the needs of constantly changing production systems, and there is a long time lag in the manual control process, which cannot make the system operate in the best working state.

[0005] 2) The gas pipeline network system has a large number of devices, a complex structure, and a huge amount of information. It is difficult for dispatchers to summarize and analyze a large amount of data, find the relationship between cabinet height and the amount of gas recovered and consumed, and make accurate balance control decisions. Furthermore, when the control decisions of the dispatchers differ from those of the on-site allocation personnel, coordination between the two parties is required, which increases the workload of the dispatchers and allocation personnel.

[0006] To address the aforementioned issues, a search revealed Chinese patent CN107523663A, which discloses a control method for converter gas recovery. The method involves a converter gas recovery system that controls the recovery and release of gas via a control system. When the converter smelting time is ≤60 seconds, the gas is released. When the converter smelting time is 60–240 seconds, gas is recovered when the CO concentration rises to >6%, and released when the CO concentration decreases to <4%. When the converter smelting time is 240–550 seconds, gas is recovered when the CO concentration rises to >9%, and released when the CO concentration decreases to <7%. When the converter smelting time is ≥550 seconds, gas is recovered when the CO concentration rises to >12%, and released when the CO concentration decreases to <10%.

[0007] For example, Chinese patent CN102978331A discloses a control method for improving the recovery of gas from a new OG converter. The steps are as follows: 1) Feed back the pressure signal of the differential pressure at the furnace mouth, the position signal of the annular seam scrubbing tower, and the speed of the dust removal fan to the PLC; 2) The gas analyzer detects the content of CO and CO in the gas. If the content does not meet the recovery requirements, the gas will be ignited and released in the venting tower.

[0008] 3) Use self-optimizing mode switching control to adjust the furnace mouth differential pressure; 4) When the gas analyzer detects that the gas meets the collection requirements, start recovering the gas.

[0009] For example, Chinese patent CN115128955A discloses a method for increasing the recovery of converter gas. The gas holder used has the following settings: low-level CO recovery concentration set at 28%; medium-level CO recovery concentration set at 30%; and high-level CO recovery concentration set at 34%. When the gas holder is at the low level, the CO recovery concentration is set relatively low to quickly replenish the holder and increase the recovery amount. When the gas holder is at the medium level, the CO recovery concentration is set to a stable state to stabilize the holder level. When the gas holder is at the high level, the CO recovery concentration is set to a pure gas concentration to neutralize the gas holder concentration.

[0010] Chinese patent CN113947331A discloses a multi-scenario gas optimization and allocation system for the steel industry. This patent describes an application server and database server connected to a switch via network cables. The switch, through a firewall, connects to routers on each client PC, enabling communication between the client and server. The client PCs deploy a basic configuration module and a multi-scenario gas allocation module. It combines human experience, allocation rules, and a gas optimization and allocation model, continuously optimizing the best match between maintenance plans and gas balance plans through result evaluation and iterative adjustments. However, this patent only optimizes gas allocation and does not study the improvement of recovered gas quality. Furthermore, the allocation module obtained through the database lacks adjustments for abnormal situations, making the results highly susceptible to error.

[0011] For example, Chinese patent CN113537541A discloses an optimized control system for converter gas in steel enterprises. This method uses a model simulation module to establish a mathematical model of the equipment and perform simulations. The simulation results are then used to provide an optimization model and solution. The optimization model and solution module establishes optimized mathematical models for equipment such as the start / stop and speed models of the compressor, valve opening and closing models, pressure models, generator energy consumption models, and pipeline models. After verification by the simulation module, the optimized results verified by the optimization model and solution module are used to provide an equipment operation plan. However, this patent's optimized control is not integrated with the gas distribution, which may result in unreasonable gas allocation. Some gas may be released due to full gas tanks, leading to increased gas release rates and energy waste. Summary of the Invention

[0012] 1. The problem to be solved

[0013] To address the problem of high high-calorific-value gas emission rate caused by the imbalance between converter gas generation and user consumption, this invention provides a dynamic control method for CO concentration during converter gas recovery. This method can optimize the control and recovery of converter gas in steel enterprises, reduce the emission rate of high-calorific-value gas, improve the quality of gas recovered from gas holders, and increase energy utilization.

[0014] 2. Technical Solution

[0015] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0016] The method for dynamic control of CO concentration during converter gas recovery of the present invention includes the following steps:

[0017] Step S101: Construct a prediction model and a gas control model;

[0018] Step S102: Retrieve data and use the prediction model to predict the amount of gas generated and consumed in future blowing cycles;

[0019] Step S103: Based on the blowing plan, determine the blowing furnace number and call up the CO characteristic curve of the corresponding furnace number for analysis;

[0020] Step S104: Calculate the gas holder position difference ΔQy in a certain full-furnace cycle based on the gas control model;

[0021] Step S105: Combine the gas holder position difference ΔQy with the holder capacity to obtain the constraint conditions;

[0022] Step S106: Using a nonlinear programming optimization algorithm to connect the CO characteristic curve with the calorific value of the recovered coal gas as the optimization objective, and combining the constraints, the optimal CO concentration start and end points for recovery are obtained through regulation.

[0023] In one possible embodiment of the present invention, in step S101, the prediction model is a mechanism model and a SARIMA prediction model.

[0024] In one possible embodiment of the present invention, in step S102, the prediction process of gas generation and gas consumption in the prediction model is as follows: based on historical data of converter oxygen flow and gas holder outlet flow, a mechanism model is established through regression algorithm to predict gas generation, and a prediction model is trained through deep learning to predict gas consumption; the prediction results are combined with real-time tank position data to obtain the change in tank height in the future blowing cycle and to determine whether to adjust. The judgment rule is: once the tank is full, adjustment is carried out.

[0025] In one possible embodiment of the present invention, the furnace cycle data is imported into the control model. A nonlinear programming optimization algorithm is employed, with the calorific value of the recovered gas as the optimization objective. The model predicts the amount of gas entering the condenser and combines this prediction with the condenser height at the start of blowing to constrain the recovery time. Then, by relating this to the CO characteristic curve, a control model is established to determine the optimal start and end points of CO concentration for recovery, providing a basis for online control. This achieves the goal of reducing the release of high-calorific-value gas and improving the quality of gas recovery.

[0026] In one possible embodiment of the present invention, in step S104, the control model is a single-furnace blowing control model: using a nonlinear programming optimization algorithm, with the calorific value of the recovered gas as the optimization objective, the amount of gas entering the cabinet is predicted by the prediction model and combined with the cabinet height at the start of blowing to constrain the recovery time, and then, in conjunction with the CO characteristic curve, a control model is established to determine the optimal recovery start and end CO concentration time points.

[0027] In one possible embodiment of the present invention, in step S104, the control model is a multi-furnace overlapping blowing control model: using a nonlinear programming optimization algorithm, with the calorific value of the recovered gas as the optimization objective, the amount of gas entering the cabinet is predicted by the prediction model and combined with the height of the starting blowing cabinet position to constrain the recovery time. Then, by referring to the CO characteristic curves corresponding to the overlapping furnaces, a control model is established to determine the optimal recovery start and end CO concentration time points for each overlapping furnace.

[0028] In one possible embodiment of the present invention, the process of adjusting the gas holder level difference ΔQy in step S104 is as follows:

[0029] ΔQy=H 回收后柜位高度 -H 柜位极限高度

[0030] ①ΔQy>0, that is, the difference in gas tank position during the blowing process is greater than 0, and the amount of gas entering the tank causes the tank to be full. High calorific value gas is recovered within the tank recovery limit.

[0031] ② ΔQy≤0, meaning the gas holder level difference during the blowing process is less than 0, the gas holder does not vent, and no control is required.

[0032] 3. Beneficial effects

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] (1) The present invention provides a dynamic control method for the CO concentration of converter gas recovery at the start and end of the process. It combines the equipment production plan and the gas prediction model, and optimizes the calculation through the gas control model to finally obtain the gas recovery control scheme. This method can solve the energy utilization problems such as low quality of converter gas and high calorific value gas release rate, and solves the control problem of insufficient human experience.

[0035] (2) The present invention combines the amount of gas entering the cabinet with the real-time height of the cabinet according to the equipment, and uses nonlinear programming based on the calorific value to regulate the start and end concentration of the recovery through the CO characteristic curve, thereby realizing the regulation of the CO concentration at the start and end of the converter gas recovery, improving the calorific value of the recovered converter gas, reducing the high calorific value gas emission rate, and improving the energy utilization rate.

[0036] (3) The gas control model module of the present invention receives the prediction data from the gas prediction model module. After analyzing and judging the data, it calls different CO characteristic curves according to the furnace number for control, thereby improving the accuracy of the scheme.

[0037] (4) In the process of the converter gas recovery and control method of the present invention, abnormal situations can be predicted and detected in advance, and prediction results and control and treatment plans can be made in a timely, fast and accurate manner, so as to minimize the impact of abnormal failures; provide reliable theoretical basis for on-site control personnel, avoid control errors caused by lag, discontinuity, incompleteness and unscientific nature in the control process, greatly improve the working efficiency of various energy equipment in steel enterprises, reduce the gas emission rate, improve the quality of gas recovery, and ensure that steel enterprises' production is economically reasonable and energy-saving and environmentally friendly. Attached Figure Description

[0038] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that these drawings are designed for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, unless specifically indicated, these drawings are intended only to conceptually illustrate the structural construction described herein and are not necessarily drawn to scale.

[0039] Figure 1 This is a diagram showing the overall structure of the converter gas prediction and control system of the present invention.

[0040] Figure 2 This is a schematic diagram of the gas pipeline network for the converter gas prediction and control system of the present invention;

[0041] Figure 3 This is a flowchart of the method for dynamic control of CO concentration during converter gas recovery in this invention.

[0042] Figure 4 This invention provides a single-furnace cycle prediction and control result for the dynamic control method of CO concentration recovery during converter gas start-up and shutdown.

[0043] Figure 5 This invention relates to the dynamic control method for CO concentration in converter gas recovery during start-up and shutdown, and provides prediction and control results for two different furnace cycles.

[0044] Figure 6 This invention relates to a method for dynamically controlling the CO concentration during the start and end of converter gas recovery, and provides prediction and control results for three furnace cycles. Detailed Implementation

[0045] The following detailed description of exemplary embodiments of the invention is taken with reference to the accompanying drawings, which form part of the description and illustrate exemplary embodiments in which the invention may be practiced. While these exemplary embodiments have been described in sufficient detail to enable those skilled in the art to practice the invention, it should be understood that other embodiments may be implemented and various changes may be made to the invention without departing from the spirit and scope thereof. The more detailed description of embodiments of the invention below is not intended to limit the scope of the claimed invention, but is merely illustrative and not restrictive of the description of the features and characteristics of the invention, to suggest the best mode for carrying out the invention, and is sufficient to enable those skilled in the art to practice the invention. Therefore, the scope of the invention is defined only by the appended claims.

[0046] It should be noted that currently, there is no reasonable predictive model for converter gas in the future, nor is there a balanced control scheme between the generation and consumption of converter gas and the height of the gas holder. This results in the lack of timeliness, continuity, comprehensiveness, and scientific rigor in the control of converter gas by steel enterprises. Therefore, a converter gas control scheme should maximize the recovery of high-calorific-value gas while ensuring that the converter gas holder does not vent, thereby improving the high-calorific-value gas recovery rate. This is of great significance for the development of the steel industry, reducing energy consumption, lowering costs, and improving the environment.

[0047] like Figure 1 As shown, the converter gas start-up and stop CO concentration dynamic control method of the present invention includes control of gas generation, gas consumption, and start-up and stop CO concentration control. It can predict gas generation and gas consumption, and formulate corresponding control schemes according to constraints. The above prediction model predicts the gas generation and gas consumption during the future blowing period through historical oxygen flow and gas holder outlet flow. The control method is as follows: based on the prediction results and real-time tank position data, the change of tank position height during the future blowing cycle is obtained and it is determined whether to control.

[0048] The control model imports the data of the furnaces that need to be controlled into the control model, and adopts a nonlinear programming optimization algorithm with the calorific value of the recovered gas as the optimization objective. By predicting the amount of gas entering the cabinet and combining it with the height of the cabinet at the start of blowing, the recovery time is constrained. Then, by referring to the CO characteristic curves of each overlapping furnace, the control model is established to determine the optimal start and end CO concentration time points for recovery of each overlapping furnace. This can provide on-site staff with a theoretical basis for balanced control and reasonable control of the gas.

[0049] like Figure 3 As shown, the control model is divided into single-furnace control and multi-furnace overlapping control:

[0050] 1) The single-furnace control strategy employs a nonlinear programming optimization algorithm, with the calorific value of the recovered gas as the optimization objective. A predictive model is used to forecast the amount of gas entering the furnace and the furnace height at the start of blowing, constraining the recovery time. Then, by relating this to the CO characteristic curve, a control model is established to determine the optimal start and end points for CO concentration recovery. The single-furnace control cycle is the single-furnace blowing time.

[0051] 2) The multi-furnace overlapping control strategy employs a nonlinear programming optimization algorithm, with the calorific value of the recovered gas as the optimization objective. A predictive model is used to forecast the amount of gas entering the furnace and the starting height of the blowing furnace, constraining the recovery time. Furthermore, by considering the CO characteristic curves of each overlapping furnace, a control model is established to determine the optimal start and end CO concentration times for each overlapping furnace. Compared to single-furnace gas production, multi-furnace overlapping control requires overall control to ensure that the calorific value of the recovered gas in each furnace is high and similar. The multi-furnace control cycle is the overall blowing time of the overlapping furnaces, and the number of control cycles is equal to the number of overlapping furnaces. The first control cycle applies to the entire overlapping furnace. Subsequent cycles undergo secondary optimization control before the start of each blowing cycle, with the same control logic as the first control cycle.

[0052] Example 1

[0053] Taking a steel plant's converter gas pipeline network as an example, the generating equipment includes three converters: #1, #2, and #3. The consuming equipment is the user, the venting equipment is the converter gas venting tower, and the buffer equipment includes a recovery limit of 69,000 m³. 3 The No. 1 converter gas holder. During the control process, the prediction model module, based on historical and real-time data input from the database server, uses a pre-established prediction model to predict the gas generation and consumption within the blowing cycle according to the blowing plan. It then combines this with real-time holder position and capacity to determine if the holder is full. If full, the data from the prediction model module is imported into the control model module for further control. Three sets of data on furnace cycles resulting in full holders are taken from the gas control model module, corresponding to three control methods: single-furnace control, two-furnace overlapping control, and three-furnace overlapping control.

[0054] To verify the accuracy of the control model results and analyze them, historical data from steel production were collected for verification. Software was used to display the results before and after control. The left graph shows the CO concentration curve, and the right graph shows the tank height curve. The blue line represents the CO concentration and tank height curves without control, and the red line represents the CO concentration and tank height curves after control. The control results are displayed in two ways: single-furnace prediction and control results and analysis, and multi-furnace overlapping prediction and control results and analysis.

[0055] like Figure 2 As shown, taking a steel plant as an example, the various equipment in the aforementioned gas pipeline network are categorized into gas generating equipment, gas venting equipment, gas consuming equipment, and gas buffering equipment. Taking the converter gas pipeline network as an example, the aforementioned gas generating equipment includes converters #1, #2, #3, #4, #5, and #6; the aforementioned gas venting equipment includes venting towers; the aforementioned gas consuming equipment refers to users who consume converter gas; and the aforementioned gas buffering equipment includes gas holders #1, #2, #3, and #4.

[0056] like Figure 3 As shown, the generation and consumption of various equipment in the above-mentioned gas generating equipment and gas buffer equipment are predicted using a predictive model.

[0057] The amount of coal gas generated is predicted based on the oxygen flow rate. In actual production, the oxygen flow rate is basically constant during each furnace blowing process and can be considered as a fixed value, unaffected by time. Therefore, the oxygen flow rate consumed per furnace is determined by averaging. During the model establishment process, historical data was studied and it was found that the oxygen flow rate and the coal gas flow rate have a linear relationship, and the ratio coefficient δ1 of oxygen flow rate to coal gas flow rate was obtained.

[0058] The gas consumption is predicted based on the SARIMA model. In actual production, the gas outlet flow rate is relatively stable and has a certain periodicity. Therefore, the gas consumption per furnace is q. 消耗 The SARIMA model is used for prediction. The SARIMA modeling approach involves filtering and analyzing a large amount of historical export flow data to obtain the patterns of data change, and then training the SARIMA model to establish a SARIMA prediction model.

[0059] like Figure 3 As shown, the gas prediction model predicts the amount of gas entering the gas tank during the future blowing cycle. Combined with the actual tank height, the gas tank height difference ΔQy is obtained. Based on the control principle, it is determined whether the furnace needs to be controlled.

[0060] To predict the CO concentration variation curve during the blowing process, this paper establishes a characteristic model of CO concentration in flue gas. Analysis of historical data reveals a certain regularity in the CO concentration variation curve during the blowing process, with an effective data length of 15 minutes. Based on the variation characteristics of the mean curve, mathematical processing software is used to perform piecewise fitting of the mean curve, yielding a characteristic model and mathematical expression for the instantaneous CO concentration.

[0061] Through data preprocessing, the CO change curves of different blowing cycles were superimposed, and the curves were filtered by deviation. CO concentration data of 110 heats of each of converters 1, 2, and 3 were used. After data correction, the rejection rate was less than 10%. The curves were fitted and segmented to obtain the CO concentration fitting curves of converters 1, 2, and 3. The fitting curves of CO concentration of converters 1, 2, and 3 are displayed using piecewise functions. The fitting curve functions of CO concentration of converters 1, 2, and 3 are shown in Table 1.

[0062] Table 1. Fitting curves of CO concentration in converters #1, #2, and #3

[0063]

[0064] The model building process is divided into predictive model building and regulatory model building.

[0065] The prediction model for converter gas generation is as follows:

[0066]

[0067] The prediction model for converter gas consumption is as follows:

[0068]

[0069] Based on the above prediction model for gas generation and consumption, and combined with the real-time tank height H, the prediction model for converter gas tank level changes is as follows:

[0070]

[0071] in: —Oxygen flow rate, km 3 / h;

[0072] q 消耗 —User data consumption, km 3 / h;

[0073] δ1—Oxygen flow rate to coal gas flow rate ratio coefficient;

[0074] t2, t1 — End and start times of gas recovery, in minutes;

[0075] H 柜位高度 —Cabinet height at the end, in meters3 ;

[0076] After processing and predicting a large amount of data, the final predicted data is compared and analyzed with the actual on-site operating data. The prediction accuracy can reach over 95%, and the predicted data has reliable guiding significance for gas regulation.

[0077] The aforementioned gas prediction model predicts the amount of gas entering the gas tank during future blowing cycles. This prediction is then combined with the actual tank height to obtain the gas tank level difference ΔQy, and a decision is made regarding whether to adjust the gas supply. The decision-making principles are as follows:

[0078] ΔQy=H 回收后柜位高度 -H 柜位极限高度

[0079] ① ΔQy>0, meaning that the increase in the height of the gas holder during the blowing process is greater than the gas holder recovery limit, the amount of gas entering the holder causes the holder to be full, and the control principle is to recover high-calorific-value gas within the holder limit.

[0080] ②ΔQy≤0, meaning the increase in gas holder height during the blowing process is less than the recovery limit, no gas is released from the gas holder, and no control scheme needs to be determined.

[0081] The control model in this embodiment is divided into a single-furnace control model and a multi-furnace overlapping control model.

[0082] 1) Single-furnace control:

[0083] The single-furnace control strategy employs a nonlinear programming optimization algorithm, with the calorific value of the recovered gas as the optimization objective. It combines the predicted gas flow rate into the condenser with the condenser height at the start of blowing to constrain the recovery time. Then, by relating this to the CO characteristic curve, a control model is established to determine the optimal start and end points for CO concentration recovery. The single-furnace control cycle is the single-furnace blowing time.

[0084] Object(objective function)

[0085]

[0086] St (Constraints)

[0087]

[0088] in —Oxygen flow rate, km 3 / h;

[0089] q 消耗 —User data consumption, km 3 / h;

[0090] t2, t1 — End and start times of gas recovery, in minutes;

[0091] t2', t1' — the times when gas recovery ends and begins after regulation, in minutes;

[0092] Y(x) — CO characteristic curve for the corresponding furnace batch, %;

[0093] H max —The maximum amount of gas a gas holder can hold, in meters. 3 ;

[0094] 2) Multi-furnace overlapping control:

[0095] The multi-furnace overlapping control strategy employs a nonlinear programming optimization algorithm, with the recovered gas calorific value as the optimization objective. It uses a predictive model to combine the predicted gas flow rate into the furnace with the furnace height at the start of the blowing process, constraining the recovery time. Furthermore, by considering the CO characteristic curves of each overlapping furnace, a control model is established to determine the optimal start and end CO concentration times for each overlapping furnace. Compared to single-furnace gas production, multi-furnace overlapping control requires overall control to ensure that the recovered gas calorific value is high and similar across furnaces. The multi-furnace control cycle is the overall blowing time of the overlapping furnaces, and the number of control cycles is equal to the number of overlapping furnaces. The first control cycle applies to the entire overlapping furnace group, and subsequent cycles undergo secondary optimization control before each furnace begins blowing, with the control logic identical to the first control cycle.

[0096] Taking the overlapping gas production of two furnaces as an example:

[0097] Object(object function).

[0098]

[0099] St (Constraints)

[0100]

[0101] in: —Oxygen flow rate, km 3 / h;

[0102] q 消耗 —User data consumption, km 3 / h;

[0103] t2, t1 — End and start times of gas recovery, in minutes;

[0104] t2', t1' — the time, in minutes, when the No. 1 converter finishes and starts gas recovery after regulation;

[0105] t2”, t1” – the time, in minutes, when the No. 2 converter finishes and starts gas recovery after regulation;

[0106] Y1(x) — CO characteristic curve for the corresponding furnace batch, %;

[0107] Y2(x) — CO characteristic curve for the corresponding furnace batch, %;

[0108] H max —The maximum amount of gas a gas holder can hold, in meters. 3 .

[0109] like Figure 4 As shown, 2-3 minutes after the start of single-furnace blowing, oxygen begins to react with carbon, and the CO concentration curve gradually rises. Without control, CO recovery begins when the concentration reaches 25%. Five minutes into blowing, the reaction of other metals ends, and a large amount of carbon reacts with oxygen, causing the CO concentration to reach its peak and remain stable for 7-8 minutes. This represents the period of highest quality recovered gas. However, without control, due to poor coordination between the gas holder level and the start and end recovery concentrations, gas holder recovery stops after 10 minutes of blowing, at which point the CO concentration is 55%, resulting in the release of a large amount of high-calorific-value gas.

[0110] After adjustment, the control model, combined with the prediction results of the forecasting model and the actual gas holder position, set the gas recovery interval to 10 minutes. Recovery began when the CO concentration rose to 40% after the start of blowing. After another 2 minutes, the CO concentration rose to a stable level, and the gas holder continued to recover. At the 12-minute mark of blowing, due to the decrease in carbon content, the CO concentration gradually decreased, stopping recovery when it reached 38%. At this point, the gas holder height just reached its capacity limit of 69,000 m³. 3 .

[0111] Analysis of the single-furnace blowing control results revealed that before control, a full-tank venting occurred during the blowing process, at which point the CO characteristic curve showed a high CO concentration, and subsequent gas recovery was impossible, leading to the venting of high-calorific-value gas. Control not only improved the gas recovery quality but also reduced the gas venting rate. The calorific value of the converter gas entering the tank decreased from 1522 Kcal / m³. 3 Increase by 1582 kcal / m 3 The calorific value of the recovered gas increased by 4%. Analysis of the tank fullness curve revealed that before the adjustment, the mismatch between the gas tank level and the start and end recovery concentrations led to premature tank fullness and cessation of recovery, resulting in a low tank height at the end of the blowing process. After the adjustment, the tank height increased at the end of recovery, meeting the subsequent gas usage requirements.

[0112] Overlapping furnace runs are categorized into two types: two-furnace gas production and three-furnace gas production. Compared to single-furnace gas production, overall control needs to be considered to ensure that the calorific value of the recovered gas in each furnace is high and similar. Due to the long blowing cycle, an optimization control model will also be added.

[0113] like Figure 5 , Figure 6As shown, during the overlapping blowing process, other furnaces begin blowing some time after the first furnace starts. The CO concentration reaches its highest and most stable stage at different time periods, and all overlapping furnaces are connected to the same gas holder. Without regulation, the first furnace in the overlapping process starts recovery when the CO concentration is 35%. Eight minutes after the first furnace starts, the second furnace starts recovery when the CO concentration is 35%. Due to poor coordination between the gas holder position and the start and end recovery concentrations, the gas blowing process ends after 10 minutes. At this time, the CO concentration of the first furnace is 50%, the CO concentration of the second furnace is 55%, and the third furnace has no recovery at all, resulting in the release of a large amount of high-calorific-value gas.

[0114] After adjustment, the control model, combined with the prediction results of the forecast model, is linked to the actual gas holder position. Recovery begins when the CO concentration in the first furnace reaches 47% after the first furnace blowdown starts. During the first furnace blowdown, the second furnace blowdown begins, and recovery starts when the CO concentration in the second furnace reaches 49%. Recovery stops when the CO concentration in converter #1 drops to 45% after 12 minutes of blowdown. Recovery stops when the gas holder is full after 14 minutes of blowdown, and continues until the gas holder height reaches the safe recovery height of 65,000 m. 3 Secondary recycling begins when the CO concentration rises to 47% after 25 minutes of blowing. Recycling ends when the CO concentration in the second batch drops to 47%. Subsequent blowing continues until the CO concentration in the third batch drops to 45% after 38 minutes.

[0115] Analysis of the control results for multiple overlapping blowing processes revealed that, before control, multiple instances of full-tank venting occurred during the blowing process, and the overlapping gas production from multiple furnaces caused conflicts. At this point, the CO concentration curve was at a high CO concentration level, and subsequent gas recovery was not possible, leading to the venting of high-calorific-value gas from later furnaces. After control, a balanced control was implemented between each furnace. While ensuring the recovery limit of the tank, the CO concentration at the start and end of recovery for each furnace was increased, thereby improving the calorific value of the converter gas entering the tank for each furnace.

[0116] Taking a two-furnace overlapping blowing process as an example, calculations show that the calorific value of the converter gas entering the first furnace in the overlapping furnace process is 1569 kcal / m³. 3 Increased to 1669 kcal / m 3 The calorific value of the recovered gas increased by 6.3%. In the overlapping furnace operation, the calorific value of the converter gas entering the second furnace was increased from 1735 kcal / m³. 3 Increased to 1866 kcal / m 3 The calorific value of the recovered coal gas increased by 7.5%.

[0117] Analysis of the tank level change curve reveals that before regulation, poor coordination between the gas tank level and the start and end recovery concentrations led to premature tank fullness and cessation of recovery, resulting in a low tank level at the end of the blowing cycle. After regulation, the tank level increased at the end of recovery. Furthermore, due to the unique characteristics of overlapping gas production, the tank level recovery varied significantly, necessitating secondary recovery regulation. After the regulation ended recovery, if the tank level dropped to the secondary recovery limit before the blowing cycle was complete, secondary recovery would be initiated, increasing the tank level at the end of the cycle, reducing the converter gas venting rate, and meeting subsequent gas usage needs.

[0118] The method for dynamic control of CO concentration during converter gas recovery of the present invention can be used to improve the situation of high-calorific-value gas release caused by the mismatch between gas production and user consumption during the converter gas recovery process in steel enterprises. The optimal control scheme is obtained through data retrieval, prediction and regulation, and the equipment can be operated stably through real-time on-site monitoring, resulting in significant overall benefits.

Claims

1. A method for dynamic control of CO concentration during converter gas recovery, characterized in that, Includes the following steps: Step S101: Construct a prediction model and a gas control model; Step S102: Retrieve data and use the prediction model to predict the amount of gas generated and consumed in future blowing cycles; In step S102, the prediction process for the amount of gas generated and the amount of gas consumed in the prediction model is as follows: Based on the historical data of converter oxygen flow and gas holder outlet flow, a mechanism model is established through regression algorithm to predict the amount of gas generated, and a prediction model is trained through deep learning to predict the amount of gas consumed; the prediction results are combined with real-time tank position data to obtain the change of tank position height in the future blowing cycle and to determine whether to adjust. The judgment rule is: once the tank is full, adjustment is carried out. Step S103: Based on the blowing plan, determine the blowing furnace number and call up the CO characteristic curve of the corresponding furnace number for analysis; The furnace data for regulation is imported into the regulation model. The optimization algorithm of nonlinear programming is adopted, with the calorific value of the recovered gas as the optimization target. The gas flow rate into the cabinet is predicted by the prediction model and combined with the cabinet height at the start of blowing to constrain the recovery time. Then, by referring to the CO characteristic curve, the regulation model is established to determine the optimal time points for the start and end of recovery CO concentration, which provides a basis for online regulation. This aims to reduce the release of high-calorific-value gas and improve the quality of gas recovery. Step S104: Calculate the gas holder position difference ΔQy in a certain full-furnace cycle based on the gas control model; In step S104, the adjustment process for the gas holder level difference ΔQy is as follows: ΔQy=H 回收后柜位高度 -H 柜位极限高度 ①ΔQy>0, that is, the difference in gas tank position during the blowing process is greater than 0, and the amount of gas entering the tank causes the tank to be full. High calorific value gas is recovered within the tank recovery limit. ②ΔQy≤0, meaning the gas holder level difference during the blowing process is less than 0, the gas holder does not vent, and no control is required; Step S105: Combine the gas holder position difference ΔQy with the holder capacity to obtain the constraint conditions; Step S106: Using a nonlinear programming optimization algorithm to connect the CO characteristic curve with the calorific value of the recovered coal gas as the optimization objective, and combining the constraints, the optimal CO concentration start and end points for recovery are obtained through regulation.

2. The method for dynamic control of CO concentration during converter gas recovery according to claim 1, characterized in that, In step S101, the prediction model is a combination of the mechanistic model and the SARIMA prediction model.

3. The method for dynamic control of CO concentration during converter gas recovery according to claim 1, characterized in that, In step S104, the control model is a single-furnace blowing control model: a nonlinear programming optimization algorithm is adopted, with the calorific value of the recovered gas as the optimization objective. The amount of gas entering the cabinet is predicted by the prediction model and combined with the cabinet height at the start of blowing to constrain the recovery time. Then, by referring to the CO characteristic curve, the control model is established to determine the optimal time points for the start and end of CO concentration recovery.

4. The method for dynamic control of CO concentration during converter gas recovery according to claim 1, characterized in that, In step S104, the control model is a multi-furnace overlapping blowing control model: a nonlinear programming optimization algorithm is adopted, with the calorific value of the recovered gas as the optimization objective. The gas flow rate into the cabinet is predicted by the prediction model and combined with the height of the starting blowing cabinet position to constrain the recovery time. Then, by referring to the CO characteristic curves corresponding to the overlapping furnaces, the control model is established to determine the optimal CO concentration start and end time points for each overlapping furnace.

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