A blast furnace gas network balancing system and gas control method through power generation regulation

Abstract

The present invention discloses a blast furnace gas network balancing system and a gas control method through power generation regulation, constructs a pressure-related model, including a pressure change model from production difference to pressure, a single power plant model, and a multi-power plant model; calls the pressure-related model in real time, predicts future pressure changes, and dynamically determines the optimal regulation strategy, and finally outputs a power generation suggestion based on the gas regulation amount for each power plant, and each power plant executes the suggestion to stably control the network pressure within the upper and lower limits of the pressure warning value. It can accurately predict future pressure changes, improve the real-time and response speed of regulation, has strong adaptability, flexible regulation strategy, reduces energy waste, improves system efficiency, and enhances system stability and safety.

Description

Technical Field

[0001] The invention belongs to the technical field of blast furnace gas pipeline network balance control, and in particular relates to a blast furnace gas pipeline network balance system and a control method through power generation regulation. Background Art

[0002] In a blast furnace ironmaking system without a gas holder, the unstable use of gas generated by blast furnace ironmaking and by various production units will cause large changes in pipeline pressure, leading to economic losses.

[0003] The existing technology lacks feasible means to predict the amount of gas generated, consider the lag problem of the impact of gas generation and consumption on pipeline pressure, and make detailed power generation adjustments.

[0004] For example, many methods directly predict the amount of blast furnace gas generated, but it is difficult to make an accurate estimate of the amount due to many influencing factors. Past methods failed to take into account the lag effect of gas generation and consumption on pipeline pressure. Past methods rarely describe detailed power generation adjustment methods, especially for complex systems with multiple power plants, which are rarely studied and cannot be applied.

[0005] At the same time, in the existing technology, the regulation between multiple power plants often lacks coordination, which may lead to problems such as over-regulation of a single power plant or untimely response of multiple power plants, resulting in unbalanced system regulation. Summary of the invention

[0006] The purpose of this application is to provide a blast furnace gas pipeline balancing system and a gas control method through power generation regulation, so as to accurately predict future pressure changes, improve the real-time and response speed of regulation, have strong adaptability, flexible regulation strategy, reduce energy waste, improve system efficiency, and enhance system stability and safety.

[0007] Furthermore, the present invention realizes coordinated regulation of multiple power plants, avoiding the problem of over-regulation of one power plant or untimely response of multiple power plants.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A blast furnace gas network balancing system regulated by power generation, characterized by comprising:

[0010] Data acquisition module, used to collect blast furnace gas generation, consumption and continuous pressure data;

[0011] Pressure-related models include pressure change models, single power plant models, and multi-power plant models; among them: the pressure change model is configured to obtain the total gas production and use difference by subtracting the consumption from the blast furnace gas generation, and the total gas production and use difference is used as the independent variable, and the pressure difference change corresponding to the lag time is used as the dependent variable, and a regression model from production and use difference to pressure is established as the pressure change model; the single power plant model is configured to calculate the total gas production and use difference in the pipeline network of a single power plant, and predict the pressure change of the target measuring point; the multi-power plant model is configured to traverse and mark all power plants from near to far, and predict the pressure change of the target measuring point of the gas pipeline network respectively, so as to determine the adjustment order and specific pressure adjustment amount of each power plant;

[0012] The real-time regulation module is used to dynamically adjust the gas consumption of the power generation unit according to the pressure-related model output.

[0013] In the above technical solution, the pressure change model is set to calculate the total gas production and use difference and the lag time based on the acquired gas volume, consumption, pressure data and time coordinates to predict the pressure change of the target measuring point at the future set time.

[0014] In the above technical solution, in the pressure change model, based on the past setting time of a single power plant n The continuous pressure data of blast furnace gas in the system is obtained by subtracting the production gas consumption per unit time and the power generation gas consumption per unit time from the blast furnace gas generation per unit time to obtain the total gas production and consumption difference per unit time.

[0015] In the above technical solution, in the pressure change model, the upper limit of the hysteresis time threshold is set u , and divide the continuous pressure data into u The length is NU +1 pressure subset, the front NU The correlation coefficients of the total production-use difference data and all pressure subsets are calculated; the pressure difference change is obtained by subtracting the pressure subset with the largest correlation coefficient from the first pressure subset; the total production-use difference of gas per unit time is taken as the independent variable, and the pressure difference change is taken as the dependent variable, and a pressure change model with a regression model structure is established.

[0016] In the above technical solution, the pressure change model can be one of linear regression, polynomial regression, elastic network regression, random forest regression, etc.

[0017] In the above technical solution, the single power plant model is set to obtain the maximum pressure adjustment range and the maximum adjustment speed respectively according to the adjustment range and adjustment speed of a single power plant through the pressure change model, so as to predict the maximum impact speed and pressure adjustment range of the power plant on the pipeline pressure change in the future.

[0018] In the above technical solution, the multi-power plant model is used to coordinate the gas consumption of multiple power plants to achieve a dynamic balance of the pipeline network pressure; it is set to traverse the marked m power plants in sequence from near to far, set the upper and lower limits of the pressure warning value respectively, obtain the real-time total production and use difference, set the expansion coefficient, calculate the corrected production and use difference, and calculate the pipeline network pressure after a set time in the future. If the pipeline network pressure after the set time in the future is within the upper and lower limits of the pressure warning value, it will not be adjusted; if it is less than the lower limit of the pressure warning value, it will be increased; if it is greater than the lower limit of the pressure warning value, it will be decreased; the total adjustment amount is determined according to the pipeline network pressure after the set time in the future, and the adjustment order and specific gas adjustment amount of each power plant are determined.

[0019] The above technical solution also includes a safety monitoring module for monitoring the system stability and safety during the adjustment process to ensure timely response when the pressure changes suddenly.

[0020] The above technical solution also includes a user interface module for displaying real-time data, prediction results and adjustment strategies to operators, and receiving manual adjustment instructions from operators.

[0021] Therefore, the present invention realizes real-time monitoring, prediction, regulation and safety monitoring of the blast furnace gas pipeline network pressure through an integrated modular design, thereby improving the stability and safety of the system, reducing energy waste, and improving overall efficiency.

[0022] The present invention also provides a method for controlling balanced gas in a blast furnace gas network through power generation regulation, which is characterized by comprising the following steps:

[0023] Construct pressure-related models, including pressure variation models, single power plant models, and multi-power plant models;

[0024] The pressure change model is set up based on the obtained gas volume, consumption, pressure data and time coordinates. The total gas production and use difference is calculated by subtracting the consumption from the blast furnace gas generation, and the lag time is calculated. The total gas production and use difference is used as the independent variable, and the pressure difference change corresponding to the lag time is used as the dependent variable. A regression model from production and use difference to pressure is established as the pressure change model.

[0025] The single power plant model is set to obtain the maximum impact speed of the power plant regulation on the pipeline network pressure change based on the pressure change model according to the regulation range and gas regulation speed of the power plant's maximum gas consumption, and determine the impact of the power plant on the pipeline network pressure based on the future set time;

[0026] The multi-power plant model is set to sort and mark each power plant according to the position of the target point of the pressure measurement, set the upper and lower limits of the pipeline network pressure warning value respectively, obtain the real-time total production and use difference, set the expansion coefficient, and calculate the corrected production and use difference; input the corrected production and use difference into the pressure change model to obtain the pipeline network pressure after the future set time, and judge the adjustment direction of the generator set based on the pipeline network pressure at the upper and lower limits of the pressure warning value after the future set time; traverse all power plants in order from near to far to determine the sequence number of the power plant that needs to be adjusted and the total adjustment amount;

[0027] The pressure-related model is called in real time to predict future pressure changes and dynamically determine the optimal regulation strategy. The final output is to form power generation recommendations for each power plant based on the gas regulation amount. Each power plant implements the recommendations to stabilize the pipeline network pressure within the upper and lower limits of the pressure warning value.

[0028] In the above technical solution, the multi-power plant model dynamically determines the optimal regulation strategy according to the distance between each power plant and the target point and their respective regulation capabilities.

[0029] In the above technical scheme, if the pipeline pressure after the set time in the future is within the upper and lower limits of the pressure warning value, the adjustment direction of the generator set will not be adjusted; if it is less than the lower limit of the pressure warning value, the adjustment direction of the generator set will be increased; if it is greater than the lower limit of the pressure warning value, the adjustment direction of the generator set will be decreased.

[0030] In the above technical solution, the multi-power plant model determines the order number of the power plant to be adjusted and the total adjustment amount according to the following steps:

[0031] Input all power plants from near to far into the single power plant model, and obtain all the regulation parameters of each power plant: the speed of the influence of the single power plant regulation on the pipeline network pressure, the maximum amount of pipeline network pressure increase that can be set in the future, and the maximum amount of pipeline network pressure decrease that can be set in the future;

[0032] Determine the power plants that need to be adjusted according to their serial numbers from near to far;

[0033] According to the power plant serial number, retrieve the maximum impact speed of the power plant or a generator in the power plant on the change of pipeline pressure or the maximum gas regulation amount per minute; traverse all the power plant serial numbers that need to be adjusted so that the pipeline pressure can be adjusted as quickly as possible while the number of power plants adjusted is minimized; obtain the generator set regulation direction and the maximum gas regulation amount per minute of all power plants and / or all generators, and combine the regulation direction and the maximum gas regulation amount per minute of each generator set into a power generation recommendation.

[0034] In the above technical solution, when the pressure-related model is called in real time, it is set to call the multi-power plant model once every minute.

[0035] In the above technical solution, the future setting time unit is a number of minutes.

[0036] The present invention may also provide a readable storage medium having a computer program stored thereon, which, when executed, is used to implement the above-mentioned blast furnace gas network balanced gas control method through power generation regulation.

[0037] The present invention relates to a blast furnace gas network balancing system regulated by power generation, which aims to solve the problem of large changes in pipeline network pressure in blast furnace ironmaking systems without gas cabinets. The system accurately predicts future pressure changes by constructing a quantitative relationship model from the difference between production and use to pressure, and considering the hysteresis of the impact of gas generation and consumption on pipeline network pressure. Through the multi-power plant coordination model, the system can dynamically determine the optimal regulation strategy to improve the overall regulation efficiency and stability of the system. In addition, the system calls the model in real time every minute, responds and adjusts quickly, has strong adaptability, and flexible regulation strategies, reduces energy waste, improves system efficiency, and enhances system stability and safety.

[0038] The beneficial effects are explained from the following aspects:

[0039] 1. Accurately predict future pressure changes:

[0040] By constructing a pressure-related model (Model 1), the present invention can accurately predict future pressure changes based on historical gas volume, consumption and pressure data, which enables the system to take measures in advance to avoid sudden pressure fluctuations.

[0041] Compared with traditional technologies that rely on experience or simple real-time data adjustments, which make it difficult to accurately predict future pressure changes and may cause regulation lags, the present invention can reduce economic losses and safety hazards caused by inaccurate pressure predictions.

[0042] Compared with the prior art, the present invention can more accurately predict future pressure changes, improve the overall regulation efficiency and stability of the system, reduce energy waste, improve system efficiency, and enhance system stability and safety.

[0043] 2. Coordination and regulation of power plants:

[0044] The present invention uses a multi-power plant coordination model (Model 3) to dynamically determine the optimal regulation strategy based on the distance between each power plant and the target point and their respective regulation capabilities.

[0045] Through the collaborative work of multiple models, the impact of sudden pressure changes on the system can be effectively avoided, and the stability and safety of system operation can be significantly improved.

[0046] Compared with the prior art in which there is a lack of coordination in the regulation between multiple power plants, which may lead to problems such as over-regulation of a single power plant or untimely response of multiple power plants, the present invention greatly improves the overall regulation efficiency and stability of the system.

[0047] 3. Real-time adjustment and response speed:

[0048] The present invention can call the model in real time every minute, dynamically adjust the gas consumption of the power generation unit, and achieve rapid response and regulation.

[0049] Compared with the traditional method which has the problems of long response time and adjustment lag, and cannot make effective adjustments quickly when the pressure fluctuates, the present invention ensures that the pipeline network pressure can be restored to stability in a relatively short time.

[0050] 4. Strong adaptability and flexible adjustment strategies:

[0051] The present invention can flexibly respond to different pipe network pressure conditions through adaptive regression models (such as linear regression, polynomial regression, elastic network regression, random forest regression, etc.) and dynamically adjust the regulation strategy according to current data.

[0052] In contrast, the regulation strategies in traditional technologies are often fixed and cannot be flexibly adjusted according to actual conditions, and the regulation effect is limited. The present invention can more effectively cope with various pressure changes.

[0053] 5. Reduce energy waste and improve system efficiency:

[0054] By optimizing the adjustment process, the present invention can reduce unnecessary gas consumption, reduce energy waste, and improve the overall energy utilization efficiency of the system while maintaining pressure stability.

[0055] Compared with traditional regulation technologies that may cause excessive energy consumption and reduced system efficiency due to delayed response or inaccurate regulation, the present invention can achieve more efficient energy utilization.

[0056] 6. Enhance system stability and security:

[0057] The present invention effectively avoids the impact of sudden pressure changes on the system through the collaborative work of multiple models, and significantly improves the stability and safety of system operation.

[0058] In the prior art, due to the lack of advance prediction and coordinated regulation of pressure changes, the system is easily affected by sudden pressure changes, and there are certain safety hazards. The present invention significantly reduces this risk through advance prediction and coordinated regulation.

[0059] The above beneficial effects are achieved based on the technical solution and working principle of the present invention. Through accurate data models and algorithms, precise control and regulation of the blast furnace gas pipeline pressure are achieved, thereby improving the efficiency and safety of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 It is the overall flow chart of the method of the present invention.

[0062] Figure 2 The model workflow of the gas volume change in the pipeline network and the pressure change at the target measuring point of the present invention is shown. It shows how to predict future pressure changes through the model of production and consumption difference to pressure.

[0063] Figure 3 This is the model workflow of the pressure regulation of a single power plant according to the method of the present invention, showing how a single power plant regulates the pipe network pressure according to the gas regulation range and speed.

[0064] Figure 4 This is the workflow of the multi-power plant regulation model of the present invention, showing how multiple power plants work together to dynamically determine the optimal regulation strategy.

[0065] Figure 5 This is a diagram of the actual pressure fluctuations in Example 2.

[0066] Figure 6 This is a diagram of the actual pressure fluctuations in Example 3.

[0067] Figure 7 This is a diagram of the actual pressure fluctuations in Example 4. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0069] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0070] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0071] In the description of this application, it should also be noted that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0072] In the present application, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0073] The features and performance of the present application are further described in detail below in conjunction with the embodiments.

[0074] Example 1

[0075] The present invention aims to adjust the power generation unit by anchoring the target point pressure so as to achieve the purpose of stabilizing the blast furnace gas network pressure. Figure 1-4 As shown, the following steps are included:

[0076] 1. Constructing a pressure-related model

[0077] 1.1 Constructing a model for changes in gas volume in the pipeline network and pressure changes at the target measuring point ;

[0078] (1) Get the past The amount of blast furnace gas generated per minute is recorded as , the blast furnace gas consumption per minute is recorded as , the blast furnace gas consumption per power generation unit per minute, recorded as , the target point pressure data per minute, recorded as , the time coordinate corresponding to each data is recorded as ;

[0079] (2) Since the gas pipeline network is relatively long, it takes a certain amount of time for changes in gas generation and consumption to be transmitted through the pipeline network to the target pressure measurement point and have an impact. The blast furnace gas pipeline network is relatively complex, and it is necessary to find the most appropriate impact lag time from a data perspective.

[0080] 1) Calculate the difference between total gas production and use per minute ;

[0081] ;

[0082] The subscript Indicates Based on the data obtained in step (1), the total production-use difference can be obtained. .

[0083] 2) Set the upper limit of the time lag threshold , and divide the continuous pressure data into The length is A subset of ,in .

[0084] 3) Take the front Total production and use difference data , and The correlation coefficient is calculated for all subsets in , and the calculation formula of the correlation coefficient is:

[0085]

[0086] Can get , the subscript of the maximum value is the lag time, and this lag time is recorded as .

[0087] (3) and Make a difference, get ;

[0088] (4) As an independent variable, As the dependent variable, a regression model is established. The model can be linear regression, polynomial regression, elastic network regression, random forest regression, etc., denoted as The function of this model is to obtain the future total output-use difference data based on the current total output-use difference data. Potential change in pressure after minutes .

[0089] 2. Constructing a regulatory correlation model

[0090] 2.1 Model of single power plant regulation pressure ;

[0091] (1) Obtain the maximum gas regulation range of the power plant. The regulation range is , that is, if the gas consumption of the power plant is , then its gas consumption can be adjusted to Any value in .

[0092] (2) Obtain the gas regulation speed of the power plant, denoted as This speed is the amount of gas that can be used by the power plant per minute. The maximum adjustment speed is , which is usually a fixed value.

[0093] (3) Based on The speed of its regulation on the pipe network pressure can be obtained as , which means that the power plant can change the gas network pressure every minute. , which can change the gas network pressure by up to .

[0094] (4) Based on The influence of the pressure on the pipe network can be obtained as The maximum pressure drop of the pipeline network is , the maximum allowable network pressure rise is: .make ,if ,So ,otherwise ;if ,So ,otherwise .

[0095] future The maximum pressure rise in the pipeline network can be within minutes. , the maximum pressure drop in the pipeline network .

[0096] Enter the power plant name The output is the relevant parameters of the power plant's impact on the pipeline network pressure at the current moment, including .

[0097] 2.2 Multi-power plant regulation model ;

[0098] (1) Obtain the names of all adjustable power plants and sort them according to their distance from the target point for measuring pressure, from near to far, recorded as ,in The closest to the target point. The farthest distance from the target point.

[0099] (2) Set the upper and lower limits of the pipeline network pressure warning value, and record the upper limit as , the lower limit is .

[0100] (3) Obtain real-time total production and use difference data , set the expansion coefficient , calculate the corrected production-use difference Since there are certain errors in the readings of the measuring meters, it is necessary to make certain corrections to the production and use difference readings here. At the same time, in order to prevent problems such as untimely adjustments of the power plant, some redundancy needs to be added.

[0101] (4) enter get The possible change in network pressure after minutes is , take the target measuring point pressure at that time , can be obtained The pressure after 1 minute is ,if , no adjustment is made, if or Then go to step (5) to determine whether the generator set adjustment direction is "upward" or "downward", recorded as ,if , To "raise", if , To “lower”.

[0102] (5) Determine the power plant number and total regulation amount that needs to be regulated. After 15 minutes, control the gas network pressure between the upper and lower limits set in step (2). Note that the pressure to be adjusted is ,like ,but ,like ,but .

[0103] 1) Calculate all power plant regulation parameters

[0104] Will Enter in sequence All adjustment parameters are obtained, denoted as ),…,( ).

[0105] 2) Determine the power plants that need to be adjusted from near to far. The pseudo code for the calculation method is as follows:

[0106] initialization ,

[0107] Initialize an empty list ;

[0108] For from arrive Each number of

[0109] If you need to increase the pressure

[0110] make ;

[0111] Otherwise, reduce the pressure if necessary.

[0112] make ;

[0113] Will Add to middle

[0114] if ;

[0115] Continue the cycle;

[0116] otherwise

[0117] End the loop;

[0118] return ;

[0119] Included in it are the serial numbers of all power plants that need to be adjusted.

[0120] 3) According to The power plant number in the adjustment data is taken from 2.1 (2) , representing The maximum gas regulation amount per minute of each generator is composed of a dictionary corresponding to the key value , where the power plant number is the key, The adjustment result obtained by this method can achieve the fastest adjustment of the pipeline network pressure while adjusting the minimum number of power plants. From (4), the direction in which the power plant needs to be adjusted is obtained . Output adjustment direction and specific adjustments .

[0121] 3. Use the model

[0122] Called every minute ,according to The output result will be and The combination is a power generation suggestion. The pseudo code of the power generation suggestion is as follows:

[0123] enter and ;

[0124] Initialize an empty string ;

[0125] for Each key-value pair in and ;

[0126] Will" The number of power plants required per minute The amount of gas" added to ;

[0127] if Not the last key,

[0128] Add ", " to ;

[0129] End the loop;

[0130] return ;

[0131] The final power generation recommendation is shown in Figure 1. Each power plant can implement the recommendation to stably control the pipeline network pressure within the upper and lower limits of the threshold.

[0132] Example 2

[0133] The following case uses real pressure data from a steel plant to demonstrate the effectiveness of the method and system of the present invention.

[0134] At 17:04:00 on July 24, 2024, the discharge tower measurement point showed that the discharge began. After checking the data, it was found that the pipeline pressure began to break through the threshold of 12kpa at 16:53, and then rose rapidly. It reached a pressure of 19kpa at around 17:05 and began to drop rapidly. It returned to normal pressure at around 17:21 and continued to drop. The lowest pressure of the third ironmaking discharge tower was only 5.28kpa, and then rose again at 17:38, breaking through the 12kpa threshold and continued to rise. It reached a pressure of 14.61kpa at 17:44 before slowly dropping. Figure 5 , which is the actual pressure fluctuation.

[0135] Part of the data records in the power generation suggestion database in Example 2 are shown in Table 1, including the production and utilization difference of different target measurement points and the corresponding power generation suggestions.

[0136] Table 1 Partial data records in the power generation suggestion database in Example 2

[0137]

[0138] Power generation suggestion using the system and method of this embodiment: the system is based on It was analyzed that the pressure might continue to rise, so a suggestion was given to increase the amount of gas used for power generation, and the suggestion was continued until 17:09 when the pressure returned to normal. Subsequently, when the pressure was too low at 17:13, a suggestion was also given to reduce the amount of gas used for power generation, and relevant suggestions for increasing the load were given before the pressure increased again at 17:36.

[0139] Example 3

[0140] like Figure 6 At 23:04:00 on August 2, 2024, the discharge tower measuring point showed that it began to release. After checking the data, it was found that the pressure began to break through the threshold of 12kpa at 22:52, and then continued to rise to a high point of 16.16kpa at 23:05. It then fluctuated and maintained a high level, and only dropped to the normal pressure range at 23:45.

[0141] Table 2 is a partial data record in the power generation suggestion database of this embodiment, including the production and utilization difference of different target measurement points and the corresponding power generation suggestions.

[0142] Table 2 Partial data records in the power generation suggestion database in Example 3

[0143]

[0144] The power generation suggestion using the method and system of this embodiment is as follows: a suggestion to increase the load is given at 22:55, and warnings are continued thereafter until the suggestion is stopped at 23:44.

[0145] Example 4

[0146] like Figure 7 As shown, the release tower measuring point showed that the release began at 02:57:00 on 2024-08-03, and the release lasted until 4:43. The data showed that the pressure was high from 2:00 to 5:58, except from 3:30 to 4:00, and the maximum pressure could reach 18.2kpa. Among them, the release pressure fluctuation of the third ironmaking was particularly violent.

[0147] Table 3 is a partial data record in the power generation suggestion database of this embodiment, including the production-consumption difference and the corresponding power generation suggestion.

[0148] Table 3 Partial data records in the power generation suggestion database in Example 4

[0149]

[0150] The power generation suggestion using the method and system of this embodiment is as follows: the power generation suggestion of increasing the load is given starting at 1:59, the pressure is normal during part of the time from 3:30 to 4:00 so no suggestion is given, and the suggestion of increasing the load continues to be given subsequently until the suggestion is stopped at 5:49.

[0151] In summary, in the three cases of Embodiments 2-4 above, the method and system of the present invention have grasped the possible potential emission time, and have a significant effect on reducing gas emission and controlling pipe network pressure.

[0152] The embodiments described above are part of the embodiments of the present application, rather than all of the embodiments. The detailed description of the embodiments of the present application is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

Claims

1. A blast furnace gas network balancing system regulated by power generation, characterized in that: include: Data acquisition module, used to collect blast furnace gas generation, consumption and continuous pressure data; Pressure-related models include pressure change models, single power plant models, and multi-power plant models; among them: the pressure change model is configured to obtain the total gas production and use difference by subtracting the consumption of blast furnace gas, and use the total gas production and use difference as the independent variable, and the pressure difference change corresponding to the lag time as the dependent variable, to establish a pressure change model with a regression model structure from production and use difference to pressure; set the upper limit of the lag time threshold u , and divide the continuous pressure data into u The length is NU +1 pressure subset, the front NU Calculate the correlation coefficient between the total production and use difference data and all pressure subsets; subtract the pressure subset with the largest correlation coefficient from the first pressure subset to obtain the pressure difference change; The single power plant model is configured to calculate the total gas production and consumption difference in the pipeline network of a single power plant and predict the pressure change at the target measuring point; The multi-power plant model is used to coordinate the gas consumption of multiple power plants to achieve a dynamic balance of the network pressure. The multi-power plant model is configured to traverse and mark all m power plants in order from near to far, and predict the pressure changes of the target measuring points of the gas network respectively, so as to determine the adjustment order and specific pressure adjustment amount of each power plant. Specifically: Set the upper and lower limits of the pressure warning value respectively, obtain the real-time total production and use difference, set the expansion coefficient, calculate the corrected production and use difference, and calculate the pipeline network pressure after the future set time. If the pipeline network pressure after the future set time is within the upper and lower limits of the pressure warning value, no adjustment will be made. If it is less than the lower limit of the pressure warning value, it will be adjusted up; if it is greater than the lower limit of the pressure warning value, it will be adjusted down; determine the total regulation amount according to the pipeline network pressure after the future set time, and determine the regulation order and specific gas regulation amount of each power plant; The real-time regulation module is used to dynamically adjust the gas consumption of the power generation unit according to the pressure-related model output.

2. The blast furnace gas network balancing system through power generation regulation according to claim 1 is characterized in that: The single power plant model is configured to obtain the maximum pressure adjustment range and the maximum adjustment speed respectively according to the adjustment range and adjustment speed of a single power plant through the pressure change model, so as to predict the maximum impact speed and pressure adjustment range of the power plant on the pipeline network pressure change in the future.

3. The blast furnace gas network balancing system through power generation regulation according to claim 1 is characterized in that It also includes a safety monitoring module to monitor system stability and safety during the regulation process to ensure timely response in the event of a sudden pressure change.

4. The blast furnace gas network balancing system through power generation regulation according to claim 1 is characterized in that It also includes a user interface module for displaying real-time data, prediction results and adjustment strategies to operators, and receiving manual adjustment instructions from operators.

5. A method for controlling balanced gas in a blast furnace gas network through power generation regulation, characterized in that The steps include: Construct pressure-related models, including pressure variation models, single power plant models, and multi-power plant models; The pressure change model is set up based on the obtained gas volume, consumption, pressure data and time coordinates. The total gas production and use difference is obtained by subtracting the consumption from the blast furnace gas generation. The total gas production and use difference is used as the independent variable, and the pressure difference change corresponding to the lag time is used as the dependent variable. The pressure change model of the regression model structure from production and use difference to pressure is established. Set the upper limit of the latency threshold u , and divide the continuous pressure data into u The length is NU +1 pressure subset, the front NU The correlation coefficients are calculated between the total production-use difference data and all pressure subsets; The pressure difference change is obtained by subtracting the pressure subset with the largest correlation coefficient from the first pressure subset; The single power plant model is set to obtain the maximum impact speed of the power plant regulation on the pipeline network pressure change based on the regulation range and gas regulation speed of the power plant's maximum gas consumption, and determine the impact of the power plant on the pipeline network pressure based on the future set time; Multi-power plant model, used to coordinate gas consumption of multiple power plants to achieve dynamic balance of pipeline network pressure; The multi-power plant model is configured to traverse and mark all m power plants in order from near to far, and predict the pressure changes of the target measuring points of the gas pipeline network respectively, so as to determine the adjustment order and specific pressure adjustment amount of each power plant; specifically: Set the upper and lower limits of the pressure warning value respectively, obtain the real-time total production and use difference, set the expansion coefficient, calculate the corrected production and use difference, and calculate the pipeline network pressure after the future set time. If the pipeline network pressure after the future set time is within the upper and lower limits of the pressure warning value, no adjustment will be made. If it is less than the lower limit of the pressure warning value, it will be adjusted up; if it is greater than the lower limit of the pressure warning value, it will be adjusted down; determine the total regulation amount according to the pipeline network pressure after the future set time, and determine the regulation order and specific gas regulation amount of each power plant; The pressure-related model is called in real time to predict future pressure changes and dynamically determine the optimal regulation strategy. The final output is to form power generation recommendations for each power plant based on the gas regulation amount. Each power plant will follow the recommendations to stabilize the pipeline network pressure within the upper and lower limits of the pressure warning value.

6. The method for controlling the balanced gas in the blast furnace gas network through power generation regulation according to claim 5, characterized in that If the pipeline pressure after the set time in the future is within the upper and lower limits of the pressure warning value, the generator set adjustment direction will not be adjusted. If it is less than the lower limit of the pressure warning value, the generator set adjustment direction will be increased; if it is greater than the lower limit of the pressure warning value, the generator set adjustment direction will be decreased.

7. The method for controlling the balanced gas in a blast furnace gas network through power generation regulation according to claim 5, characterized in that The multi-power plant model determines the power plant number and total regulation amount that need to be regulated according to the following steps: Input all power plants from near to far into the single power plant model, and obtain all the regulation parameters of each power plant: the speed of the influence of the single power plant regulation on the pipeline network pressure, the maximum amount of pipeline network pressure increase that can be set in the future, and the maximum amount of pipeline network pressure decrease that can be set in the future; Determine the power plants that need to be adjusted according to their serial numbers from near to far; According to the power plant serial number, the maximum speed of influence of the power plant or a generator in the power plant on the change of the pipeline network pressure or the maximum gas adjustment amount per minute is retrieved; the serial numbers of all power plants that need to be adjusted are traversed so that the pipeline network pressure is adjusted at the fastest speed while the number of power plants adjusted is the least; The generator set regulation directions and the maximum gas regulation amount per minute of all power plants and / or all generators are obtained, and the regulation direction and the maximum gas regulation amount per minute of each generator set are combined into a power generation recommendation.

8. A computer-readable storage medium having a computer program stored thereon, which, when executed, is used to implement the blast furnace gas network balanced gas control method through power generation regulation as described in any one of claims 5 to 7.

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