Expansion pressure detection method applied to electric heating coke oven

Abstract

The invention relates to the technical field of coke oven operation control, in particular to an expansion pressure detection method applied to an electric heating coke oven, which comprises the following steps: dividing coking stages based on coal state change in a historical coking period of the electric heating coke oven, and setting a reference acquisition frequency for each coking stage; for each coking stage, real-time data acquisition is carried out based on the reference acquisition frequency, and expansion pressure detection data is obtained; performing pressure anomaly calculation on the expansion pressure detection data to obtain an expansion pressure change rate and an expansion pressure distribution characteristic; correcting the reference acquisition frequency according to the reference acquisition frequency to obtain a dynamic acquisition frequency corresponding to the coking stage; and generating expansion pressure detection alarm information in response to the condition that at least one of the expansion pressure detection data, the change rate, the distribution characteristic and the dynamic acquisition frequency exceeds a corresponding preset warning threshold value. Acquisition frequency can be dynamically adjusted along with coal expansion characteristics, timeliness of pressure peak capture is guaranteed, and redundant data in a stable stage is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coke oven operation control, and particularly relates to an expansion pressure detection method applied to an electric heating coke oven. BACKGROUND

[0002] In the coking process of an electric heating coke oven, coal will undergo complex physical and chemical changes, during which the coal will expand in volume to generate expansion pressure, which is prone to cause damage to the oven wall, directly affecting the safety of the oven structure and the stability of the coke quality. Accurate detection of the dynamic changes of the expansion pressure is conducive to precise assessment of the damage risk of the oven wall and optimization of the coal blending scheme.

[0003] The existing expansion pressure detection of an electric heating coke oven mostly uses a fixed collection frequency; for example, the expansion pressure detection device of a 300 kg test coke oven usually collects pressure data at preset intervals; however, the expansion pressure of coal briquettes changes dynamically with the coking time, the temperature rising system and the coal blending scheme, and the pressure fluctuation characteristics are significantly different at different coking stages; for example, the pressure rises gently in the early coking stage, the pressure may suddenly increase in the middle coking stage, and the pressure tends to be stable in the late coking stage; a fixed collection frequency is difficult to adapt to the pressure change law at each stage, and in the middle coking stage and other stages with severe pressure fluctuations, the pressure peak may be captured with a lag due to a too wide sampling interval, and the damage risk of the oven wall cannot be assessed in a timely manner; and in the stable pressure stage, fixed high-frequency collection will produce redundant data, increasing the system processing load.

[0004] Therefore, there is an urgent need for an expansion pressure detection method that can dynamically adjust the collection frequency according to the characteristics of the coking stage and optimize the detection accuracy in combination with the data characteristics of multiple detection points, to solve the above problems. SUMMARY

[0005] The present application provides an expansion pressure detection method applied to an electric heating coke oven, which can effectively solve the problems in the background art.

[0006] In order to achieve the above purpose, the present application provides an expansion pressure detection method applied to an electric heating coke oven, comprising: dividing the coking stages based on the state changes of the coal in the historical coking period of the electric heating coke oven, and setting a corresponding reference collection frequency for each coking stage; for each coking stage, based on the reference collection frequency, real-time data collection is performed through at least two preset expansion pressure detection points to obtain expansion pressure detection data; performing pressure anomaly calculation on the expansion pressure detection data to obtain the expansion pressure change rate and the expansion pressure distribution characteristics; According to the expansion pressure change rate and the expansion pressure distribution characteristic, the reference collection frequency is corrected to obtain a dynamic collection frequency corresponding to the coking stage; In response to at least one of the expansion pressure detection data, the expansion pressure change rate, the expansion pressure distribution characteristic and the dynamic collection frequency exceeding a corresponding preset warning threshold, expansion pressure detection alarm information is generated according to the exceeding state.

[0007] Further, the coking stages are divided based on the state changes of the coal materials in the historical coking cycle of the electrically heated coke oven, including: Multi-dimensional state characteristic parameters of the coal materials in the historical coking cycle are extracted; The coking cycle is divided into a drying preheating stage, a pyrolysis initial stage, a pyrolysis peak stage and a coke maturation stage in combination with the physical and chemical characteristics of the coal pyrolysis process.

[0008] Further, the multi-dimensional state characteristic parameters include a historical coke cake temperature sequence, a historical expansion pressure change rate sequence and a historical volatile matter escape rate sequence.

[0009] Further, the coking cycle is divided into a drying preheating stage, a pyrolysis initial stage, a pyrolysis peak stage and a coke maturation stage in combination with the physical and chemical characteristics of the coal pyrolysis process, including: When the historical coke cake temperature is in a first temperature interval, the historical expansion pressure change rate is less than a first pressure rate threshold and the historical volatile matter escape rate is less than a first volatile matter threshold, the drying preheating stage is divided; When the historical coke cake temperature is in a second temperature interval, the historical expansion pressure change rate is not less than the first pressure rate threshold and less than a second pressure rate threshold, and the historical volatile matter escape rate is not less than the first volatile matter threshold and less than a second volatile matter threshold, the pyrolysis initial stage is divided; When the historical coke cake temperature is in a third temperature interval, the historical expansion pressure change rate is not less than the second pressure rate threshold and the historical volatile matter escape rate is not less than the second volatile matter threshold, the pyrolysis peak stage is divided; When the historical coke cake temperature is in a fourth temperature interval, the historical expansion pressure change rate is less than a third pressure rate threshold and the historical volatile matter escape rate is less than a third volatile matter threshold, the coke maturation stage is divided.

[0010] Further, a corresponding reference collection frequency is set for each of the coking stages, including: Based on the historical expansion pressure change rate sequence, historical expansion pressure fluctuation characteristics of each of the coking stages are extracted; the historical expansion pressure fluctuation characteristics include a historical expansion pressure peak value appearance frequency, a standard deviation of the historical expansion pressure change rate and a historical pressure sustained fluctuation duration; According to the corresponding historical expansion pressure fluctuation characteristics, the expansion pressure fluctuation intensity of the coking stage is calculated; Based on the expansion pressure fluctuation intensity, the reference collection frequency of the coking stage is determined in the preset collection frequency mapping table.

[0011] Further, at least two expansion pressure detection points are determined for each coking stage, including: Based on the spatial distribution characteristics of the coal expansion pressure of each coking stage, at least two detection points are selected from the preset plurality of expansion pressure detection points for each coking stage, and the selected detection points can reflect the typical distribution state of the expansion pressure of the corresponding stage. The spatial distribution characteristics of the coal expansion pressure include the pressure mean value, pressure fluctuation amplitude and pressure gradient change of each detection point in the corresponding stage in the historical coking period; and the typical distribution state represents the action area of the expansion pressure of the coal in the stage.

[0012] Further, the expansion pressure detection data is calculated to obtain the expansion pressure change rate and the expansion pressure distribution characteristics, including: The expansion pressure detection data of the same detection point at two continuous collection time points is selected, and the difference value and the time interval of the two are calculated to obtain the expansion pressure change rate of the detection point in the corresponding period; The standard deviation and the mean value of the expansion pressure detection data of a plurality of detection points at the same time are calculated, and the ratio of the standard deviation and the mean value is taken as the expansion pressure distribution characteristic.

[0013] Further, when calculating the expansion pressure change rate, in response to the existence of a plurality of continuous collection time points, the mean value or the maximum value of a plurality of expansion pressure change rates is taken as the current expansion pressure change rate of the detection point.

[0014] Further, the calculation formula for correcting the reference collection frequency is: ; Wherein, f represents the dynamic collection frequency; f0 represents the reference collection frequency of the current coking stage; v represents the real-time expansion pressure change rate; v0 represents the reference pressure change rate of the current coking stage, which is the typical value of the coking stage based on historical data statistics; CV represents the expansion pressure distribution characteristic; k1 i represents the stage-specific sensitivity coefficient of the i-th coking stage, which is used to reflect the response weight of the coking stage to the pressure change; k2 i represents the stage-specific stability coefficient of the i-th coking stage, which is used to reflect the dependence of the coking stage on the data consistency.

[0015] Further, the stage-specific sensitivity coefficient and the stage-specific stability coefficient are set according to historical pressure characteristics of each coking stage.

[0016] The technical scheme of the present application can achieve the following technical effects: the present application can accurately adapt to the pressure change law of different coking stages by dynamically adjusting the collection frequency according to the coking stage characteristics; the stress condition of the furnace wall can be more comprehensively reflected by selecting at least two expansion pressure detection points in each coking stage and analyzing the data characteristics of the multiple detection points; the expansion pressure change rate and distribution characteristics can be obtained by calculating the pressure anomaly of the expansion pressure detection data, so that the risk of furnace wall damage can be more accurately evaluated; in the pressure stable stage, the generation of redundant data is reduced by reducing the collection frequency, the system processing load is reduced, and the operation efficiency of the monitoring system is improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The logic flow chart of the expansion pressure detection method applied to the electric heating coking furnace in the present application. DETAILED DESCRIPTION

[0018] The present application will be described below in conjunction with the drawings in the present application.

[0019] As shown in Figure 1 the expansion pressure detection method applied to the electric heating coking furnace of the present application specifically includes the following steps: Step S1, dividing the coking stage based on the coal state change in the historical coking period of the electric heating coking furnace, and setting the corresponding reference collection frequency for each coking stage; Step S2, for each coking stage, based on the reference collection frequency, real-time data collection is performed through the preset at least two expansion pressure detection points to obtain expansion pressure detection data; Step S3, performing pressure anomaly calculation on the expansion pressure detection data to obtain the expansion pressure change rate and the expansion pressure distribution characteristics; the expansion pressure distribution characteristics are used to represent the consistency of the expansion pressure detection data collected by multiple expansion pressure detection points at the same time; Step S4, according to the expansion pressure change rate and the expansion pressure distribution characteristics, performing collection frequency correction to obtain the dynamic collection frequency corresponding to the coking stage; the collection frequency correction includes positive correction and negative correction; Step S5, in response to at least one of the expansion pressure detection data, the expansion pressure change rate, the expansion pressure distribution characteristics and the dynamic collection frequency exceeding the corresponding preset warning threshold, generating expansion pressure detection alarm information according to the exceeding state.

[0020] In the embodiment, the method can accurately adapt to the pressure change law of different coking stages by dynamically adjusting the collection frequency according to the characteristics of the coking stage. For example, when the pressure fluctuates violently in the middle of coking, the collection frequency is automatically increased to ensure that the pressure peak value can be captured in time. In the stable pressure stage, the collection frequency is reduced to avoid the generation of redundant data. The detection method can flexibly cope with complex production conditions, improve the adaptability and flexibility of detection, and more comprehensively reflect the stress condition of the furnace wall by selecting at least two expansion pressure detection points in each coking stage and analyzing the data characteristics of multiple detection points, effectively improving the detection accuracy and avoiding misjudgment caused by abnormal single detection point data. By calculating the pressure abnormality of the expansion pressure detection data, the expansion pressure change rate and distribution characteristics can be obtained to more accurately evaluate the risk of furnace wall damage. For example, when the expansion pressure change rate or distribution characteristics exceeds the preset warning threshold, the system will generate an alarm information in time to remind the operator to take measures, thereby effectively reducing the risk of furnace wall damage and ensuring the safety of the furnace structure. In the stable pressure stage, by reducing the collection frequency, the generation of redundant data is reduced, the system processing load is reduced, the operation efficiency of the system is improved, and the service life of the equipment is prolonged. At the same time, the combination of dynamic collection frequency adjustment and multi-detection point data fusion can effectively reduce false positives and omissions. For example, in the case of violent pressure fluctuations, the increase in dynamic collection frequency can capture the pressure peak value in time to avoid omissions. In the stable pressure stage, the fusion of multi-detection point data can avoid false positives caused by abnormal single detection point data, making the detection method more reliable in actual application and improving the overall performance of the system.

[0021] In some embodiments of the present application, the existing coking stage division relies on empirical time nodes and is not associated with the actual pyrolysis state of the coal, resulting in a disconnection between the stage boundary and the expansion characteristics of the coal. For example, the key stage of sudden increase in pressure in the middle of coking may be blurredly divided, resulting in unreasonable setting of the reference collection frequency. Therefore, it is necessary to divide the stage based on the real state change of the coal in the historical coking cycle, deeply bind the stage characteristics with the physical and chemical characteristics of the coal pyrolysis process, and ensure that the subsequent detection strategy can adapt to the expansion pressure fluctuation law of each stage. The specific implementation is as follows: Step S101, extract multi-dimensional state characteristic parameters from the historical coking cycle operation, including a historical coke cake temperature sequence, a historical expansion pressure change rate sequence, and a historical volatile matter escape rate sequence; wherein the historical coke cake temperature sequence is derived from the center thermocouple temperature monitoring data of the carbonization chamber, reflecting the heating degree of the coal; the historical expansion pressure change rate sequence is derived from the historical data of the expansion pressure detection point, reflecting the dynamic strength of the coal expansion; and the historical volatile matter escape rate sequence is derived based on the historical gas flow and component analysis of the raw coal gas combustion system, reflecting the intensity of the coal pyrolysis reaction.

[0022] Step S102, according to the multi-dimensional state characteristic parameters, and combined with the physical and chemical characteristics of the coal pyrolysis process, that is, the conversion law of drying, pyrolysis, semi-coke, and coke, the coking stage is divided by parameter threshold judgment, which is specifically divided into: Drying and preheating stage: the historical coke cake temperature is in the first temperature interval, such as room temperature to 300℃, the historical expansion pressure change rate < the first pressure rate threshold value, such as 0.2kPa / min, and the historical volatile matter escape rate < the first volatile matter threshold value, such as 0.5kg / h; Pyrolysis initial stage: the historical coke cake temperature is in the second temperature interval, such as 300℃ to 600℃, the historical expansion pressure change rate ≥ the first pressure rate threshold value and < the second pressure rate threshold value, such as 1kPa / min, and the historical volatile matter escape rate ≥ the first volatile matter threshold value and < the second volatile matter threshold value, such as 2kg / h; Pyrolysis peak stage: the historical coke cake temperature is in the third temperature interval, such as 600℃ to 800℃, the historical expansion pressure change rate ≥ the second pressure rate threshold value, and the historical volatile matter escape rate ≥ the second volatile matter threshold value; Coke mature stage: the historical coke cake temperature is in the fourth temperature interval, such as 800℃ to 1050℃, the historical expansion pressure change rate < the third pressure rate threshold value, such as 0.1kPa / min, and the historical volatile matter escape rate < the third volatile matter threshold value, such as 0.3kg / h; Among them, the first to fourth temperature intervals are sequentially increased, the third pressure rate threshold value < the first pressure rate threshold value, and the third volatile matter threshold value < the first volatile matter threshold value.

[0023] In this embodiment, through the cooperative judgment of the three dimensions of temperature, expansion pressure change rate, and volatile matter escape rate, the stage misjudgment caused by a single parameter is avoided, the stage boundary is highly matched with the real pyrolysis state of the coal, the four stages after division correspond to the characteristics of the expansion pressure of the coal being flat, rising, suddenly increasing, and stable, respectively, so that the setting of the reference collection frequency is more targeted; the parameter extraction is based on the existing detection system of the electric heating coking furnace, without the need for additional equipment, which is convenient for industrial application; at the same time, through threshold adjustment, the coking characteristics under different coal blending schemes or temperature rising systems can be adapted; combined with the physical and chemical characteristics of the coal pyrolysis, the stage division not only serves the pressure detection, but also indirectly reflects the rationality of the coal blending scheme.

[0024] As a preferred embodiment of the present application, during the coking process, the expansion pressure of the coal charge in different coking stages fluctuates differently. For example, the pressure in the pyrolysis peak stage is prone to sudden increase and frequent fluctuation, while the pressure in the drying preheating stage and the coke maturation stage is relatively stable. If a uniform reference collection frequency is set for all stages, two problems may occur: first, in the stage with severe pressure fluctuation, the sampling interval is too wide to capture the instantaneous peak value, resulting in delayed assessment of the risk of furnace wall damage; second, in the stable pressure stage, a large amount of redundant data is generated by high-frequency collection, increasing the system storage and processing load. Therefore, the reference collection frequency needs to be set differently according to the expansion pressure fluctuation characteristics of each coking stage, so that the collection frequency is accurately matched with the pressure change law in the stage, ensuring the detection accuracy in the key stage and avoiding resource waste. The specific implementation is as follows: Step S111, based on the historical expansion pressure change rate sequence, the historical expansion pressure fluctuation characteristics of each stage are extracted for the drying preheating stage, the initial pyrolysis stage, the pyrolysis peak stage and the coke maturation stage, including: Frequency of historical expansion pressure peak value: the number of pressure peak values in a unit time is counted to reflect the frequency of sudden increase of pressure; Standard deviation of historical expansion pressure change rate: the dispersion degree of pressure change rate is calculated to quantify the severity of pressure fluctuation; Duration of historical pressure sustained fluctuation: the duration of a single pressure fluctuation from start to stabilization is recorded to reflect the persistence of fluctuation.

[0025] Step S112, for each coking stage, the above three types of fluctuation characteristics are comprehensively considered, and the expansion pressure fluctuation intensity of the stage is calculated by a weighting algorithm; for example, the pyrolysis peak stage has a significantly higher fluctuation intensity than other stages due to high peak value frequency, large change rate standard deviation and long sustained fluctuation duration; while the coke maturation stage has the lowest fluctuation intensity due to stable pressure.

[0026] Step S113, a preset collection frequency reference table is provided, in which different expansion pressure fluctuation intensities correspond to different reference collection frequencies, i.e. the higher the fluctuation intensity, the higher the corresponding reference collection frequency; according to the calculated fluctuation intensity of each stage, the reference collection frequency of the stage is determined by matching in the reference table; for example, the pyrolysis peak stage corresponds to a higher reference collection frequency due to the highest fluctuation intensity; the drying preheating stage and the coke maturation stage correspond to a lower reference collection frequency due to lower fluctuation intensity.

[0027] In the embodiment, the fluctuation characteristics are extracted and the fluctuation intensity is quantified through historical data, so that the reference collection frequency is no longer dependent on empirical judgment, but is determined based on the actual pressure fluctuation law of each stage, avoiding the disadvantages of fixed frequency setting; the high reference frequency in the high fluctuation stage can quickly respond to the sudden increase in pressure, and only a small positive correction is needed to capture the peak value; the low reference frequency in the low fluctuation stage reduces the frequency of negative correction, so that the dynamic frequency is more stable, and the system runs more efficiently; when the coal blending scheme or the temperature rising system is adjusted, the expansion pressure fluctuation characteristics of each stage will change; through the mapping relationship between the fluctuation intensity and the frequency, the reference frequency can be automatically adjusted based on the updated historical data, without the need for manual re-setting, and the adaptability is stronger.

[0028] In some embodiments of the present application, the coal expansion regions are different in different coking stages, for example, in the pyrolysis peak stage, the coal expansion is mainly concentrated in the middle to top of the carbonization chamber, while in the drying and preheating stage, the bottom expansion is mainly used; if the same expansion pressure detection point is fixedly used, the detection data of some stages cannot reflect the typical pressure distribution state of the stage, for example, in the pyrolysis peak stage, only detecting the bottom point may miss the pressure sudden increase signal at the top, affecting the detection accuracy; therefore, it is necessary to select a detection point that can represent the pressure distribution of each coking stage according to the expansion characteristics of each coking stage to ensure the effectiveness and representativeness of the collected data; the specific implementation is as follows: Step S201, based on the pressure data of different detection points in the historical coking period, the spatial distribution characteristics of each coking stage are extracted, including: The pressure mean value of each detection point in the corresponding stage: reflecting the pressure level of the point in the stage; Pressure fluctuation amplitude: the difference between the maximum and minimum values of the pressure data of the point, reflecting the pressure change range; Pressure gradient change: analyzing the pressure difference between different points to determine the spatial distribution trend of the pressure.

[0029] Step S202, combining the above spatial distribution characteristics, the action region of the coal expansion pressure in each coking stage, i.e. the typical distribution state, is determined; for example, in the drying and preheating stage, the detection point with low pressure mean value and small fluctuation amplitude is usually located in the middle of the furnace wall; in the initial pyrolysis stage, the detection point with gradually increasing pressure mean value and moderate fluctuation amplitude is usually located in the middle and upper part of the furnace wall; in the pyrolysis peak stage, the detection point with the highest pressure mean value and the largest fluctuation amplitude is usually located in the middle and lower part of the furnace wall; in the coke maturation stage, the detection point with low pressure mean value and small fluctuation amplitude is usually located in the middle and lower part of the furnace wall.

[0030] Step S203, from the preset multiple expansion pressure detection points, at least two detection points located in the typical distribution area of each coking stage are selected to ensure that the selected points can reflect the pressure distribution characteristics of the stage; for example, the detection points at the middle and top of the pyrolysis peak stage are selected, and the detection point at the bottom of the drying preheating stage is selected.

[0031] In the embodiment, by matching the typical expansion area of each stage to select the detection point, the misjudgment of the fixed point to the pressure characteristics of part of the stage is avoided, so that the collected data can truly reflect the expansion pressure distribution of the stage; as the coking stage advances, the expansion area of the coal material changes dynamically, and the selection of the detection point in stages can adapt to this change in real time, while ensuring the coverage of the detection, avoiding the redundant collection of invalid points, and reducing the consumption of system resources; by selecting multiple detection points and comprehensively analyzing the data thereof, the misjudgment and omission caused by the abnormal data of a single detection point can be reduced.

[0032] In some embodiments of the application, in order to more accurately evaluate the pressure characteristics of each coking stage, the expansion pressure change rate and the expansion pressure distribution characteristics need to be extracted from the original expansion pressure data; for example, if only the absolute value of the pressure is concerned in the pyrolysis peak stage, the rapid pressure rise process in a short time may be missed; if only the data of a single detection point is relied on, the sensor failure may be misjudged as a real pressure anomaly; therefore, the expansion pressure change rate and the distribution characteristics need to be calculated to quantify the dynamic change trend of the pressure and the consistency of the data of multiple detection points, and the original data is analyzed from two dimensions of time dynamics and spatial consistency.

[0033] Specifically, for each detection point, the expansion pressure detection data of two continuous collection time points are selected, and the change rate is calculated by the following method: the pressure difference value of the two time points, i.e. the data of the latter time point minus the data of the former time point, is calculated; the time interval of the two time points is determined based on the current collection frequency; the expansion pressure change rate of the period is obtained by dividing the pressure difference value by the time interval; if there are 3 or more continuous collection time points, the mean or maximum value of the change rates of multiple periods is taken as the current expansion pressure change rate of the detection point, so as to more comprehensively reflect the dynamic trend of the pressure. For the data of multiple detection points at the same time, the consistency thereof is analyzed by a statistical method, specifically: the mean value of the pressure data of all detection points is calculated to reflect the overall pressure level at the time; the standard deviation of the pressure data of all detection points is calculated to reflect the dispersion degree of the data; the expansion pressure distribution characteristic is obtained by dividing the standard deviation by the mean value; the smaller the value of the expansion pressure distribution characteristic is, the higher the consistency of the data of multiple detection points is; the larger the value is, the more significant the difference between the data is, and the lower the consistency is.

[0034] In the embodiment, the expansion pressure change rate can accurately reflect the increase or decrease amplitude of the pressure in a unit time, is especially suitable for capturing the pressure sudden increase signal in the pyrolysis peak stage, provides a clear basis for timely triggering the positive frequency correction and risk warning, and avoids the delay of the risk assessment of the furnace wall damage caused by the lag of the peak value capture; the expansion pressure distribution characteristics can be effectively identified through statistical analysis of the data of multiple detection points, such as abnormal values caused by the jump data of a single detection point; for example, when a detection point shows a sudden increase in pressure but the data of other points are stable, it can be determined that it is a local error rather than a real pressure anomaly, the subsequent frequency correction and warning are avoided, and the anti-interference ability of the method is improved. In some embodiments of the application, the physical and chemical change characteristics of the coal charge in different coking stages are different, such as the intense expansion of the coal charge in the pyrolysis peak stage, the fast pressure change, and the great influence on the furnace wall, while the pressure is relatively stable in the drying and preheating stage; if a unified frequency correction logic is used, it cannot accurately adapt to the pressure change characteristics in each stage, which may lead to insufficient acquisition frequency in the key stage of pressure change or excessive acquisition in the stable stage; therefore, the reference acquisition frequency needs to be dynamically corrected according to the real-time pressure change rate and data distribution characteristics, so that the acquisition frequency can adapt to the pressure dynamics in the current coking stage in real time.

[0035] Specifically, the reference acquisition frequency in the current coking stage is corrected to obtain the dynamic acquisition frequency by using the following formula: ; Wherein, f represents the dynamic acquisition frequency; f0 represents the reference acquisition frequency in the current coking stage; v represents the real-time expansion pressure change rate; v0 represents the reference pressure change rate in the current coking stage, which is a typical value in this coking stage based on historical data statistics; CV represents the expansion pressure distribution characteristics; k1 i represents the stage-specific sensitivity coefficient of the i-th coking stage, which is used to reflect the response weight of the coking stage to the pressure change, and is set according to the historical pressure characteristics of each coking stage; the greater the k1 i value is, the more active the pressure change in the stage is; k2 i represents the stage-specific stability coefficient of the i-th coking stage, which is used to reflect the dependence of the coking stage on data consistency, and is set according to the consistency of the historical multi-detection point data in each coking stage; the greater the k2 i value is, the higher the data consistency requirement in the stage is.

[0036] In the above calculation formula, different coking stages have different sensitivities to pressure changes. For example, during the pyrolysis peak stage, the coal expansion reaction is intense, and the pressure change has a great impact on the furnace wall, so it is necessary to capture the pressure change more sharply. The stage-specific sensitivity coefficient is set according to the historical pressure characteristics of the stage, so that the stage with active pressure change can significantly increase the acquisition frequency when the pressure changes, and capture key signals such as sudden pressure increase in time, ensuring the timeliness of risk monitoring. The pressure change rate ratio reflects the deviation of the current pressure change from the typical situation of the stage. When v>v0, it means that the pressure change is more intense than usual, which will promote the increase of the acquisition frequency. When v is an exponential function that increases and monotonically decreases with the expansion pressure distribution characteristic. When the expansion pressure distribution characteristic is small, the data consistency is high, the pressure change is reliable, , the influence of the rate correction term is small, ensuring that the frequency can be adjusted flexibly according to the rate. When the expansion pressure distribution characteristic is large, the data consistency is low, and there may be single-point anomalies, , which will weaken the effect of the rate correction term and avoid blindly increasing the frequency due to unreliable data. i k2 represents the stage-specific stability coefficient of the i-th coking stage, which is the dependence coefficient of the stage on data consistency. For example, during the pyrolysis peak stage, more reliable multi-point data is needed to judge the sudden pressure increase, k2 i is set to a large value to make the influence of the expansion pressure distribution characteristic more significant; the coke maturation stage has a slightly lower dependence on consistency, k2 i is set to a smaller value. The above calculation formula of dynamic acquisition frequency is based on the baseline acquisition frequency, which adapts to the stage characteristics through the stage-specific sensitivity coefficient, captures real-time changes through the pressure change rate ratio, , and ensures data reliability. The three work together to dynamically adjust the frequency according to the current pressure, rather than relying solely on historical statistics. Different stages have adapted correction logic according to their own risks and data needs, avoiding false corrections due to single-point anomalies or unreliable data. In addition, it can also be flexibly set according to the historical characteristics of the type of coking furnace and production process.

[0037] Further, in the coking process, abnormal changes in coal expansion pressure can directly cause damage to the furnace wall, and monitoring of a single parameter cannot fully reflect the risk state; for example, the absolute value of the pressure does not exceed the standard but the change rate increases sharply, which may indicate that a peak pressure will occur in the short term; or the consistency of data at multiple detection points is poor, which may reflect abnormal expansion of local coal; if only one parameter is used to determine the risk, it is easy to miss or misjudge, and protective measures cannot be taken in time; therefore, it is necessary to comprehensively consider four types of parameters, including expansion pressure detection data, change rate, distribution characteristics and dynamic acquisition frequency, to trigger an alarm through multi-dimensional threshold monitoring, so as to ensure the safety of the furnace wall and the stability of the coke quality.

[0038] Specifically, based on historical coking data and furnace wall tolerance standards, the following warning thresholds are set: Expansion pressure detection data threshold: set according to the pressure limit of the furnace wall material, such as 5 kPa for a certain coke oven; Expansion pressure change rate threshold: set according to the safe change range at different coking stages, such as 0.8 kPa / min at the pyrolysis peak stage; Expansion pressure distribution characteristic threshold: set according to the normal consistency range of multi-detection point data, such as an upper limit of 0.2 for the ratio of standard deviation to mean value; Dynamic acquisition frequency threshold: set according to the maximum processing capacity of the system, such as a highest frequency threshold of 1 time per second to avoid system overload caused by high-frequency acquisition; During the coking process, the real-time acquired parameters are continuously compared with the corresponding thresholds, and if any parameter exceeds its preset warning threshold, it is determined to be an abnormal state; if multiple parameters exceed the threshold at the same time, it is determined to be a high-risk state; For different abnormal states, generate graded alarm information, when a single parameter slightly exceeds the limit, generate a prompt-level alarm, such as "pressure change rate is slightly high, please pay attention to monitoring"; when multiple parameters exceed the limit, generate a warning-level alarm, such as "pressure increases sharply at the pyrolysis peak stage and data consistency is poor, it is recommended to adjust the heating rate"; when key parameters such as absolute pressure value exceed the limit, generate an emergency-level alarm, such as "furnace wall pressure exceeds the standard, heating must be stopped immediately".

[0039] On the other hand, when the expansion pressure detection data, the expansion pressure change rate, the expansion pressure distribution characteristics and the dynamic acquisition frequency all do not exceed their respective preset warning thresholds, real-time data acquisition is carried out based on the corrected acquisition frequency, and the threshold judgment detection process is repeated.

[0040] In the present embodiment, the expansion pressure detection data directly reflects the current pressure absolute value, the threshold value of which ensures that the furnace wall will not bear pressure exceeding the tolerance limit, avoiding physical damage; the expansion pressure change rate reflects the emergency degree of pressure rise, the threshold value of which can give an early warning when the pressure is not at the limit but is rising rapidly, giving time for intervention; the expansion pressure distribution characteristic reflects the consistency of multi-detection point data, the threshold value of which can identify local abnormal expansion and avoid ignoring local risks due to normal overall pressure; the dynamic acquisition frequency reflects the matching degree of system load and pressure fluctuation, the threshold value of which can prevent system overload caused by high-frequency acquisition and ensure detection continuity. Different levels of alarms correspond to differentiated processing strategies: prompt-level alarms can be responded to by the system automatically adjusting the acquisition frequency; pre-warning level alarms require operator intervention to optimize the coal blending or temperature rising system; emergency level alarms trigger emergency shutdown protection, avoiding excessive reaction or insufficient response caused by single type of alarm, balancing production efficiency and furnace safety.

[0041] The basic principles, main features and advantages of the present application are shown and described above; those skilled in the art should understand that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application; without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application; the scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for detecting expansion pressure in an electrically heated coking oven, characterized in that, include: The coking stages are divided based on the changes in the state of coal during the historical coking cycle of the electric heating coking oven, and a corresponding benchmark acquisition frequency is set for each coking stage. For each coking stage, based on the reference acquisition frequency, real-time data acquisition is performed through at least two preset expansion pressure detection points to obtain expansion pressure detection data. Pressure anomaly calculations are performed on the expansion pressure detection data to obtain the expansion pressure change rate and expansion pressure distribution characteristics; Based on the expansion pressure change rate and the expansion pressure distribution characteristics, the reference acquisition frequency is corrected to obtain the dynamic acquisition frequency corresponding to the coking stage. If at least one of the expansion pressure detection data, the expansion pressure change rate, the expansion pressure distribution characteristics, and the dynamic acquisition frequency exceeds its respective preset warning threshold, an expansion pressure detection alarm message is generated based on the exceeding state.

2. The expansion pressure detection method for an electrically heated coking oven according to claim 1, characterized in that, The coking stages are divided based on the changes in coal state during the historical coking cycle of electrically heated coking ovens, including: Extract multi-dimensional state characteristic parameters of coal within historical coking cycles; Based on the physicochemical characteristics of the coal pyrolysis process, the coking cycle is divided into the drying and preheating stage, the initial pyrolysis stage, the peak pyrolysis stage, and the coke maturation stage.

3. The expansion pressure detection method for an electrically heated coking oven according to claim 2, characterized in that, The multidimensional state characteristic parameters include historical coke temperature sequence, historical expansion pressure change rate sequence, and historical volatile matter escape rate sequence.

4. The expansion pressure detection method for an electrically heated coking oven according to claim 3, characterized in that, Based on the physicochemical properties of the coal pyrolysis process, the coking cycle is divided into four stages: drying and preheating, initial pyrolysis, peak pyrolysis, and coke maturation. When the historical coke cake temperature is in the first temperature range, the historical expansion pressure change rate is less than the first pressure rate threshold, and the historical volatile matter escape rate is less than the first volatile matter threshold, it is classified as the drying preheating stage. When the historical coke temperature is in the second temperature range, the historical expansion pressure change rate is not less than the first pressure rate threshold and less than the second pressure rate threshold, and the historical volatile matter escape rate is not less than the first volatile matter threshold and less than the second volatile matter threshold, it is classified as the initial stage of pyrolysis. When the historical coke temperature is in the third temperature range, the historical expansion pressure change rate is not less than the second pressure rate threshold, and the historical volatile matter escape rate is not less than the second volatile matter threshold, it is classified as the pyrolysis peak stage. When the historical coke temperature is in the fourth temperature range, the historical expansion pressure change rate is less than the third pressure rate threshold, and the historical volatile matter escape rate is less than the third volatile matter threshold, it is classified as the coke maturity stage.

5. The expansion pressure detection method for an electrically heated coking oven according to claim 4, characterized in that, To set a corresponding reference acquisition frequency for each of the aforementioned coking stages, including: Based on the historical expansion pressure change rate sequence, the historical expansion pressure fluctuation characteristics of each coking stage are extracted; the historical expansion pressure fluctuation characteristics include the frequency of historical expansion pressure peak occurrence, the standard deviation of historical expansion pressure change rate, and the duration of historical pressure fluctuation. For each coking stage, the intensity of the expansion pressure fluctuation is calculated based on its corresponding historical expansion pressure fluctuation characteristics. Based on the intensity of the expansion pressure fluctuation, the reference acquisition frequency for the coking stage is determined by mapping it to a preset acquisition frequency lookup table.

6. The expansion pressure detection method for an electrically heated coking oven according to claim 1, characterized in that, For each of the aforementioned coking stages, at least two expansion pressure detection points are determined, including: Based on the spatial distribution characteristics of coal expansion pressure at each coking stage, at least two detection points are selected for each coking stage from a set of multiple preset expansion pressure detection points. The selected detection points can reflect the typical distribution of coal expansion pressure at the corresponding stage. The spatial distribution characteristics of the coal expansion pressure include the average pressure, pressure fluctuation amplitude, and pressure gradient change of each detection point in the corresponding stage of the historical coking cycle; the typical distribution state represents the area of ​​effect of the coal expansion pressure in that stage.

7. The expansion pressure detection method for an electrically heated coking oven according to claim 1, characterized in that, The step of performing pressure anomaly calculations on the expansion pressure detection data to obtain the expansion pressure change rate and expansion pressure distribution characteristics includes: The expansion pressure detection data of the same detection point at two consecutive collection times are selected, and the ratio of the difference between the two data points to the time interval is calculated to obtain the rate of change of expansion pressure at the detection point in the corresponding time period. The ratio of the standard deviation to the mean of the expansion pressure detection data from multiple detection points at the same time is calculated as a characteristic of the expansion pressure distribution.

8. The expansion pressure detection method for an electrically heated coking oven according to claim 7, characterized in that, When calculating the rate of change of expansion pressure, in response to the existence of multiple continuous acquisition times, the average or maximum value of the multiple rates of change of expansion pressure is taken as the current rate of change of expansion pressure at the detection point.

9. The expansion pressure detection method for an electrically heated coking oven according to claim 7, characterized in that, The formula for correcting the reference acquisition frequency is as follows: ； Where f represents the dynamic acquisition frequency; f0 represents the baseline acquisition frequency for the current coking stage; v represents the real-time expansion pressure change rate; v0 represents the baseline pressure change rate for the current coking stage, a typical value for this coking stage based on historical data statistics; CV represents the expansion pressure distribution characteristics; k1 i k2 represents the stage-specific sensitivity coefficient for the i-th coking stage, used to reflect the response weight of this coking stage to pressure changes; i This represents the stage-specific stability coefficient for the i-th coking stage, which reflects the degree of dependence of this coking stage on data consistency.

10. The method for detecting expansion pressure in an electrically heated coking oven according to claim 8, characterized in that, The stage-specific sensitivity coefficient and the stage-specific stability coefficient are set based on the historical pressure characteristics of each coking stage.

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