Expansion pressure detection method applied to electrically heated coke oven

Abstract

The present application relates to coke oven operation control technical field, especially to a kind of applied to electric heating coke oven expansion pressure detection method, comprising: based on the coal material state change in the historical coking cycle of electric heating coke oven division coking stage, and set reference collection frequency for each coking stage;For each coking stage, based on reference collection frequency, real-time data acquisition is carried out, and expansion pressure detection data are obtained;Expansion pressure detection data are calculated for pressure anomaly, and expansion pressure change rate and expansion pressure distribution characteristics are obtained;And according to this, reference collection frequency is revised, and the dynamic collection frequency corresponding to the coking stage is obtained;In response to at least one of expansion pressure detection data, change rate, distribution characteristics and dynamic collection frequency exceeds corresponding preset warning threshold, expansion pressure detection alarm information is generated.It can make collection frequency dynamic adjustment with coal material expansion characteristics, ensure the timeliness of pressure peak capture, reduce redundant data in stable stage.

Description

Technical Field

[0001] This invention relates to the technical field of coking oven operation control, and in particular to a method for detecting expansion pressure in an electrically heated coking oven. Background Technology

[0002] During the coking process in an electrically heated coking oven, the coal undergoes complex physicochemical changes. During this process, the coal expands in volume, generating expansion pressure. This pressure can easily cause damage to the furnace walls, directly affecting the structural safety of the furnace and the stability of coke quality. Accurately detecting the dynamic changes in expansion pressure is beneficial for precisely assessing the risk of furnace wall damage and optimizing coal blending schemes.

[0003] Existing methods for detecting the expansion pressure of electrically heated coking ovens mostly employ fixed sampling frequencies. For example, the expansion pressure measuring device for a 300kg test coking oven typically collects pressure data at preset intervals. However, the expansion pressure of coal cake changes dynamically with coking time, heating regime, and coal blending scheme. The pressure fluctuation characteristics at different coking stages differ significantly. For instance, the pressure rises gradually in the early stage of coking, may surge in the middle stage, and tends to stabilize in the later stage. Fixed sampling frequencies are difficult to adapt to the pressure change patterns at each stage. In stages with drastic pressure fluctuations, such as the middle stage of coking, the sampling interval is too wide, leading to a delay in capturing pressure peaks and making it impossible to assess the risk of furnace wall damage in a timely manner. Furthermore, in the stable pressure stage, fixed high-frequency sampling generates 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 acquisition frequency based on the characteristics of the coking stage and optimize the detection accuracy by combining the data characteristics of multiple detection points, in order to solve the above problems. Summary of the Invention

[0005] This invention provides a method for detecting expansion pressure in an electrically heated coking oven, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides a method for detecting expansion pressure in an electrically heated coking oven, comprising:

[0007] 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.

[0008] 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.

[0009] Pressure anomaly calculations are performed on the expansion pressure detection data to obtain the expansion pressure change rate and expansion pressure distribution characteristics;

[0010] 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.

[0011] 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.

[0012] Furthermore, the coking stages are divided based on the changes in coal state during the historical coking cycle of the electrically heated coking oven, including:

[0013] Extract multi-dimensional state characteristic parameters of coal within historical coking cycles;

[0014] 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.

[0015] Furthermore, the multi-dimensional state characteristic parameters include historical coke temperature sequence, historical expansion pressure change rate sequence, and historical volatile matter escape rate sequence.

[0016] Furthermore, based on the physicochemical properties 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, including:

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] Furthermore, a corresponding reference acquisition frequency is set for each of the aforementioned coking stages, including:

[0022] 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.

[0023] For each coking stage, the intensity of the expansion pressure fluctuation is calculated based on its corresponding historical expansion pressure fluctuation characteristics.

[0024] 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.

[0025] Furthermore, at least two expansion pressure detection points are determined for each of the aforementioned coking stages, including:

[0026] 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.

[0027] 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.

[0028] Further, 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:

[0029] 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.

[0030] 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.

[0031] Furthermore, 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 multiple rates of change of expansion pressure is taken as the current rate of change of expansion pressure at the detection point.

[0032] Furthermore, the calculation formula for correcting the reference acquisition frequency is as follows:

[0033] ;

[0034] 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.

[0035] Furthermore, the stage-specific sensitivity coefficient and the stage-specific stability coefficient are set according to the historical pressure characteristics of each coking stage.

[0036] The technical solution of this invention achieves the following technical effects: By dynamically adjusting the acquisition frequency according to the characteristics of the coking stage, this invention can accurately adapt to the pressure change patterns of different coking stages; by selecting at least two expansion pressure detection points in each coking stage and combining the data characteristics of multiple detection points for analysis, it can more comprehensively reflect the stress situation of the furnace wall; by performing pressure anomaly calculation on the expansion pressure detection data to obtain the rate of change and distribution characteristics of expansion pressure, it can more accurately assess the risk of furnace wall damage; in the pressure stabilization stage, by reducing the acquisition frequency, the generation of redundant data is reduced, the system processing load is reduced, and thus the operating efficiency of the monitoring system is improved. Attached Figure Description

[0037] Figure 1 This is a logic flowchart of the expansion pressure detection method applied to an electrically heated coking oven in this invention. Detailed Implementation

[0038] This application will now be described with reference to the accompanying drawings.

[0039] like Figure 1 As shown, the expansion pressure detection method of the present invention applied to an electrically heated coking oven specifically includes the following steps:

[0040] Step S1: Divide the coking stages based on the changes in the state of coal during the historical coking cycle of the electric heating coking oven, and set a corresponding benchmark acquisition frequency for each coking stage.

[0041] Step S2: 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.

[0042] Step S3: Perform pressure anomaly calculation on the expansion pressure detection data to obtain the expansion pressure change rate and expansion pressure distribution characteristics; the expansion pressure distribution characteristics are used to characterize the consistency of the expansion pressure detection data collected by multiple expansion pressure detection points at the same time.

[0043] Step S4: Based on the expansion pressure change rate and the expansion pressure distribution characteristics, perform sampling frequency correction to obtain the dynamic sampling frequency corresponding to the coking stage; the sampling frequency correction includes positive correction and negative correction;

[0044] 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 acquisition frequency exceeding their respective preset warning thresholds, an expansion pressure detection alarm message is generated based on the exceeding state.

[0045] In this embodiment, the method dynamically adjusts the acquisition frequency according to the characteristics of the coking stage, which can accurately adapt to the pressure change patterns of different coking stages. For example, when the pressure fluctuates drastically in the middle of coking, the acquisition frequency will automatically increase to ensure that the pressure peak can be captured in time. In the pressure stabilization stage, the acquisition frequency will decrease to avoid the generation of redundant data, so that the detection method can flexibly cope with complex production conditions and improve the adaptability and flexibility of detection. By selecting at least two expansion pressure detection points in each coking stage and combining the data characteristics of multiple detection points for analysis, the stress situation of the furnace wall can be more comprehensively reflected, effectively improving the detection accuracy and avoiding misjudgment caused by abnormal data from a single detection point.

[0046] By performing pressure anomaly calculations on the expansion pressure detection data, the rate of change and distribution characteristics of expansion pressure can be obtained, enabling a more accurate assessment of the risk of furnace wall damage. For example, when the rate of change or distribution characteristics of expansion pressure exceeds a preset warning threshold, the system will promptly generate an alarm message to remind operators to take measures, thereby effectively reducing the risk of furnace wall damage and ensuring the safety of the furnace structure. During the pressure stabilization phase, by reducing the acquisition frequency, redundant data generation is reduced, and the system processing load is lowered, which not only improves the system's operating efficiency but also extends the service life of the equipment.

[0047] Meanwhile, the combination of dynamic acquisition frequency adjustment and multi-detection point data fusion can effectively reduce false alarms and missed alarms. For example, when the pressure fluctuates drastically, increasing the dynamic acquisition frequency can capture the pressure peak in time and avoid missed alarms. In the pressure stabilization stage, the fusion of multi-detection point data can avoid false alarms caused by abnormal data from a single detection point, making the detection method more reliable in practical applications and improving the overall performance of the system.

[0048] In some embodiments of the present invention, existing coking stage divisions rely heavily on empirical time points, failing to correlate with the actual pyrolysis state of the coal. This leads to a disconnect between stage boundaries and coal expansion characteristics. For example, the critical stage of a sudden pressure increase during the mid-coking process may be vaguely defined, resulting in an unreasonable baseline sampling frequency setting. Therefore, it is necessary to divide stages based on the actual state changes of the coal during historical coking cycles, deeply binding stage characteristics with the physicochemical properties of the coal pyrolysis process, ensuring that subsequent detection strategies can adapt to the expansion pressure fluctuation patterns of each stage. Specific implementation is as follows:

[0049] Step S101: Extract multi-dimensional state characteristic parameters from historical coking cycle operations, including historical coke cake temperature sequence, historical expansion pressure change rate sequence, and historical volatile matter escape rate sequence; wherein the historical coke cake temperature sequence is derived from the temperature monitoring data of the thermocouple in the center of the carbonization chamber, reflecting the degree of heating 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 intensity of coal expansion; the historical volatile matter escape rate sequence is derived from the historical gas flow and composition analysis of the raw coal gas combustion system, reflecting the intensity of the coal pyrolysis reaction.

[0050] Step S102: Based on multi-dimensional state characteristic parameters and combined with the physicochemical properties of the coal pyrolysis process, namely the conversion laws of drying, pyrolysis, semi-coke, and coke, the coking stage is determined and divided by parameter thresholds, specifically as follows:

[0051] Drying and preheating stage: The historical coke temperature is in the first temperature range, such as room temperature to 300℃, the historical expansion pressure change rate is less than the first pressure rate threshold, such as 0.2kPa / min, and the historical volatile matter escape rate is less than the first volatile matter threshold, such as 0.5kg / h;

[0052] In the initial stage of pyrolysis: the historical coke temperature is in the second temperature range, such as 300℃ to 600℃, the historical expansion pressure change rate is ≥ the first pressure rate threshold and < the second pressure rate threshold, such as 1 kPa / min, and the historical volatile matter escape rate is ≥ the first volatile matter threshold and < the second volatile matter threshold, such as 2 kg / h.

[0053] Peak pyrolysis stage: The historical coke cake temperature is in the third temperature range, such as 600℃ to 800℃, the historical expansion pressure change rate is ≥ the second pressure rate threshold, and the historical volatile matter escape rate is ≥ the second volatile matter threshold.

[0054] Coke maturation stage: The historical coke cake temperature is in the fourth temperature range, such as 800℃ to 1050℃, the historical expansion pressure change rate is less than the third pressure rate threshold, such as 0.1 kPa / min, and the historical volatile matter escape rate is less than the third volatile matter threshold, such as 0.3 kg / h.

[0055] Among them, the first to fourth temperature ranges increase sequentially, the third pressure rate threshold is less than the first pressure rate threshold, and the third volatile matter threshold is less than the first volatile matter threshold.

[0056] In this embodiment, the stage determination is achieved through the coordinated use of parameters from three dimensions: temperature, rate of change of expansion pressure, and rate of volatile matter release. This avoids misjudgment of stages caused by a single parameter, ensuring a high degree of match between stage boundaries and the actual pyrolysis state of the coal. The four stages correspond to the gradual, rising, sudden, and stable characteristics of the coal's expansion pressure, making the setting of the benchmark acquisition frequency more targeted. Parameter extraction is based on the existing detection system of the electrically heated coking oven, eliminating the need for additional equipment and facilitating industrial applications. Furthermore, threshold adjustment can adapt to coking characteristics under different coal blending schemes or heating regimes. Combined with the physicochemical properties of coal pyrolysis, the stage division not only serves pressure detection but also indirectly reflects the rationality of the coal blending scheme.

[0057] In a preferred embodiment of the present invention, during the coking process, the expansion pressure fluctuations of coal differ at different coking stages. For example, the pressure tends to increase sharply and fluctuate frequently during the peak pyrolysis stage, while the pressure is relatively stable during the drying preheating stage and the coke maturation stage. If a uniform benchmark sampling frequency is set for all stages, two problems will arise: first, during stages of severe pressure fluctuations, the sampling interval is too wide, making it impossible to capture instantaneous peak values, leading to a lag in the assessment of furnace wall damage risk; second, during stages of stable pressure, high-frequency sampling generates a large amount of redundant data, increasing the system's storage and processing load. Therefore, it is necessary to differentiate the benchmark sampling frequency according to the expansion pressure fluctuation characteristics of each coking stage, so that the sampling frequency accurately matches the pressure change pattern within the stage, ensuring the detection accuracy of key stages while avoiding resource waste. The specific implementation is as follows:

[0058] Step S111: Based on the historical expansion pressure change rate sequence, extract the historical expansion pressure fluctuation characteristics for each stage: drying preheating stage, initial pyrolysis stage, peak pyrolysis stage, and coke maturation stage. These characteristics include:

[0059] Historical expansion pressure peak frequency: The number of times pressure peaks occur per unit time, reflecting the frequency of sudden pressure increases;

[0060] Standard deviation of historical expansion pressure change rate: Quantifies the severity of pressure fluctuations by calculating the dispersion of the pressure change rate;

[0061] Duration of historical pressure fluctuations: Records the duration from the onset to stabilization of a single pressure fluctuation, reflecting the persistence of the fluctuation.

[0062] Step S112: For each coking stage, the intensity of expansion pressure fluctuation is calculated by weighted algorithm based on the above three types of fluctuation characteristics. For example, the peak pyrolysis stage has a significantly higher fluctuation intensity than other stages due to the high frequency of peak occurrence, large standard deviation of change rate, and long duration of continuous fluctuation. The coke maturation stage has the lowest fluctuation intensity due to stable pressure.

[0063] Step S113: Preset a sampling frequency reference table, where different expansion pressure fluctuation intensities correspond to different benchmark sampling frequencies, i.e., the higher the fluctuation intensity, the higher the corresponding benchmark sampling frequency; based on the fluctuation intensity calculated for each stage, match and determine the benchmark sampling frequency for that stage in the reference table; for example, the peak pyrolysis stage has the highest fluctuation intensity and corresponds to a higher benchmark sampling frequency; the drying preheating stage and the coke maturation stage have lower fluctuation intensities and correspond to lower benchmark sampling frequencies.

[0064] In this embodiment, fluctuation characteristics are extracted and fluctuation intensity is quantified by historical data, so that the reference acquisition frequency no longer relies on experience judgment, but is determined based on the actual pressure fluctuation pattern of each stage, avoiding the drawbacks 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; the low reference frequency in the low fluctuation stage reduces the frequency of negative correction, making the dynamic frequency more stable and the system operation more efficient; when the coal blending scheme or heating regime is adjusted, the expansion pressure fluctuation characteristics of each stage will change accordingly; through the mapping relationship between fluctuation intensity and frequency, the reference frequency can be automatically adjusted based on updated historical data, without the need for manual reset, and the adaptability is stronger.

[0065] In some embodiments of the present invention, the coal expansion regions differ at different coking stages. For example, during the peak pyrolysis stage, coal expansion is mainly concentrated in the middle to top of the carbonization chamber, while during the drying and preheating stage, expansion is mainly at the bottom. If the same expansion pressure detection points are used consistently, the detection data for some stages may not reflect the typical pressure distribution of that stage. For instance, if only the bottom point is detected during the peak pyrolysis stage, the sudden pressure increase signal at the top may be missed, affecting the detection accuracy. Therefore, it is necessary to select detection points that represent the pressure distribution of each coking stage based on its expansion characteristics to ensure the validity and representativeness of the collected data. The specific implementation is as follows:

[0066] Step S201: Based on the pressure data from different detection points in the historical coking cycle, extract the spatial distribution characteristics of each coking stage, including:

[0067] Average pressure at each detection point during the corresponding stage: reflects the pressure level at that point during that stage;

[0068] Pressure fluctuation range: The difference between the maximum and minimum pressure data at this point reflects the range of pressure variation;

[0069] Pressure gradient variation: Analyze the pressure difference between different points to determine the spatial distribution trend of pressure.

[0070] Step S202: Based on the above spatial distribution characteristics, identify the area of ​​effect of coal expansion pressure in each coking stage, i.e., the typical distribution state; for example, in the drying and preheating stage, select the detection point with a lower average pressure and smaller fluctuation range, usually located in the middle of the furnace wall; in the initial stage of pyrolysis, select the detection point with a gradually increasing average pressure and moderate fluctuation range, usually located in the middle and upper part of the furnace wall; in the peak stage of pyrolysis, select the detection point with the highest average pressure and the largest fluctuation range, usually located in the middle and lower part of the furnace wall; in the coke maturation stage, select the detection point with a lower average pressure and smaller fluctuation range, usually located in the middle and lower part of the furnace wall.

[0071] Step S203: From the preset multiple expansion pressure detection points, select at least two detection points located in the typical distribution area for each coking stage to ensure that the selected points can reflect the pressure distribution characteristics of the stage; for example, select the detection points in the middle and top of the pyrolysis peak stage, and select the detection points at the bottom of the drying preheating stage.

[0072] In this embodiment, by matching typical expansion areas of each stage to select detection points, misjudgments of pressure characteristics of some stages by fixed points are avoided, so that the collected data can truly reflect the expansion pressure distribution of that stage. As the coking stage progresses, the coal expansion area changes dynamically, and the stage-based selection of detection points can adapt to this change in real time. While ensuring detection coverage, it avoids redundant collection of invalid points and reduces system resource consumption. By selecting multiple detection points and comprehensively analyzing their data, misjudgments and omissions caused by abnormal data from a single detection point can be reduced.

[0073] In some embodiments of the present invention, in order to more accurately assess the pressure characteristics of each coking stage, it is necessary to extract the rate of change of expansion pressure and the distribution characteristics of expansion pressure from the original expansion pressure data. For example, if only the absolute value of pressure is focused on during the peak pyrolysis stage, the rapid pressure increase process in a short period of time may be missed. If only data from a single detection point is relied upon, sensor failure may be misjudged as a real pressure anomaly. Therefore, it is necessary to quantify the dynamic trend of pressure change and the consistency of data from multiple detection points by calculating the rate of change of expansion pressure and the distribution characteristics, and to analyze the original data from the two dimensions of temporal dynamics and spatial consistency.

[0074] Specifically, for each detection point, expansion pressure detection data from two consecutive acquisition times are selected, and the rate of change is calculated as follows: the pressure difference between the two times is calculated, i.e., the data from the later time point minus the data from the earlier time point; the time interval between the two times is determined based on the current acquisition frequency; the pressure difference is divided by the time interval to obtain the rate of change of expansion pressure during that time period; if there are three or more consecutive acquisition times, the average or maximum value of the rate of change for multiple time periods is taken as the current rate of change of expansion pressure at that detection point, so as to more comprehensively reflect the dynamic trend of pressure.

[0075] For data from multiple monitoring points at the same time, statistical methods are used to analyze their consistency. Specifically, the mean of pressure data from all monitoring points is calculated to reflect the overall pressure level at that time; the standard deviation of pressure data from all monitoring points is calculated to reflect the dispersion of the data; the standard deviation is divided by the mean to obtain the expansion pressure distribution characteristics; the smaller the value of the expansion pressure distribution characteristics, the higher the consistency of the data from multiple monitoring points; the larger the value, the more significant the data differences and the lower the consistency.

[0076] In this embodiment, the rate of change of expansion pressure can accurately reflect the increase or decrease of pressure per unit time, which is especially suitable for capturing the sudden increase in pressure during the peak of pyrolysis. This provides a clear basis for timely triggering of positive frequency correction and risk warning, and avoids delays in furnace wall damage risk assessment caused by peak capture lag. The expansion pressure distribution characteristics can effectively identify abnormal values ​​such as jump data caused by faults at individual detection points through statistical analysis of data from multiple detection points. For example, when a certain detection point shows a sudden increase in pressure but the data at other points are stable, it can be determined as a local error rather than a true pressure anomaly, avoiding false triggering of subsequent frequency correction and warning, and improving the anti-interference capability of the method.

[0077] In some embodiments of the present invention, the physicochemical changes of coal at different coking stages differ. For example, during the peak pyrolysis stage, the coal expands violently, the pressure changes rapidly, and it has a significant impact on the furnace wall, while the pressure is relatively stable during the drying and preheating stage. If a uniform frequency correction logic is used, it cannot accurately adapt to the pressure change characteristics of each stage, which may lead to insufficient sampling frequency during critical pressure change stages or excessive sampling during stable stages. Therefore, it is necessary to dynamically correct the benchmark sampling frequency according to the real-time pressure change rate and data distribution characteristics so that the sampling frequency can adapt to the pressure dynamics of the current coking stage in real time.

[0078] Specifically, the dynamic acquisition frequency is obtained by correcting the reference acquisition frequency for the current coking stage using the following formula:

[0079] ;

[0080] Where f represents the dynamic acquisition frequency;

[0081] f0 represents the reference acquisition frequency of the current coking stage;

[0082] v represents the real-time rate of change of the swelling pressure;

[0083] v0 represents the reference rate of change of pressure in the current coking stage, which is a typical value of this coking stage based on historical data statistics;

[0084] CV represents the swelling pressure distribution characteristic;

[0085] k1 i represents the stage-specific sensitivity coefficient of the i-th coking stage, which is used to reflect the response weight of this coking stage to pressure changes. It is set according to the historical pressure characteristics of each coking stage. The more active the pressure change stage, the i greater the value of k1;

[0086] k2 i represents the stage-specific stability coefficient of the i-th coking stage, which is used to reflect the degree of dependence of this coking stage on data consistency. It is set according to the consistency of historical multi-detection point data of each coking stage. The higher the data consistency requirement of the stage, the i greater the value of k2.

[0087] In the above calculation formula, the sensitivity of different coking stages to pressure changes is different. For example, during the pyrolysis peak stage, the swelling reaction of the coal material is intense, and the pressure change has a great impact on the furnace wall, so it is necessary to capture the pressure change more sensitively; the stage-specific sensitivity coefficient is set according to the stage historical pressure characteristics, which can make the stage with active pressure change significantly increase the acquisition frequency when the pressure changes, and timely capture key signals such as sudden pressure increase, ensuring the timeliness of risk monitoring;

[0088] The ratio of the rate of change of pressure reflects the degree of deviation of the current pressure change from the typical situation of this stage; when v > v0, it means that the pressure change is more intense than normal, and this part will promote the increase of the acquisition frequency; when v < v0, it will cause a tendency for the acquisition frequency to decrease, achieving the effect of real-time adjustment of the acquisition frequency according to the dynamic change of pressure;

[0089] is an exponential function that monotonically decreases with the increase of the swelling pressure distribution characteristic; when the swelling pressure distribution characteristic is small, the data consistency is high, the pressure change is reliable, , and the influence on the rate correction term is small, ensuring that the frequency can follow the rate flexibly; when the swelling pressure distribution characteristic is large, the data consistency is low, and there may be single-point anomalies, significantly decreases, which will weaken the role of the rate correction term and avoid blindly increasing the frequency due to unreliable data; k2 iThis represents the stage-specific stability coefficient for the i-th coking stage, and is the stage's dependence on data consistency. For example, during the peak pyrolysis stage, the pressure to make more reliable multi-point data judgments increases sharply. i Setting it to a larger value makes the influence of expansion pressure distribution characteristics more significant; the coke maturation stage has a slightly lower dependence on consistency, k2 i Set to a smaller value;

[0090] The above formula for calculating the dynamic acquisition frequency is based on the reference acquisition frequency, and uses a stage-specific sensitivity coefficient to adapt to stage characteristics and a pressure change rate ratio to capture real-time changes. To ensure data reliability, the collaboration of these three elements enables the frequency to be dynamically adjusted according to the current pressure, rather than relying solely on historical statistics; different stages can obtain appropriate correction logic based on their own risks and data needs; erroneous corrections caused by single-point anomalies or unreliable data can be avoided; and the settings can also be flexibly configured according to the coking oven type and the historical characteristics of the production process.

[0091] Furthermore, during the coking process, abnormal changes in coal expansion pressure can directly lead to furnace wall damage, and monitoring a single parameter is insufficient to fully reflect the risk status. For example, if the absolute pressure value does not exceed the standard but the rate of change increases sharply, it may indicate that peak pressure will occur in the short term; or poor consistency of data from multiple monitoring points may reflect abnormal local coal expansion. If only one parameter is relied upon to judge the risk, it is easy to miss or misjudge, and it is impossible to take protective measures in time. Therefore, it is necessary to integrate four types of parameters: expansion pressure detection data, rate of change, distribution characteristics, and dynamic acquisition frequency, and trigger alarms through multi-dimensional threshold monitoring to ensure furnace wall safety and stable coke quality.

[0092] Specifically, based on historical coking data and furnace wall tolerance standards, the following warning thresholds are set:

[0093] Expansion pressure detection data threshold: set according to the compressive strength limit of the furnace wall material, such as 5 kPa for a certain coke oven;

[0094] Expansion pressure change rate threshold: set according to the safe change range of different coking stages, such as 0.8 kPa / min for the peak pyrolysis stage;

[0095] Expansion pressure distribution characteristic threshold: set according to the normal consistency range of multi-detection point data, such as the upper limit of the ratio of standard deviation to mean is 0.2;

[0096] Dynamic acquisition frequency threshold: set according to the system's maximum processing capacity, such as a maximum frequency threshold of 1 time / second, to avoid high-frequency acquisition causing system overload;

[0097] During the coking process, the parameters acquired in real time are continuously compared with the corresponding thresholds. If any parameter exceeds its preset warning threshold, it is judged as an abnormal state; if multiple parameters exceed the threshold at the same time, it is judged as a high-risk state.

[0098] For different abnormal states, graded alarm information is generated. When a single parameter slightly exceeds the limit, a prompt alarm is generated, such as "Pressure change rate is slightly high, please pay attention to monitoring"; when multiple parameters exceed the limit, a warning alarm is generated, such as "Pressure increases suddenly during the peak of pyrolysis and data consistency is poor, it is recommended to adjust the heating rate"; when key parameters such as absolute pressure exceed the limit seriously, an emergency alarm is generated, such as "Furnace wall pressure exceeds the standard, heating must be stopped immediately".

[0099] On the other hand, if the expansion pressure detection data, expansion pressure change rate, expansion pressure distribution characteristics, and dynamic acquisition frequency do not exceed their respective preset warning thresholds, then real-time data acquisition is performed based on the corrected acquisition frequency, and the threshold judgment detection process is repeated.

[0100] In this embodiment, the expansion pressure detection data directly reflects the current absolute pressure value. Its threshold ensures that the furnace wall will not bear pressure exceeding its tolerance limit, avoiding physical damage. The expansion pressure change rate reflects the urgency of the pressure rise. Its threshold can provide early warning when the pressure rises rapidly before reaching its limit, giving time for intervention. The expansion pressure distribution characteristics reflect the consistency of data from multiple detection points. Its threshold can identify local abnormal expansion, avoiding the neglect of local risks due to normal overall pressure. The dynamic acquisition frequency reflects the matching degree between system load and pressure fluctuations. Its threshold can prevent system overload caused by high-frequency acquisition, ensuring continuous detection. Different levels of alarms correspond to different handling strategies: prompt alarms can be handled by the system automatically adjusting the acquisition frequency; warning alarms require operator intervention to optimize coal blending or heating procedures; emergency alarms trigger emergency shutdown protection, avoiding overreaction or insufficient response caused by single alarms, balancing production efficiency and furnace safety.

[0101] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention 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, then an expansion pressure detection alarm message is generated based on the exceeding state. 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. Calculate the ratio of the standard deviation to the mean of the expansion pressure detection data from multiple detection points at the same time, and use it as a characteristic of the expansion pressure distribution; 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.

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 pre-set 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, 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.

8. The expansion pressure detection method for an electrically heated coking oven according to claim 7, 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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