Intelligent control method and system for self-falling of flue gas desulfurizer

Abstract

The present application belongs to the technical field of flue gas desulfurization material falling control, and specifically discloses a flue gas desulfurizer self-falling intelligent control method and system. The method calculates the incoming material weight change rate by collecting the material bin weighing signal in real time, and calculates the actual feeding rate by collecting the feeder body mass signal. When the actual feeding rate is lower than the target set value and the deviation is out of limit, the current time is taken as the end point to construct an analysis window. Trend consistency indexes are obtained by trend matching analysis of the two signals in the window, and the material risk index and the incoming material risk index are calculated respectively. Based on the three indexes, the feeding deviation type is determined as incoming material deviation and / or discharging deviation, and corresponding speed reduction or PID parameter adjustment instructions are generated. The present application realizes accurate differentiation between incoming material interruption and insufficient feeding capacity, avoids invalid regulation and control and equipment loss caused by blind speed regulation, and thus guarantees the accuracy of desulfurizer feeding.

Description

Technical Field

[0001] This invention belongs to the field of flue gas desulfurization material feeding control technology, specifically, it relates to an intelligent control method and system for the self-feeding of flue gas desulfurizing agent. Background Technology

[0002] In the calcium-based dry desulfurization process for flue gas, highly active calcium powder desulfurizer is transported from the silo to the desulfurization terminal via a discharge valve and a loss-in-weight feeder. The feeding rate is controlled by adjusting the feeder speed, thereby achieving precise and continuous feeding.

[0003] Highly active calcium powder is highly hygroscopic and easily absorbs moisture during transportation, forming bridging or agglomeration at the discharge port of the silo cone. When bridging, agglomeration, or other disturbances cause interruption of upstream material supply, the material in the feeder gradually decreases, and the measured rate drops. Currently, the feeding rate is usually restored by increasing the feeder speed. However, the physical root cause of bridging and other disturbances is the interruption of material supply rather than insufficient feeding capacity. Increasing the speed cannot restore upstream material supply. After the internal material is emptied, the screw spins idly, the rate is zero and uncontrollable, resulting in ineffective final control. Currently, there is a lack of perception of the two intermediate state variables: the material inventory inside the feeder and the state of upstream material supply. When the rate drops, it is impossible to distinguish whether the fault source is insufficient output capacity or interruption of input material supply. Only a preset single adjustment action can be executed, thus failing to guarantee the accuracy and timeliness of feeding. Summary of the Invention

[0004] In view of this, in order to solve the above problems, a method and system for intelligent control of flue gas desulfurizing agent self-feeding is proposed.

[0005] The objective of this invention can be achieved through the following technical solution: The first aspect of this invention provides a method for intelligent control of flue gas desulfurizing agent self-feeding, the method comprising: real-time acquisition of silo weighing signals and calculation of incoming material weight change rate, and real-time acquisition of feeder body mass signals and calculation of actual feeding rate by differentiation.

[0006] If the current actual feeding rate is lower than the target setting value and the deviation exceeds the allowable range, an analysis window is constructed with the current time as the endpoint.

[0007] A trend matching analysis is performed on the incoming material weight change rate and the actual feeding rate within the analysis window to calculate the trend consistency index. At the same time, the incoming material risk index is calculated by the ratio of the standard deviation of the incoming material weight change rate within the analysis window to the upper limit of the preset normal fluctuation range. The outgoing material risk index is calculated by the cumulative deviation of the actual feeding rate from the target set value at each corresponding sampling time within the analysis window.

[0008] The feeding deviation type is determined based on the trend consistency index, the incoming material risk index, and the outgoing material risk index. The feeding deviation type includes one or more of the incoming material deviation and the outgoing material deviation.

[0009] Based on the type of feeding deviation, a corresponding adjustment control command is generated and sent to the corresponding control terminal.

[0010] The second aspect of the present invention also provides an intelligent control system for self-feeding flue gas desulfurizing agent. The system includes: a feeding signal acquisition module, which acquires the weighing signal of the silo in real time and calculates the rate of change of the incoming material weight, and simultaneously acquires the mass signal of the feeder body in real time and calculates the actual feeding rate by differentiation.

[0011] The analysis window construction module constructs an analysis window with the current time as the endpoint if the current actual feeding rate is lower than the target setting value and the deviation exceeds the allowable range.

[0012] The trend matching analysis module performs trend matching analysis on the incoming material weight change rate and the actual feeding rate within the analysis window, and calculates the trend consistency index.

[0013] The material feeding risk analysis module calculates the material feeding risk index by the ratio of the standard deviation of the material weight change rate within the analysis window to the upper limit of the preset normal fluctuation range, and calculates the material output risk index by the cumulative deviation of the actual feeding rate from the target set value at each corresponding sampling time within the analysis window.

[0014] The deviation type determination module determines the feeding deviation type based on the trend consistency index, incoming material risk index, and outgoing material risk index. The feeding deviation type includes one or more of the incoming material deviation and outgoing material deviation.

[0015] The control command generation module generates corresponding adjustment control commands based on the type of feeding deviation and sends them to the corresponding control terminal.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention, by using a dual signal acquisition method based on the simultaneous acquisition of the silo weighing signal and the feeder body mass signal, constructs an analysis window with the current deviation time as the endpoint to perform trend matching analysis on the two signals, breaks the limitation of focusing on a single feeding rate signal while ignoring the upstream material status, solves the problem that the current method can only sense the decrease in feeding rate but cannot trace its root cause to whether it is the interruption of upstream material or the insufficient downstream feeding capacity, realizes the synchronous monitoring and effective differentiation of the input material status and the output feeding status, and provides a complete field data basis for subsequent regulation.

[0017] (2) This invention calculates the trend consistency index, the incoming material risk index, and the outgoing material risk index separately. The trend consistency index characterizes the degree of trend coordination between changes in incoming material and changes in feed, serving as a preliminary basis for determining the type of deviation. The incoming material risk index quantifies the severity of fluctuations in upstream incoming material, and the outgoing material risk index quantifies the cumulative degree of deviation of feed relative to the target feed, serving as a basis for correcting and verifying the preliminary determination results. The three-layer index forms a progressive determination model, thereby providing a hierarchical quantitative data foundation for the accurate positioning of subsequent deviation types.

[0018] (3) The present invention identifies the deviation type based on the trend consistency index, the incoming material risk index and the outgoing material risk index. This makes up for the current defect that the interruption of incoming material such as bridging and agglomeration is misjudged as insufficient feeding capacity due to the lack of incoming material status monitoring. It avoids the situation that blindly increasing the feeder speed will cause the screw to run idle after the internal material is emptied and the control is ineffective. It realizes the accurate distinction and directional identification of incoming material deviation and outgoing material deviation, and reduces the ineffective control and equipment wear caused by misjudgment.

[0019] (4) Based on the differentiated control method of feeding deviation type, the corresponding adjustment control command is generated for the incoming material deviation and the outgoing material deviation and sent to the corresponding control terminal. This avoids the current shortcomings of simply performing a single feeder speed adjustment action regardless of the root cause of the fault, which cannot achieve effective control. It realizes the matching and correspondence between control action and deviation type, and ensures the control effectiveness and feeding accuracy of the desulfurizer feeding process. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall implementation process of the method of the present invention;

[0021] Figure 2 This is a schematic diagram of the system module connection structure of the present invention;

[0022] Figure 3 This is a schematic diagram of the trend matching analysis process of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Currently, when the measured feeding rate decreases, the feeding rate is usually controlled by adjusting the feeder speed. When bridging or agglomeration occurs at the discharge port of the silo cone due to the moisture absorption of highly active calcium powder, the upstream material supply gradually stops, and the material in the feeder continues to decrease, causing the measured feeding rate to decrease. This can easily be misjudged as insufficient feeding capacity, leading to a blind increase in the feeder speed. However, increasing the speed when the material supply has been interrupted cannot restore the upstream material supply. Instead, it causes the screw to idle after the material inside the feeder is emptied, ultimately rendering the control ineffective.

[0025] Based on this, this invention discloses an intelligent control method for the self-feeding of flue gas desulfurizing agent, which can effectively improve the accuracy and effectiveness of feed regulation. The control method provided by this invention is applicable to normal operating conditions where there is an effective desulfurizing agent level in the silo, i.e., the subsequent feed deviation judgment and regulation steps are executed under the condition that the silo level is not lower than a preset safety lower limit. When the silo level is lower than the preset safety lower limit, a level alarm is triggered and the intelligent self-feeding control process involved in this method is suspended.

[0026] Specifically, please refer to Figure 1 As shown, the present invention provides an intelligent control method for self-feeding of flue gas desulfurizing agent. The method includes: S1, real-time acquisition of the weighing signal of the silo and calculation of the change rate of the incoming material weight, and real-time acquisition of the mass signal of the feeder body and calculation of the actual feeding rate by differentiation.

[0027] During the desulfurizer feeding process, when bridging or agglomeration of the highly active calcium powder occurs at the cone outlet of the silo due to moisture absorption, the rate of change in the incoming material weight approaches zero. However, the feeder can still maintain a certain feeding rate before the residual material inside is emptied. Conversely, when the feeder's screw or feeding channel is blocked, the incoming material may discharge normally, but the actual feeding rate cannot reach the target set value. Both of these abnormal states manifest as a decrease in the feeding rate. If the actual feeding rate is obtained solely from the feeder's own quality signal, it is impossible to distinguish whether the root cause of the rate decrease is an interruption in the upstream material supply or insufficient downstream feeding capacity.

[0028] Based on this, this embodiment adopts a two-channel synchronous acquisition method to acquire the hopper weighing signal and the feeder body mass signal in real time.

[0029] Specifically, load cells are installed at the support structure of the silo to collect the overall weight signal of the silo in real time. The current sampling time is recorded as... The weighing signal of the silo collected at the current sampling time is Previous sampling time The collected silo weighing signal is Then the rate of change of incoming material weight Calculate according to the following formula: ,in, The sampling period.

[0030] The rate at which the weighing signal in the silo decreases over time represents the instantaneous flow rate of the desulfurizing agent discharged from the silo. When the rate of change of the incoming material weight is zero or close to zero, it indicates that the upstream material supply has been interrupted.

[0031] Simultaneously, a weighing sensor is installed at the main support structure of the feeder to collect the overall mass signal of the feeder body in real time. Record the current sampling time. The mass signal of the feeder body collected is The rate of change of the feeder body's mass, i.e., the actual feeding rate, is obtained by differentiating the mass signal. The calculation formula is as follows: .

[0032] In practical applications, the above differential operation can also be approximated using a difference method, that is, calculating the ratio of the difference between the current sampling time and the previous sampling time of the feeder's mass signal to the sampling period. When the feeder has sufficient material and is operating normally, the actual feeding rate remains consistent with the target set value. As the material inside the feeder gradually decreases, the actual feeding rate decreases accordingly.

[0033] It should be noted that the two signals mentioned above are acquired synchronously using the same clock reference to ensure accurate time alignment of the two signals in the subsequent analysis window. Furthermore, considering that environmental vibrations and electromagnetic interference may introduce high-frequency noise components, the implementation method prioritizes low-pass filtering preprocessing of the acquired weighing signals before calculation. The cutoff frequency of the low-pass filter is set based on the spectral characteristics of the on-site weighing signal, aiming to filter out high-frequency noise components introduced by equipment vibration while retaining the true mass change signal. This ensures that the phase delay introduced by the filter when the filtered signal is used for differential calculation does not exceed one sampling period, thus avoiding deviations in the time alignment of the two signals in the subsequent calculation.

[0034] In this embodiment, the cutoff frequency of the low-pass filter is determined according to the upper limit of the dynamic response frequency band specified in the weighing sensor product specification. That is, the cutoff frequency is preferably set to the upper limit of the dynamic response frequency band. If the measured noise frequency band is lower than this upper limit, the upper limit frequency of the measured noise energy concentration band is used as the cutoff frequency.

[0035] Through this step, this embodiment achieves synchronous real-time monitoring of the feeding input and output ends, providing a reliable data basis for subsequently distinguishing between incoming material deviation and outgoing material deviation.

[0036] S2. If the current actual feeding rate is lower than the target setting value and the deviation exceeds the allowable range, construct an analysis window with the current time as the endpoint.

[0037] It should be noted that during the operation of the feeder, the actual feeding rate may vary randomly within a certain range due to factors such as fluctuations in incoming material and equipment vibration. Therefore, this embodiment pre-sets an allowable deviation range, for example, set to ±5% of the target set value. When the deviation between the actual feeding rate and the target set value is within this allowable range, the current feeding status is considered normal, and no further analysis is required. Only when the actual feeding rate is lower than the target set value and the difference between the two exceeds this allowable range is a significant feeding deviation determined, triggering subsequent processes such as the construction of the analysis window.

[0038] When determining a significant deviation in feeding, it's important to consider that the rate of change in incoming material and the actual feeding rate at a single moment are easily affected by signal noise and instantaneous disturbances. Therefore, deviation determination based on single-point data involves significant randomness and uncertainty. Furthermore, there is an inherent physical transmission delay in the transport of desulfurizing agent from the silo outlet to the feeder outlet. The incoming material change corresponding to the detected discharge deviation at the current moment actually occurred some time prior to the current moment. If the start and end points of the window are not corrected for delay, there will be a time misalignment between the incoming and outgoing signals, preventing the formation of an effective causal relationship.

[0039] Based on this, this embodiment constructs an analysis window covering the previous continuous time period with the current time as the endpoint. The starting point of the window is set as the current time minus the total delay time of powder transmission at the feeder's discharge end, and the ending point of the window is set as the current time. This ensures that the incoming material weight change rate signal and the discharge deviation signal within the window are accurately aligned on the physical transmission time axis, laying the time reference for the subsequent joint analysis of the two signals.

[0040] The total delay time of powder transmission at the feeder's discharge end is the ratio of the theoretical powder conveying speed to the length of the conveying channel.

[0041] After completing the analysis window construction, proceed to the next step.

[0042] S3. Perform trend matching analysis on the incoming material weight change rate and actual feeding rate within the analysis window, calculate the trend consistency index, and calculate the incoming material risk index and the outgoing material risk index.

[0043] The desulfurizing agent feeding system consists of an upstream silo and a downstream feeder connected in series via a conveying channel. During operation, the incoming material end experiences inherent unloading instability due to fluctuations in powder flow characteristics. The feeder's actual output deviates from the target output due to changes in its mechanical state and drive characteristics. However, there is a definite causal relationship between the incoming and outgoing material states under normal operating conditions. These three physical levels are independent yet coexist; an anomaly in any level may manifest as a decrease in the feeding rate, but their respective physical causes and handling methods are completely different.

[0044] Based on this, this embodiment calculates the trend consistency index, incoming material risk index, and outgoing material risk index in parallel within the analysis window.

[0045] Preferably, the trend consistency index is used to quantify the degree of trend coordination between changes in incoming materials and changes in outgoing materials, thereby determining whether the causal relationship between the two is maintained normally. Please refer to [link to relevant documentation]. Figure 3 As shown, the specific calculation process is as follows:

[0046] A1. Calculate the mean values ​​of the incoming material weight change rate sequence and the actual feeding rate sequence within the analysis window, respectively.

[0047] A2. Construct an incoming material deviation sequence based on the deviation of the value at each sampling time in the incoming material weight change rate sequence from its mean value, and construct an outgoing material deviation sequence based on the deviation of the value at each sampling time in the actual feeding rate sequence from its mean value.

[0048] A3. The ratio of the number of data points with the same sign for the incoming material deviation value and the outgoing material deviation value at the same time to the total number of data points in the analysis window is used as the directional consistency index.

[0049] A4. If the absolute values ​​of the incoming material deviation and the outgoing material deviation are both zero at the same time, the amplitude matching degree at that time is set to 1. Otherwise, the ratio of the smaller value to the larger value of the absolute values ​​of the incoming material deviation and the outgoing material deviation at the same time is taken as the amplitude matching degree at that time. Then, the average value of the amplitude matching degree at all times in the analysis window is taken as the amplitude consistency index.

[0050] It should be understood that the directional consistency index reflects whether the directions of change of the two signals are consistent, while the amplitude consistency index reflects whether the amplitudes of change of the two signals are similar.

[0051] A5. Calculate the weighted sum of the directional consistency index and the amplitude consistency index as the trend consistency index. The higher the trend consistency index, the higher the degree of trend coordination between changes in incoming material and changes in feed, and the greater the likelihood that the change in the discharge rate originates from changes in the state of the incoming material. Conversely, it indicates that there is no coordinated change between the two, and the decrease in the discharge rate is caused by an independent anomaly at the discharge end itself.

[0052] It should be further explained that before calculating the weighted sum of the trend consistency index, the specific values ​​of the directional weight and amplitude weight need to be determined according to the current operating conditions. Specifically, this includes: obtaining the incoming material weight change rate at each sampling time within the current analysis window, and statistically analyzing the proportion of sampling times where the incoming material weight change rate is lower than a preset threshold. If the incoming material weight change rate is lower than the preset threshold, it indicates that the unloading volume of the upstream silo at the corresponding time is lower than the lower limit of the normal range. If the proportion of sampling times is greater than or equal to the preset proportion threshold, it is determined that the current operating condition is low material level. Under the low material level condition, there is a significant shortage of material supply at the incoming material end. At this time, the directional consistency of the two signals can better reflect the degree of abnormality of the incoming material status than the amplitude consistency. That is, the directional weight is configured as the first weight value, the amplitude weight is configured as the second weight value, and the first weight value is greater than the second weight value. The preset proportion threshold is any value between 0.7 and 0.9, and is preferably 0.9 in this embodiment of the invention.

[0053] Otherwise, it is determined that the current operating condition is normal, and the directional weight and amplitude weight are configured with the same weight value.

[0054] It should be understood that the sum of the weights of the directional consistency index and the magnitude consistency index is always 1. For example, when taking the same weight value, both are 0.5. When taking different weights, the first weight value is 0.7 and the second weight value is 0.3.

[0055] It should be noted that the weight configuration is determined once after the analysis window is constructed. That is, the working condition is determined and the weight configuration is based on the material weight change rate data of the entire time period from the start to the end of the window. The determination result is fixed and applied to the trend consistency index calculation of all sampling times within the window, and is not dynamically updated with the data changes of local time periods within the window.

[0056] Preferably, the incoming material risk index is used to quantify the degree of fluctuation of the incoming material weight change rate within the window, so as to assess whether there are flow anomalies such as bridging and agglomeration at the incoming material end. The incoming material risk index is the ratio of the standard deviation of the incoming material weight change rate sequence within the analysis window to the upper limit of the preset normal fluctuation range. The larger the incoming material risk index, the more drastic the fluctuation of the incoming material weight change rate within the analysis window, the more unstable the incoming material status, and the higher the possibility of incoming material deviation.

[0057] It should be noted that the upper limit of the preset normal fluctuation range is the upper boundary of the normal fluctuation range. The normal fluctuation range is determined as follows: a continuous 72-hour operating period after commissioning and confirmation that there is no bridging, no agglomeration, and the feed rate deviation does not exceed ±5% of the target set value is used as the benchmark calibration period. The incoming material weight change rate data within this calibration period is extracted, and the interval formed by its minimum and maximum values ​​is taken as the preset normal fluctuation range. The confirmation of no bridging and no agglomeration is based on the fact that no abnormal action alarm of the hopper cone vibrator was triggered during this period, no manual arch breaking operation was performed, and the fluctuation rate of the feeder body quality signal is continuously within the design-allowed random fluctuation range.

[0058] In another preferred embodiment, the discharge risk index is used to quantify the cumulative degree of deviation of the actual feeding rate from the target set value, in order to assess whether the feeder's output deviates from expectations. The specific calculation process is as follows:

[0059] B1. Calculate the difference between the actual feed rate sequence and the target set value sequence at the same sampling time within the analysis window.

[0060] B2. Record the times when the difference is positive as positive deviation times and the times when the difference is negative as negative deviation times. Sum the differences of each positive deviation time to obtain the cumulative positive deviation value. Sum the absolute values ​​of the differences of each negative deviation time to obtain the cumulative negative deviation value.

[0061] B3. If both the cumulative positive deviation and the cumulative negative deviation are less than the preset effective deviation threshold, it is considered that there is no effective deviation between the actual feeding rate and the target set value within the analysis window, and the discharge risk index is assigned a value of 0.

[0062] Understandably, the preset effective deviation threshold is determined as follows: the actual feeding rate data of the feeding system is collected for 24 consecutive hours under stable operating conditions, the average value of the cumulative positive deviation and the cumulative negative deviation in the analysis window during this period is calculated, and twice the average value is used as the preset effective deviation threshold to avoid ineffective control caused by small deviations.

[0063] B4. Otherwise, divide the cumulative positive deviation value by the sum of the cumulative positive deviation value and the cumulative negative deviation value to obtain the proportion of the cumulative positive deviation value. Determine the dominant deviation type based on the proportion of the cumulative positive deviation value. The dominant deviation type is either dominant positive deviation, dominant negative deviation, or no dominant type.

[0064] If the percentage is greater than or equal to the preset first percentage threshold, the deviation dominance type is positive deviation dominance. If the percentage is greater than or equal to the preset second percentage threshold and less than the preset first percentage threshold, the deviation dominance type is no dominance type. If the first percentage threshold is greater than the second percentage threshold, and the percentage is less than the second percentage threshold, the deviation dominance type is negative deviation dominance.

[0065] It should be noted that the sum of the first percentage threshold and the second percentage threshold is 1. In this embodiment of the invention, the first percentage threshold is 70%, and the second percentage threshold is 30%. A percentage falling within the 30% to 70% range indicates that the positive and negative deviations are relatively balanced, and no action based on the dominant deviation type is required. When the cumulative percentage of positive deviation exceeds 70% or falls below 30%, it indicates that the deviation is continuously accumulating in a single direction, exceeding the normal equilibrium range, and targeted regulation is necessary.

[0066] B5. After determining the dominant type of deviation, calculate the discharge risk index based on the preset maximum allowable cumulative deviation value:

[0067] B51. If the positive deviation is dominant, the cumulative value of the positive deviation and the preset maximum permissible cumulative value of the deviation are respectively denoted as: and ,Will This serves as the value for the material discharge risk index.

[0068] B52. If the negative bias is dominant, the cumulative value of the negative bias is recorded as follows: ,Will This serves as the value for the material discharge risk index.

[0069] B53. If it is a type without a dominant group, As the value of the material discharge risk index, among which, and These represent the percentage weights for positive and negative deviations, respectively, and for example, each is set to 0.5.

[0070] It should be understood that the preset maximum permissible cumulative deviation value is determined as follows: The permissible error is determined based on the accuracy class of the feeder. For example, the permissible weighing error for a 0.5-class loss-in-weight scale is ±0.25%. The product of the target setpoint and this permissible error is taken as the maximum permissible deviation at a single point. This product is then multiplied by the number of sampling points within the analysis window, and the resulting product is taken as the preset maximum permissible cumulative deviation value. When the feeder does not have a clearly defined accuracy class, ±1% of the target setpoint is taken as the permissible error.

[0071] After calculating the trend consistency index, incoming material risk index, and outgoing material risk index, this step provides a quantitative description of the feeding system's operating status within the analysis window from three dimensions: trend synergy, incoming material fluctuation degree, and outgoing material deviation accumulation degree. This provides a comparable quantitative data basis for subsequent deviation type determination, avoiding the occurrence of blindly increasing the feeder speed, which would lead to the screw running idle after the internal material is emptied and the control would be ineffective.

[0072] S4. Determine the feeding deviation type based on the trend consistency index, incoming material risk index, and outgoing material risk index. The feeding deviation type includes one or more of the incoming material deviation and outgoing material deviation.

[0073] It should be noted that the weighting of the directional consistency index and amplitude consistency index differs from that of the normal operating condition under low material level conditions, resulting in a numerical deviation in the directly calculated trend consistency index under this condition compared to the normal operating condition. To unify the judgment benchmark, under low material level conditions, the currently calculated trend consistency index is multiplied by a preset compensation factor to obtain a compensated equivalent consistency index, which is then used as the trend consistency index for subsequent judgments. Under normal operating conditions, the compensation factor is set to 1. Under low material level conditions, the compensation factor is equal to the ratio of the historical average trend consistency index over 72 hours under normal operating conditions to the historical average trend consistency index over 72 hours under low material level conditions. After this compensation, the trend consistency index under all operating conditions is on the same numerical scale and is uniformly compared with its preset threshold.

[0074] Specifically, the initial determination is based on the trend consistency index: when the trend consistency index is lower than the preset threshold, it indicates that the two sequences do not show synchronous changes, and the decrease in the discharge rate is caused by the independent abnormality of the discharge end itself. Based on this, it is initially determined to be a discharge deviation. When the trend consistency index is higher than or equal to the preset threshold, it indicates that the incoming material weight change rate sequence and the actual feeding rate sequence show a synchronous change trend within the analysis window, and the change in the discharge rate comes from the abnormal change in the state of the incoming material end. Based on this, it is initially determined to be an incoming material deviation.

[0075] Based on the initial determination of incoming material deviation, the incoming material risk index and the outgoing material risk index are further compared with their corresponding preset thresholds. If both the incoming material risk index and the outgoing material risk index are greater than their respective preset thresholds, it indicates that there are anomalies at both the incoming and outgoing ends. In this case, the determination result is revised to indicate that both incoming and outgoing material deviations coexist. For scenarios initially determined to be outgoing material deviations, since the root cause of the rate decrease has been identified as an anomaly at the outgoing end itself, the determination of coexistence is not involved.

[0076] Through the above-mentioned hierarchical judgment logic, the feeding deviation type covers three states: incoming material deviation, outgoing material deviation, and both.

[0077] It should be added that the above judgment process involves three preset thresholds: the trend consistency index threshold, the incoming material risk index threshold, and the outgoing material risk index threshold. These three thresholds are determined based on at least 72 hours of historical data accumulated under a manually confirmed, anomaly-free operating condition. Specifically, a continuous 72-hour dataset, after production operation and manual confirmation of no bridging, no agglomeration, and a feeding rate deviation not exceeding ±5% of the target set value, is used as the benchmark dataset. The trend consistency index, incoming material risk index, and outgoing material risk index corresponding to this benchmark dataset at each time point are calculated according to the aforementioned steps S1 to S3. The 95th percentile of each index in the benchmark dataset is then taken as the corresponding preset threshold.

[0078] This step identifies deviation types based on a comprehensive analysis of the trend consistency index, incoming material risk index, and outgoing material risk index. It addresses the current deficiency of misjudging incoming material interruptions such as bridging and clumping as insufficient feeding capacity due to the lack of incoming material status monitoring. It also avoids the occurrence of blindly increasing the feeder speed, which leads to the screw running idle after the internal material is emptied and the control becomes ineffective. This achieves accurate differentiation and directional identification of incoming material deviation and outgoing material deviation, reducing ineffective control and equipment wear caused by misjudgment.

[0079] S5. Generate corresponding adjustment control commands based on the type of feeding deviation and send them to the corresponding control terminal.

[0080] Specifically, when the feed rate is determined to be due to insufficient feed material from upstream, it indicates that the current decrease in feed rate is caused by insufficient feed material from upstream. If the original speed is maintained, the feeder will gradually empty the internal residual material. The solution is to obtain the average value of the feed weight change rate sequence within the analysis window. This average value is used as the new target setting value for the feeder speed, and a speed reduction command is generated to match the feeder speed with the current actual feed rate, thus preventing the feeder from emptying the internal material prematurely due to the speed being higher than the feed rate.

[0081] When a discharge deviation is detected, it indicates an abnormality in the feeder's output capacity. In this case, the PID parameters of the feed controller need to be adjusted to restore output. This involves obtaining the current integral time constant value of the feed controller. The feed controller has an online parameter adjustment function, and its integral time constant can be dynamically modified during operation, based on the discharge risk index. Correct the current integration time constant value, and then set the corrected value to... The adjusted integral time constant is used as the basis for generating PID parameter adjustment instructions.

[0082] It should be added that the specific correction process for adjusting the current integral time constant value based on the discharge risk index is as follows: if the discharge risk index is less than the preset first risk threshold, the current integral time constant value is maintained unchanged.

[0083] If the discharge risk index is greater than or equal to the first risk threshold and less than the preset second risk threshold, the discharge risk index and the current integral time constant value are respectively recorded as follows: and Calculate the corrected integral time constant value. , , This is the preset correction factor.

[0084] If the discharge risk index is greater than or equal to the second risk threshold, the integral time constant value is corrected to a preset safety lower limit value. The second risk threshold is greater than the first risk threshold. The first risk threshold is preferably set to 0.3, and the second risk threshold is preferably set to 0.7. The safety lower limit value is the minimum allowable value of the integral time constant of the feed controller, which is preset according to the hardware specifications of the feed controller.

[0085] The correction coefficient controls the decay rate of the integral time constant as the discharge risk index increases. When the discharge risk index increases, the integral time constant decreases accordingly to accelerate the response speed of the PID controller. If... If the value is greater than or equal to the second risk threshold, it indicates that the material deviation is serious. To ensure stability, the integral time constant is directly corrected to the preset safety lower limit value to avoid control oscillation caused by excessive reduction of the integral time constant.

[0086] The correction coefficient for the integral time constant is preset based on the response characteristics of the PID controller, and its value ranges from 0 to 1. The specific value is determined by the settling time of the feeding control system. The settling time refers to the time required for the actual feeding rate to rise from 10% to 90% of the steady-state value and remain within that range under a step input signal excitation. When the settling time is less than or equal to 5 seconds, the correction coefficient is 0.2. When the settling time is greater than or equal to 15 seconds, the correction coefficient is 0.8. When the settling time is greater than 5 seconds but less than 15 seconds, the correction coefficient is determined through linear interpolation.

[0087] Specifically, when both are determined to exist simultaneously, it indicates that there are abnormalities at both the incoming and outgoing ends. In this case, the following control measures are implemented in sequence: First, the incoming material deviation adjustment is performed, and the feeder speed is reduced to match the current incoming material rate. After the material inventory inside the feeder stabilizes, for example, the fluctuation rate of the feeder body quality signal is lower than the upper limit of the preset normal fluctuation range for 5 consecutive sampling periods as a stabilization judgment condition. After stabilization, the outgoing material deviation adjustment is performed to adjust the PID parameters to avoid mutual interference between the two types of adjustment actions.

[0088] After generating the corresponding adjustment control commands, the commands are sent to the corresponding control terminals for execution. For incoming material deviation, the control terminal is the feeder's drive controller. For outgoing material deviation, the control terminal is the parameter configuration interface for the feeder controller.

[0089] Please see Figure 2 As shown, the present invention also provides an intelligent control system for self-feeding of flue gas desulfurizing agent. The system includes: a feeding signal acquisition module, an analysis window construction module, a trend matching analysis module, a feeding risk analysis module, a deviation type determination module, and a control command generation module.

[0090] In the above, the material feeding signal acquisition module is connected to the analysis window construction module, the trend matching analysis module, and the material feeding risk analysis module, respectively, and is used to output the incoming material weight change rate and the actual feeding rate to the analysis window construction module, the trend matching analysis module, and the material feeding risk analysis module, respectively; the analysis window construction module is connected to the trend matching analysis module and the material feeding risk analysis module, respectively, and is used to output the analysis window information to the trend matching analysis module and the material feeding risk analysis module, respectively, when the window construction conditions are met; the trend matching analysis module and the material feeding risk analysis module are connected to the deviation type determination module, respectively, and are used to output the trend consistency index, the incoming material risk index, and the outgoing material risk index to the deviation type determination module; the deviation type determination module is connected to the control command generation module, and is used to output the determination result of the feeding deviation type to the control command generation module.

[0091] Specifically, the execution content of each of the above modules is as follows: The material feeding signal acquisition module is used to acquire the weighing signal of the hopper in real time and calculate the rate of change of the incoming material weight, and at the same time acquire the mass signal of the feeder body in real time and calculate the actual feeding rate through differentiation.

[0092] If the current actual feeding rate is lower than the target setting value and the deviation exceeds the allowable range, the analysis window construction module will construct an analysis window with the current time as the endpoint.

[0093] The trend matching analysis module performs trend matching analysis on the incoming material weight change rate and the actual feeding rate within the analysis window, and calculates the trend consistency index.

[0094] The material feeding risk analysis module calculates the material feeding risk index by the ratio of the standard deviation of the material weight change rate within the analysis window to the upper limit of the preset normal fluctuation range, and calculates the material output risk index by the cumulative deviation of the actual feeding rate from the target set value at each corresponding sampling time within the analysis window.

[0095] The deviation type determination module determines the feeding deviation type based on the trend consistency index, the incoming material risk index, and the outgoing material risk index. The feeding deviation type includes one or more of the incoming material deviation and the outgoing material deviation.

[0096] The control command generation module generates corresponding adjustment control commands based on the type of feeding deviation and sends them to the corresponding control terminal.

[0097] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for intelligent control of self-feeding of flue gas desulfurizing agent, characterized in that, The method includes: Real-time acquisition of silo weighing signals and calculation of incoming material weight change rate, and real-time acquisition of feeder body mass signal and calculation of actual feeding rate through differentiation; If the current actual feeding rate is lower than the target set value and the deviation exceeds the allowable range, an analysis window is constructed with the current time as the endpoint. A trend matching analysis is performed on the incoming material weight change rate and the actual feeding rate within the analysis window to calculate the trend consistency index. At the same time, the incoming material risk index is calculated by the ratio of the standard deviation of the incoming material weight change rate within the analysis window to the upper limit of the preset normal fluctuation range. The outgoing material risk index is calculated by the cumulative deviation of the actual feeding rate and the target set value at each corresponding sampling time within the analysis window. The feeding deviation type is determined based on the trend consistency index, the incoming material risk index, and the outgoing material risk index. The feeding deviation type includes one or more of the incoming material deviation and the outgoing material deviation. Based on the type of feeding deviation, a corresponding adjustment control command is generated and sent to the corresponding control terminal.

2. The intelligent control method for self-feeding of flue gas desulfurizing agent as described in claim 1, characterized in that: The specific method for constructing the analysis window is as follows: The total delay time of powder transfer from the silo outlet to the feeder outlet is used as the target traceability time. The window start time is obtained by subtracting the target tracing duration from the window end time, and the time interval from the window start time to the window end time is used as the analysis window.

3. The intelligent control method for self-feeding of flue gas desulfurizing agent as described in claim 1, characterized in that: The specific analytical process for trend matching analysis includes: Calculate the mean values ​​of the incoming material weight change rate sequence and the actual feed rate sequence within the analysis window, respectively. A material deviation sequence is constructed based on the deviation of the value at each sampling time in the material weight change rate sequence from its mean value, and a material discharge deviation sequence is constructed based on the deviation of the value at each sampling time in the actual feeding rate sequence from its mean value. The ratio of the number of data points with the same sign for the incoming material deviation value and the outgoing material deviation value at the same time to the total number of data points in the analysis window is used as the directional consistency index. If the absolute values ​​of the incoming material deviation and the outgoing material deviation are both zero at the same time, the amplitude matching degree at that time is set to 1. Otherwise, the ratio of the smaller value to the larger value of the absolute values ​​of the incoming material deviation and the outgoing material deviation at the same time is taken as the amplitude matching degree at that time, and the average value of the amplitude matching degree at all times within the analysis window is taken as the amplitude consistency index. The weighted sum of the directional consistency index and the magnitude consistency index is calculated as the trend consistency index.

4. The intelligent control method for self-feeding of flue gas desulfurizing agent as described in claim 3, characterized in that: Before calculating the weighted sum of the directional consistency index and the amplitude consistency index, the process includes steps of operating condition identification and weight configuration, specifically: Obtain the rate of change of incoming material weight at each sampling time within the current analysis window, and count the proportion of sampling times where the rate of change of incoming material weight is lower than a preset threshold; If the sampling time ratio is greater than or equal to the preset ratio threshold, it is determined that the current working condition is low material level. The weight of the directional consistency index is configured as the first weight value, and the weight of the amplitude consistency index is configured as the second weight value, and the first weight value is greater than the second weight value. Otherwise, the weights of the directional consistency index and the amplitude consistency index will be set to the same value. The sum of the weights of the directional consistency index and the amplitude consistency index is 1.

5. The intelligent control method for self-feeding of flue gas desulfurizing agent as described in claim 1, characterized in that: The calculation of the material discharge risk index specifically includes: Calculate the difference between the actual feed rate sequence and the target setpoint sequence at the same sampling time within the analysis window; The time when the difference is positive is recorded as the positive deviation time, and the time when the difference is negative is recorded as the negative deviation time. The absolute values ​​of the differences between each positive deviation time and each negative deviation time are accumulated to obtain the cumulative positive deviation value and the cumulative negative deviation value. If both the cumulative positive deviation and the cumulative negative deviation are less than the preset effective deviation threshold, the discharge risk index will be set to 0. Otherwise, the dominant type of deviation is determined based on the proportion of the cumulative positive deviation value. The dominant type of deviation is either positive deviation dominant, negative deviation dominant, or no dominant type. If the positive deviation is dominant or the negative deviation is dominant, the cumulative value of the positive deviation or the cumulative value of the negative deviation is linearly normalized based on the preset maximum allowable cumulative value, and the result is used as the discharge risk index. If there is no dominant type, the positive deviation accumulation value and the negative deviation accumulation value are normalized based on the preset maximum allowable cumulative deviation value, and the two normalization results are linearly weighted and summed to obtain the discharge risk index.

6. The intelligent control method for self-feeding of flue gas desulfurizing agent as described in claim 5, characterized in that: The specific process for determining the dominant type of deviation is as follows: Divide the cumulative positive deviation value by the sum of the cumulative positive deviation value and the cumulative negative deviation value to obtain the percentage of the cumulative positive deviation value; If the proportion is greater than or equal to the preset first proportion threshold, then the deviation dominance type is positive deviation dominance; If the proportion is greater than or equal to the preset second proportion threshold and less than the preset first proportion threshold, then the dominant type of the deviation is no dominant type, wherein the first proportion threshold is greater than the second proportion threshold. If the proportion is less than the second proportion threshold, then the deviation-dominant type is negative deviation-dominant.

7. The intelligent control method for self-feeding of flue gas desulfurizing agent as described in claim 1, characterized in that: The specific process for determining the type of feed deviation includes: When the trend consistency index is lower than the preset threshold, it is initially determined to be a material output deviation; when the trend consistency index is higher than or equal to the preset threshold, it is initially determined to be a material incoming deviation. If the initial determination is that the material deviation is in the incoming material, the incoming material risk index and the outgoing material risk index are compared with their respective preset thresholds. If both the incoming material risk index and the outgoing material risk index are greater than their respective preset thresholds, the feeding deviation type is determined to be a combination of incoming material deviation and outgoing material deviation.

8. The intelligent control method for self-feeding of flue gas desulfurizing agent as described in claim 7, characterized in that: The specific steps of generating corresponding adjustment control commands based on the type of feed deviation include: When the incoming material deviation is determined, the average value of the sequence of incoming material weight change rate in the analysis window is obtained, and the average value is used as the target setting value for adjusting the feeding rate. Based on the target setting value, a speed reduction command is generated. When a discharge deviation is determined, the current integral time constant value of the feed controller is obtained, and the current integral time constant value is corrected according to the discharge risk index. The corrected value is used as the adjustment integral time constant value, and a PID parameter adjustment instruction is generated based on the adjustment integral time constant value.

9. The intelligent control method for self-feeding of flue gas desulfurizing agent as described in claim 8, characterized in that: The specific correction process for adjusting the current integral time constant value based on the discharge risk index is as follows: If the discharge risk index is less than the preset first risk threshold, the current integral time constant value will remain unchanged. If the discharge risk index is greater than or equal to the first risk threshold and less than the preset second risk threshold, the discharge risk index and the current integral time constant value are respectively recorded as follows: and Calculate the corrected integral time constant value. , , This is the preset correction factor; If the discharge risk index is greater than or equal to the second risk threshold, the integral time constant value will be corrected to the preset safety lower limit value, and the second risk threshold is greater than the first risk threshold.

10. A self-feeding intelligent control system for flue gas desulfurizing agent, characterized in that: The system includes: The material feeding signal acquisition module collects the weighing signal of the hopper in real time and calculates the rate of change of the incoming material weight. At the same time, it collects the mass signal of the feeder body in real time and calculates the actual feeding rate through differentiation. The analysis window construction module constructs an analysis window with the current time as the endpoint if the current actual feeding rate is lower than the target set value and the deviation exceeds the allowable range. The trend matching analysis module performs trend matching analysis on the rate of change of incoming material weight and the actual feeding rate within the analysis window, and calculates the trend consistency index. The material feeding risk analysis module calculates the material feeding risk index by the ratio of the standard deviation of the material weight change rate within the analysis window to the upper limit of the preset normal fluctuation range, and calculates the material discharge risk index by the cumulative deviation of the actual feeding rate and the target set value at each corresponding sampling time within the analysis window. The deviation type determination module determines the feeding deviation type based on the trend consistency index, the incoming material risk index, and the outgoing material risk index. The feeding deviation type includes one or more of the incoming material deviation and the outgoing material deviation. The control command generation module generates corresponding adjustment control commands based on the type of feeding deviation and sends them to the corresponding control terminal.

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