A sinter alkalinity control method and system based on online detection of a mixture

Abstract

The application discloses a sinter alkalinity control method and system based on online detection of a mixture, and belongs to the technical field of sinter proportioning control. The control method comprises the following steps: determining whether the current moment satisfies an alkalinity adjustment condition according to sinter alkalinity detection values of the current moment and a plurality of continuous moments before the current moment output by an online component analyzer; when the current moment satisfies the alkalinity adjustment condition, generating a feedforward adjustment amount, and synchronously acquiring a plurality of groups of continuous alkalinity time series data before the current moment output by laboratory sampling detection; generating a feedback adjustment amount according to the plurality of groups of continuous alkalinity time series data before the current moment output by the laboratory sampling detection; and superimposing the feedforward adjustment amount and the feedback adjustment amount to obtain a final lime proportioning adjustment amount. The feedforward is provided by the online component analyzer, and the feedback is provided by the laboratory sampling detection, so that the alkalinity qualification rate and the robustness are significantly improved.

Description

Technical Field

[0001] This application belongs to the field of sintering batching control technology, and specifically relates to a method and system for controlling the basicity of sintered ore based on online detection of the mixture. Background Technology

[0002] Currently, the control of sinter basicity generally adopts the method of offline testing of finished sinter composition by laboratory sampling. For example, XRF (X-Ray Fluorescence) is used to offline test the composition of finished sinter. Sampling is performed every 3 to 4 hours, and the basicity value is obtained after laboratory testing. The basicity value is the ratio of CaO to SiO2 test values. Based on the deviation between this basicity value and the target basicity value, the operator manually or automatically adjusts the lime ratio in the mixture.

[0003] Meanwhile, existing technologies have attempted to install LIBS (Laser-Induced Breakdown Spectroscopy) online analyzers on the conveyor belt of the mixture to detect the alkalinity of the mixture in real time and use it as a feedforward reference. However, existing feedforward schemes typically require adjustment after each detection, but the mixture adjusted with quicklime takes about 10 minutes to reach the LIBS detection point, causing the same batch of material to be repeatedly adjusted, resulting in a "ringing" phenomenon.

[0004] Furthermore, XRF sampling can cause abrupt changes, such as segregation due to belt transport or particle size distribution. For example, the SiO2 content of a single fluorescent sample may fluctuate in isolation (e.g., a sudden increase from 5.4% to 5.8% and then back to normal), while the CaO content may also fluctuate by more than 1%. Adjusting CaO based on these anomalous values ​​would worsen previously acceptable operating conditions, causing alkalinity fluctuations.

[0005] Therefore, there is an urgent need for a new technical solution for controlling the basicity of sintered ore. Summary of the Invention

[0006] To address the aforementioned issues, this application provides a method and system for controlling the basicity of sintered ore based on online detection of the mixture.

[0007] In a first aspect, this application provides a method for controlling the basicity of sintered ore based on online detection of the mixture, the control method comprising: Determine whether the alkalinity adjustment conditions are met at the current moment based on the sintering alkalinity detection values ​​of the current moment and the previous multiple consecutive moments output by the online component analyzer. When the alkalinity adjustment conditions are met at the current moment, a feedforward adjustment amount is generated, and multiple sets of continuous alkalinity time series data prior to the current moment are simultaneously acquired from the laboratory sampling and testing output. A feedback adjustment amount is generated based on multiple sets of continuous alkalinity time-series data prior to the current moment, as output by laboratory sampling and testing. The final white-to-gray ratio adjustment is obtained by superimposing the feedforward adjustment and the feedback adjustment.

[0008] Furthermore, the generation of the feedforward adjustment amount includes: Calculate the average basicity deviation between the sintering basicity detection value and the target sintering basicity value at the current moment and multiple consecutive moments preceding it; The feedforward adjustment amount is determined based on the average alkalinity deviation value, the maximum adjustment amount of a single feedforward, and the preset feedforward adjustment coefficient.

[0009] Furthermore, the step of generating a feedback adjustment amount based on multiple sets of continuous alkalinity time-series data prior to the current moment, as output by laboratory sampling and testing, includes: Determine whether the most recent alkalinity value of the laboratory sample is qualified; wherein, the most recent alkalinity value of the laboratory sample is used to characterize the sintering alkalinity detection value in the first set of alkalinity time series data most recent before the current time of the laboratory sample detection output; If the most recent alkalinity value from the laboratory sample is unqualified, additional feedback adjustment parameters are determined based on the most recent alkalinity deviation value from the laboratory sample, the first alkalinity deviation threshold, and the second alkalinity deviation threshold. The most recent alkalinity deviation value from the laboratory sample is used to characterize the absolute value of the difference between the most recent alkalinity value from the laboratory sample and the sintering alkalinity detection value in the most recent second set of alkalinity time series data before the current time. The feedback adjustment amount is generated based on the additional feedback adjustment parameters, the most recent alkalinity value from the laboratory sample, and the target alkalinity value.

[0010] Furthermore, the determination of whether the most recent alkalinity value of the laboratory sample is qualified includes: If the most recent alkalinity value of the laboratory sample is between the lower and upper alkalinity thresholds, then the most recent alkalinity value of the laboratory sample is acceptable; otherwise, it is unacceptable.

[0011] Furthermore, the step of determining additional feedback adjustment parameters based on the most recent alkalinity deviation value from laboratory sampling, the first alkalinity deviation threshold, and the second alkalinity deviation threshold includes: If the most recent alkalinity deviation value of the laboratory sample is less than or equal to the first alkalinity deviation threshold, the additional feedback adjustment parameter is the first preset feedback parameter. If the most recent alkalinity deviation value of the laboratory sample is greater than or equal to the second alkalinity deviation threshold, the additional feedback adjustment parameter is the second preset feedback parameter; If the most recent alkalinity deviation value of the laboratory sample is greater than the first alkalinity deviation threshold but less than the second alkalinity deviation threshold, the laboratory determines whether a data anomaly has occurred based on multiple sets of continuous alkalinity time series data output before the current time. If a data anomaly has occurred, the additional feedback adjustment parameter is the second preset feedback parameter. If no data anomaly has occurred, the third preset feedback parameter is used.

[0012] Furthermore, the step of determining whether data anomalies have occurred based on multiple sets of continuous alkalinity time-series data prior to the current moment, as output by laboratory sampling and testing, includes: Calculate multiple SiO2 differences between the SiO2 values ​​of the remaining groups and the most recent SiO2 values ​​in the laboratory, and multiple CaO differences between the CaO values ​​of the remaining groups and the most recent CaO values ​​in the laboratory; wherein, the most recent SiO2 value in the laboratory is used to characterize the SiO2 detection value in the first most recent set of alkalinity time series data before the current time of the laboratory sampling and detection output, and the most recent CaO value in the laboratory is used to characterize the CaO detection value in the first most recent set of alkalinity time series data before the current time of the laboratory sampling and detection output; The system determines whether SiO2 data is abnormal based on multiple SiO2 differences and a preset SiO2 deviation threshold, and determines whether CaO data is abnormal based on multiple CaO differences and a preset CaO deviation threshold. If SiO2 and / or CaO show abnormal data, it is determined that a data abnormality has occurred; otherwise, it is determined that no data abnormality has occurred.

[0013] Furthermore, the step of determining whether SiO2 data anomalies have occurred based on multiple SiO2 differences and a preset SiO2 deviation threshold includes: If the absolute values ​​of multiple SiO2 differences are all greater than the preset SiO2 deviation threshold, and the signs of the multiple SiO2 differences are the same, then SiO2 data anomaly occurs; otherwise, SiO2 data anomaly does not occur. The method of determining whether CaO data anomalies have occurred based on multiple CaO differences and a preset CaO deviation threshold includes: If the absolute values ​​of multiple CaO differences are all greater than the preset CaO deviation threshold, and the signs of the multiple CaO differences are the same, then CaO data anomaly occurs; otherwise, CaO data anomaly does not occur.

[0014] Furthermore, before generating the feedback adjustment amount based on the additional feedback adjustment parameters, the most recent alkalinity value from the laboratory sample, and the target alkalinity value, the following is also included: If the most recent alkalinity value from the laboratory sample is greater than the upper limit threshold and the alkalinity values ​​of all other groups are greater than the target alkalinity value, or if the most recent alkalinity value from the laboratory sample is less than the lower limit threshold and the alkalinity values ​​of all other groups are less than the target alkalinity value, then the laboratory determines whether a data anomaly has occurred based on the multiple sets of continuous alkalinity time-series data output before the current time from the laboratory sampling test. If a data anomaly has occurred, the additional feedback adjustment parameter is updated to the fourth preset feedback parameter; if no data anomaly has occurred, the additional feedback adjustment parameter is updated to the first preset feedback parameter.

[0015] Secondly, this application provides a sinter basicity control system based on online detection of the mixture, the control system comprising: The adjustment condition judgment module is used to determine whether the alkalinity adjustment condition is met at the current time based on the sintering alkalinity detection values ​​of the current time and the previous multiple consecutive times output by the online component analyzer. The feedforward adjustment generation module is used to generate a feedforward adjustment amount when the alkalinity adjustment conditions are met at the current moment, and simultaneously acquire multiple sets of continuous alkalinity time series data output from laboratory sampling and testing before the current moment. The feedback adjustment generation module is used to generate a feedback adjustment based on multiple sets of continuous alkalinity time series data output from laboratory sampling and testing before the current moment. The final adjustment amount determination module is used to superimpose the feedforward adjustment amount and the feedback adjustment amount to obtain the final white lime ratio adjustment amount.

[0016] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned control method.

[0017] The embodiments of this application provide a method, system, and electronic equipment for controlling the basicity of sintered ore based on online detection of mixed materials. The method determines whether to adjust the basicity by using the basicity detection data of the sintered material from an online component analyzer, thus avoiding repeated adjustments to the same batch of materials. Under the condition of meeting the basicity adjustment requirements, the basicity of the material is adjusted by combining the superposition of the feedforward adjustment amount and the feedback adjustment amount, thereby improving the basicity qualification rate and robustness.

[0018] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic flowchart of a sinter basicity control method based on online detection of the mixture is shown according to an embodiment of this application; Figure 2 A schematic diagram of the process for generating feedback adjustment amounts according to an embodiment of this application is shown; Figure 3 A schematic diagram of a sinter basicity control system based on online detection of the mixture is shown according to an embodiment of this application; Figure 4 A schematic diagram of the structure of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The detection principles, sampling methods, and material forms of online component analyzers and laboratory sampling tests differ fundamentally. After high-temperature calcination, some components of the mixture are lost (e.g., changes in loss on ignition), and the physical form changes from powder to lumps, leading to significant and non-linear deviations in the detection results. Directly using the detection values ​​from the online component analyzer to replace the laboratory sampling test values ​​of the finished product for closed-loop control will introduce systematic biases, causing incorrect adjustments to the proportions.

[0023] Existing technologies typically assume high accuracy in online detection, without considering the dynamic changes in the deviation between online component analyzers and laboratory sampling and detection. When the deviation exceeds the allowable fluctuation range, the formula is still calculated based on online data, which actually reduces the alkalinity pass rate.

[0024] Furthermore, in existing technologies, the sampling process for sintered ore is highly susceptible to factors such as insufficient representativeness of laboratory sampling points and material particle size segregation, which can lead to false abrupt changes in laboratory sampling test values ​​at certain times. For example, the SiO2 content may jump upwards from the normal value of 5.4% to over 5.8%, and then return to normal. This abrupt change is not a fluctuation in the actual composition, but rather caused by the accidental sampling of high-silicon material blocks. Such abnormal values ​​will directly lead to unqualified alkalinity calculation values. If they are not identified and are directly used for feedback control, they will cause incorrect ratio adjustments and further deteriorate alkalinity stability.

[0025] Figure 1 A schematic flowchart of a sinter basicity control method based on online detection of the mixture is shown according to an embodiment of this application.

[0026] like Figure 1 As shown in the figure, an embodiment of this application provides a method for controlling the basicity of sintered ore based on online detection of the mixture, which includes: Step S101: Determine whether the alkalinity adjustment conditions are met at the current time based on the sintering alkalinity detection values ​​of the current time and the previous multiple consecutive times output by the online component analyzer. Step S102: When the alkalinity adjustment conditions are met at the current time, generate a feedforward adjustment amount and simultaneously acquire multiple sets of continuous alkalinity time series data output from laboratory sampling and testing before the current time. Step S103: Generate a feedback adjustment amount based on multiple sets of continuous alkalinity time series data output from laboratory sampling and testing before the current moment; Step S104: The feedforward adjustment and the feedback adjustment are superimposed to obtain the final white-gray ratio adjustment.

[0027] Specifically, when implementing the sinter basicity control method based on online detection of mixed materials according to an embodiment of this application, an online component analyzer is set at the detection point of the online component analyzer. The detection point can be on the material conveyor belt. The online component analyzer is used to detect the basicity value of the material on the conveyor belt. For example, the detection cycle of the online component analyzer is 5 to 10 minutes, and the basicity value of the material is obtained by periodic detection.

[0028] Specifically, the alkalinity, CaO and SiO2 values ​​of the finished product are tested simultaneously using laboratory sampling. For example, the testing cycle of laboratory sampling is set to 3 hours, and the alkalinity, CaO and SiO2 values ​​of the finished product are obtained through periodic testing.

[0029] For example, the online component analyzer can be LIBS or PGNAA (Prompt Gamma Neutron Activation Analysis); laboratory sampling and detection can be performed using XRF.

[0030] Specifically, such as Figure 1 As shown, when the online component analyzer obtains the sinter basicity detection value at the current moment, it starts to execute the sinter basicity control method based on online detection of the mixture according to the embodiment of this application, that is, it is executed in each detection cycle of the online component analyzer.

[0031] Specifically, such as Figure 1 As shown, in step S101, the sintering basicity detection value of the online component analyzer at the current moment is obtained, as well as the sintering basicity detection values ​​at multiple consecutive moments preceding the current moment, with each moment corresponding to one sintering basicity detection value. For example, a LIBS is installed on a conveyor belt, with a detection cycle of 6 minutes. When executing step S101, the sintering basicity detection values ​​at the current moment and at the previous 6 consecutive moments are obtained.

[0032] It is understandable that the sintering alkalinity test value output by the online component analyzer represents the alkalinity value of the material on the conveyor belt.

[0033] It is worth noting that the number of sintering basicity test values ​​used can be reasonably set according to the actual scenario, such as 6 to 10.

[0034] Specifically, such as Figure 1 As shown, after obtaining the sintering basicity detection values ​​of the current time and several consecutive previous times in step S101, it is determined whether the basicity adjustment conditions are met at the current time; if the basicity adjustment conditions are met at the current time, then step S102 is executed; if the basicity adjustment conditions are not met at the current time, then the execution ends.

[0035] Preferably, the step of determining whether the alkalinity adjustment conditions are met at the current moment based on the sintering alkalinity detection values ​​at the current moment and multiple consecutive moments preceding it, as output by the online component analyzer, includes: If the sintering basicity detection values ​​at the current moment and at several consecutive moments preceding it are all greater than the upper limit of basicity threshold, or if the sintering basicity detection values ​​at the current moment and at several consecutive moments preceding it are all less than the lower limit of basicity threshold, then the basicity adjustment condition is met at the current moment.

[0036] Specifically, the target sintering basicity value is set according to actual production needs. For example, the target sintering basicity value is set to 2.05.

[0037] Specifically, based on the target sintering basicity value and actual production needs, an upper limit threshold and a lower limit threshold for basicity are set. For example, the upper limit threshold for basicity is set to 2.10 and the lower limit threshold for basicity is set to 2.00.

[0038] Specifically, the sintering basicity detection values ​​at the current moment and several consecutive preceding moments are compared with the upper and lower basicity thresholds, respectively: If the sintering alkalinity detection values ​​at the current moment and at multiple consecutive moments preceding it are all greater than the upper limit threshold of alkalinity, then the alkalinity adjustment condition is met at the current moment, and step S102 is continued. If the sintering basicity detection values ​​at the current moment and at several consecutive moments preceding it are all less than the lower limit threshold of basicity, then the basicity adjustment condition is met at the current moment, and step S102 is continued. Otherwise, if the alkalinity adjustment conditions are not met at the current moment, step S102 will not be executed.

[0039] Preferably, the alkalinity adjustment conditions further include: The time difference between the current moment and the last time the alkalinity adjustment condition was triggered is greater than the time difference between the material's journey from the discharge port to the detection point of the online component analyzer.

[0040] Specifically, the online component analyzer can only detect the alkalinity value of the material at the detection point, and it takes a certain amount of time for the material to travel from the feed port to the detection point of the online component analyzer, for example, 10 minutes.

[0041] Specifically, to avoid repeated adjustments to the same batch of materials, which could cause a "ringing" effect, the time difference between two consecutive triggers of alkalinity adjustment conditions must be greater than the time difference between the material's journey from the discharge port to the detection point of the online component analyzer. This ensures that each adjustment corresponds to a different batch of materials. For example, if the alkalinity adjustment conditions are met at the current moment, the next time the conditions are met should be postponed by at least 10 minutes.

[0042] Specifically, such as Figure 1 As shown, in step S102, when the alkalinity adjustment condition is met at the current time, a feedforward adjustment amount is generated.

[0043] Preferably, the generation of the feedforward adjustment amount includes: Calculate the average basicity deviation between the sintering basicity detection value and the target sintering basicity value at the current moment and multiple consecutive moments preceding it; The feedforward adjustment amount is determined based on the average alkalinity deviation value, the maximum adjustment amount of a single feedforward, and the preset feedforward adjustment coefficient.

[0044] Specifically, the maximum adjustment amount for a single feedforward needs to be set in advance. For example, the maximum adjustment amount for a single feedforward is 0.35%.

[0045] Specifically, the feedforward adjustment coefficient needs to be set in advance. For example, the range of the feedforward adjustment coefficient is 1.5 to 3.5.

[0046] Specifically, the basicity deviation between the current sintering basicity detection value and the target sintering basicity value is calculated, as well as the basicity deviation between the sintering basicity detection value and the target sintering basicity value at multiple consecutive preceding times. The basicity deviation between the current sintering basicity detection value and the target sintering basicity value at multiple consecutive preceding times is averaged to obtain the average basicity deviation value e_L_avg.

[0047] Specifically, the feedforward adjustment amount is determined based on the average alkalinity deviation value, the maximum single feedforward adjustment amount, and the feedforward adjustment coefficient. For example, the product of the average alkalinity deviation value and the feedforward adjustment coefficient is calculated. If this product is less than or equal to the maximum single feedforward adjustment amount, then the product of the average alkalinity deviation value and the feedforward adjustment coefficient is used as the feedforward adjustment amount for this adjustment; if the product is greater than the maximum single feedforward adjustment amount, then the maximum single feedforward adjustment amount is used as the feedforward adjustment amount for this adjustment.

[0048] It is worth noting that it takes about 7 hours from adjusting the lime ratio to obtaining the corresponding alkalinity feedback of the finished product (2-3 hours of sintering + 1-2 hours of cooling + 1-2 hours of sampling and testing). During this period, the control effect cannot be known, which can easily lead to a large number of unqualified products. This application can control the alkalinity in a timely manner through feedforward adjustment.

[0049] Specifically, such as Figure 1 As shown, in step S102, while generating the feedforward adjustment amount, multiple sets of continuous alkalinity time series data before the current time are acquired from the laboratory sampling and detection output.

[0050] It is understandable that the detection cycles of laboratory sampling and testing and online component analyzer are different, and the two may not acquire alkalinity data simultaneously. For example, the detection cycle of laboratory sampling and testing is 3 hours, while the detection cycle of online component analyzer is 6 minutes. If the laboratory sampling and testing at the current moment has not acquired alkalinity time-series data for the current moment, then in step S102, it is only necessary to acquire multiple sets of continuous alkalinity time-series data output by the laboratory sampling and testing prior to the current moment.

[0051] Specifically, such as Figure 1 As shown, in step S102, when the alkalinity adjustment conditions are met at the current time, multiple sets of continuous alkalinity time-series data output by the laboratory sampling and detection before the current time are acquired. Each set of alkalinity time-series data includes sintering alkalinity detection value, SiO2 value and CaO value.

[0052] For example, three sets of continuous alkalinity time series data prior to the current time can be used.

[0053] Specifically, such as Figure 1 As shown, in step S103, a feedback adjustment amount is generated based on multiple sets of continuous alkalinity time series data output from laboratory sampling and testing before the current time.

[0054] Figure 2 A schematic diagram of the process for generating feedback adjustment amounts according to an embodiment of this application is shown.

[0055] Preferably, the step of generating a feedback adjustment amount based on multiple sets of continuous alkalinity time-series data prior to the current moment output by laboratory sampling and testing includes: Step S501: Determine whether the most recent alkalinity value of the laboratory sample is qualified; wherein, the most recent alkalinity value of the laboratory sample is used to characterize the sintering alkalinity detection value in the first set of alkalinity time series data most recent before the current time of the laboratory sample detection output; Step S502: If the most recent alkalinity value from the laboratory sample is unqualified, then additional feedback adjustment parameters are determined based on the most recent alkalinity deviation value from the laboratory sample, the first alkalinity deviation threshold, and the second alkalinity deviation threshold; wherein, the most recent alkalinity deviation value from the laboratory sample is used to characterize the absolute value of the difference between the most recent alkalinity value from the laboratory sample and the sintering alkalinity detection value in the most recent second set of alkalinity time series data before the current time. Step S503: Generate feedback adjustment amount based on additional feedback adjustment parameters, the most recent alkalinity value from laboratory sampling, and the target alkalinity value.

[0056] Specifically, such as Figure 2 As shown, in step S501, the most recent alkalinity value of the laboratory sample is determined and it is judged whether the most recent alkalinity value of the laboratory sample is qualified. The most recent alkalinity value of the laboratory sample represents the sintering alkalinity detection value in the first set of alkalinity time series data most recent before the current time.

[0057] Preferably, determining whether the most recent alkalinity value of the laboratory sample is qualified includes: If the most recent alkalinity value of the laboratory sample is between the lower and upper alkalinity thresholds, then the most recent alkalinity value of the laboratory sample is acceptable; otherwise, it is unacceptable.

[0058] Specifically, when the most recent alkalinity value of a laboratory sample is between the lower and upper alkalinity thresholds, the most recent alkalinity value is considered acceptable; when the most recent alkalinity value is less than the lower alkalinity threshold or greater than the upper alkalinity threshold, the most recent alkalinity value is considered unacceptable.

[0059] Specifically, such as Figure 2As shown, in step S502, when the most recent alkalinity value of the laboratory sample is unqualified, additional feedback adjustment parameters are determined based on the most recent alkalinity deviation value of the laboratory sample, the first alkalinity deviation threshold, and the second alkalinity deviation threshold. The first alkalinity deviation threshold and the second alkalinity deviation threshold need to be set in advance. For example, the first alkalinity deviation threshold is 0.07%, and the second alkalinity deviation threshold is 0.13%.

[0060] Specifically, the most recent alkalinity deviation value of laboratory sampling represents the absolute value of the difference between the most recent alkalinity value of laboratory sampling and the sintering alkalinity detection value in the second most recent set of alkalinity time series data before the current time.

[0061] Preferably, determining the additional feedback adjustment parameters based on the most recent alkalinity deviation value from laboratory sampling, a first alkalinity deviation threshold, and a second alkalinity deviation threshold includes: Step S701: If the most recent alkalinity deviation value of the laboratory sample is less than or equal to the first alkalinity deviation threshold, the additional feedback adjustment parameter is the first preset feedback parameter; Step S702: If the most recent alkalinity deviation value of the laboratory sample is greater than or equal to the second alkalinity deviation threshold, the additional feedback adjustment parameter is the second preset feedback parameter; Step S703: If the most recent alkalinity deviation value of the laboratory sample is greater than the first alkalinity deviation threshold and less than the second alkalinity deviation threshold, determine whether a data anomaly has occurred based on the multiple sets of continuous alkalinity time series data output by the laboratory sample detection before the current time; if a data anomaly has occurred, the additional feedback adjustment parameter is the second preset feedback parameter; if no data anomaly has occurred, the third preset feedback parameter is used.

[0062] Specifically, such as Figure 2 As shown, in step S701, when the most recent alkalinity deviation value of the laboratory sample is less than or equal to the first alkalinity deviation threshold, the data output by the laboratory sample detection can be considered a reliable value. At this time, only normal logic adjustment is required, without any amplitude constraint, and the additional feedback adjustment parameter adopts the first preset feedback parameter. For example, the first preset feedback parameter is 1.

[0063] Specifically, such as Figure 2 As shown, in step S702, when the most recent alkalinity deviation value of the laboratory sample is greater than or equal to the second alkalinity deviation threshold, the data output by the laboratory sample detection can be considered unreliable. No adjustment is needed in this case because it is practically impossible for the most recent alkalinity deviation value of the laboratory sample to fluctuate so significantly in a short period. If it does occur, it can be considered a sudden change in the sampling, which is not a fluctuation in the actual composition but rather caused by accidentally sampling a high-silica material block. No adjustment is needed in this case, and the additional feedback adjustment parameter adopts the second preset feedback parameter. For example, the second preset feedback parameter is 0.

[0064] It is worth noting that the first alkalinity deviation threshold and the second alkalinity deviation threshold can be set reasonably according to actual needs.

[0065] For example, the first alkalinity deviation threshold can be set to infinitely small and the second alkalinity deviation threshold can be set to infinitely large. Then, when the most recent alkalinity value of the laboratory sample is unqualified, it can be directly determined whether a data anomaly has occurred based on the multiple sets of continuous alkalinity time series data before the current time output by the laboratory sampling test.

[0066] Specifically, such as Figure 2 As shown, in step S703, when the most recent alkalinity deviation value of the laboratory sample is greater than the first alkalinity deviation threshold but less than the second alkalinity deviation threshold, if it is determined that a data anomaly has occurred, i.e., the data is unreliable, no adjustment is needed, and the additional feedback adjustment parameter adopts the second preset feedback parameter. If it is determined that no data anomaly has occurred, although the data is reliable, it is not entirely reliable, and a conservative adjustment strategy can be adopted. In this case, the additional feedback adjustment parameter adopts the third preset feedback parameter. For example, the third preset feedback parameter is 0.7.

[0067] Preferably, when the most recent alkalinity deviation value from laboratory sampling is greater than a first alkalinity deviation threshold but less than a second alkalinity deviation threshold, the step of determining whether a data anomaly has occurred based on multiple sets of continuous alkalinity time-series data output from the laboratory sampling test prior to the current time includes: Step S801: Calculate multiple SiO2 differences between the SiO2 values ​​of the remaining groups and the most recent SiO2 values ​​in the laboratory, and multiple CaO differences between the CaO values ​​of the remaining groups and the most recent CaO values ​​in the laboratory; wherein, the most recent SiO2 value in the laboratory is used to characterize the SiO2 detection value in the first most recent set of alkalinity time series data before the current time of the laboratory sampling and detection output, and the most recent CaO value in the laboratory is used to characterize the CaO detection value in the first most recent set of alkalinity time series data before the current time of the laboratory sampling and detection output; Step S802: Determine whether SiO2 data is abnormal based on multiple SiO2 differences and a preset SiO2 deviation threshold, and determine whether CaO data is abnormal based on multiple CaO differences and a preset CaO deviation threshold; Step S803: When SiO2 and / or CaO experience data anomalies, it is determined that a data anomaly has occurred; otherwise, it is determined that no data anomaly has occurred.

[0068] For example, the laboratory sampling and testing outputs three sets of continuous alkalinity time series data before the current time, namely the first set of alkalinity time series data most recent before the current time, the second set of alkalinity time series data most recent before the current time, and the third set of alkalinity time series data most recent before the current time.

[0069] Specifically, the most recent SiO2 value in the laboratory refers to the SiO2 detection value in the first set of alkalinity time series data most recent before the current time of the laboratory sampling and testing output, and the most recent CaO value in the laboratory refers to the CaO detection value in the first set of alkalinity time series data most recent before the current time of the laboratory sampling and testing output.

[0070] Specifically, when calculating multiple SiO2 differences between the SiO2 values ​​of the remaining groups and the most recent SiO2 values ​​in the laboratory, the SiO2 difference between the SiO2 value in the second most recent alkalinity time series data before the current time and the most recent SiO2 value in the laboratory is calculated, as well as the SiO2 difference between the SiO2 value in the third most recent alkalinity time series data before the current time and the most recent SiO2 value in the laboratory is calculated.

[0071] Specifically, when calculating multiple CaO differences between the CaO values ​​of the remaining groups and the most recent CaO value in the laboratory, we calculate the CaO difference between the CaO value in the second most recent alkalinity time series data before the current time and the most recent CaO value in the laboratory, and we calculate the CaO difference between the CaO value in the third most recent alkalinity time series data before the current time and the most recent CaO value in the laboratory.

[0072] Specifically, the system determines whether SiO2 data is abnormal based on multiple SiO2 differences and a preset SiO2 deviation threshold.

[0073] Preferably, the step of determining whether SiO2 data anomalies have occurred based on multiple SiO2 differences and a preset SiO2 deviation threshold includes: If the absolute values ​​of multiple SiO2 differences are all greater than the preset SiO2 deviation threshold, and the signs of the multiple SiO2 differences are the same, then SiO2 data anomaly occurs; otherwise, SiO2 data anomaly does not occur.

[0074] Specifically, the preset SiO2 deviation threshold needs to be set in advance. For example, the preset SiO2 deviation threshold range is 0.2% to 0.3%.

[0075] The system determines whether the absolute value of the difference between the SiO2 value in the second most recent alkalinity time series data set before the current time and the most recent SiO2 value in the laboratory is greater than a preset SiO2 deviation threshold, and also determines whether the absolute value of the difference between the SiO2 value in the third most recent alkalinity time series data set before the current time and the most recent SiO2 value in the laboratory is greater than the preset SiO2 deviation threshold. If both are greater than the preset SiO2 deviation threshold, and the SiO2 differences between the second most recent alkalinity time series data set before the current time and the third most recent alkalinity time series data set before the current time have the same sign, then the determination result is that SiO2 data is abnormal. Otherwise, SiO2 data is not abnormal.

[0076] Specifically, the system determines whether CaO data is abnormal based on multiple CaO differences and a preset CaO deviation threshold.

[0077] Preferably, the step of determining whether CaO data anomalies have occurred based on multiple CaO differences and a preset CaO deviation threshold includes: If the absolute values ​​of multiple CaO differences are all greater than the preset CaO deviation threshold, and the signs of the multiple CaO differences are the same, then CaO data anomaly occurs; otherwise, CaO data anomaly does not occur.

[0078] Specifically, the preset CaO deviation threshold needs to be set in advance. For example, the preset CaO deviation threshold range is 0.8% to 1.0%.

[0079] The system determines whether the absolute value of the difference between the CaO value in the second most recent alkalinity time series data set before the current time and the most recent CaO value in the laboratory is greater than a preset CaO deviation threshold, and whether the absolute value of the difference between the CaO value in the third most recent alkalinity time series data set before the current time and the most recent CaO value in the laboratory is greater than the preset CaO deviation threshold. If both are greater than the preset CaO deviation threshold, and the CaO differences in the second most recent alkalinity time series data set before the current time and the third most recent alkalinity time series data set before the current time have the same sign, then the determination result is that CaO data is abnormal. Otherwise, CaO data is not abnormal.

[0080] Specifically, if either CaO or SiO2 data anomalies are met, then the laboratory's sampling and testing output of multiple sets of continuous alkalinity time series data prior to the current moment is considered to have data anomalies; otherwise, no data anomalies are detected.

[0081] Specifically, if multiple sets of continuous alkalinity time-series data output by the laboratory sampling and testing before the current time show abnormalities, the additional feedback adjustment parameter adopts the second preset feedback parameter; if multiple sets of continuous alkalinity time-series data output by the laboratory sampling and testing before the current time do not show abnormalities, the additional feedback adjustment parameter adopts the third preset feedback parameter.

[0082] Preferably, before generating the feedback adjustment amount based on the additional feedback adjustment parameters, the most recent alkalinity value from the laboratory sample, and the target alkalinity value, the method further includes: If the most recent alkalinity value from the laboratory sample is greater than the upper limit threshold and the alkalinity values ​​of all other groups are greater than the target alkalinity value, or if the most recent alkalinity value from the laboratory sample is less than the lower limit threshold and the alkalinity values ​​of all other groups are less than the target alkalinity value, then the laboratory determines whether a data anomaly has occurred based on the multiple sets of continuous alkalinity time-series data output before the current time from the laboratory sampling test. If a data anomaly has occurred, the additional feedback adjustment parameter is updated to the fourth preset feedback parameter; if no data anomaly has occurred, the additional feedback adjustment parameter is updated to the first preset feedback parameter.

[0083] Specifically, after determining the additional feedback adjustment parameters based on the most recent alkalinity deviation value from the laboratory sample, the first alkalinity deviation threshold, and the second alkalinity deviation threshold, a mechanism for updating the additional feedback adjustment parameters can be added before generating the feedback adjustment amount based on the additional feedback adjustment parameters, the most recent alkalinity value from the laboratory sample, and the target alkalinity value.

[0084] Specifically, if the most recent alkalinity value of a laboratory sample is greater than the upper limit threshold for alkalinity and the alkalinity values ​​of the remaining groups are all greater than the target alkalinity value, for example, the target alkalinity value is set to 2.05 and the upper limit threshold for alkalinity is set to 2.10. If the most recent alkalinity value of a laboratory sample is greater than 2.10 and the alkalinity values ​​of the remaining groups are all greater than 2.05, then the alkalinity value can be considered to have an increasing trend.

[0085] Specifically, if the most recent alkalinity value of a laboratory sample is less than the lower alkalinity threshold and the alkalinity values ​​of the remaining groups are all less than the target alkalinity value, for example, the target alkalinity value is set to 2.05 and the lower alkalinity threshold is set to 2.00. If the most recent alkalinity value of a laboratory sample is less than 2.05 and the alkalinity values ​​of the remaining groups are all less than 2.05, then the alkalinity value can be considered to have a decreasing trend.

[0086] When the alkalinity value shows an increasing trend or a decreasing trend, it is necessary to determine whether a data anomaly has occurred based on multiple sets of continuous alkalinity time series data output from the laboratory sampling and testing output before the current time. If a data anomaly occurs, the additional feedback adjustment parameter is updated to the fourth preset feedback parameter; if no data anomaly occurs, the additional feedback adjustment parameter is updated to the first preset feedback parameter.

[0087] Specifically, such as Figure 2 As shown, in step S503, a feedback adjustment amount is generated based on the additional feedback adjustment parameters, the most recent alkalinity value from the laboratory sample, and the target alkalinity value.

[0088] For example, the alkalinity difference is obtained by subtracting the most recent alkalinity value from the target alkalinity value, and then multiplying it by the additional feedback adjustment parameter to obtain the feedback adjustment amount.

[0089] It is worth noting that the data obtained from laboratory sampling and testing may contain abnormal fluctuations caused by sampling. This application makes a judgment on the data anomaly when the alkalinity value of the most recent laboratory sampling is unqualified, so as to obtain more appropriate additional feedback to adjust the parameters and avoid the original qualified working conditions from deteriorating.

[0090] Specifically, such as Figure 1 As shown, in step S104, the feedforward adjustment amount obtained in step S102 and the feedback adjustment amount obtained in step S103 are superimposed to obtain the final white lime ratio adjustment amount, which is used as the white lime ratio adjustment amount for this adjustment.

[0091] It is worth noting that this application, by combining feedforward adjustment and feedback adjustment, can not only make full use of the lag data from laboratory sampling and testing to judge the true trend and random fluctuations, but also provide timely feedback adjustments.

[0092] Figure 3 A schematic diagram of a sinter basicity control system based on online detection of the mixture is shown according to an embodiment of this application.

[0093] like Figure 3 As shown in the embodiment of this application, a sinter basicity control system based on online detection of the mixture is provided. The control system includes: The adjustment condition judgment module 301 is used to determine whether the alkalinity adjustment condition is met at the current time based on the sintering alkalinity detection values ​​of the current time and the previous multiple consecutive times output by the online component analyzer. The feedforward adjustment generation module 302 is used to generate a feedforward adjustment amount when the alkalinity adjustment conditions are met at the current time, and simultaneously acquire multiple sets of continuous alkalinity time series data output by the laboratory sampling and testing before the current time. The feedback adjustment generation module 303 is used to generate a feedback adjustment based on multiple sets of continuous alkalinity time series data output from laboratory sampling and testing before the current moment. The final adjustment amount determination module 304 is used to superimpose the feedforward adjustment amount and the feedback adjustment amount to obtain the final white lime ratio adjustment amount.

[0094] Figure 4 A schematic diagram of the structure of an electronic device according to an embodiment of this application is shown.

[0095] like Figure 4 As shown in the figure, an electronic device according to an embodiment of this application includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned control method.

[0096] The embodiments of this application provide a method, system, and electronic equipment for controlling the basicity of sintered ore based on online detection of mixed materials. The method determines whether to adjust the basicity by using the basicity detection data of the sintered material from an online component analyzer, thus avoiding repeated adjustments to the same batch of materials. Under the condition of meeting the basicity adjustment requirements, the basicity of the material is adjusted by combining the superposition of the feedforward adjustment amount and the feedback adjustment amount, thereby improving the basicity qualification rate and robustness.

[0097] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for controlling the basicity of sintered ore based on online detection of the mixture, characterized in that, The control method includes: Determine whether the alkalinity adjustment conditions are met at the current moment based on the sintering alkalinity detection values ​​of the current moment and the previous multiple consecutive moments output by the online component analyzer. When the alkalinity adjustment conditions are met at the current moment, a feedforward adjustment amount is generated, and multiple sets of continuous alkalinity time series data prior to the current moment are simultaneously acquired from the laboratory sampling and testing output. A feedback adjustment amount is generated based on multiple sets of continuous alkalinity time-series data prior to the current moment, as output by laboratory sampling and testing. The final white-to-gray ratio adjustment is obtained by superimposing the feedforward adjustment and the feedback adjustment.

2. The control method according to claim 1, characterized in that, The generated feedforward adjustment includes: Calculate the average basicity deviation between the sintering basicity detection value and the target sintering basicity value at the current moment and multiple consecutive moments preceding it; The feedforward adjustment amount is determined based on the average alkalinity deviation value, the maximum adjustment amount of a single feedforward, and the preset feedforward adjustment coefficient.

3. The control method according to claim 1, characterized in that, The process of generating a feedback adjustment based on multiple sets of continuous alkalinity time-series data prior to the current moment, as output by laboratory sampling and testing, includes: Determine whether the most recent alkalinity value of the laboratory sample is qualified; wherein, the most recent alkalinity value of the laboratory sample is used to characterize the sintering alkalinity detection value in the first set of alkalinity time series data most recent before the current time of the laboratory sample detection output; If the most recent alkalinity value from the laboratory sample is unqualified, additional feedback adjustment parameters are determined based on the most recent alkalinity deviation value from the laboratory sample, the first alkalinity deviation threshold, and the second alkalinity deviation threshold. The most recent alkalinity deviation value from the laboratory sample is used to characterize the absolute value of the difference between the most recent alkalinity value from the laboratory sample and the sintering alkalinity detection value in the most recent second set of alkalinity time series data before the current time. The feedback adjustment amount is generated based on the additional feedback adjustment parameters, the most recent alkalinity value from the laboratory sample, and the target alkalinity value.

4. The control method according to claim 3, characterized in that, The determination of whether the most recent alkalinity value of a laboratory sample is acceptable includes: If the most recent alkalinity value of the laboratory sample is between the lower and upper alkalinity thresholds, then the most recent alkalinity value of the laboratory sample is acceptable; otherwise, it is unacceptable.

5. The control method according to claim 4, characterized in that, The determination of additional feedback adjustment parameters based on the most recent alkalinity deviation value from laboratory sampling, the first alkalinity deviation threshold, and the second alkalinity deviation threshold includes: If the most recent alkalinity deviation value of the laboratory sample is less than or equal to the first alkalinity deviation threshold, the additional feedback adjustment parameter is the first preset feedback parameter. If the most recent alkalinity deviation value of the laboratory sample is greater than or equal to the second alkalinity deviation threshold, the additional feedback adjustment parameter is the second preset feedback parameter; If the most recent alkalinity deviation value of the laboratory sample is greater than the first alkalinity deviation threshold but less than the second alkalinity deviation threshold, the laboratory determines whether a data anomaly has occurred based on multiple sets of continuous alkalinity time series data output before the current time. If a data anomaly has occurred, the additional feedback adjustment parameter is the second preset feedback parameter. If no data anomaly has occurred, the third preset feedback parameter is used.

6. The control method according to claim 5, characterized in that, The process of determining whether data anomalies have occurred based on multiple sets of continuous alkalinity time-series data prior to the current moment, as output by laboratory sampling and testing, includes: Calculate multiple SiO2 differences between the SiO2 values ​​of the remaining groups and the most recent SiO2 values ​​in the laboratory, and multiple CaO differences between the CaO values ​​of the remaining groups and the most recent CaO values ​​in the laboratory; wherein, the most recent SiO2 value in the laboratory is used to characterize the SiO2 detection value in the first most recent set of alkalinity time series data before the current time of the laboratory sampling and detection output, and the most recent CaO value in the laboratory is used to characterize the CaO detection value in the first most recent set of alkalinity time series data before the current time of the laboratory sampling and detection output; The system determines whether SiO2 data is abnormal based on multiple SiO2 differences and a preset SiO2 deviation threshold, and determines whether CaO data is abnormal based on multiple CaO differences and a preset CaO deviation threshold. If SiO2 and / or CaO show abnormal data, it is determined that a data abnormality has occurred; otherwise, it is determined that no data abnormality has occurred.

7. The control method according to claim 6, characterized in that, The method of determining whether SiO2 data is abnormal based on multiple SiO2 differences and a preset SiO2 deviation threshold includes: If the absolute values ​​of multiple SiO2 differences are all greater than the preset SiO2 deviation threshold, and the signs of the multiple SiO2 differences are the same, then SiO2 data anomaly occurs; otherwise, SiO2 data anomaly does not occur. The method of determining whether CaO data anomalies have occurred based on multiple CaO differences and a preset CaO deviation threshold includes: If the absolute values ​​of multiple CaO differences are all greater than the preset CaO deviation threshold, and the signs of the multiple CaO differences are the same, then CaO data anomaly occurs; otherwise, CaO data anomaly does not occur.

8. The control method according to any one of claims 3 to 7, characterized in that, Before generating the feedback adjustment amount based on the additional feedback adjustment parameters, the most recent alkalinity value from the laboratory sample, and the target alkalinity value, the following steps are also included: If the most recent alkalinity value from the laboratory sample is greater than the upper limit threshold and the alkalinity values ​​of all other groups are greater than the target alkalinity value, or if the most recent alkalinity value from the laboratory sample is less than the lower limit threshold and the alkalinity values ​​of all other groups are less than the target alkalinity value, then the laboratory determines whether a data anomaly has occurred based on the multiple sets of continuous alkalinity time-series data output before the current time from the laboratory sampling test. If a data anomaly has occurred, the additional feedback adjustment parameter is updated to the fourth preset feedback parameter; if no data anomaly has occurred, the additional feedback adjustment parameter is updated to the first preset feedback parameter.

9. A sinter basicity control system based on online detection of mixed materials, characterized in that, The control system includes: The adjustment condition judgment module is used to determine whether the alkalinity adjustment condition is met at the current time based on the sintering alkalinity detection values ​​of the current time and the previous multiple consecutive times output by the online component analyzer. The feedforward adjustment generation module is used to generate a feedforward adjustment amount when the alkalinity adjustment conditions are met at the current moment, and simultaneously acquire multiple sets of continuous alkalinity time series data output from laboratory sampling and testing before the current moment. The feedback adjustment generation module is used to generate a feedback adjustment based on multiple sets of continuous alkalinity time series data output from laboratory sampling and testing before the current moment. The final adjustment amount determination module is used to superimpose the feedforward adjustment amount and the feedback adjustment amount to obtain the final white lime ratio adjustment amount.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the control method according to any one of claims 1-8.

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