Rotary cement kiln working condition identification method based on multi-modal data analysis

Abstract

The invention provides a rotary cement kiln working condition identification method based on multi-modal data analysis, and belongs to the technical field of rotary cement kiln working condition identification. The method comprises the following steps of: 1, constructing a multivariable time sequence prediction model by monitoring core process parameters of the rotary kiln in real time, and predicting a future change trend of each process parameter; 2, performing retraining and incremental updating on the prediction model based on a fixed period to realize online evolution and performance self-optimization of the model; and step 3, extracting dynamic evolution characteristics and coupling association modes of the core parameters of the rotary kiln, and constructing a kiln condition grade quantification model based on trend combination discrimination. According to the method, the operation state of the cement kiln can be accurately evaluated, and an accurate and dynamic kiln condition grade quantitative model is formed; meanwhile, the method has an online updating mechanism, long-term effectiveness of the model is ensured, and the problem of model failure caused by kiln condition drift in a traditional method is avoided.

Description

Technical Field

[0001] This invention belongs to the field of cement rotary kiln operating condition identification technology. Specifically, this invention relates to a cement rotary kiln operating condition identification method based on multimodal data analysis. Background Technology

[0002] With the advancement of industrialization, energy consumption and environmental pollution in cement production have received increasing attention. Traditional optimization methods for cement rotary kilns mainly rely on empirical rules and manual adjustments, which have drawbacks such as low optimization accuracy, slow response speed, and inability to adapt to changes in operating conditions.

[0003] Chinese Patent 103776262A discloses a method and device for real-time adjustment of the combustion conditions of a cement rotary kiln. The method includes the following steps: (1) real-time monitoring of the radiation intensity inside the cement rotary kiln; (2) experimentally adjusting the air supply volume to determine whether the real-time operating condition of the cement rotary kiln is in the fuel-rich combustion section or the oxygen-rich combustion section based on the trend of radiation intensity changes; (3) adjusting the air supply volume of the cement rotary kiln according to the real-time operating condition of the cement rotary kiln until the radiation intensity no longer increases. The device includes a CCD sensor, a tail gas analyzer, an air volume opening actuator, and a processor; the CCD sensor is electrically connected to the processor, the tail gas analyzer is electrically connected to the processor, and the processor and the air volume opening actuator are electrically connected.

[0004] Existing methods for identifying the operating conditions of cement rotary kilns mostly rely on prediction and control based on a single key parameter. This ignores the dynamic coupling relationship between multiple key parameters and fails to accurately identify the real-time operating conditions of the cement rotary kiln. Summary of the Invention

[0005] The present invention aims to provide a method for identifying the operating conditions of a cement rotary kiln based on multimodal data analysis, so as to achieve online quantitative identification of kiln conditions and provide technical support for optimizing the control strategy of the firing process.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] This invention provides a method for identifying the operating conditions of a cement rotary kiln based on multimodal data analysis:

[0008] Step 1: By monitoring the core process parameters of the rotary kiln in real time, a multivariate time series prediction model is constructed to predict the future trends of each process parameter.

[0009] Step 2: Retrain and incrementally update the prediction model based on a fixed period to achieve online evolution and performance self-optimization of the model;

[0010] Step 3: Extract the dynamic evolution characteristics and coupling correlation patterns of the core parameters of the rotary kiln, construct a kiln condition level quantification model based on trend combination discrimination, and realize online kiln condition identification.

[0011] Step one includes the following sub-steps:

[0012] (1) Real-time acquisition of core process parameters of rotary kiln, including NOx concentration at kiln tail, kiln main current, secondary air temperature and fCaO, etc.

[0013] (2) Construct a multivariate time series prediction model to model and predict the future change trends of each process parameter.

[0014] In step one, sub-step (2) involves a multivariate time-series prediction model for process parameters, where the real-time prediction model for fCaO is modeled using the following method:

[0015] 1) Select process variables that are highly correlated with kiln conditions and fCaO, and apply time series and statistical transformations within different time windows to construct a high-dimensional candidate feature set;

[0016] 2) Using a gradient boosting tree model, the subset of features that contribute the most to the prediction is selected based on feature importance scores;

[0017] 3) Using the selected features, hyperparameters are optimized through cross-validation, and the final prediction model is constructed using model fusion technology.

[0018] In step one, sub-step (2), the multivariate time series prediction model for process parameters and the time series prediction model for other parameters are modeled as follows: select highly correlated covariates of process parameters, then extract the periodic and fluctuation characteristics of the covariate sequence simultaneously, and use the Spline Regression model to fuse cross-variable dynamics to achieve future trend prediction of process parameters.

[0019] The specific update mechanism for step two is as follows:

[0020] (1) The model is retrained based on a fixed period, and the latest DCS and historical data are incrementally fused to retrain the complete model;

[0021] (2) Deployment updates are triggered only when the new model performs significantly better than the online version on the recent validation set.

[0022] The validation metric for model performance in sub-step (2) of step two is:

[0023] ,

[0024] Where RMSE is the root mean square error between the predicted sequence and the true sequence on the model validation set; Trend is the degree of agreement between the predicted trend and the true trend, calculated as a percentage; α and β are the weighting coefficients of the two indicators.

[0025] Step 3 includes the following sub-steps:

[0026] (1) By using real-time data and prediction models, extract the dynamic evolution characteristics of the core parameters of the rotary kiln, including the average value and trend of change over a period of time, as well as the predicted value and trend of the future period.

[0027] (2) Based on historical data of rotary kilns and expert experience, construct kiln condition level judgment rules based on core process parameters and their future trends;

[0028] (3) Through multivariate trend coupling and dynamic threshold distribution analysis, the kiln condition is classified and identified online from good to bad.

[0029] The method for judging the kiln condition level in sub-step (2) of step three is as follows:

[0030] Level 5 (Excellent Operating Condition): When the fCaO prediction is within a good range, if the average NOx concentration at the kiln tail reaches the excellent standard and there is no significant fluctuation, or if it is close to the excellent standard and the secondary air temperature and kiln current are within the normal range, it is judged as an excellent operating condition.

[0031] Level 4 (Superior Operating Condition): When the fCaO prediction is within a good range, if the NOx concentration at the kiln tail shows an upward trend, and the secondary air temperature or kiln current rises synchronously and steadily, it is judged as a superior operating condition.

[0032] Level 3 (Medium Operating Condition): When the fCaO prediction is in a medium range, if the operating status does not meet the criteria of any other level, it is judged as a medium operating condition.

[0033] Level 2 (Poor Operating Condition): When the fCaO prediction is in a poor range, if the average NOx concentration at the kiln tail is lower than the set threshold, or multiple parameters decrease in tandem, it is judged as a poor operating condition.

[0034] Level 1 (Poor Operating Condition): When the fCaO prediction is in a very poor range, if any core process parameter is lower than the poor operating condition judgment standard, it is judged as a poor operating condition.

[0035] The technical effects of this invention are as follows:

[0036] (1) By monitoring multiple key parameters in real time and employing a multivariate coupling analysis and trend prediction model, this invention can accurately assess the operating status of cement kilns, providing a reference for improving the efficiency and stability of the cement firing process. Compared with existing single-parameter models, this invention can comprehensively consider multi-dimensional process parameters and operating condition changes, forming a more accurate and dynamic kiln condition level quantification model;

[0037] (2) The present invention has the advantage of accurate kiln condition identification. In response to the technical pain points of strong coupling of multiple variables in rotary kilns and reliance on human experience for condition judgment, the present invention innovatively adopts multivariate time series decomposition, periodic feature extraction and cross-variable regression fusion technology to effectively capture the dynamic coupling relationship between parameters, identify changes in operating conditions in advance, and establish five-level quantitative identification rules to transform traditional subjective judgment into standardized algorithm decision-making, significantly reduce the misjudgment rate, and provide accurate input for subsequent control strategies.

[0038] (3) The present invention has the advantage of reliable prediction model. In the modeling stage, when constructing the real-time prediction model of fCaO, the feature importance is screened by gradient boosting tree. Combined with cross-validation optimization and model fusion technology, the prediction accuracy is significantly improved, providing a reliable basis for quality prediction for control strategy. In the model maintenance stage, an update mechanism is designed to continuously optimize the model adaptability by integrating the latest DCS data and historical data. At the same time, only new models with significantly better performance than the online version are allowed to be deployed online, avoiding production fluctuations during the update process and ensuring that the model is always synchronized with the actual working conditions. Attached Figure Description

[0039] This manual includes the following figures, which illustrate the following:

[0040] Figure 1 This is a flowchart of a cement rotary kiln condition identification method based on multimodal data analysis according to the present invention.

[0041] Figure 2 This is a flowchart of the online update mechanism for the prediction model of the present invention;

[0042] Figure 3 This is the kiln condition level determination rule table of the present invention. Detailed Implementation

[0043] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, in order to help those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention, and to facilitate its implementation.

[0044] Taking a rotary kiln equipped with a high-temperature NOx concentration monitoring device at the kiln tail as an example, the specific implementation process of the present invention will be described, such as... Figure 1 As shown, it includes the following steps:

[0045] Step 1: Construct a prediction model for core process parameters.

[0046] This step includes the following sub-steps:

[0047] (1) Data collection.

[0048] Real-time acquisition of core process parameters of the rotary kiln, such as NOx concentration at the kiln tail, kiln main current, and secondary air temperature, and statistical analysis of fCaO content detection data.

[0049] (2) Construct a time series prediction model.

[0050] The following are the steps for constructing a real-time prediction model for fCaO content:

[0051] 1) Record the fCaO content as The process variables that are related to kiln conditions and high fCaO content were selected and denoted as follows:

[0052] ,

[0053] Where l represents the number of variables, including raw material KH value, secondary air temperature, tertiary air temperature, kiln main transmission current, and smoke chamber O2 concentration.

[0054] Applying time series and statistical transformations within different time windows is denoted as...

[0055] ,

[0056] in This provides time-series and statistical information on the variable, including mean, standard deviation, linear trend slope, first-order autocorrelation coefficient, and cross-correlation coefficient with fCaO content. A collection of information.

[0057] Construct a high-dimensional candidate feature set, denoted as

[0058] .

[0059] 2) Using the XGBoost gradient boosting tree model, based on feature importance scores, the subset of features that contribute the most to the prediction is selected, denoted as...

[0060] , ,

[0061] in Score the feature weights of this parameter. This is an important indicator.

[0062] 3) Using the selected features, for a single sub-model k, the hyperparameters are evaluated through cross-validation. By performing optimization, an optimized model is obtained:

[0063] ,

[0064] in For the prediction output of a single model, These are the optimal hyperparameters.

[0065] Then, model fusion technology is used to construct the final fCaO content prediction model:

[0066] ,

[0067] in The optimal weighting coefficients are obtained through training and optimization using a Bayesian search method.

[0068] Next, predictive models for parameters such as NOx concentration at the kiln tail, kiln main current, and secondary air temperature will be constructed.

[0069] Among them, the covariates selected for NOx concentration at the kiln tail are decomposition furnace outlet temperature, coal feed rate, kiln tail oxygen concentration, and ammonia flow rate; the covariates selected for kiln main transmission current are kiln output, coal feed rate, and kiln speed; and the covariates selected for secondary air temperature are grate pressure, clinker feed rate, kiln head negative pressure, and primary air pressure.

[0070] The following are the modeling steps:

[0071] 1) The target parameter is denoted as Select covariates that are highly correlated with the target parameter, denoted as...

[0072] ,

[0073] Where n is the number of covariates.

[0074] 2) Use the STL decomposition method to extract the periodicity and volatility characteristics of the covariate sequence, i.e.

[0075] ,

[0076] in For trend items, It is a periodic term. This is the residual term.

[0077] 3) Using the periodic and residual terms of the covariates as inputs, a spline regression model is used to fuse cross-variable dynamics to obtain...

[0078] ,

[0079] in This is the intercept term of the model; and It is a univariate spline function; It is a tensor product spline function used to capture nonlinear interactions between variables.

[0080] Finally, Bayesian optimization was used for training to achieve prediction of process parameters for the next 20 to 30 minutes.

[0081] Step 2: Update the prediction model online.

[0082] like Figure 2 As shown, to prevent performance degradation of the prediction model due to kiln condition drift, an online update mechanism is established. The model is retrained at fixed intervals, incrementally fusing the latest DCS and historical data to retrain the complete model; deployment updates are only triggered when the new model significantly outperforms the online version on the recent validation set.

[0083] The validation metric for model performance is

[0084] ,

[0085] Where RMSE is the root mean square error between the predicted and actual sequences on the validation set; Trend is the percentage of agreement between the predicted and actual trends; α and β are the weighting coefficients of the two indicators. In summary, the smaller the F value, the better the model performance.

[0086] Step 3: Online identification of kiln condition level.

[0087] (1) Feature extraction.

[0088] Using real-time data from the cement firing process and the current optimal prediction model from step two, the dynamic evolution characteristics of the core parameters of the rotary kiln are extracted, including the average value over a past period and the predicted value and trend over a future period.

[0089] (2) Establish a quantitative model for kiln condition levels.

[0090] Based on historical data of rotary kilns and expert experience, a kiln condition level determination rule is constructed based on core process parameters and their future trends. The following section combines... Figure 3 The rules for determining kiln condition levels are explained in detail:

[0091] Level 5 (Optimal operating condition, fCaO content prediction: C1-C2): Calculate the average value of each parameter over a period of time. When one of the following two conditions is met, the condition is judged as optimal: Condition 1: The average NOx value at the kiln tail is greater than N5 and there is no significant fluctuation; Condition 2: The average NOx value at the kiln tail is close to N5 and there is no significant fluctuation. At this time, the average secondary air temperature is greater than F5 and there is no rapid rise or fall, and the average kiln main transmission current is greater than I5 and there is no downward trend. If only one of the latter two conditions is met, the condition is judged as relatively optimal.

[0092] Level 4 (Superior operating condition, fCaO content prediction: C2-C3): Calculate the average value of each parameter over a period of time. When the average NOx value at the kiln tail is greater than N4 and the upward trend is obvious, it can be judged as a superior operating condition if either of the following two conditions is met: the average secondary air temperature is greater than F4 and there is no downward trend over a period of time; the average kiln main transmission current is greater than I4 and there is no downward trend over a period of time.

[0093] Level 3 (Medium working condition, fCaO content prediction: C3-C4): If it does not belong to Level 1, 2, 4, or 5 working conditions, it is judged as a medium working condition;

[0094] Level 2 (Poor Operating Condition, fCaO Content Prediction: C4-C5): Calculate the average value of each parameter over a period of time. If one of the following two conditions is met, it is judged as a poor operating condition: Condition 1: The average value of NOx at the kiln tail is less than N2; Condition 2: The NOx at the kiln tail has shown a significant downward trend over a period of time, the average value of the secondary air temperature is less than F2 and has shown a downward trend over a period of time, and the average value of the kiln main transmission current is less than I2 and has shown a downward trend over a period of time. Any two or more of the above three conditions must be met.

[0095] Level 1 (Poor operating condition, predicted fCaO content: >C5): Calculate the average value of each parameter over a period of time. If any of the following conditions are met, it is judged as a poor operating condition: the average value of NOx at the kiln tail is less than N1; the average value of secondary air temperature is less than F1; the average value of kiln main transmission current is less than I1.

[0096] (3) Kiln condition identification.

[0097] By combining multivariate trend coupling with dynamic threshold distribution analysis, online classification of kiln conditions from excellent to poor is achieved. The specific values ​​of each threshold need to be initially determined based on production line conditions and expert experience, and then dynamically optimized and adjusted according to actual kiln age and changes in raw material batches.

[0098] The beneficial effects of the present invention are described in detail below.

[0099] This invention, by real-time monitoring of multiple key parameters and employing multivariate coupling analysis and trend prediction models, can accurately assess the operating status of cement kilns, providing a reference for improving the efficiency and stability of the cement firing process. Compared with existing single-parameter models, this invention can comprehensively consider multi-dimensional process parameters and operating condition changes, forming a more accurate and dynamic kiln condition level quantification model.

[0100] This invention has the advantage of accurate kiln condition identification. Addressing the technical pain points of strong coupling of multiple variables in rotary kilns and reliance on human experience for condition judgment, this invention innovatively adopts multivariate time series decomposition, periodic feature extraction, and cross-variable regression fusion technology to effectively capture the dynamic coupling relationship between parameters, identify changes in operating conditions in advance, and establish a five-level quantitative identification rule to transform traditional subjective judgment into standardized algorithm decision-making, significantly reducing the misjudgment rate and providing accurate input for subsequent control strategies.

[0101] This invention has the advantage of reliable prediction models. In the modeling stage, when constructing the real-time prediction model of fCaO, feature importance is screened through gradient boosting trees. Combined with cross-validation optimization and model fusion technology, the prediction accuracy is significantly improved, providing a reliable basis for quality prediction of control strategies. In the model maintenance stage, an update mechanism is designed to continuously optimize model adaptability by fusing the latest DCS data with historical data. At the same time, only new models with significantly better performance than the online version are allowed to be deployed online, avoiding production fluctuations during the update process and ensuring that the model is always synchronized with actual working conditions.

[0102] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution; or the direct application of the inventive concept and technical solution to other situations without modification, are all within the protection scope of the present invention.

Claims

1. A cement rotary kiln working condition identification method based on multi-modal data analysis, characterized by: Step one, by real-time monitoring of the core process parameters of the rotary kiln, a multivariate time series prediction model is constructed to predict the future trend of each process parameter; Step two, based on fixed cycle, the prediction model is retrained and incrementally updated, realizing the online evolution and performance self-optimization of the model; Step three, extract the dynamic evolution characteristics and coupling correlation mode of the core parameters of the rotary kiln, construct a kiln condition grade quantization model based on trend combination discrimination, and realize online identification of kiln condition.

2. The cement rotary kiln working condition identification method based on multi-modal data analysis according to claim 1, characterized in that: Step one includes the following sub-steps: (1) Real-time acquisition of the core process parameters of the rotary kiln, including kiln tail NOx concentration, kiln main transmission current, secondary air temperature and free calcium (fCaO) etc.; (2) Construct a multivariate time series prediction model to model and predict the future trend of each process parameter.

3. The method of claim 2, wherein the method is characterized by: The multivariate time series prediction model of the process parameters in sub-step (2) is as follows: 1) Select the process variables highly correlated with kiln condition and fCaO, apply time series and statistical transformation in different time windows, and construct a high-dimensional candidate feature set; 2) Use gradient boosting tree model to select the highest contribution feature subset according to feature importance score; 3) Use the filtered features to optimize the hyperparameters through cross-validation, and use model fusion technology to construct the final prediction model.

4. The method of claim 2, wherein the method is characterized by: The multivariate time series prediction model of the process parameters in sub-step (2) is as follows:

5. The method for working condition identification of cement rotary kiln based on multi-modal data analysis according to claim 1, characterized in that: The modeling method of other parameter time series prediction model is as follows: select the covariates highly correlated with the process parameters, then extract the period and fluctuation characteristics of the covariate sequence synchronously, and realize the future trend prediction of the process parameters by fusing the cross-variable dynamics with Spline Regression model. The specific updating mechanism of step two is as follows: (1) Based on fixed cycle, retrain the model, incrementally fuse the latest DCS and historical data, and retrain the complete model; 6. The method for working condition identification of cement rotary kiln based on multi-modal data analysis according to claim 5, characterized in that: (2) Only when the new model performs significantly better than the online version on the recent validation set, the update is triggered. ， The validation index of model performance in sub-step (2) is as follows:

7. The method for working condition identification of cement rotary kiln based on multi-modal data analysis according to claim 1, characterized in that: Where RMSE is the root mean square error of the predicted sequence and the true sequence on the model validation set; Trend is the degree of agreement between the predicted trend and the true trend, calculated in percentage; α and β are the weight coefficients of the two indicators. Step three has the following sub-steps: (1) Extract the dynamic evolution characteristics of the core parameters of the rotary kiln through real-time data and prediction model, including the mean value, trend of change in the past period, and predicted value and trend in the future period; (2) Based on the historical data of the rotary kiln and expert experience, construct kiln condition grade determination rules based on core process parameters and their future trends; 8. The method of claim 7, wherein the method is characterized by: (3) Through multivariate trend coupling and dynamic threshold distribution analysis, realize the online identification of kiln condition grade from good to bad. The kiln condition grade determination rule in sub-step (2) is as follows: The fifth level (optimal working condition): when the fCaO content prediction is in a good range, if the kiln tail NOx concentration average reaches the excellent standard without significant fluctuations, or it is close to the excellent standard, and the secondary air temperature and kiln current are in the normal range, it is determined as the optimal working condition; The fourth level (better working condition): when the fCaO content prediction is in a better range, if the kiln tail NOx concentration shows an upward trend, and the secondary air temperature or the kiln current is stably rising synchronously, it is determined as the better working condition; The third level (medium working condition): when the fCaO content prediction is in a medium range, if the running state does not meet the standard of any other level, it is determined as the medium working condition; The second level (poor working condition): when the fCaO content prediction is in a poor range, if the kiln tail NOx concentration average is lower than the set threshold, or multiple parameters decrease synchronously, it is determined as the poor working condition; The first level (bad working condition): when the fCaO content prediction is in a very poor range, if any core process parameter is lower than the bad working condition determination standard, it is determined as the bad working condition.

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