CN-121979055-A - Pollution early warning and capacity intelligent regulation and control method and system based on cement production parameters

CN121979055ACN 121979055 ACN121979055 ACN 121979055ACN-121979055-A

Abstract

The application relates to the technical field of cement kiln pollutant emission control, in particular to a pollution early warning and capacity intelligent regulation and control method and system based on cement production parameters. The method comprises the steps of collecting and standardizing multidimensional time sequence parameters of a cement kiln system, extracting set basic characteristics and determining key influence parameters, extracting time dependent characteristics by utilizing a long-short-period memory network based on the key parameters to obtain key characteristic vectors, adopting a support vector machine to identify abnormal emission trend according to the characteristic vectors and record abnormal information, carrying out simulation experiments based on the abnormal information to evaluate different regulating paths to obtain an optimized regulating scheme, applying the optimized scheme when conditions are met, recording regulating data, continuously monitoring pollutant concentration by taking the regulating end as a starting point, evaluating a regulating effect and updating model parameters to form a closed-loop improvement mechanism. The method effectively improves the early warning accuracy of the pollutant emission trend of the cement kiln and the adaptability of the regulation and control process.

Inventors

MA SHEXIA
ZHAO GUOYIN
LIN XIHUA
XING JIANHUA
QI YINGJIE
GAO YAN

Assignees

生态环境部华南环境科学研究所(生态环境部生态环境应急研究所)
鹤壁市生态环境监测和安全中心

Dates

Publication Date: 20260505
Application Date: 20260202

Claims (10)

1. The pollution early warning and capacity intelligent regulation and control method based on cement production parameters is characterized by comprising the following steps: S1, acquiring a multidimensional parameter data set from a cement kiln system, performing standardized processing to acquire a time sequence data set, and analyzing the time sequence data set to acquire set basic characteristics; s2, determining key influence parameters according to influence relations among the set basic feature analysis parameters; S3, extracting time-dependent features based on the key influence parameters and combining a long-term and short-term memory network model to obtain key feature vectors representing pollutant emission trends; S4, according to the key feature vector, adopting a support vector machine model to identify whether the pollutant emission trend is abnormal, generating an abnormal emission precursor mark when the pollutant emission trend is abnormal, and recording an abnormal time point and related parameter values; S5, performing a simulation experiment based on the abnormal time point and the related parameter value, recording influence paths of different regulation and control schemes in simulation, evaluating the influence paths to obtain an optimized regulation and control scheme, and generating an evaluation report; s6, judging whether the productivity adjustment value in the evaluation report meets implementation conditions, if so, applying a corresponding optimized regulation and control scheme to the cement kiln system, and recording final regulation and control data; And S7, continuously monitoring the pollutant concentration by taking the end time of the final regulation data as a starting point, recording the change trend of the pollutant concentration, judging the regulation effect, and updating the parameters of the long-short-period memory network model and the parameters of the support vector machine model to form a closed-loop feedback and continuous improvement mechanism.
2. The method according to claim 1, wherein S1 comprises: synchronously acquiring multidimensional parameter data comprising raw meal proportion, kiln temperature and pollutant concentration from a cement kiln system through a real-time sensor; normalizing the multidimensional parameter data to generate a standardized multidimensional parameter data set; performing outlier filtering and format verification on the standardized multidimensional parameter data set to form a time sequence data set with consistent quality and structuring; And carrying out preliminary statistical analysis on the time sequence data set, and extracting set basic features, wherein the set basic features comprise the mean value, variance and trend features of each parameter.
3. The method according to claim 1, wherein S2 comprises: calculating an interaction coefficient between the raw material proportion and the kiln temperature based on the aggregate basic characteristics; According to the mutual influence coefficient, evaluating a nonlinear influence relation among parameters; Verifying the nonlinear influence relation based on historical data, and determining the nonlinear influence relation strength between parameters; And judging whether the nonlinear influence relation strength exceeds a preset strength threshold, if so, ordering the mutual influence coefficients in a descending order, and selecting the top two parameters as key influence parameters.
4. The method according to claim 1, wherein S3 comprises: Extracting corresponding time sequence data from the time sequence data set based on the key influence parameters; Inputting the time series data into a long-term and short-term memory network model, and extracting time dependent characteristics in pollutant emission trend; integrating the time-dependent features into key feature vectors, and performing dimension reduction on the key feature vectors to obtain light key feature vectors; And carrying out consistency matching verification on the key feature vector and the actual pollutant emission trend to obtain a final key feature vector.
5. The method according to claim 1, wherein S4 comprises: Classifying the key feature vectors by adopting a support vector machine model, distinguishing normal and abnormal states of pollutant emission trend, and determining classification results of the key feature vectors; Judging whether the pollutant emission trend deviates from a normal range according to the classification result; if yes, generating an abnormal emission precursor mark; Based on the abnormal emission precursor identification, an abnormal time point and related parameter values are recorded.
6. The method according to claim 1, wherein S5 comprises: simulating linkage effects of production energy adjustment and process parameter change on pollutant emission trend based on the abnormal time points and the related parameter values in combination with a dynamic programming algorithm, and recording influence paths of different regulation and control schemes in simulation; Carrying out feasibility assessment on the influence path to obtain feasibility scores of the corresponding schemes, and taking the scheme with the highest feasibility score as an optimized regulation scheme; performing multi-scene stability simulation test on the optimized regulation scheme to generate a specific regulation parameter combination; and carrying out quantitative analysis on the influence effect of the adjustment parameter combination, and generating an evaluation report.
7. The method according to claim 1, wherein S6 comprises: judging whether the capacity adjustment value in the evaluation report is lower than a preset capacity threshold value, if yes, applying the optimized regulation scheme to a cement kiln system through a closed-loop regulation mechanism, adjusting production parameters in real time, and collecting production state data; Integrating the production state data to form preliminary historical regulation data; performing preliminary analysis on the pollutant concentration in the preliminary historical regulation data, and evaluating the immediate effect of regulation; And if the instant effect accords with the preset regulation and control index, taking the preliminary historical regulation and control data as final regulation and control data.
8. The method according to claim 1, wherein said S7 comprises: Continuously monitoring the subsequent pollutant concentration by adopting a sliding window method by taking the ending time of the final regulation and control data as a starting point to acquire the variation trend of the pollutant concentration; judging whether the change trend reaches an expected target trend or not; if the expected target trend is not reached, recording deviation data, updating the multidimensional parameter data set by combining the deviation data, and triggering a new round of regulation and control flow; If the expected target trend is reached, updating the multi-dimensional parameter data set according to the final regulation data, and optimizing the parameters of the long-short-term memory network model and the parameters of the support vector machine model according to the multi-dimensional parameter data set to form a closed-loop feedback and continuous improvement mechanism.
9. A pollution early warning and capacity intelligent regulation system based on cement production parameters, for implementing the method as claimed in any one of claims 1 to 8, characterized in that the pollution early warning and capacity intelligent regulation system based on cement production parameters comprises: The data acquisition module is used for acquiring a multidimensional parameter data set from the cement kiln system and performing standardized processing to acquire a time sequence data set, analyzing the time sequence data set and acquiring set basic characteristics; the parameter extraction module is used for determining key influence parameters according to the influence relation among the set basic feature analysis parameters; The feature extraction module is used for extracting time-dependent features based on the key influence parameters and combining a long-period memory network model to obtain key feature vectors representing pollutant emission trends; The anomaly identification module is used for identifying whether the pollutant emission trend is abnormal or not by adopting a support vector machine model according to the key feature vector, generating an abnormal emission precursor mark when the pollutant emission trend is abnormal, and recording an abnormal time point and related parameter values; The regulation and control evaluation module is used for carrying out a simulation experiment based on the abnormal time point and the related parameter value, recording influence paths of different regulation and control schemes in simulation, evaluating the influence paths to obtain an optimized regulation and control scheme, and generating an evaluation report; the implementation feedback module is used for judging whether the productivity adjustment value in the evaluation report meets the implementation condition, if so, applying the corresponding optimized regulation and control scheme to the cement kiln system, and recording final regulation and control data; And the closed loop updating module is used for continuously monitoring the pollutant concentration by taking the ending time of the final regulation and control data as a starting point, recording the change trend of the pollutant concentration, judging the regulation and control effect, and updating the parameters of the long-period memory network model and the parameters of the support vector machine model to form a closed loop feedback and continuous improvement mechanism.
10. The pollution early warning and capacity intelligent regulation and control equipment based on cement production parameters is characterized by comprising a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory to cause the pollution early warning and capacity intelligent regulation device for cement production parameter based pollution early warning and capacity intelligent regulation method according to any one of claims 1-8.

Description

Pollution early warning and capacity intelligent regulation and control method and system based on cement production parameters Technical Field The application relates to the technical field of cement kiln pollutant emission control, in particular to a pollution early warning and capacity intelligent regulation and control method and system based on cement production parameters. Background Cement production is an important industrial field of support infrastructure construction, and pollutants discharged in the production process directly affect environmental protection and achievement of green manufacturing targets. Cement kiln systems can produce a variety of pollutants in operation, and the emission levels are complexly affected by multidimensional production parameters such as temperature, pressure, raw material proportion, equipment state and the like in the kiln. These parameters are not only numerous, but also have dynamically interwoven nonlinear interactions with each other, making accurate predictions of pollutant emission trends and effective regulation a central challenge in achieving clean production. Currently, many pollutant emission control methods focus on the monitoring and adjustment of a single or a few key parameters, or on regulatory strategies based on fixed rules. Such methods often have difficulty adequately capturing and quantifying nonlinear interactions between individual parameters over time. For example, adjusting the kiln tail temperature may affect the efficiency of subsequent dust removal equipment, which is not a simple proportional relationship, and the effect may delay appearance with fluctuations in the raw material composition. The limitation of the existing methods is that they lack a systematic depiction of such a dynamic, nonlinear correlation chain between parameters, resulting in a lack of predictive prediction of the possible linkage reactions that may be triggered by regulatory measures. Therefore, how to accurately determine how each parameter affects each other in a nonlinear manner and evolves with time in a dynamically changing production system, so that potential risk precursors are identified before obvious abnormality occurs in pollutant emission, and an optimal regulation and control scheme capable of predicting linkage effect is designed, which becomes a key problem of accurate control and green production of cement kiln pollutants. In order to cope with the challenge, the invention provides the pollution early warning and capacity intelligent regulation method based on cement production parameters, which combines the standardized processing of multidimensional parameter data, the time sequence feature extraction and the machine learning model, can more accurately identify the pollutant emission trend and realize intelligent regulation, improves the pollution emission control effect of a cement kiln system, and provides effective support for green manufacturing. Disclosure of Invention The application provides a pollution early warning and capacity intelligent regulation and control method and system based on cement production parameters, which are used for improving the accuracy of cement kiln pollutant emission trend early warning and the adaptability of regulation and control process. In a first aspect, the application provides a pollution early warning and capacity intelligent regulation method based on cement production parameters, which comprises the following steps: S1, acquiring a multidimensional parameter data set from a cement kiln system, performing standardized processing to acquire a time sequence data set, and analyzing the time sequence data set to acquire set basic characteristics; s2, determining key influence parameters according to influence relations among the set basic feature analysis parameters; S3, extracting time-dependent features based on the key influence parameters and combining a long-term and short-term memory network model to obtain key feature vectors representing pollutant emission trends; S4, according to the key feature vector, adopting a support vector machine model to identify whether the pollutant emission trend is abnormal, generating an abnormal emission precursor mark when the pollutant emission trend is abnormal, and recording an abnormal time point and related parameter values; S5, performing a simulation experiment based on the abnormal time point and the related parameter value, recording influence paths of different regulation and control schemes in simulation, evaluating the influence paths to obtain an optimized regulation and control scheme, and generating an evaluation report; s6, judging whether the productivity adjustment value in the evaluation report meets implementation conditions, if so, applying a corresponding optimized regulation and control scheme to the cement kiln system, and recording final regulation and control data; And S7, continuously monitoring the pollutant concentration by taking th