Efficient quality management system for cement production line

Abstract

The invention provides an efficient quality management system for a cement production line, and the system comprises an online analysis device which is used for obtaining the chemical component data of a raw material and the technological parameters in the production process; the data processing unit is used for preprocessing the chemical component data and the process parameters to obtain standardized data; the mathematical model is obtained based on historical production data and product quality target training and used for predicting product quality according to the standardized data; the data acquisition module is used for continuously acquiring real-time data in the production process and forming a dynamic data set; the model optimization unit is used for carrying out iterative optimization on the mathematical model by utilizing the dynamic data set; the automatic equipment is used for executing raw material ratio adjustment and process parameter optimization according to the optimized control strategy; and the quality detection equipment is used for detecting the final product in real time, acquiring key index data and comparing the key index data with a preset quality target to form a closed-loop feedback mechanism.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly to an efficient quality management system for a cement production line. Background Art

[0002] The core technical problems faced by cement production quality control are how to achieve accurate prediction and intelligent regulation of product quality in a complex and changeable production environment. Traditional methods are difficult to cope with the comprehensive effects of factors such as fluctuations in raw material composition and changes in process parameters on the quality of the final product. At the same time, abnormal situations in the production process are often difficult to detect and handle in a timely manner, resulting in unstable product quality. In addition, production experience and expert knowledge are difficult to effectively inherit and apply, restricting the improvement of production efficiency. These problems are interrelated and form a complex technical contradiction: how to improve production efficiency and reduce costs while ensuring product quality.

[0003] Around this core problem, there are also several related technical difficulties: one is how to establish a mathematical model that accurately reflects the relationship between raw material ratio, process parameters and product quality; the second is how to achieve real-time monitoring and intelligent diagnosis of the entire production process; the third is how to make accurate decisions and adjustments quickly in case of abnormalities. These problems are all important challenges derived from solving the core technical contradiction, and they jointly constitute the technical problem system of intelligent control of cement production quality.

[0004] To solve these problems, the key lies in how to organically combine advanced machine learning technologies with the professional knowledge of cement production to build an intelligent control system that can learn independently and continuously optimize. This system should not only be able to accurately predict product quality, but also be able to make timely and reasonable adjustment decisions for different situations, and ultimately achieve the full intelligentization of the cement production process and the stable improvement of product quality. Summary of the Invention

[0005] The present invention provides an efficient quality management system for a cement production line, mainly including: An online analysis device for obtaining chemical composition data of raw materials and process parameters during the production process; a data processing unit for preprocessing the chemical composition data and the process parameters to obtain standardized data; a mathematical model trained based on historical production data and product quality targets for predicting product quality according to the standardized data; an intelligent decision-making system integrating expert rules and machine learning algorithms for full-process optimization according to the prediction results of the mathematical model, triggering an early warning mechanism and generating countermeasures when abnormal working conditions are detected; a data acquisition module for continuously obtaining real-time data during the production process and forming a dynamic data set; a model optimization unit for iteratively optimizing the mathematical model using the dynamic data set; an automated device for adjusting the raw material ratio and optimizing process parameters according to the optimized control strategy; a quality inspection device for real-time inspection of the final product and obtaining key indicator data, and comparing it with the preset quality target to form a closed-loop feedback mechanism.

[0006] Further, the online analysis device includes: real-time collection of chemical composition data of raw materials through the online analysis device, including the percentage content of calcium, silicon, aluminum, and iron elements, using chemical composition sensors to transmit the chemical composition data to the data processing unit in the form of digital signals; obtaining temperature parameters during the production process, using temperature sensors to measure the temperature value of the production environment in real time, and the temperature sensors are installed at key positions on the production line to transmit the collected temperature data in the form of digital signals; obtaining pressure parameters during the production process, using pressure sensors to measure the pressure value of the production environment in real time, and the pressure sensors are installed at key positions on the production line to transmit the collected pressure data in the form of digital signals; obtaining flow parameters during the production process, using flow sensors to measure the material flow on the production line in real time, and the flow sensors are installed on the material conveying pipeline to transmit the collected flow data in the form of digital signals.

[0007] Further, the data processing unit includes: aggregating the collected chemical composition data, temperature data, pressure data, and flow data to the data processing unit, and performing denoising and normalization processing on various types of data through a data preprocessing module to obtain standardized data; inputting the preprocessed standardized data into a machine learning model, using the support vector machine algorithm to perform real-time prediction and classification on the quality status of the production process, and when the prediction result shows quality anomalies, outputting an early warning signal; according to the quality early warning signal, automatically adjusting the process parameters of the production line through an industrial control system, including adjusting the material ratio, temperature, pressure, and flow, to make the production process in an excellent working condition, and pushing the quality early warning information to the management personnel.

[0008] Furthermore, the mathematical model includes: taking the chemical composition data and process parameters obtained during the production process as input data and feeding them into a pre-established mathematical model for processing; the mathematical model is obtained by performing regression analysis on historical production data and product quality target data and training it in combination with a neural network algorithm; after inputting the chemical composition data and process parameters into the mathematical model, a predicted product quality parameter output value is obtained through the calculation of multiple layers of neurons inside the model; comparing the predicted quality parameter value output by the mathematical model with a preset product quality target threshold, if the predicted value meets the quality target, it is determined that the current process parameter settings can be used to produce qualified products; if the quality parameter predicted by the model does not reach the preset target, the comparison result is fed back to the process control system, and the chemical composition ratio and production process parameters are automatically adjusted through the control system.

[0009] Furthermore, the intelligent decision-making system includes: obtaining the output result of the mathematical model and judging whether the raw material composition deviates from the preset range; if it deviates from the preset range, automatically adjusting the feeding ratios of limestone, clay, and iron powder raw materials according to the deviation degree by using an optimization algorithm; establishing an association model between the raw material ratio and product quality based on historical data and raw material characteristics through a machine learning algorithm; determining the raw material feeding ratio according to the association model and generating a new raw material ratio plan; inputting the new raw material ratio plan into the production control system to automatically adjust the working parameters of the raw material feeding equipment; during the production process, collecting product quality data in real time and dynamically optimizing the raw material ratio plan through a feedback control algorithm; continuously collecting production data and using an incremental learning algorithm to continuously update and improve the association model between the raw material ratio and product quality to achieve the optimization of the ratio plan.

[0010] Furthermore, the model optimization unit includes: obtaining a dynamic data set, preprocessing the data, converting the data into a format acceptable to the model, and dividing the data set into a training set and a test set; selecting an optimization algorithm according to the type and characteristics of the mathematical model and setting the hyperparameters of the optimization algorithm; training the mathematical model on the training set, continuously updating the model parameters by minimizing the loss function to improve the prediction accuracy of the model; at the same time, evaluating the performance of the model on the test set and calculating the prediction error of the model; if the prediction error of the model on the test set exceeds the preset threshold, it is determined to retrain the model; selecting a model structure according to the characteristics of the data set and the complexity of the model; retraining the mathematical model on the training set using the new model structure and optimization algorithm to obtain updated model parameters; at the same time, adjusting the control strategy according to the prediction result of the model; evaluating the performance of the updated model on the test set and calculating the prediction error of the model.

[0011] Furthermore, the automated equipment includes: according to the optimized control strategy, obtaining the real-time operation parameters and raw material ratio data of the production line, and judging whether the current production parameters and raw material ratio meet the requirements of the optimization strategy through the support vector machine algorithm; if not, taking the target parameters and ratio in the optimization strategy as the input, using the genetic algorithm to optimize the process parameters and raw material ratio of the production line, and obtaining the optimized process parameter values and raw material ratio plan; sending the optimized process parameter values and raw material ratio plan to the automated control system of the production line, and adjusting the operation parameters and raw material feeding ratio of the production equipment through the programmable logic controller to realize the real-time optimization control of the production process; during the production process, continuously collecting various parameter data and product quality data of the production line, and analyzing the data through the convolutional neural network algorithm to judge whether the production process deviates from the control target of the optimization strategy.

[0012] The technical solution provided by the embodiment of the present invention may include the following beneficial effects: The present invention discloses an intelligent control method for cement production quality based on machine learning. This method collects production data in real time and uses machine learning algorithms to establish a mathematical model of raw material ratio, process parameters and product quality. Based on this model, the present invention can predict product quality and optimize adjustments, and at the same time diagnose the entire production process in combination with an expert knowledge base and case reasoning. When an abnormality is detected, the present invention can give an early warning in time and generate an emergency decision-making plan. Through incremental learning, the present invention continuously updates the model and decision-making system, and continuously improves the prediction and decision-making level. Finally, the optimization result realizes closed-loop control through the automated system, realizes the intelligentization of the cement production process and the stable improvement of product quality, and greatly improves production efficiency and product qualification rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flowchart of an efficient quality management system for a cement production line of the present invention.

[0014] Figure 2 It is a schematic diagram of an efficient quality management system for a cement production line of the present invention.

[0015] Figure 3 It is another schematic diagram of an efficient quality management system for a cement production line of the present invention.

[0016] Figure 4 It is another schematic diagram of an efficient quality management system for a cement production line of the present invention.

[0017] Figure 5 It is another schematic diagram of an efficient quality management system for a cement production line of the present invention.

[0018] Figure 6Another schematic diagram of an efficient quality management system for a cement production line according to the present invention.

[0019] Figure 7 Another schematic diagram of an efficient quality management system for a cement production line according to the present invention.

[0020] Figure 8 Another schematic diagram of an efficient quality management system for a cement production line according to the present invention.

[0021] Figure 9 Another schematic diagram of an efficient quality management system for a cement production line according to the present invention. Detailed implementation manners

[0022] In order to enable those skilled in the art of the present technology to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0023] As Figures 1-9 , an efficient quality management system for a cement production line in this embodiment may specifically include: S101. Obtain real-time production data of the cement production line, where the real-time production data includes chemical components of raw materials, process parameters during the production process, and product quality inspection results.

[0024] The chemical composition data of raw materials, including the percentage of calcium, silicon, aluminum, iron and other elements, are collected in real time through online analysis equipment. A dedicated chemical composition sensor is used to transmit the chemical composition data to the data processing unit in the form of a digital signal to obtain standardized raw material chemical composition data. The temperature parameters in the production process are obtained by using a temperature sensor to measure the temperature value of the production environment in real time. The temperature sensor is installed at a key position of the production line. The collected temperature data is transmitted in the form of a digital signal to obtain standardized temperature process parameters. The pressure parameters in the production process are obtained by using a pressure sensor to measure the pressure value of the production environment in real time. The pressure sensor is installed at a key position of the production line. The collected pressure data is transmitted in the form of a digital signal to obtain standardized pressure process parameters. The flow parameters in the production process are obtained by using a flow sensor to measure the material flow on the production line in real time. The flow sensor is installed on the material conveying pipeline. The collected flow data is transmitted in the form of a digital signal to obtain standardized flow process parameters. Through the online product quality detection equipment, various quality indicators of the product, including strength, coagulation time, stability, etc., are detected in real time. The quality detection results are transmitted in the form of a digital signal to obtain standardized product quality data. The collected raw material chemical composition data, temperature data, pressure data, flow data and product quality data are summarized in the data processing unit, and the data preprocessing module performs preprocessing operations such as denoising and normalization on various types of data to obtain high-quality real-time data sets for the production process. Data fusion technology is used to integrate real-time data of the production process from different sources and in different formats, and a unified data model and data warehouse are constructed to achieve centralized management and efficient use of production data. Based on the constructed real-time data warehouse of the production process, the association rule mining algorithm is used to discover the inherent correlation and rules between raw material composition, process parameters and product quality indicators, and obtain the quality influencing factors of the production process. The association rule mining results are applied to real-time production process quality monitoring. Through real-time data analysis, it is determined whether there is a quality abnormality risk in the current production process. If there is an abnormality, a quality warning signal is output, otherwise the real-time monitoring of the production process continues.

[0025] Exemplarily, the chemical composition data of raw materials are collected in real time by an online X-ray fluorescence spectrometer, such as a calcium content of 45%, a silicon content of 13%, an aluminum content of 3%, and an iron content of 2.5%. The data are transmitted to the DCS system in the form of a 4-20 mA analog signal using the RS-485 bus transmission method. The temperature data are collected in real time by PT100 temperature sensors arranged at key process points such as the rotary kiln and the decomposition furnace, with a range of 0-1400 °C and an accuracy of ±1 °C, and are transmitted in the form of digital signals through the PROFIBUS-DP bus. The pressure sensor uses a metal resistance strain type pressure transmitter with a range of 0-1 MPa and an accuracy of 0.1% FS, and is installed at equipment such as the kiln head, the kiln tail, and the mill, and the pressure data are transmitted to the PLC in the form of a 4-20 mA analog signal. A vortex flowmeter is installed on the material conveying pipeline with a range of 0-100 m³ / h and an accuracy of 0.5%, and the flow data are uploaded in the form of digital signals through the RS-485 bus. The quality indicators such as the compressive strength and soundness of the cement finished product are automatically detected by an online detection device, and the detection results are uploaded to the MES system through the Ethernet communication method. The real-time data of each collection point are aggregated to the data processing unit through an OPC server, and denoising processing is performed using the Kalman filter algorithm. The data are mapped to the interval [0,1] using the min-max normalization method. Through the data integration middleware, the data of heterogeneous data sources such as relational databases and MES systems are subjected to ETL processing, and a data warehouse for the cement production process is established based on a unified data model. Using the Apriori algorithm with parameters of a minimum support of 0.2 and a minimum confidence of 0.8, the association rules between production data are mined, and quality influence rules such as "high-silicon and high-aluminum raw materials + low-temperature firing process → high-strength cement" are discovered. Combining parameters such as the firing temperature and material ratio collected in real time, the quality of the product is judged whether it is abnormal through a logistic regression model. If the abnormal probability exceeds 0.6, an early warning prompt is output to notify the DCS system to adjust the process parameters in time to ensure the stable and controllable quality of the cement.

[0026] S102. According to the real-time production data, a mathematical model between the raw material ratio, process parameters and product quality is established by using a machine learning algorithm, and the mathematical model is obtained by training with historical production data.

[0027] The data acquisition module is used to obtain multi-dimensional real-time production data such as raw material components, process parameters, and product quality indicators. Through data cleaning and preprocessing of the acquired multi-dimensional data, noise data and outliers are removed to obtain a high-quality production process dataset. The data fusion technology is adopted to integrate production process data from different sources and in different formats, construct a unified data model and data warehouse, and provide a basis for subsequent data analysis and modeling. According to the historical production process data in the data warehouse, the association rule mining algorithm is used to discover the association relationships and laws between raw material components, process parameters, and product quality indicators, and determine the key factors affecting product quality. According to the laws obtained by association rule mining, combined with the real-time production process data collected, machine learning algorithms such as support vector machines and decision trees are used to establish a mathematical model between raw material ratio, process parameters, and product quality. The historical production data is divided into a training set and a test set. The established mathematical model is trained using the training set data. By continuously adjusting the model parameters and optimizing the model performance, a product quality prediction model with high accuracy is obtained. The trained mathematical model is tested using the test set data to evaluate the generalization ability and prediction accuracy of the model. The model is further optimized and improved according to the test results. The optimized mathematical model is applied to the real-time production process data. According to the real-time changes in raw material components and process parameters, the product quality indicators are dynamically predicted to realize the real-time monitoring and early warning of product quality. The quality prediction results are compared and analyzed with the actual quality indicators to identify prediction deviations and abnormal situations. Through the data feedback mechanism, the mathematical model is continuously updated and optimized to improve the accuracy and stability of the prediction. Based on the optimized mathematical model, the raw material ratio and process parameters are adjusted to formulate an optimal production plan to guide the optimization control of the production process, realize the continuous improvement and enhancement of product quality, and finally achieve the product quality goal.

[0028] Exemplarily, by installing sensors on the production line, process parameters such as temperature and pressure and raw material composition data are collected in real time, and sampling is performed every 5 seconds. The Gaussian noise model is used to remove outliers with 99% confidence to obtain a clean data set. ETL processing is performed on 100 batches of historical data to eliminate format differences and store them in the Hadoop data warehouse. Using the Aprior algorithm, with a support of 5% and a confidence of 80%, 10 association rules such as "when the sulfur content > 0.1%, the product qualification rate is lower than 90%" are obtained. An SVM model is established, using 10-fold cross-validation, 8000 groups in the training set, 2000 groups in the test set, the penalty factor C = 1, and the RBF kernel function. The final model accuracy reaches 95%. Real-time data is input into the model every minute for prediction. If the predicted qualification rate is lower than 95%, an early warning is issued in a timely manner. The prediction results and measured values are compared and analyzed weekly. When the deviation is greater than 5%, model retraining is triggered. Based on the optimized model, when the proportion of raw material A increases by 1%, the product strength increases by 5 MPa, the process parameters are adjusted, and the product quality is continuously improved, and finally the first-pass qualification rate of the product is stabilized above 98%.

[0029] S103. Input the real-time production data into the mathematical model to predict the product quality under the current production conditions. If the predicted quality deviates from the preset target, trigger the optimization and adjustment of production parameters.

[0030] Based on the real-time production process data obtained by the data acquisition module, multi-dimensional data such as raw material composition, process parameters, and product quality indicators are obtained. Through data cleaning and preprocessing of the obtained multi-dimensional data, noise data and outliers are removed to obtain a high-quality production process dataset. Data fusion technology is adopted to integrate production process data from different sources and in different formats, construct a unified data model and data warehouse, and use ETL tools to achieve data extraction, transformation, and loading, realizing standardized and normalized management of data. In the data warehouse, the Apriori association rule mining algorithm is used, with the minimum support set to 0.5 and the minimum confidence set to 0.8, to discover the association relationships and rules between raw material composition, process parameters, and product quality indicators, obtaining a set of association rules. The real-time collected production process data is input into the association rule matching module. Through the association rule matching algorithm, it is judged whether the current production process data meets the association rules. If it meets, an early warning is triggered to indicate the existence of production anomaly risks. The support vector machine algorithm is adopted, the Gaussian kernel function is selected, and the penalty factor C = 10 to construct a product quality prediction model. The model parameters are optimized through the cross-validation method, and a quality prediction model with an accuracy rate of over 90% is obtained through training. The real-time collected production process data is input into the quality prediction model. The model predicts the product quality indicators under the current production conditions based on factors such as raw material composition and process parameters, obtaining a quality prediction value. The quality prediction value is compared with the preset quality target threshold. If the predicted quality deviates from the preset target, the optimization adjustment process of production parameters is triggered. Through process parameter optimization algorithms such as genetic algorithms and particle swarm algorithms, the optimal process parameter combination is searched. The optimized process parameter combination is set for the control system of the production equipment, and instructions are sent through the control system to adjust the process parameters of the production equipment, such as temperature, pressure, speed, etc., so that the production process operates under the optimal process parameters. During the production process, data collection, data analysis, quality prediction, and parameter optimization are continuously carried out. Through real-time data feedback and closed-loop control, the production process is continuously iteratively optimized, and the product quality is continuously improved, realizing the automated control and management of product quality.

[0031] Exemplarily, during the production process, multi-dimensional data such as raw material components, process parameters, and product quality indicators are collected in real time by sensors. The data is cleaned and preprocessed. The median filtering method is used to remove noise data, and the 3σ criterion is used to identify outliers, obtaining a high-quality production process dataset. Then, data fusion technology is adopted to integrate production process data from different sources and in different formats, construct a unified data model and data warehouse, and use ETL tools to achieve data extraction, transformation, and loading. In the data warehouse, the Apriori association rule mining algorithm is used, with the minimum support set to 0.5 and the minimum confidence set to 0.8, to discover the association relationships and rules between raw material components, process parameters, and product quality indicators. For example, when the temperature rises by 1°C, the product hardness increases by 5%. Real-time analysis is performed on the real-time collected production process data. Using the EWMA control chart method and combining with association rules, the upper control limit is set to 5 times the standard deviation, and the lower control limit is set to 5 times the standard deviation to monitor the abnormal conditions of the production process in real time. If the data exceeds the control limit, an early warning is given in a timely manner and the production parameters are adjusted. The support vector machine algorithm is adopted, the Gaussian kernel function is selected, and the penalty factor C = 10 to construct a product quality prediction model. The model parameters are optimized through the cross-validation method, obtaining a quality prediction model with an accuracy rate of over 90%. The quality prediction results are compared and analyzed with the actual quality indicators, the root mean square error RMSE is calculated, and the quality prediction model is continuously optimized to reduce RMSE to below 0.5, achieving continuous improvement and enhancement of product quality. When the quality prediction value deviates from the preset target, the genetic algorithm is used to optimize the production parameters. The population size is set to 100, the crossover probability is set to 0.8, the mutation probability is set to 0.1, and it is iterated 500 times to search for the optimal combination of production process parameters. The optimized parameters are sent to the production equipment through the control system to achieve automatic adjustment and optimized control of the production process.

[0032] S104. An optimization algorithm is used to search for the optimal combination of raw material ratios and process parameters to make the predicted quality meet the preset target, and the optimization algorithm is solved based on the mathematical model.

[0033] According to the chemical composition data and process parameters obtained during the production process, they are used as input data and fed into a pre-established mathematical model for processing to obtain the predicted output values of product quality parameters. The predicted quality parameter values output by the mathematical model are compared with the preset product quality target threshold to determine whether the current process parameter settings can produce qualified products. If the quality parameters predicted by the model do not meet the preset target, an optimization algorithm is used to search for the process parameters and raw material ratios to obtain the optimal parameter combination. The optimization algorithm is solved based on the mathematical model. Through multiple iterative calculations, the process parameters and raw material ratios are continuously adjusted until the optimal combination that can meet the quality target is found. During the search process of the optimization algorithm, the support vector machine algorithm is used to determine whether the current parameter combination meets the requirements of the optimization strategy. If not, continue the iterative optimization. The genetic algorithm is used to optimize the parameter combination. New parameter combinations are generated through operations such as crossover and mutation, and their fitness function values are calculated to determine the optimal solution. The optimal parameter combination obtained by the optimization algorithm is compared with the prediction result of the mathematical model to determine whether the preset quality target is achieved. If so, this combination is applied to actual production. According to the optimized parameter combination, the PLC control system automatically adjusts the process parameters and raw material feeding ratios of the production equipment to achieve real-time optimization control of the production process. During the production process, various data are continuously collected and analyzed through the convolutional neural network algorithm. If it is found that the production deviates from the optimization target, a new round of parameter optimization is triggered to achieve dynamic closed-loop control and continuously improve product quality and production efficiency.

[0034] Exemplarily, during the production process, chemical composition data of clinker is obtained through on-line detection equipment, including CaO content of 62.5%, SiO2 content of 21.3%, Al2O3 content of 5.2%, Fe2O3 content of 3.1%, etc. At the same time, process parameters such as the kiln head temperature of 1450 °C, the kiln tail temperature of 350 °C, and the kiln speed of 1.2 r / min are collected. These data are input into a prediction model constructed based on the BP neural network. Through the calculation of 5 hidden layers, the predicted value of the f-CaO content of the clinker is obtained as 0.82%. Comparing the predicted value with the target value of 0.80%, it is found that the prediction quality does not meet the requirements. Then, the particle swarm optimization algorithm is adopted to search for the optimal process parameter combination on the basis of the prediction model. The algorithm takes the minimum f-CaO content as the optimization goal. Through 20 iterations, the optimal parameters are obtained as the kiln head temperature of 1460 °C, the kiln tail temperature of 345 °C, and the kiln speed of 1.25 r / min. The optimized parameters are input into the support vector machine model for verification, and it is confirmed that they can meet the quality requirements. According to the optimization results, the production control system automatically adjusts the kiln head temperature to 1460 °C, the kiln tail temperature to 345 °C, and the kiln speed to 1.25 r / min. At the same time, the relationship between the chemical composition and the f-CaO content is analyzed by partial least squares regression. It is obtained that for every 0.1% increase in the SiO2 content in the raw meal, the f-CaO content will increase by 0.05%. Accordingly, the raw meal formula is adjusted, and the SiO2 content is reduced from 21.3% to 21.1%. After the production line is started, the on-line quality detection system collects data every 5 minutes and conducts real-time analysis through the convolutional neural network algorithm. If it is found that the f-CaO content exceeds 0.85% continuously for 3 times, a new round of parameter optimization process will be automatically triggered, so as to continuously improve the product quality and production efficiency.

[0035] S105. Input the optimized raw material ratio and process parameters into the intelligent decision-making system. The intelligent decision-making system integrates the expert knowledge base and case reasoning to diagnose and optimize the entire production process.

[0036] Based on the expert knowledge base and historical production data, an optimization model for raw material ratio and process parameters is established. The model is trained using machine learning algorithms to obtain the optimized raw material ratio and process parameter settings. The optimized raw material ratio and process parameters are input into the intelligent decision-making system. The intelligent decision-making system integrates expert rules and machine learning algorithms to diagnose and optimize the entire production process. The intelligent decision-making system collects various parameter data of the production process in real time. After data cleaning and feature engineering processing, it is input into the production process anomaly detection model to determine whether each parameter is within the normal operating range. If a certain parameter is detected to exceed the normal range, the warning mechanism is triggered, an abnormal parameter report is generated, and it is input into the case-based reasoning module for processing. The case-based reasoning module, according to the abnormal parameter report and combined with the expert knowledge base, uses a similarity calculation algorithm to search for the historical case most similar to the current abnormal working condition and extracts the disposal plan of this case. The extracted historical case disposal plan is compared with the current production status, and through rule reasoning and constraint satisfaction technology, an optimized decision for the current abnormal working condition is generated. The generated optimized decision is converted into executable control instructions and sent to the on-site equipment through the production execution system to achieve real-time regulation and optimization of the production process. While executing the optimized decision, the intelligent decision-making system continuously monitors the changes in the production process state. Through real-time data feedback and iterative optimization, the optimized decision is dynamically adjusted to ensure that the entire production process is always in the optimal state. The various parameters and decision-making data generated during the optimization process are stored in the knowledge base, and the expert rules and optimization model are continuously updated and improved through machine learning algorithms to achieve the self-learning and iterative upgrade of the intelligent decision-making system.

[0037] Exemplarily, in the raw material ratio and process parameter optimization model, the support vector machine algorithm is used to train the historical production data, and the optimal hyperparameters of the model are determined through grid search and cross-validation. For example, the penalty factor C is set to 1.0, and the Gaussian kernel is selected as the kernel function, so as to obtain the optimal combination of raw material ratio and process parameters. The optimized parameters are input into the intelligent decision-making system. Based on 200 rules in the expert knowledge base and combined with the random forest algorithm, the production process is diagnosed in real time. When the system detects that key parameters such as furnace temperature exceed the normal range of 1500±10°C, an early warning is immediately triggered and an exception report is generated. The case reasoning module retrieves the top 5 cases most similar to the current exception from 10,000 historical cases through the KNN algorithm, and extracts the disposal solutions of reducing temperature and adjusting the raw material ratio when the furnace temperature is too high. The system compares the solution with the current state and uses constraint-based reasoning technology to generate a series of control instructions, such as lowering the furnace temperature set value to 1480°C and adjusting the ratio of raw material A from 20% to 18%. The instructions are sent to the production execution system, and the precise control of the furnace temperature is realized through the PID control algorithm, and the production data is collected and analyzed in real time, and the optimization decision is updated every 5 seconds. At the same time, the optimization process data is fed back to the knowledge base, and the incremental learning algorithm is used to iteratively update the expert rules and the optimization model to improve the adaptive ability and decision-making level of the system.

[0038] S106. If the intelligent decision-making system detects production anomalies, an early warning signal is output, and an emergency decision-making plan is inferred based on similar historical cases to guide the adjustment of production parameters.

[0039] Input the optimized raw material ratio and process parameters into the production execution system, collect various real-time data during the production process, and use an anomaly detection model to determine whether each parameter is within the normal operating range. If a certain parameter is detected to exceed the normal range, it is determined as an abnormal condition, triggering an early warning mechanism to generate an abnormal parameter report and an early warning signal. According to the abnormal parameter report, combined with the expert rule base and the case-based reasoning algorithm, search for the historical case most similar to the current abnormal condition, and calculate the similarity between the current condition and the historical case. If the similarity is greater than the set threshold, extract the disposal plan of this historical case as the emergency decision-making plan for the current abnormal condition. Compare the disposal plan of the historical case with the current production status, adjust and optimize the key parameters in the disposal plan through an optimization algorithm, and generate a targeted emergency decision-making plan. Send the generated emergency decision-making plan to the production execution system, and guide the real-time adjustment of production parameters by adjusting relevant parameters or switching to standby process routes, etc., to achieve dynamic optimization of the production process. The production execution system automatically adjusts the equipment parameters according to the emergency decision-making plan, and at the same time monitors the changes of the adjusted parameters in real time to determine whether the abnormal state has been eliminated. If the parameters return to normal within the set time and the abnormal state is eliminated, record the effectiveness of this emergency decision-making plan and update the case base; if the abnormal state is not eliminated, start the standby emergency plan. Through the collaboration of modules such as optimization decision-making, real-time monitoring, anomaly diagnosis, historical case reasoning, emergency decision-making generation and execution, form a closed-loop optimization of the entire production process to ensure product quality and production efficiency.

[0040] Exemplarily, first, the process parameters are set to a temperature of 360 °C, a pressure of 2 MPa, and a reaction time of 120 minutes. These are input into the production execution system, and parameters such as temperature, pressure, flow rate, and liquid level at 10 key processes on the production line are collected in real-time, once per minute. After the collected data is processed by outlier removal, data normalization, etc., it is input into an anomaly detection model using the Isolation Forest algorithm. Whether a sample point is abnormal is judged by calculating the average distance between the sample point and other data points. If the distance is greater than the threshold, it is determined to be abnormal and a warning is triggered, generating an anomaly warning message. The system automatically searches the historical case library and finds that there was a similar working condition a month ago, which was solved by increasing the input of raw material B by 3%, reducing the reaction temperature to 340 °C, and extending the reaction time to 150 minutes. The similarity between the current working condition and this historical case is 85%. Based on this, a coping strategy is generated: increase the input of raw material B from 40% to 45%, adjust the temperature set value from 360 °C to 340 °C, and extend the reaction time from 120 minutes to 150 minutes. The above decisions are sent to the production execution system, and the equipment parameters are automatically adjusted. The system monitors in real-time and finds that the temperature returns to the normal level after 15 minutes, and the abnormal state is eliminated, and the product quality and output return to normal. If the parameters still do not return to normal within 30 minutes, the backup plan is activated and the standby process route is switched. Through the above closed-loop optimization process, intelligent control of the entire production process is achieved.

[0041] S107. According to the product quality inspection results, the mathematical model and the intelligent decision-making system are dynamically updated in an incremental learning manner to continuously improve the prediction performance and decision-making level of the system.

[0042] Obtain the product quality inspection result data, including key quality indicators such as product strength, fineness, setting time, etc., and input the quality inspection data into the quality evaluation model. Evaluate the product quality inspection results through the quality evaluation model, calculate the deviation value between the actual inspection indicators and the preset quality target, and judge whether the product quality meets the requirements. If the quality evaluation result shows that the product quality does not meet the requirements, then feedback the deviation value and related parameters to the mathematical model and the intelligent decision-making system as new training data. Adopt the incremental learning algorithm, and dynamically adjust and optimize the weights and parameters of the original mathematical model according to the newly obtained product quality deviation data to achieve online learning and updating of the model. Continuously improve the quality prediction performance of the mathematical model through incremental learning, so that the model can more accurately predict the product quality and provide a reliable basis for process optimization. Embed the updated mathematical model into the intelligent decision-making system, and dynamically adjust the decision tree and association rules according to the quality trend predicted by the model to optimize the quality control strategy. Optimally solve the key process parameters through the intelligent decision-making system to obtain the optimal production parameter combination and guide the production process to achieve precise control of product quality. Obtain the production process data and product quality inspection data after the process parameters are adjusted, and input them into the quality evaluation model again to evaluate the effect of process optimization and form a quality improvement closed loop. Through incremental learning and dynamic optimization, continuously improve the prediction performance and control level of the mathematical model and the intelligent decision-making system, and achieve continuous improvement and stable control of product quality.

[0043] Exemplarily, by obtaining product quality inspection result data, such as the cement strength inspection value of 42.5 MPa, the fineness inspection value of 3.5%, the setting time inspection value of 180 min, etc., these data are input into the quality evaluation model. The quality evaluation model adopts the support vector machine algorithm. By comparing the actual inspection indicators with the preset quality targets, such as the strength target of 42.5 ± 1.5 MPa, the fineness target of 3.2 ± 0.3%, the setting time target of 180 ± 15 min, the strength deviation value is calculated as 0 MPa, the fineness deviation value is 0.3%, and the setting time deviation value is 0 min, and it is determined that the product quality meets the requirements. If the quality evaluation result shows that the strength deviation value exceeds ±1.5 MPa, the deviation value is fed back to the BP neural network mathematical model and the intelligent decision-making system based on association rules for retraining. The Hoeffding Tree incremental learning algorithm is used to dynamically adjust the model weights according to the newly obtained quality deviation data. For example, the number of hidden layer neurons in the strength prediction model is increased from 10 to 15, and the learning rate is increased from 0.01 to 0.02. After continuous training 500 times, the strength prediction accuracy of the model is increased from 95% to 98%. The updated strength prediction model is embedded in the intelligent decision-making system. By optimizing process parameters such as the cement clinker ratio and the firing temperature curve, the optimal parameter combination is predicted as follows: the calcium-silicon ratio in the clinker is 2.2, the silicon-aluminum ratio is 3.5, the iron-aluminum ratio is 1.5, and the firing temperature curve is 200 °C for 30 min, 800 °C for 60 min, 1450 °C for 120 min. It is predicted that the product strength can stably reach 42.5 MPa ± 1 MPa. Applying the optimized process parameters to production and detecting the product quality again, the actual measured strength value is 42.8 MPa, which meets the quality requirements and verifies the effectiveness of the incremental learning optimization. By continuously cycling through the closed-loop process of "quality inspection - deviation analysis - incremental learning - process optimization - production verification", the accuracy of the quality prediction model is steadily improved, and the first-pass inspection qualification rate of the product is increased from 90% to 99%.

[0044] S108. Convert the optimized decision result into a production execution instruction, and realize the real-time adjustment of the raw material ratio and process parameters through the automatic control system to form a closed-loop control of the product quality.

[0045] According to the target parameters and ratios in the optimization strategy, the genetic algorithm is used to optimize the process parameters and raw material ratios of the production line, and the optimized process parameter values and raw material ratio plans are obtained. The optimized process parameter values and raw material ratio plans are sent to the automatic control system of the production line, and the operating parameters of the production equipment and the raw material feeding ratio are adjusted through the PLC controller to achieve real-time optimization control of the production process. During the production process, various parameter data of the production line and product quality data are continuously collected, and the data is analyzed through the convolutional neural network algorithm to judge whether the production process deviates from the control target of the optimization strategy. If the production process deviates from the control target, the latest production data is used as the input, and the genetic algorithm is triggered again to optimize the process parameters and raw material ratios, and the new optimization results are updated to the automatic control system to achieve dynamic optimization of the production process. Obtain the output result of the mathematical model and judge whether the raw material composition deviates from the preset range. If it deviates from the preset range, the feeding ratios of raw materials such as limestone, clay, and iron powder are automatically adjusted according to the deviation degree by using the optimization algorithm. Through the machine learning algorithm, according to the historical data and raw material characteristics, a correlation model between the raw material ratio and the product quality is established. According to the correlation model, the optimal raw material feeding ratio is determined, and a new raw material ratio plan is generated. The new raw material ratio plan is input into the production control system, and the working parameters of the raw material feeding equipment are automatically adjusted. During the production process, the product quality data is collected in real time, and the raw material ratio plan is dynamically optimized through the feedback control algorithm. The optimization results are sent to the PLC control system in the workshop to adjust the program parameters of the numerical control machine tool and the ratio valves of the feeding device to achieve online optimization of the process parameters and ratios. Continuously collect production data, and use the incremental learning algorithm to continuously update and improve the correlation model between the raw material ratio and the product quality to achieve adaptive optimization of the ratio plan and form a closed-loop control of the product quality.

[0046] Exemplarily, the operating parameters collected in real time on the production line are compared with the target values in the optimization strategy through the support vector machine algorithm. It is found that the current parameters deviate from the target values by more than 5%, and optimization and adjustment are required. The genetic algorithm takes the spindle speed of 8500 rpm, feed rate of 850 mm / min, cutting depth of 9 mm, and new material ratio of 45% determined in the optimization strategy as the optimization objectives. After 50 generations of evolutionary iteration, the optimal parameter combination is obtained as the spindle speed of 8600 rpm, feed rate of 820 mm / min, cutting depth of 85 mm, and new material ratio of 43%. The optimization results are sent to the PLC control system in the workshop to adjust the program parameters of the numerically controlled machine tool and the ratio valves of the feeding device, realizing the online optimization of process parameters and ratios. Multiple sensors arranged on the production line collect parameters such as temperature, vibration, and current in real time, as well as quality data such as the weight and size of products. The convolutional neural network algorithm analyzes and judges these data every 5 minutes. If it is found that the temperature rises by 5°C, the vibration amplitude exceeds 0.1 mm, or the product quality continuously deviates from the mean by more than 5% for 3 consecutive times, it is determined that the optimization target is deviated, and a new round of genetic algorithm optimization is triggered. According to the output results of the mathematical model, it is judged that the raw material composition deviates from the preset range. For example, the limestone feeding ratio is lower than 38%, the clay is higher than 42%, and the iron powder exceeds 3%. Then, according to the degree of deviation, the opening degrees of the feeding valves of the three raw materials are automatically adjusted by the optimization algorithm, increasing the limestone to 40%, reducing the clay to 40%, and controlling the iron powder within 2%. By analyzing the historical production data through the association rule algorithm, a new raw material ratio plan is obtained: quicklime 42%, silica 38%, iron powder 15%, pulverized coal 3%, grinding aid 2%. The ratio parameters are input into the batching system of the production line to replace the original ratio, and quality data such as the burning temperature and free calcium content are collected in real time. The ratio of the grinding aid is dynamically adjusted through the PID control algorithm and controlled between 15% and 25% to realize the online optimization of the ratio plan. Continuously collect production data such as the kiln head temperature, clinker specific gravity, and cement fineness, and establish a ratio-quality model using the BP neural network algorithm, which is updated once a day to continuously improve the prediction accuracy of the model. When the raw material ratio changes, it automatically predicts the change trend of the product quality, optimizes the ratio plan in advance, and realizes the closed-loop control of the product quality parameters.

[0047] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An efficient quality management system for a cement production line, characterized in that, Including: An on-line analysis device for obtaining the chemical composition data of raw materials and process parameters during the production process; A data processing unit for preprocessing the chemical composition data and the process parameters to obtain standardized data; A mathematical model, trained based on historical production data and product quality targets, for predicting product quality according to the standardized data; An intelligent decision-making system, integrating expert rules and machine learning algorithms, for optimizing the entire production process according to the prediction results of the mathematical model, and triggering an early warning mechanism and generating countermeasures when abnormal working conditions are detected; A data acquisition module for continuously obtaining real-time data during the production process and forming a dynamic data set; A model optimization unit for iteratively optimizing the mathematical model using the dynamic data set; An automated device for adjusting the raw material ratio and optimizing process parameters according to the optimized control strategy; A quality inspection device for performing real-time inspection on the final product and obtaining key index data, and comparing it with the preset quality target to form a closed-loop feedback mechanism.

2. The system according to claim 1, wherein The on-line analysis device includes: Real-time collection of the chemical composition data of raw materials through the on-line analysis device, including the content percentages of calcium, silicon, aluminum, and iron elements. Using a chemical composition sensor, the chemical composition data is transmitted to the data processing unit in the form of digital signals; Obtaining the temperature parameter during the production process. Using a temperature sensor to measure the temperature value of the production environment in real time. The temperature sensor is installed at key positions on the production line and transmits the collected temperature data in the form of digital signals; Obtaining the pressure parameter during the production process. Using a pressure sensor to measure the pressure value of the production environment in real time. The pressure sensor is installed at key positions on the production line and transmits the collected pressure data in the form of digital signals; Obtaining the flow parameter during the production process. Using a flow sensor to measure the material flow on the production line in real time. The flow sensor is installed on the material conveying pipeline and transmits the collected flow data in the form of digital signals.

3. The system according to claim 1, characterized in that, The data processing unit includes: Summarize the collected chemical composition data, temperature data, pressure data, and flow data to the data processing unit, and perform denoising and normalization processing on various types of data through a data preprocessing module to obtain standardized data; Input the preprocessed standardized data into a machine learning model, and use the support vector machine algorithm to perform real-time prediction and classification on the quality status of the production process. When the prediction result shows quality abnormality, an early warning signal is output; According to the quality early warning signal, automatically adjust the process parameters of the production line through the industrial control system, including adjusting the material ratio, temperature, pressure, and flow, so that the production process is in an excellent working condition, and push the quality early warning information to the management personnel.

4. The system according to claim 1, wherein The mathematical model includes: According to the chemical composition data and process parameters obtained during the production process, use them as input data and input them into a pre-established mathematical model for processing; The mathematical model is obtained through regression analysis of historical production data and product quality target data, and is trained in combination with neural network algorithms; After inputting chemical composition data and process parameters into the mathematical model, through the calculation of multiple layers of neurons inside the model, an output value of the predicted product quality parameter is obtained; Compare the predicted quality parameter value output by the mathematical model with the preset product quality target threshold. If the predicted value meets the quality target, it is determined that the current process parameter setting can be used to produce qualified products; If the quality parameter predicted by the model does not reach the preset target, the comparison result is fed back to the process control system, and the chemical composition ratio and production process parameters are automatically adjusted through the control system.

5. The system according to claim 1, characterized in that, The intelligent decision-making system includes: Obtain the output result of the mathematical model and judge whether the raw material composition deviates from the preset range; If it deviates from the preset range, according to the degree of deviation, use an optimization algorithm to automatically adjust the feeding ratio of limestone, clay, and iron powder raw materials; Through machine learning algorithms, establish an association model between raw material ratio and product quality based on historical data and raw material characteristics; According to the association model, determine the raw material feeding ratio and generate a new raw material ratio plan; Input the new raw material ratio plan into the production control system and automatically adjust the working parameters of the raw material feeding equipment; During the production process, collect product quality data in real time and dynamically optimize the raw material ratio plan through feedback control algorithms; Continuously collect production data, use incremental learning algorithms, continuously update and improve the association model between raw material ratio and product quality, and realize the optimization of the ratio plan.

6. The system according to claim 1, wherein The model optimization unit includes: Obtain the dynamic data set, preprocess the data, convert the data into a format acceptable to the model, and divide the data set into a training set and a test set; According to the type and characteristics of the mathematical model, select an optimization algorithm and set the hyperparameters of the optimization algorithm; Train the mathematical model on the training set. By minimizing the loss function, continuously update the model parameters to improve the prediction accuracy of the model; At the same time, evaluate the performance of the model on the test set and calculate the prediction error of the model; If the prediction error of the model on the test set exceeds the preset threshold, it is determined to retrain the model; According to the characteristics of the data set and the complexity of the model, select the model structure; Use the new model structure and optimization algorithm to retrain the mathematical model on the training set to obtain updated model parameters; At the same time, adjust the control strategy according to the prediction result of the model; Evaluate the performance of the updated model on the test set and calculate the prediction error of the model.

7. The system according to claim 1, characterized in that The automated equipment includes: According to the optimized control strategy, obtain the real-time operation parameters of the production line and the raw material ratio data, and judge whether the current production parameters and raw material ratio meet the requirements of the optimization strategy through the support vector machine algorithm; If the requirements are not met, use the target parameters and ratio in the optimization strategy as the input, and use the genetic algorithm to optimize the process parameters and raw material ratio of the production line to obtain the optimized process parameter values and raw material ratio plan; Send the optimized process parameter values and raw material ratio plan to the automated control system of the production line, and adjust the operation parameters of the production equipment and the raw material feeding ratio through the programmable logic controller to realize the real-time optimization control of the production process; During the production process, continuously collect various parameter data and product quality data of the production line, and analyze the data through the convolutional neural network algorithm to determine whether the production process deviates from the control objectives of the optimization strategy.

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