Cement grinding parameter adjustment method and system based on particle size distribution

Abstract

The present application relates to the technical field of cement grinding control, and particularly relates to a cement grinding parameter adjustment method and system based on particle size distribution, which can improve the quality control ability and efficiency in the cement production process, and reduce the production cost and environmental impact; the method comprises the following steps: collecting a cement grinding production sample in real time, and performing a screening test on the production sample to obtain real-time particle size distribution data of the cement grinding; calculating the error between the real-time particle size distribution data and preset target particle size distribution data to obtain a particle size distribution error vector; obtaining a plurality of particle size distribution error vectors according to a preset frequency; aligning the error values of the same particle size at different time points in the plurality of particle size distribution error vectors to the same column, aligning the error values of each particle size at the same time point to the same row, and arranging in time sequence to obtain a cement grinding particle error feature matrix; and inputting the cement grinding particle error feature matrix into a pre-constructed particle size distribution error analysis model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cement grinding control, and particularly relates to a cement grinding parameter adjustment method and system based on particle size distribution. BACKGROUND

[0002] With the rapid development of the cement industry, the performance requirements of users for cement products are continuously improved, especially in terms of particle size distribution; the particle size distribution not only affects the physical properties of cement such as strength and setting time, but also directly affects the adaptability of cement and admixtures and the workability of concrete.

[0003] The existing cement grinding process mainly relies on equipment such as ball mills, and the grinding effect is controlled by adjusting parameters such as the rotational speed of the mill, the filling rate of the balls, the size and ratio of the balls, etc.; the setting of the above parameters is usually based on historical data and the experience of operators; however, due to the performance degradation of cement grinding equipment, it is difficult to cope with particle size distribution errors caused by performance degradation and other factors by only referring to historical data and experience, and at the same time, there is a lack of flexibility to adjust according to specific production conditions, resulting in that in the actual production process, even if the particle size distribution deviates from the target value is monitored, it is also difficult to quickly take corresponding adjustment measures. SUMMARY

[0004] To solve the above technical problems, the present application provides a cement grinding parameter adjustment method and system based on particle size distribution, which can improve the quality control ability and efficiency in the cement production process, and reduce production costs and environmental impact.

[0005] In a first aspect, the present application provides a cement grinding parameter adjustment method based on particle size distribution, which comprises:

[0006] Real-time collection of cement grinding production samples, and sieve test of the production samples to obtain real-time particle size distribution data of the cement grinding;

[0007] Calculation of the error between the real-time particle size distribution data and the preset target particle size distribution data to obtain a particle size distribution error vector;

[0008] According to a preset frequency, a plurality of particle size distribution error vectors are obtained;

[0009] The error values of the same particle size at different time points in the plurality of particle size distribution error vectors are aligned to the same column, the error values of each particle size at the same time point are aligned to the same row, and the error values are arranged in time sequence to obtain a cement grinding particle error feature matrix;

[0010] The cement grinding particle error feature matrix is input into a pre-constructed particle size distribution error analysis model to obtain a corresponding particle size distribution production quality grade of the cement grinding;

[0011] determining whether the particle size distribution production quality level exceeds the preset production quality level; if the particle size distribution production quality level exceeds the preset production quality level, maintaining the current cement grinding operation; if the particle size distribution production quality level does not exceed the preset production quality level, calculating a production quality level gap between the particle size distribution production quality level and the preset production quality level;

[0012] According to the production quality level gap, performing optimization matching in the preset cement grinding parameter adjustment database to obtain a cement grinding parameter set corresponding to the production quality level gap;

[0013] According to the cement grinding parameter set, performing real-time adjustment control on the cement grinding.

[0014] Further, the cement grinding particle error feature matrix is:

[0015]

[0016] wherein N represents the number of sampling times, i.e. the number of time points, M represents the number of particle sizes, e NM represents the error value of the Mth particle size at the Nth time point.

[0017] Further, the particle size distribution error vector is:

[0018] E=[R1-T1,R2-T2,…,R n -T n ];

[0019] wherein E represents the particle size distribution error vector, R n represents the real-time particle percentage of the nth particle size section, T n represents the target particle percentage of the nth particle size section.

[0020] Further, the influencing factors of the preset frequency setting include production line stability, equipment performance decay speed, particle size distribution change sensitivity, adjustment measure implementation effect and data processing capacity.

[0021] Further, the construction method of the cement grinding parameter adjustment database comprises:

[0022] Collecting particle size distribution data and corresponding production quality levels under different parameter settings in the historical production process;

[0023] Obtaining parameters of the equipment, including rotational speed data, filling rate of the balls, size ratio of the balls and feeding rate;

[0024] Collecting production environment information, including raw material characteristics, temperature and humidity;

[0025] Performing standardization processing on the collected historical data;

[0026] generating a list of different parameter combinations, including mill speed, ball filling rate, ball size ratio and feed rate;

[0027] for each parameter combination, recording its corresponding production quality level;

[0028] designing a database structure, including parameter fields and production quality level fields;

[0029] entering the sorted data into the database.

[0030] Further, the factors affecting the setting of the preset production quality level include user demand, industry standard, market competition, material characteristics, production equipment capacity, environmental factors and cost-benefit analysis.

[0031] Further, the set of cement grinding parameters includes mill speed, ball filling rate, ball size ratio and feed rate.

[0032] In another aspect, the application also provides a cement grinding parameter adjustment system based on particle size distribution, which comprises:

[0033] a sample collection and screening module, which collects cement grinding production samples in real time and performs screening tests on the production samples to obtain real-time particle size distribution data of the cement grinding;

[0034] an error calculation module, which calculates the error between the real-time particle size distribution data and the preset target particle size distribution data to obtain a particle size distribution error vector;

[0035] an error vector management module, which acquires a plurality of particle size distribution error vectors according to a preset frequency; aligns the error values of the same particle size at different time points in the plurality of particle size distribution error vectors to the same column, aligns the error values of each particle size at the same time point to the same row, and arranges them in time sequence to obtain a cement grinding particle error feature matrix;

[0036] a particle size distribution error analysis module, which inputs the cement grinding particle error feature matrix into a pre-constructed particle size distribution error analysis model to obtain the corresponding particle size distribution production quality level of the cement grinding;

[0037] a production quality level judgment module, which judges whether the particle size distribution production quality level exceeds the preset production quality level; if the particle size distribution production quality level exceeds the preset production quality level, the cement grinding operation status is maintained; if the particle size distribution production quality level does not exceed the preset production quality level, the production quality level gap between the particle size distribution production quality level and the preset production quality level is calculated;

[0038] The parameter adjustment data matching module performs optimization matching in a preset cement grinding parameter adjustment database according to the production quality grade gap, and obtains a cement grinding parameter set corresponding to the production quality grade gap.

[0039] The real-time adjustment control module performs real-time adjustment control on the cement grinding according to the cement grinding parameter set.

[0040] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory and the processor are connected through the bus, and the computer program is executed by the processor to implement the steps of the method in any one of the preceding aspects.

[0041] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the method in any one of the preceding aspects.

[0042] Compared with the prior art, the method can quickly obtain accurate particle size distribution data by collecting cement grinding production samples in real time and performing screening tests, and can timely find deviations in particle size distribution and take corresponding adjustment measures to ensure that the performance of the cement product meets the requirements.

[0043] The method uses a plurality of particle size distribution error vectors to construct an error feature matrix, which is input into a pre-constructed particle size distribution error analysis model for analysis, making the adjustment process more scientific and reasonable and avoiding the limitations of adjusting parameters based on experience. At the same time, through intelligent analysis, the production quality level can be more accurately judged, and parameter adjustment can be performed accordingly.

[0044] The method can flexibly adjust the cement grinding parameters according to the specific production situation and the size of the particle size distribution error, so that the system can cope with challenges brought by different production conditions and equipment performance degradation and other factors, ensuring the continuity and stability of the production process. At the same time, by continuously accumulating and optimizing data, the system can also adapt to changes in the production environment, improving the accuracy and efficiency of the adjustment.

[0045] By constructing the error feature matrix and performing intelligent analysis, the system can predict possible future particle size distribution problems, take measures in advance to prevent problems from occurring, reduce production interruptions and scrap rates caused by particle size distribution problems, improve production efficiency and product quality, and optimize the production process by adjusting the cement grinding parameters in real time, reducing production delays and rework caused by particle size distribution problems, reducing production costs, and improving production efficiency and market competitiveness of the product.

[0046] In summary, the cement grinding parameter adjustment method based on particle size distribution can improve the quality control ability and efficiency in the cement production process, and reduce the production cost and environmental impact. BRIEF DESCRIPTION OF DRAWINGS

[0047] Fig. 1 is a flowchart of the present application;

[0048] Fig. 2 is a flowchart of the construction method of the cement grinding parameter adjustment database;

[0049] Fig. 3 is a structural diagram of the cement grinding parameter adjustment system based on particle size distribution. DETAILED DESCRIPTION

[0050] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, device, electronic device and computer readable storage medium. Therefore, the present application can be specifically implemented as the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), hardware and software combined form. In addition, in some embodiments, the present application can also be implemented as a computer program product in one or more computer readable storage media, which contains computer program code.

[0051] The above computer readable storage medium can adopt any combination of one or more computer readable storage media. The computer readable storage medium includes: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination thereof. More specific examples of computer readable storage medium include: portable computer disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, compact disk read-only memory, optical storage device, magnetic storage device or any combination thereof. In the present application, the computer readable storage medium can be any tangible medium containing or storing programs, which can be used or combined with instruction execution system, device or device.

[0052] The acquisition, storage, use, processing and other data in the technical solution of the present application comply with the relevant provisions of national laws.

[0053] The present application describes the provided method, device and electronic equipment through flowchart and / or block diagram.

[0054] It should be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0055] These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable storage medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0056] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable data processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0057] The application is described below with reference to the accompanying drawings.

[0058] Embodiment one: as shown in the figure, the particle size distribution based cement grinding parameter adjustment method of the application specifically includes the following steps: Figs. 1-2

[0059] S1, real-time collection of cement grinding production samples, and sieve test on the production samples to obtain real-time particle size distribution data of the cement grinding;

[0060] The method for obtaining real-time particle size distribution data of the cement grinding includes:

[0061] On the cement grinding production line, an automatic sampling device is arranged to collect samples in the cement grinding production process at a preset time interval; the sampling process needs to follow standardized operating procedures to ensure that the collected samples are representative and avoid the influence of human factors or sampling tools on the quality of the samples;

[0062] The collected production samples are sent to a sieve test in a sieve device; the sieve device contains sieves with different pore sizes for classifying the cement powder according to particle size; during the sieve test, the cleanliness and dryness of the sieves need to be ensured to avoid the influence of impurities on the sieve test results; at the same time, the sieve time and vibration intensity are controlled to ensure the accuracy and repeatability of the sieve test results; ​

[0063] After the sieving is completed, the cement powder on each screen is collected separately, and its mass is measured to calculate the proportion of each particle size, i.e., the real-time particle size distribution data.

[0064] In this step, by setting up an automatic sampling device and collecting samples at preset time intervals, real-time particle size distribution data during the cement grinding production process can be obtained; this real-time nature ensures the timeliness and accuracy of the data, providing a basis for quick response for subsequent error analysis and adjustment measures; the sampling process follows standardized operating procedures, ensuring that the samples taken are representative; this avoids the influence of human factors or sampling tools on sample quality, ensuring the reliability and consistency of the data; the sieving equipment uses screens of different aperture sizes to accurately classify the cement powder by particle size; at the same time, by controlling the sieving time, vibration intensity, and keeping the screens clean and dry, the accuracy and repeatability of the sieving results are ensured; this high-precision sieving method provides a strong guarantee for obtaining accurate particle size distribution data; S1 step obtains accurate real-time particle size distribution data by real-time collection of cement grinding production samples and sieving tests; not only improves the real-time, representativeness and accuracy of the data, but also realizes efficient operation and traceable records, providing important data support for cement grinding control.

[0065] S2, calculate the error between the real-time particle size distribution data and the preset target particle size distribution data to obtain a particle size distribution error vector;

[0066] The method for obtaining the particle size distribution error vector comprises:

[0067] Real-time particle size distribution data is obtained by real-time collection of cement grinding production samples and sieving tests; the real-time particle size distribution data represents the current cement particle distribution in the production process, including the mass percentage of particles in different size intervals;

[0068] The preset target particle size distribution data is set according to the performance requirements of the cement product, user demand, and production process standards;

[0069] For each size interval, the difference between the real-time particle size distribution data and the preset target particle size distribution data is calculated; this difference is the particle size distribution error of the size interval;

[0070] The particle size distribution errors of all size intervals are arranged in order to form a vector; this vector is the particle size distribution error vector, which shows the difference between the current particle size distribution and the target distribution in the production process;

[0071] The particle size distribution error vector is:

[0072] E = [R1-T1, R2-T2, …, Rn-Tn]n -T n ]

[0073] wherein E represents the particle size distribution error vector, R n represents the real-time particle percentage of the nth particle size section, T n represents the target particle percentage of the nth particle size section.

[0074] In this step, the particle size distribution error vector intuitively shows the specific difference between the particle size distribution in the current production process and the target distribution, providing clear data support for subsequent analysis and adjustment; based on the particle size distribution error vector, it can more accurately identify which particle size interval deviates from the target value and the degree of deviation; it helps to more targetedly adjust the cement grinding parameters, thereby improving the accuracy and effect of adjustment; real-time particle size distribution data is obtained; the particle size distribution error vector can be calculated in time, and real-time adjustment control is carried out accordingly; through cooperation with the subsequent error analysis model and parameter adjustment database, automatic monitoring and adjustment of the production process can be realized, thereby optimizing the entire production process, reducing production cost and improving production benefit; this step can improve the real-time, accuracy and automation level of the production process, optimize the production process and improve product quality and production benefit.

[0075] S3, acquiring a plurality of particle size distribution error vectors according to a preset frequency;

[0076] The preset frequency is the time interval for collecting the particle size distribution error vector at specified periods, and the factors affecting the setting of the preset frequency include:

[0077] Production line stability: if the production line stability is high, the preset frequency can be appropriately reduced; on the contrary, if the production line stability is poor, there is greater volatility and uncertainty, then the preset frequency needs to be correspondingly increased in order to respond to these changes faster;

[0078] Equipment performance attenuation speed: the performance of cement grinding equipment will gradually decrease with the increase of use time, which will affect the stability and accuracy of particle size distribution; if the equipment performance attenuation speed is fast, the preset frequency needs to be set higher in order to timely find and adjust the particle size distribution deviation caused by the decline of equipment performance;

[0079] Sensitivity of particle size distribution change: different users have different requirements for the particle size distribution of cement products, and some products are very sensitive to the change of particle size distribution, even a slight deviation can have a significant impact on performance; for such products, the preset frequency needs to be set higher in order to timely find and correct the slight deviation of particle size distribution;

[0080] The implementation effect of the adjustment measure: the implementation effect of the adjustment measure also affects the setting of the preset frequency; if the adjustment measure can quickly and effectively improve the particle size distribution condition, the preset frequency can be appropriately reduced; on the contrary, if the effect of the adjustment measure is not obvious or needs a long time to appear, the preset frequency needs to be increased to timely find and adjust the particle size distribution deviation before the adjustment measure takes effect;

[0081] Data processing capacity: the setting of the preset frequency needs to consider the limitation of data processing and calculation capacity; if the data acquisition, processing and analysis capacity is limited, the preset frequency cannot be set too high to exceed the processing capacity of the system.

[0082] In this step, by setting a reasonable preset frequency, the particle size distribution condition on the production line can be ensured to be monitored in a timely manner; for cement products sensitive to particle size distribution changes, high-frequency monitoring can ensure the stability of product quality; by timely finding and correcting the slight deviation of particle size distribution, product quality problems caused by deviation accumulation can be avoided, and the overall performance and market competitiveness of the product can be improved; by setting the preset frequency, the monitoring frequency can be adjusted according to the implementation effect of the adjustment measure; when setting the preset frequency, the limitation of data processing and calculation capacity is fully considered, which can avoid the problem of system overload caused by high-frequency data acquisition and processing; by reasonably setting the monitoring frequency, it can be ensured that the data processing system can efficiently operate without exceeding its capacity range, providing strong support for the stable operation of the production line and the guarantee of product quality; this step obtains multiple particle size distribution error vectors by setting a reasonable preset frequency, which not only helps to respond to production line changes and improve the stability of product quality, but also optimizes the implementation of adjustment measures and reasonably utilizes data processing capacity, providing strong support for quality control and production management of cement production enterprises.

[0083] S4, aligning the error values of the same particle size at different time points in the multiple particle size distribution error vectors to the same column, aligning the error values of each particle size at the same time point to the same row, and arranging in time sequence to obtain a cement grinding particle error feature matrix;

[0084] Since there may be a time difference in each collection, it is necessary to ensure that the error vectors are aligned on the time axis; by selecting a unified time reference, all error vectors are adjusted to the corresponding position of the time reference;

[0085] At each time point, the error values of different particle sizes need to be arranged in the order of particle size from small to large; it is ensured that each row of the error feature matrix represents the error distribution of all particle sizes at a time point;

[0086] The error values are filled into the matrix in time sequence and particle size sequence; each row in the matrix represents the particle size distribution error data at a time point, that is, the error vector at each time point; each column represents the error value of a specific particle size at all time points; and the cement grinding particle error feature matrix is:

[0087]

[0088] wherein N represents the number of sampling times, that is, the number of time points, M represents the number of particle sizes, e NM represents the error value of the Mth particle size at the Nth time point.

[0089] In this step, the particle size distribution error vectors at multiple time points are integrated into a two-dimensional matrix, realizing the structured representation of data; not only facilitating data storage and management, but also facilitating subsequent data analysis and processing; by selecting a unified time reference and adjusting all error vectors to the corresponding position of the time reference, the consistency of error data on the time axis is ensured; which helps to identify the trend of particle size distribution error over time, providing a basis for analyzing the influence of factors such as equipment performance degradation and operating condition changes on particle size distribution; at each time point, the error values of different particle sizes are arranged in the order of particle size from small to large, ensuring that each row of the error feature matrix represents the error distribution of all particle sizes at a time point; so that the analyst can clearly see the error of each particle size at each time point, which helps to identify which particle size is more prone to deviation, so as to develop targeted adjustment measures; the constructed cement grinding particle error feature matrix provides a rich data source for subsequent pattern recognition; by performing statistical analysis, cluster analysis, association rule mining and other operations on the data in the matrix, the potential relationship between particle size distribution error and cement grinding parameters can be found, providing a scientific basis for optimizing the grinding process and improving product quality; in real-time production process, by continuously updating the data in the error feature matrix, the change of particle size distribution can be monitored in real time; once the particle size distribution deviates from the target value, the production quality grade gap can be calculated according to the information in the error feature matrix, and the optimal matching in the preset cement grinding parameter adjustment database is performed to obtain the corresponding adjustment parameter set, so as to realize real-time adjustment control of cement grinding.

[0090] S5, inputting the cement grinding particle error feature matrix into a pre-constructed particle size distribution error analysis model to obtain a corresponding particle size distribution production quality grade of the cement grinding;

[0091] The particle size distribution error analysis model is a machine learning model for analyzing the particle size distribution error feature matrix.

[0092] input the particle size distribution error characteristic matrix of the cement grinding particles in step into the particle size distribution error analysis model; the matrix contains error values of each particle size at multiple time points, comprehensively reflecting the changes of the particle size distribution in the time sequence; the model outputs the particle size distribution production quality level of the current cement grinding production through processing and analysis of these input data;

[0093] The production quality level is the result of the model's comprehensive evaluation based on the particle size distribution error characteristic matrix, which reflects the degree of excellence of the particle size distribution under current production conditions;

[0094] For each node, the model selects a feature and a threshold value, divides the data set into two subsets according to the feature and the threshold value, and maximizes the information gain; this process is recursively applied to each subset until the stopping condition is met;

[0095] For new inputs, the model starts from the root node, makes decisions based on the node's features and threshold values, and finally reaches a leaf node; the leaf node gives the corresponding production quality level prediction.

[0096] In this step, by collecting and processing particle size distribution data in real time during the cement grinding production process, the model can quickly respond to changes in production conditions and accurately evaluate the current particle size distribution production quality level; based on a large amount of historical data and complex algorithms, the model can automatically learn and identify the complex relationship between particle size distribution and production quality level; it can rely on the model's prediction results to make more scientific and reasonable decisions, reducing the dependence on artificial experience and subjective judgment; since the model is data-driven, it can automatically adjust the evaluation criteria according to changes in production conditions, thus maintaining the accuracy and effectiveness of production quality level evaluation, improving the stability and reliability of the production process; by monitoring particle size distribution errors and evaluating production quality levels in real time, the model can detect potential production problems or performance decline trends in advance; it helps production managers take preventive maintenance or optimize production parameters in a timely manner, avoiding production accidents and product quality decline; by optimizing particle size distribution and improving production quality level, the model helps improve the overall performance and quality of cement products; not only can it meet the increasing performance requirements of users for cement products, but also can reduce costs such as returns, claims, and other losses due to product quality problems; at the same time, by reducing waste and unnecessary adjustments in the production process, it improves production efficiency and reduces production costs.

[0097] S6, determine whether the particle size distribution production quality level exceeds the preset production quality level; if the particle size distribution production quality level exceeds the preset production quality level, maintain the current status of the cement grinding; if the particle size distribution production quality level does not exceed the preset production quality level, calculate the production quality level gap between the particle size distribution production quality level and the preset production quality level;

[0098] When the particle size distribution production quality level exceeds the preset production quality level, it means that the current cement grinding production process has reached or exceeded the established quality standards, and no additional adjustments are needed; therefore, the system will maintain the current operation status of the cement grinding equipment and continue to monitor the changes in the particle size distribution during the production process to ensure the stability of the production quality;

[0099] When the particle size distribution production quality level does not exceed the preset production quality level, it indicates that there are quality problems in the current production process, and measures need to be taken to improve it; at this time, the system will further calculate the production quality level gap between the particle size distribution production quality level and the preset production quality level to quantify the degree of deviation between the current production state and the desired state;

[0100] The factors affecting the setting of the preset production quality level include:

[0101] User requirements: User requirements for the performance of cement products, including strength, setting time, fluidity, etc.; user requirements for cement particle size distribution to ensure good adaptability of cement with additives and excellent work performance of concrete;

[0102] Industry standards: Relevant industry standards and specifications of the country or region, which stipulate the basic performance indicators of cement products and special requirements in specific application fields;

[0103] Market competition situation: Performance of similar products in the market to ensure the competitiveness of the produced cement products; market trends and technological progress to predict possible future product performance requirements;

[0104] Raw material characteristics: Chemical composition and physical properties of raw materials required for cement production; the influence of different raw materials on the grinding process and how to adjust the grinding parameters to optimize the particle size distribution;

[0105] Production equipment capacity: Actual production capacity of cement grinding equipment, including maximum and minimum capacity range; wear and tear and maintenance status of the equipment and how these factors affect the particle size distribution of the final product;

[0106] Environmental factors: Dust emissions and other environmental impacts during the production process and relevant regulatory restrictions; sustainability requirements for cement production and use;

[0107] Cost-benefit analysis: Cost-benefit ratio, including factors such as raw material cost, energy consumption, equipment depreciation; trade-off between producing high-quality cement products and cost.

[0108] In this step, by judging the relationship between the production quality level and the preset production quality level in real time, the quality status in the production process can be immediately fed back; when the production quality level exceeds the preset value, the present situation is maintained to ensure stable production; when the production quality is not up to standard, the adjustment mechanism is quickly started to realize precise regulation and control of the production process; after judging that the production quality level is not up to standard, the system further calculates the production quality level gap, providing a quantitative basis for subsequent parameter adjustment; it helps to optimize resource allocation, avoid unnecessary waste, and at the same time ensure the effectiveness and pertinence of the adjustment measures; by timely discovering and correcting quality problems in the production process, this step helps to reduce the rate of defective products and improve the product pass rate, thereby directly improving production efficiency and product quality; by considering various factors to set the preset production quality level, the enterprise can reasonably control production costs while ensuring product quality, which helps the enterprise maintain a competitive advantage in fierce market competition; as market demand and industry standards change, the preset production quality level can be flexibly adjusted; the implementation of this step enables the enterprise to quickly respond to market changes and adjust production strategies to meet the needs of different customers and industries, thereby enhancing the market adaptability and competitiveness of the enterprise; considering environmental factors and sustainability requirements when setting the preset production quality level helps the enterprise reduce its impact on the environment during production and promote green production; it helps the enterprise to fulfill its social responsibility and also helps to enhance the enterprise's image and brand value.

[0109] S7、According to the production quality level gap, perform optimization matching in the preset cement grinding parameter adjustment database to obtain a set of cement grinding parameters corresponding to the production quality level gap;

[0110] The method for constructing the cement grinding parameter adjustment database comprises:

[0111] Collect particle size distribution data and corresponding production quality levels under different parameter settings in the historical production process;

[0112] Obtain basic parameters of the equipment, including rotational speed data, ball filling rate, ball size ratio, and feed rate;

[0113] Collect production environment information, including raw material characteristics, temperature, and humidity;

[0114] Standardize the collected historical data to ensure data consistency and comparability;

[0115] Generate a list of different parameter combinations, including mill rotational speed, ball filling rate, ball size ratio, and feed rate;

[0116] For each parameter combination, record the corresponding production quality level;

[0117] Design the database structure, including parameter fields and production quality grade fields;

[0118] Enter the organized data into the database;

[0119] Create indexes for key fields in the database to improve query efficiency.

[0120] In this step, by collecting and analyzing historical production data, the impact of different parameter settings on particle size distribution and production quality grade can be accurately understood. During the production process, once a deviation from the target value in particle size distribution or a failure to meet the production quality grade is detected, the optimal or near-optimal parameter set can be quickly matched from the database to achieve real-time adjustment and control, thereby improving production efficiency and product quality. Traditional cement grinding control often relies on the operator's experience and intuition, lacking systematic data support. After constructing a cement grinding parameter adjustment database, the reliance on operator experience and skills is reduced through automated data analysis and matching processes, making operation simpler and more intuitive, and reducing the possibility of human error and incorrect operation. With changes in the production environment and the degradation of equipment performance, traditional control methods based on fixed parameter settings are often difficult to cope with. However, the database-based parameter adjustment method can flexibly adjust parameter settings according to specific production conditions, enhancing the system's flexibility and adaptability, and ensuring optimal production results under different conditions. By constructing a cement grinding parameter adjustment database and combining historical and real-time data, powerful data support is provided for enterprises. Enterprises can use this data for deeper analysis and mining, discovering potential optimization opportunities and improvement points, and providing a more scientific and accurate basis for enterprise decision-making.

[0121] S8. Based on the set of cement grinding parameters, perform real-time adjustment and control of cement grinding;

[0122] The set of cement grinding parameters includes:

[0123] Mill speed: Mill speed is a key factor affecting grinding efficiency and particle distribution; by adjusting the mill speed, the movement trajectory and impact force of the material inside the mill can be changed, thereby affecting the crushing and refining effect of particles.

[0124] Ball filling rate: The ball filling rate refers to the proportion of grinding media in the total volume of the mill. An appropriate filling rate can ensure effective collision and grinding between the grinding media and the material, thereby improving grinding efficiency. Too high or too low a filling rate will result in poor grinding effect.

[0125] Ball size and ratio: Balls of different sizes and ratios will produce different grinding effects in the mill; by adjusting the size and ratio of the balls, the energy transfer and material crushing effect during the grinding process can be optimized, thereby improving the particle size distribution.

[0126] Feed rate: The feed rate controls the speed of the material entering the mill; a proper feed rate can ensure that the material load in the mill is moderate, avoiding over-grinding or under-grinding phenomena.

[0127] In this step, by precisely adjusting the mill speed, fine control of the grinding efficiency and particle distribution is achieved; optimization of the mill speed can directly change the motion state and stress condition of the material inside the mill, thereby ensuring uniform and effective crushing and refinement of particles during the grinding process, which helps to improve the homogeneity and performance stability of the cement product; reasonable ball filling rate ensures effective collision and grinding between the grinding medium and the material; this not only improves the grinding efficiency, but also reduces energy consumption and wear, prolonging the service life of the mill; at the same time, it avoids over-grinding or under-grinding due to improper filling rate, further improving the quality of the cement product; by optimizing the size and ratio of the balls, precise control of energy transfer and material crushing effect during grinding is achieved; making the grinding process more efficient and uniform, which helps to form an ideal particle size distribution, thereby meeting the high requirements of users for the performance of the cement product; proper control of the feed rate is the key to ensuring that the material load in the mill is moderate and avoiding over-grinding or under-grinding phenomena; not only improves the stability and controllability of the grinding process, but also reduces product quality problems caused by improper operation, further improving production efficiency and product quality; the real-time adjustment control strategy in step S8 is based on a scientific set of cement grinding parameters, by precisely adjusting the key parameters, the overall optimization of the cement grinding process is achieved; this not only improves the grinding efficiency and product quality, but also reduces energy consumption and cost.

[0128] Example two: As shown in the figure, the cement grinding parameter adjustment system based on particle size distribution of the application specifically includes the following modules; Fig. 3

[0129] The sample collection and screening module collects cement grinding production samples in real time, and performs screening tests on the production samples to obtain real-time particle size distribution data of the cement grinding;

[0130] The error calculation module calculates the error between the real-time particle size distribution data and the preset target particle size distribution data to obtain a particle size distribution error vector;

[0131] The error vector management module acquires a plurality of particle size distribution error vectors according to a preset frequency; aligns the error values of the same particle size at different time points in the plurality of particle size distribution error vectors to the same column, aligns the error values of each particle size at the same time point to the same row, and arranges them in time sequence to obtain a cement grinding particle error feature matrix;

[0132] ​a particle size distribution error analysis module, which inputs the cement grinding particle error feature matrix into a pre-constructed particle size distribution error analysis model to obtain the corresponding particle size distribution production quality grade of the cement grinding;

[0133] a production quality grade judgment module, which judges whether the particle size distribution production quality grade exceeds the preset production quality grade; if the particle size distribution production quality grade exceeds the preset production quality grade, the cement grinding operation status is maintained; if the particle size distribution production quality grade does not exceed the preset production quality grade, a production quality grade gap between the particle size distribution production quality grade and the preset production quality grade is calculated;

[0134] a parameter adjustment data matching module, which performs optimization matching in a preset cement grinding parameter adjustment database according to the production quality grade gap to obtain a cement grinding parameter set corresponding to the production quality grade gap;

[0135] a real-time adjustment control module, which performs real-time adjustment control on the cement grinding according to the cement grinding parameter set.

[0136] The system can collect cement grinding production samples in real time and perform screening to quickly obtain particle size distribution data; once the particle size distribution deviates from the target value, the system can immediately perform calculation and analysis and take corresponding adjustment measures, thereby realizing real-time control and optimization of the production process;

[0137] Through the error calculation module and the error vector management module, the error between the real-time particle size distribution and the target particle size distribution can be accurately calculated, and a particle error feature matrix can be constructed; this enables the system to more accurately understand the current production state and make intelligent analysis and decision based on these data;

[0138] The system not only can monitor and adjust the current particle size distribution, but also can predict possible future particle size distribution problems through accumulation of historical data and error feature matrices; this enables the system to take measures in advance to prevent problems from occurring and improve the stability and reliability of production;

[0139] The system can flexibly adjust the cement grinding parameters according to the specific production conditions and the size of the particle size distribution error; this enables the system to cope with challenges brought by different production conditions and equipment performance degradation and the like, ensuring the continuity and stability of the production process;

[0140] The system analyzes and decides based on a large amount of real-time data and historical data, which makes the adjustment measures more scientific and reasonable; at the same time, the system can continuously accumulate and optimize data to improve the accuracy of analysis and the precision of adjustment, thereby realizing continuous optimization and upgrading of the production process;

[0141] Through real-time, accurate, and flexible adjustment control, the system can ensure that the particle size distribution of the cement product always remains within the target range, thereby improving the physical properties of the product and its adaptability to additives; at the same time, the system can also improve production efficiency due to the reduction of production interruptions and waste rates caused by particle size distribution problems;

[0142] In summary, the cement grinding parameter adjustment system based on particle size distribution improves the quality control ability and efficiency in the cement production process, while also reducing production costs and environmental impact.

[0143] The various variations and specific embodiments of the cement grinding parameter adjustment method based on particle size distribution in the foregoing embodiment one are also applicable to the cement grinding parameter adjustment system based on particle size distribution in this embodiment. Through the foregoing detailed description of the cement grinding parameter adjustment method based on particle size distribution, those skilled in the art can clearly understand the implementation method of the cement grinding parameter adjustment system based on particle size distribution in this embodiment. Therefore, in the interest of brevity, the cement grinding parameter adjustment system based on particle size distribution in this embodiment will not be described in detail here.

[0144] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor, the transceiver, the memory and the processor are connected through the bus respectively, the computer program is executed by the processor to realize each process of the method for controlling output data, and the same technical effect can be achieved, to avoid repetition, here will not be repeated.

[0145] The above only describes the preferred embodiments of the present application. It should be noted that for those skilled in the art, without departing from the technical principles of the present application, several improvements and modifications can be made, which should also be considered as the protection scope of the present application.

Claims

1. A method for adjusting cement grinding parameters based on particle size distribution, characterized in that, The method includes: Real-time collection of cement grinding production samples and sieving tests on the production samples to obtain real-time particle size distribution data of cement grinding. Calculate the error between real-time particle size distribution data and preset target particle size distribution data to obtain the particle size distribution error vector; Based on a preset frequency, multiple particle size distribution error vectors are obtained; Align the error values ​​of the same particle size at different time points in multiple particle size distribution error vectors to the same column, align the error values ​​of each particle size at the same time point to the same row, and arrange them in chronological order to obtain the cement grinding particle error feature matrix. The particle error feature matrix of cement grinding is input into a pre-constructed particle size distribution error analysis model to obtain the production quality grade of particle size distribution corresponding to cement grinding. Determine whether the particle size distribution production quality grade exceeds the preset production quality grade; if the particle size distribution production quality grade exceeds the preset production quality grade, maintain the current cement grinding operation status; if the particle size distribution production quality grade does not exceed the preset production quality grade, calculate the production quality grade difference between the particle size distribution production quality grade and the preset production quality grade. Based on the production quality grade difference, the optimal matching is performed in the preset cement grinding parameter adjustment database to obtain the cement grinding parameter set corresponding to the production quality grade difference. Based on the set of cement grinding parameters, the cement grinding process is adjusted and controlled in real time. The method for constructing the cement grinding parameter adjustment database includes: Collect particle size distribution data and corresponding production quality grades under different parameter settings during historical production processes; Obtain the equipment parameters, including rotation speed data, ball filling rate, ball size ratio, and feed rate; Collect production environment information, including raw material characteristics, temperature, and humidity; The collected historical data is standardized. Generate a list of different parameter combinations, including mill speed, ball filling rate, ball size ratio, and feed rate; For each combination of parameters, record its corresponding production quality level; Design the database structure, including parameter fields and production quality grade fields; Enter the organized data into the database.

2. The method for adjusting cement grinding parameters based on particle size distribution as described in claim 1, characterized in that, The particle error characteristic matrix of cement grinding is as follows: ； Where N represents the number of sampling times, M represents the number of granularities, and e NM This represents the error value of the Mth particle size at the Nth time point.

3. The method for adjusting cement grinding parameters based on particle size distribution as described in claim 1, characterized in that, The particle size distribution error vector is: E =[ R 1− T 1, R 2− T 2,…, Rn − Tn ]； in, E This represents the particle size distribution error vector. Rn Indicates the first n Real-time particle percentage within each particle size range Tn Indicates the first n Percentage of target particles in the particle size range.

4. The method for adjusting cement grinding parameters based on particle size distribution as described in claim 1, characterized in that, Factors affecting the setting of the preset frequency include production line stability, equipment performance degradation rate, sensitivity to changes in particle size distribution, the effectiveness of adjustment measures, and data processing capabilities.

5. The method for adjusting cement grinding parameters based on particle size distribution as described in claim 1, characterized in that, Factors influencing the setting of preset production quality levels include user needs, industry standards, market competition, raw material characteristics, production equipment capacity, environmental factors, and cost-benefit analysis.

6. The method for adjusting cement grinding parameters based on particle size distribution as described in claim 1, characterized in that, The set of cement grinding parameters includes mill speed, ball filling rate, ball size ratio, and feed rate.

7. A cement grinding parameter adjustment system based on particle size distribution, said system being applied to the cement grinding parameter adjustment method based on particle size distribution as described in claim 1, characterized in that, The system includes: The sample collection and sieving module collects cement grinding production samples in real time and performs sieving tests on the production samples to obtain real-time particle size distribution data of cement grinding. The error calculation module calculates the error between real-time particle size distribution data and preset target particle size distribution data to obtain the particle size distribution error vector. The error vector management module obtains multiple particle size distribution error vectors according to a preset frequency; it aligns the error values ​​of the same particle size at different time points in the multiple particle size distribution error vectors to the same column, aligns the error values ​​of each particle size at the same time point to the same row, and arranges them in chronological order to obtain the cement grinding particle error feature matrix. The particle size distribution error analysis module inputs the particle error feature matrix of cement grinding into a pre-built particle size distribution error analysis model to obtain the production quality grade of particle size distribution corresponding to cement grinding. The production quality grade judgment module determines whether the particle size distribution production quality grade exceeds the preset production quality grade. If the particle size distribution production quality grade exceeds the preset production quality grade, the current cement grinding operation status is maintained. If the particle size distribution production quality grade does not exceed the preset production quality grade, the production quality grade difference between the particle size distribution production quality grade and the preset production quality grade is calculated. The parameter adjustment data matching module performs optimal matching in the preset cement grinding parameter adjustment database according to the production quality grade difference, and obtains the cement grinding parameter set corresponding to the production quality grade difference. The real-time adjustment and control module adjusts and controls the cement grinding process in real time based on the set of cement grinding parameters.

8. An electronic device for adjusting cement grinding parameters based on particle size distribution, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that, When the computer program is executed by the processor, it implements the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.

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