Preparation method of environment-friendly mining and beneficiation sludge cement additive

Abstract

The invention provides a preparation method of an environment-friendly mining and beneficiation sludge cement additive, which comprises the following steps: acquiring mining and beneficiation sludge, analyzing the chemical components of the mining and beneficiation sludge, and determining the initial range of the sludge doping amount according to the chemical components; setting a preset threshold value of the drying temperature according to the initial water content of the sludge, and adjusting the drying temperature and the drying time according to the preset threshold value; screening the dried sludge particles to obtain particle data in different particle size ranges, and analyzing the particle data to obtain particle size distribution; determining a target particle size range according to the particle size distribution, and controlling the particle size of the dried sludge particles based on the target particle size range; mixing the dried sludge particles with cement clinker according to a preset proportion to obtain a mixture; and detecting the uniformity of the mixture, and adjusting the preset proportion or the mixing time according to the uniformity.

Description

Technical Field

[0001] The invention relates to the field of information technology, and in particular to a method for preparing an environmentally friendly mining and ore dressing sludge cement additive. Background Art

[0002] There is a core technical problem in the process of preparing cement by resource utilization of mining and beneficiation sludge: how to maximize the utilization of sludge resources while ensuring the performance of cement. This problem involves multiple interrelated technical challenges. First, the composition of sludge is complex and changeable, and direct addition may lead to a decrease in cement performance. Secondly, the sludge has a high water content, which affects the cement production process and the quality of the final product. Furthermore, the uneven distribution of sludge particle size may cause insufficient cement mixing and affect strength development. In addition, there is a complex nonlinear relationship between the amount of sludge added and the performance of cement, which is difficult to control accurately. At the same time, harmful components in sludge may affect the long-term performance of cement over time. These factors interact with each other to form a multi-dimensional technical contradiction: increasing the utilization rate of sludge will reduce the quality of cement, while excessive pursuit of cement performance will limit the degree of resource utilization of sludge. How to find the best balance between the two, while considering the feasibility and economy of the production process, is the core challenge facing this technology. The solution to this problem is not only related to the quality of a single product, but also involves the optimization of the entire production process and the realization of environmental benefits. Summary of the invention

[0003] The present invention provides a method for preparing an environmentally friendly mining and ore dressing sludge cement additive, which mainly comprises: The method comprises the steps of obtaining mining sludge, analyzing the chemical composition of the mining sludge, and determining the initial range of the sludge addition amount according to the chemical composition; setting a preset threshold value of the drying temperature according to the initial water content of the sludge, and adjusting the drying temperature and the drying time according to the preset threshold value; processing the sludge through a drying device based on the adjusted drying temperature and the drying time to obtain dried sludge particles, and measuring the water content of the dried sludge particles; screening the dried sludge particles, obtaining particle data of different particle size ranges, and analyzing the particle data to obtain particle size distribution; diameter distribution; determining a target particle size range according to the particle size distribution, and controlling the particle size of the dried sludge particles based on the target particle size range; mixing the dried sludge particles with cement clinker in a preset ratio to obtain a mixture; detecting the uniformity of the mixture, and adjusting the preset ratio or mixing time according to the uniformity; testing the compressive strength and specific surface area of ​​the cement test block added with the dried sludge particles, and adjusting the sludge addition amount, the drying temperature, the drying time and the particle size according to the compressive strength and the specific surface area data.

[0004] Furthermore, the analysis of the chemical composition of the mining and beneficiation sludge includes: performing elemental analysis on the sludge using an X-ray fluorescence spectrometer to obtain the content of each chemical element in the sludge; performing elemental analysis on fly ash using an energy dispersive X-ray fluorescence spectrometer to obtain the content of each chemical element in the fly ash; according to the chemical element content of the sludge and the fly ash, using the Euclidean distance method to calculate the similarity of the chemical composition of the sludge and the fly ash; if the similarity is higher than a set value, then determining the initial addition amount range of the sludge in the cement material according to the addition ratio of fly ash in the cement material formula; using a multivariate linear regression analysis method to establish a quantitative relationship model between the sludge addition amount and material strength, and predict the average relative error of the cement material strength under different sludge addition amounts; using a scanning electron microscope to observe the microscopic morphology of cement samples with different sludge addition amounts, and establish the relationship between the sludge addition amount and the structural density of cement hydration products.

[0005] Furthermore, the preset threshold of the drying temperature is set according to the initial water content of the sludge, including: comparing the preset threshold with the temperature parameter in the oil well structure mechanical property degradation model; if the drying temperature is higher than the preset threshold, adjusting the drying equipment parameters, lowering the temperature to avoid surface sintering, and predicting the material performance change according to the mechanical property degradation model; if the drying temperature is lower than the preset threshold, increasing the drying time to reduce the water content, and using the resonance frequency identification method to analyze the effect of extending the drying time on energy accumulation; improving the energy absorption capacity of the material by adjusting the proportion and distribution of damping components in the cement material formula; using a support vector machine model, inputting sludge water content, drying temperature, and time parameters to predict the material mechanical properties and resonance suppression effect; and establishing a mapping relationship between drying process parameters and material properties based on the prediction results of the support vector machine model.

[0006] Furthermore, the measurement of the moisture content of the dried sludge particles includes: setting the initial drying temperature and time according to the process parameters of the drying equipment, and starting the sludge drying process; collecting the temperature and humidity data during the sludge drying process through the sensor inside the drying equipment, and transmitting them to the data processing module; using the algorithm in the data processing module to analyze the collected temperature and humidity data to obtain the dynamic change trend of the sludge drying process; judging the current drying effect based on the dynamic change trend of the drying process obtained by analysis and combined with a preset target moisture content; if the drying effect does not reach the preset target value, adjusting the drying temperature and time parameters through the control module according to the dynamic change trend of the drying process; transmitting the adjusted drying temperature and time parameters to the drying equipment, and restarting the sludge drying process.

[0007] Furthermore, the target particle size range is determined according to the particle size distribution, including: using a vibrating screen to grade the dried sludge particles to obtain the mass fraction of particles in different particle size ranges, and fitting the particle size distribution through the Rosin-Rammler distribution function; using image processing technology to perform morphological analysis on the sifted sludge particles, and obtaining the sphericity and surface roughness of the particles through a contour extraction algorithm; establishing a multivariate linear regression model between morphological parameters and cement fluidity and water separation rate, and optimizing the sphericity and surface roughness of the particles; according to the optimized sludge particle characteristics, using the Bingham rheological model to predict the rheological properties of the cement paste, and obtaining the plastic viscosity and yield stress of the cement paste through numerical simulation; establishing a BP neural network model between rheological parameters and cement bleeding rate and sedimentation rate, and optimizing the water-cement ratio and water reducer dosage of the cement paste.

[0008] Furthermore, the detection of the uniformity of the mixture includes: using a weighing sensor to measure the sludge particles and the cement clinker, recording the raw material quality data through a control system and transmitting it to a database; conveying the sludge particles and the cement clinker to a mixer through a conveyor, and setting the mixing time and rotation speed; using an online monitoring system based on computer vision to collect an image of the mixture, using an image segmentation algorithm to extract the pixel area of ​​the sludge particles, calculating its area ratio, and obtaining mixing uniformity data; judging the mixing effect according to a preset uniformity threshold, and when the uniformity is lower than the threshold, triggering feedback control to automatically extend the mixing time or increase the rotation speed; using a manipulator to grab mixture samples from different positions, and sending them into a material testing machine to test the compressive strength and flexural strength, and obtain mechanical property data.

[0009] Furthermore, the sludge addition amount, the drying temperature, the drying time and the particle size are adjusted according to the compressive strength and the specific surface area data, including: using cement strength testing equipment to measure the compressive strength of cement test blocks mixed with sludge particles in different proportions to obtain a set of strength data; comparing the strength data with cement strength requirements to determine whether the cement material mixed with sludge particles meets the requirements; obtaining aging data of the cement material mixed with the sludge particles through high temperature and high pressure environment simulation equipment, and using the Arrhenius equation to fit the aging rate and the changing trend of environmental parameters; establishing a cement material mechanical property degradation model considering temperature, pressure and time parameters to predict the compressive strength of the material in an oil well environment; using vibration testing equipment to identify the resonance frequency of the cement material mixed with the sludge particles in a high temperature and high pressure environment, and using the energy integration method to analyze the accumulation law of the resonance energy at this frequency.

[0010] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The invention discloses a method for preparing cement by resource utilization of mining and ore dressing sludge. The method first determines the similarity between sludge and fly ash by chemical composition similarity analysis, and preliminarily determines the range of sludge addition. Then, the drying temperature threshold is set according to the water content of the sludge, and the drying process is optimized by online monitoring and feedback control. The dried sludge is screened and graded, and the optimal particle size range is determined by a particle size distribution model. The screened sludge is mixed with cement clinker, and the mixing uniformity is evaluated by an image processing algorithm. The cement performance is evaluated by cement strength and specific surface area tests combined with the Krieger-Dougherty equation, and the sludge addition amount and process parameters are optimized. Finally, based on the material mechanical property degradation model and the support vector machine algorithm, a mapping relationship between material components, process parameters and performance is established to guide production practice and achieve balanced optimization of sludge resource utilization and cement performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The present invention is a flow chart of a method for preparing an environmentally friendly mining and ore dressing sludge cement additive.

[0012] Figure 2 It is a schematic diagram of a method for preparing an environmentally friendly mining and ore dressing sludge cement additive according to the present invention.

[0013] Figure 3 It is another schematic diagram of the preparation method of an environmentally friendly mining and ore dressing sludge cement additive of the present invention.

[0014] Figure 4 It is another schematic diagram of the preparation method of an environmentally friendly mining and ore dressing sludge cement additive of the present invention.

[0015] Figure 5 It is another schematic diagram of the preparation method of an environmentally friendly mining and ore dressing sludge cement additive of the present invention.

[0016] Figure 6 It is another schematic diagram of the preparation method of an environmentally friendly mining and ore dressing sludge cement additive of the present invention.

[0017] Figure 7 It is another schematic diagram of the preparation method of an environmentally friendly mining and ore dressing sludge cement additive of the present invention.

[0018] Figure 8 It is another schematic diagram of the preparation method of an environmentally friendly mining and ore dressing sludge cement additive of the present invention.

[0019] Fig. 9 It is another schematic diagram of the preparation method of an environmentally friendly mining and ore dressing sludge cement additive of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art 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 drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0021] like Figure 1-9 The method for preparing an environmentally friendly mining sludge cement additive in this embodiment may specifically include: Step S101, obtaining the chemical composition data of the mining sludge, using a similarity calculation method to analyze the similarity of the chemical composition of the sludge and fly ash, and determining the initial range of the sludge addition amount.

[0022] The steps are as follows: Obtain the chemical composition data of mining sludge, perform elemental analysis on the sludge sample by X-ray fluorescence spectrometer, and obtain the content percentage of various chemical elements in the sludge. Obtain the chemical composition data of fly ash, perform elemental analysis on the fly ash sample by energy dispersive X-ray fluorescence spectrometer, and determine the content percentage of various chemical elements in the fly ash. According to the chemical composition data of sludge and fly ash obtained, a similarity calculation method is used to judge the similarity between sludge and fly ash in chemical composition. If the similarity is higher than the preset threshold, it is considered that the sludge and fly ash have similar chemical properties. According to the similarity judgment result of sludge and fly ash, combined with the addition ratio of fly ash in the cement material formula, the initial addition amount range of sludge in cement material is determined. Obtain the mechanical properties test data of cement material under different sludge addition amounts, perform compressive strength and flexural strength tests on cement samples by universal material testing machine, and obtain the relationship curve between material strength and sludge addition amount. According to the test data of mechanical properties of cement materials, the regression analysis method was used to establish a quantitative relationship model between the amount of sludge added and the material strength. Through this model, the trend of mechanical properties of cement materials under different sludge additions was predicted. The microstructure data of cement materials under different sludge additions were obtained, and the micromorphology of cement samples was observed by scanning electron microscope to analyze the effect of sludge addition on the structure of cement hydration products. According to the microstructure data of cement materials and the mechanical properties prediction model, the mechanism of the influence of sludge addition on material properties was determined. If sludge addition caused significant changes in the structure of cement hydration products, it was considered to have an important influence on material properties. By comprehensively analyzing the relationship between sludge addition and cement material properties, the optimal sludge addition ratio range in cement materials was determined under the premise of ensuring that the mechanical properties of the materials met the requirements. This ratio range provides a reference for the subsequent optimization of cement material formulas.

[0023] For example, the elemental analysis of mining ore dressing sludge samples was performed by X-ray fluorescence spectrometer, and the chemical composition data of SiO2 content of 45%, Al2O3 content of 30%, Fe2O3 content of 8%, CaO content of 5% and so on in the sludge were obtained. At the same time, the fly ash samples were analyzed by energy dispersive X-ray fluorescence spectrometer, and the chemical composition of fly ash was determined to be 50% SiO2 content, 28% Al2O3 content, 10% Fe2O3 content, 4% CaO content and so on. According to the obtained chemical composition data of sludge and fly ash, the similarity between the two was calculated by Euclidean distance method, and the similarity was 0.92, which was higher than the preset threshold value of 0.85, so it was judged that sludge and fly ash had similar chemical properties. Combined with the addition ratio of fly ash in the cement material formula of 20%~30%, it was preliminarily determined that the addition amount of sludge in cement ranged from 15% to 25%. The compressive strength of cement samples with different sludge additions (0%, 10%, 20%, 30%) was tested by a universal material testing machine, and the quadratic function relationship between compressive strength and sludge addition was obtained: y=-0.15x^2+4.5x+30, where y is the compressive strength (MPa) and x is the sludge addition (%). The model was used to predict that the compressive strength of cement materials was about 38.4MPa when the sludge addition was 15%. The micromorphology of cement hydration products with different sludge additions was observed by scanning electron microscopy. It was found that when the sludge addition exceeded 20%, the structure of the hydration products in cement changed significantly, and more needle-shaped ettringite crystals appeared, indicating that the sludge addition had an important influence on the material properties. Based on the mechanical property prediction model and microstructure analysis results, it was determined that the optimal sludge addition ratio in cement was 18%~22% under the premise of ensuring that the compressive strength of the cement material was not less than 35MPa.

[0024] Step S102, according to the initial water content of the sludge, a preset threshold value of the drying temperature is set; when the drying temperature is higher than the preset threshold value, the drying equipment parameters are adjusted to avoid sintering of the particle surface; when the drying temperature is lower than the preset threshold value, the drying time is extended to reduce the water content of the sludge.

[0025] According to the initial moisture content of the sludge, the preset threshold of the drying temperature is set. If the temperature is higher than the preset threshold, the drying equipment parameters are adjusted to avoid surface sintering. If the temperature is lower than the preset threshold, the drying time is increased to reduce the moisture content. According to the process parameters of the drying equipment, the initial drying temperature and time are set to start the sludge drying process. Through the sensors inside the drying equipment, the temperature, humidity and other data in the sludge drying process are collected in real time and transmitted to the data processing module. The algorithm in the data processing module is used to analyze the collected temperature and humidity data to obtain the dynamic change trend of the sludge drying process. According to the dynamic change trend of the drying process obtained by the analysis, combined with the preset target moisture content, it is judged whether the current drying effect meets the requirements. If the drying effect does not reach the preset target value, the drying temperature and time parameters are adjusted through the control module according to the dynamic change trend of the drying process. The adjusted drying temperature and time parameters are transmitted to the drying equipment to restart the sludge drying process. The sludge drying process data is collected again through the sensor and transmitted to the data processing module for analysis to obtain the adjusted drying effect. According to the analysis results, it is determined whether the adjusted drying effect reaches the preset target value. If so, the drying process is terminated; otherwise, the parameters are continued to be adjusted. The drying temperature and time parameters are adjusted through multiple iterations to finally obtain dried sludge particles that meet the preset moisture content target value.

[0026] For example, according to the initial moisture content of the sludge of 80%, the preset threshold of the drying temperature is set to 200°C. When the actual temperature reaches 210°C, the drying equipment parameters are adjusted by reducing the hot air temperature and reducing the material residence time to avoid sintering of the particle surface; when the actual temperature is lower than 180°C, the sludge moisture content is reduced from 80% to below 60% by extending the residence time of the material in the drying chamber. The initial drying temperature is set to 190°C, the drying time is 30 minutes, and the sludge drying process is started. Through the temperature and humidity sensors arranged in the drying chamber, data is collected every 5 seconds and transmitted to the data processing module. The collected temperature and humidity data are fitted by the least squares method to obtain the functional relationship between the temperature and moisture content over time during the drying process. It is analyzed that under the condition of 190°C, the sludge moisture content drops from 80% to 65% within 30 minutes. According to the preset target moisture content of 60%, it is judged that the current drying effect does not meet the requirements. The drying temperature was raised to 195°C through the control module, the drying time was extended to 40 minutes, and the drying process was restarted. The data was collected and analyzed again, and it was found that the sludge moisture content was reduced to 58% after adjustment, meeting the target requirements, and the drying process was ended. After three iterative adjustments, it was finally determined that at 198°C and 45 minutes, dried sludge particles with a moisture content of 55% could be obtained, completing the sludge drying and reduction process.

[0027] Step S103, transporting the sludge to a drying device, obtaining dried sludge particles, and using an online monitoring system to obtain water content data of the sludge particles to determine whether it reaches a preset target value. If not, adjusting the drying temperature and time parameters based on a feedback control mechanism.

[0028] The sludge is transported to the drying equipment, the sludge drying process is started, the initial drying temperature and time parameters are set, and the temperature, humidity and other data of the sludge drying process are collected at regular intervals through the temperature and humidity sensors inside the drying equipment, and transmitted to the data processing module in real time through the wireless transmission module. The Kalman filter algorithm in the data processing module is used to denoise the collected temperature and humidity data, and the temperature and moisture content dynamic change curve of the sludge drying process is obtained by combining the least squares fitting method. According to the dynamic change trend of the drying process obtained by analysis, combined with the preset target moisture content, by comparing the difference between the current sludge moisture content and the target value, it is judged whether the current drying effect meets the requirements. If the sludge moisture content is higher than the preset target value, the drying temperature and time parameters are adjusted by the PID control algorithm according to the dynamic change trend of the drying process, and the temperature and time adjustment step is set. The adjusted drying temperature and time parameters are transmitted to the drying equipment PLC controller through industrial Ethernet to restart the sludge drying process. The sludge drying process data is collected again through the sensor and transmitted to the data processing module for analysis to obtain the adjusted sludge moisture content value. According to the analysis results, it is judged whether the adjusted drying effect is close to the target value. If it is close to the target value, the drying process is terminated. If not, the drying temperature and time parameters are adjusted iteratively. Through multiple iterations of adjusting the drying temperature and time parameters, the dried sludge particles that reach the preset moisture content target value are finally obtained, the dried sludge particles are discharged, and the final moisture content of the sludge particles is measured by the online infrared moisture meter, and the measured data is uploaded to the data processing module. The data processing module receives the measured value of the moisture content of the sludge particles, compares it with the preset target value, calculates the deviation between the two, and uses the fuzzy control algorithm according to the deviation value to generate the initial temperature and time parameters of the next batch of sludge drying process, and sends it to the drying equipment PLC controller through the industrial Ethernet as the initial parameters for the next batch of sludge treatment. During the sludge drying process, the vibration frequency data of the drying equipment is collected in real time through the vibration sensor to determine whether the vibration frequency exceeds the set threshold. If it exceeds, an alarm signal is triggered and sent to the data processing terminal through the wireless communication module, indicating that the equipment is abnormal and needs maintenance and repair. According to the sludge drying batch and equipment operation time, the data mining algorithm is used to analyze the historical data to obtain the association rules between equipment energy consumption and output. By optimizing the drying process parameters, while ensuring the sludge drying effect, the energy consumption of the equipment is reduced and the production efficiency is improved. Through data visualization technology, the various parameters and output quality data of the sludge drying process are generated into intuitive reports and graphs, uploaded to the cloud database, and the production operation status is displayed to the management personnel in real time through Web services, providing data support for optimizing production decisions.

[0029] Exemplarily, according to the process parameters of the drying equipment, the initial drying temperature is set to 80°C, the drying time is 60 minutes, and the sludge drying process is started. The temperature and humidity data of the sludge drying process are collected every 5 minutes through the temperature and humidity sensor inside the drying equipment, and transmitted to the data processing module in real time through the wireless transmission module. The Kalman filter algorithm in the data processing module is used to denoise the collected temperature and humidity data, and the temperature and moisture content dynamic change curve of the sludge drying process is obtained by combining the least squares fitting. According to the dynamic change trend of the drying process obtained by analysis, combined with the preset target moisture content of 30%, by comparing the difference between the current sludge moisture content and the target value, it is judged whether the current drying effect meets the requirements. If the sludge moisture content is higher than 35%, the drying temperature and time parameters are adjusted by the PID control algorithm according to the dynamic change trend of the drying process, and the temperature adjustment step is 5°C and the time adjustment step is 10 minutes. The adjusted drying temperature and time parameters are transmitted to the drying equipment PLC controller via industrial Ethernet to restart the sludge drying process. The sludge drying process data is collected again through the sensor and transmitted to the data processing module for analysis, and the adjusted sludge moisture content is 32%. According to the analysis results, it is judged that the adjusted drying effect is close to the target value, and the drying process is terminated. After adjusting the drying temperature and time parameters for 3 iterations, the dried sludge particles with a moisture content of 29% are finally obtained, which meets the preset target value requirements. The dried sludge particles are discharged, and the final moisture content of the sludge particles is measured to be 28.5% by an online infrared moisture meter, and the measured data is uploaded to the data processing module. The data processing module receives the measured value of the moisture content of the sludge particles, which is 28.5%, and compares it with the preset target value of 30%. The deviation between the two is calculated to be -1.5%. According to the deviation value, the fuzzy control algorithm is used to generate the initial temperature of the next batch of sludge drying process as 75°C and the initial time as 55 minutes, which are sent to the drying equipment PLC controller through the industrial Ethernet as the initial parameters for the next batch of sludge treatment. During the sludge drying process, the vibration frequency data of the drying equipment is collected in real time through the vibration sensor. If the vibration frequency is 60Hz and exceeds the set threshold of 50Hz, an alarm signal is triggered and sent to the data processing terminal through the wireless communication module, indicating that the equipment is abnormal and needs maintenance and repair. According to historical data analysis, it is concluded that under the process parameters of drying temperature 80℃ and drying time 60 minutes, the average sludge processing capacity per batch is 500kg and the equipment energy consumption is 50kWh. By optimizing the drying process parameters to 75℃ and 55 minutes, while ensuring the sludge drying effect, the average sludge processing capacity per batch is increased to 550kg, and the equipment energy consumption is reduced to 45kWh, which improves production efficiency by 10% and saves energy consumption by 10%.The various parameters and output quality data of the sludge drying process are generated into bar charts and line graphs through data visualization technology, uploaded to the cloud database, and the production operation status is displayed to managers in real time through Web services, providing data support for optimizing production decisions.

[0030] Step S104, using a vibration screening device to classify the dried sludge particles, obtaining particle mass fraction data of different particle size ranges, and determining the optimal particle size range through particle size distribution model fitting to meet cement uniformity and fluidity requirements.

[0031] Vibration screening equipment is used to classify the dried sludge particles, obtain the particle mass fraction data of different particle size ranges, and analyze the particle size distribution characteristics. According to the particle mass fraction data, the particle size distribution curve is obtained by fitting the particle size distribution model, and the optimal particle size range is determined to meet the cement uniformity and fluidity requirements. According to the initial water content of the sludge, the preset threshold of the drying temperature is set, and the threshold is compared with the temperature parameter in the oil well structure mechanical property degradation model to determine whether the drying temperature is appropriate. If the drying temperature is higher than the preset threshold, the drying equipment parameters are adjusted to reduce the temperature to avoid surface sintering, and the material performance changes are predicted according to the mechanical property degradation model. If the drying temperature is lower than the preset threshold, the drying time is increased to reduce the water content, and the resonance frequency identification method is used to analyze the effect of extending the drying time on energy accumulation. Through the optimized cement material formula, the proportion and distribution of the damping components are adjusted to improve the energy absorption capacity of the material to meet the requirements of different drying temperatures and times. The support vector machine model is used to input parameters such as sludge moisture content, drying temperature, and time to predict the mechanical properties of the material and the resonance suppression effect, and obtain the optimal combination of drying process parameters. According to the prediction results of the support vector machine model, the mapping relationship between the drying process parameters and the material properties is established to optimize the production process of cement materials and determine the optimal drying temperature and time. Through simulation tests in actual oil well environments, the performance data of cement materials under different drying conditions are obtained, the mechanical stability and resonance suppression effect of the materials are verified, and the cement material formula is finally optimized to ensure that it has long-term stability and effective resonance suppression capabilities under different drying temperatures and times.

[0032] For example, the dried sludge particles are classified by vibrating screening equipment to obtain the mass fraction data of particles with a particle size range of 0.1-0.5mm, 0.5-1mm, 1-2mm, and 2-5mm, which are 20%, 35%, 30%, and 15%, respectively. According to the mass fraction data, the Rosin-Rammler distribution model is used for fitting to obtain the particle size distribution curve, and the optimal particle size range is determined to be 0.5-2mm, accounting for 65%, to meet the uniformity and fluidity requirements of cement. The initial moisture content of the sludge is 30%, and the preset threshold of the drying temperature is set to 200°C. The threshold is compared with the temperature parameters in the mechanical properties degradation model of the oil well structure through finite element analysis to determine whether the drying temperature is appropriate. If the drying temperature is 220°C, which is higher than the preset threshold, the drying equipment parameters are adjusted to reduce the temperature to 190°C. At the same time, the compressive strength of the material is predicted to decrease by 5% according to the mechanical properties degradation model. If the drying temperature is 180℃, which is lower than the preset threshold, the drying time is increased from 2 hours to 3 hours. The fast Fourier transform is used to analyze the effect of extending the drying time on energy accumulation, and it is found that the energy accumulation increases by 10%. By optimizing the cement material formula, the proportion of damping components is increased from 5% to 8%. The Monte Carlo method is used to simulate the effect of different damping component distributions on energy absorption capacity, improve the energy absorption capacity of the material, and adapt to the requirements of different drying temperatures and times. The support vector machine model is used to input parameters such as sludge moisture content, drying temperature, and time. The material compressive strength and resonance frequency are predicted through 10-fold cross validation, and the optimal drying process parameter combination is obtained: moisture content 25%, temperature 190℃, and time 2.5 hours. According to the prediction results of the support vector machine model, the BP neural network mapping relationship between the drying process parameters and material properties is established to optimize the production process of cement materials and determine the optimal drying temperature and time. Through simulation tests in actual oil well environments, the performance data of cement materials under different drying conditions were obtained. The response surface methodology was used to analyze the mechanical stability and resonance suppression effects of the materials. The cement material formula was ultimately optimized to ensure long-term stability and effective resonance suppression capabilities under different drying temperatures and times.

[0033] Step S105, the screened sludge particles and cement clinker are transported to the mixing equipment according to the preset ratio, the image processing algorithm is used to obtain the mixture uniformity data, and the mixing effect is judged by setting a threshold. If the requirements are not met, the mixing ratio and time parameters are optimized through the feedback mechanism.

[0034] According to the preset mixing ratio of dried sludge particles and cement clinker, the automatic batching system is used for precise metering to obtain the quality data of the two raw materials. The metered sludge particles and cement clinker are transported to the high-speed mixing equipment, and the two raw materials are fully mixed by setting the mixing time and speed parameters. During the mixing process, the online image acquisition system is used to obtain the image data of the mixture in real time, and the distribution of sludge particles in the mixture is analyzed by the image processing algorithm to calculate the mixing uniformity value. The calculated mixing uniformity value is compared with the preset threshold to determine whether the mixing effect meets the requirements. If the uniformity value is lower than the threshold, the feedback control mechanism is triggered. According to the degree of deviation of the mixing uniformity from the preset threshold, the optimization algorithm is used to dynamically adjust the operating parameters of the mixing equipment, such as extending the mixing time or increasing the speed, to improve the mixing effect. After the mixing process is completed, the mixture is automatically sampled and the samples are sent to the mechanical properties testing system. Through a series of tests, the performance data of the mixture such as strength and durability are obtained. The mixture performance test data is input into the prediction system based on the mechanical properties degradation model of oil well structure under high temperature and high pressure environment, and the performance of the mixture in actual application is estimated by the support vector machine algorithm. According to the performance prediction results, the optimization algorithm is used to dynamically adjust the mixing ratio parameters, ensuring that the performance of the mixture meets the requirements while maximizing the use of dried sludge resources to obtain an optimized mixing formula. The optimized mixing ratio parameters are transmitted to the automatic batching system to update the batching parameters to achieve closed-loop control of the production process, ensure the stability and consistency of the mixture quality, and provide reliable raw material guarantee for subsequent production.

[0035] Exemplarily, the automatic batching system collects the mass data of the two raw materials in real time through a weighing sensor according to the preset mass ratio of sludge particles to cement clinker of 1:4, and automatically closes the feed valve when the mass data reaches the set value. After the batching is completed, the sludge particles and cement clinker are transported to a high-speed mixing device with a rotation speed of 1500rpm, and the mixing time is set to 5 minutes. During the mixing process, a high-definition camera with a resolution of 1280×720 is used to collect the image of the mixture at a speed of 10 frames per second, and the mixing uniformity is calculated by an image processing algorithm based on color threshold segmentation. When the uniformity reaches 95%, the mixing effect is judged to be qualified. If the uniformity is lower than the threshold, the mixing time and speed are dynamically adjusted by the fuzzy control algorithm, and the adjustment range is 10% each time until the requirements are met. After the mixing is completed, the automatic sampling device samples at a frequency of 5 samples per batch, and the samples are tested for compressive strength, flexural strength and antifreeze performance in turn to obtain the performance data of the mixture. The performance data is input into the prediction model based on the support vector machine, and the performance of the mixture in actual application is predicted by comparing with the mechanical performance degradation data of the oil well structure under high temperature and high pressure environment. According to the prediction results, a genetic algorithm was used to optimize the mixing ratio. While meeting the performance requirements of the mixture, the sludge dosage was increased by 5%. Finally, the optimized mixing formula was obtained, and the formula parameters were uploaded to the automatic batching system to conduct real-time regulation of the subsequent production process to ensure the consistency of production quality.

[0036] Step S106, using cement strength testing equipment to measure the compressive strength of cement test blocks mixed with sludge particles in different proportions, obtain strength data, and determine whether it meets the cement strength requirements. If not, adjust the sludge addition amount or optimize the drying process parameters.

[0037] According to the cement strength test equipment, the compressive strength of the cement test blocks mixed with different proportions of sludge particles is measured to obtain strength data. It is judged whether the obtained strength data meets the cement strength requirements. If not, the sludge addition amount or drying process parameters are adjusted. The specific surface area test equipment is used to measure the specific surface area of ​​the cement test blocks mixed with sludge particles to obtain specific surface area data. It is judged whether the obtained specific surface area data meets the requirements of cement fluidity and workability. If not, the particle size or drying process parameters are adjusted. The hydration heat of the cement test blocks mixed with sludge particles is measured by the cement hydration heat test equipment to obtain hydration heat data. It is judged whether the obtained hydration heat data meets the cement performance requirements. If not, the water-cement ratio or drying process parameters are adjusted. The cement setting time test equipment is used to measure the setting time of the cement test blocks mixed with sludge particles to obtain setting time data. It is judged whether the obtained setting time data meets the cement construction performance requirements. If not, the retarder dosage or drying process parameters are adjusted. Based on the above test results, the effect of adding sludge particles on cement performance was comprehensively evaluated to determine the optimal sludge addition amount and drying process parameters.

[0038] For example, the 28-day compressive strength of cement test blocks mixed with 5%, 10%, and 15% sludge particles was measured by cement strength testing equipment, and the strength data obtained were 45MPa, 42MPa, and 38MPa, respectively. It was judged that the strength did not meet the 42.5-grade cement strength requirement, and the sludge addition amount was adjusted to 8%. The specific surface area of ​​cement mixed with 8% sludge was measured by a specific surface area meter to be 350m2 / kg, which met the fluidity requirements. Through the hydration heat test, the 3-day hydration heat of cement mixed with 8% sludge was 280J / g, which met the performance requirements. The initial setting time of cement mixed with 8% sludge was measured by a Vicat instrument to be 45min, and the final setting time was 8h, which met the construction performance. Comprehensive evaluation determined that the optimal sludge addition amount was 8%, the drying temperature was 105°C, and the drying time was 2h.

[0039] Step S107, using a specific surface area testing device to measure the specific surface area of ​​the cement test block mixed with sludge particles, obtain specific surface area data, and based on the Krieger-Dougherty equation, determine whether it meets the requirements of cement fluidity and workability. If not, adjust the particle size of the sludge particles or the drying process parameters.

[0040] The specific surface area of ​​cement test blocks mixed with sludge particles of different particle sizes is measured by specific surface area testing equipment to obtain a series of specific surface area data. According to the Krieger-Dougherty equation, the theoretical fluidity and workability parameters of cement mixed with sludge particles of different particle sizes are calculated. The specific surface area data obtained by measurement are compared with the theoretical calculated value to determine whether the cement mixed with sludge particles meets the fluidity and workability requirements. If the measured specific surface area data deviates from the theoretical value, resulting in cement fluidity and workability not meeting the requirements, the particle size distribution of the sludge particles is adjusted or the drying process parameters are optimized. New cement test blocks mixed with sludge are prepared using the optimized sludge particle size and drying process, and their specific surface area data are remeasured. The sludge particle size and drying process are iteratively optimized until the sludge-mixed cement with good fluidity and workability that meets the prediction of the Krieger-Dougherty equation is obtained. The compressive strength of the cement test blocks mixed with sludge particles after optimization is measured by cement strength testing equipment to obtain strength data. Determine whether the measured cement strength meets the application requirements. If the strength is insufficient, appropriately reduce the amount of sludge added while ensuring fluidity and workability. Determine the optimal sludge addition amount and sludge particle preparation process parameters that meet the requirements of fluidity, workability and strength, and complete the optimization of the sludge cement preparation process.

[0041] For example, the specific surface area of ​​cement test blocks mixed with sludge particles of different particle sizes (such as 0.1mm, 0.5mm, 1mm, etc.) is measured by specific surface area testing equipment, and a series of data such as 0.3m² / g, 0.4m² / g, and 0.5m² / g are obtained. According to the Krieger-Dougherty equation η=η_s / (1-Φ / Φ_m )^[η]Φ_m (where η is the viscosity of the suspension, η_s is the viscosity of the dispersion medium, Φ is the particle volume fraction, [η] is the intrinsic viscosity, and Φ_m is the maximum stacking volume fraction), the theoretical fluidity and workability parameters of cement under different particle size sludge particle additions are calculated. Comparing the measured specific surface area data with the theoretical value, it is found that the measured value of 0.5m² / g under the addition of 1mm particles is significantly higher than the theoretical predicted value of 0.35m² / g, indicating that the cement fluidity and workability are not good under this ratio. Therefore, the particle size of sludge particles was adjusted to 0.8mm, the optimized drying temperature was 105℃, and the specific surface area of ​​cement test blocks was measured to be 0.38m² / g, which was basically consistent with the theoretical value. After three iterative optimizations, it was finally determined that when the particle size of sludge particles was 0.6mm and the drying temperature was 110℃, the deviation between the test value of cement specific surface area and the predicted value of Krieger-Dougherty equation was less than 5%, achieving good fluidity and workability. Further cement strength testing showed that the compressive strength of this mix at 28d could reach 45MPa, meeting the application requirements. It was finally determined that under the optimized process conditions of sludge addition of 15%, sludge particle size of 0.6mm, and drying temperature of 110℃, sludge-added cement with excellent fluidity, workability and mechanical properties could be prepared.

[0042] Step S108, based on the cement material formula and the material mechanical properties degradation model under high temperature and high pressure environment, a support vector machine algorithm is used to establish a mapping relationship between material components, process parameters and material properties. Through the optimization model, production practice is guided to obtain the optimal formula and process parameters that take into account strength, durability and resource utilization.

[0043] The steps are as follows:According to the cement material formula, the cement strength test equipment is used to measure the compressive strength of cement test blocks mixed with different proportions of sludge particles to obtain a set of strength data. The obtained strength data is compared with the cement strength requirements to determine whether the requirements are met. The high temperature and high pressure environment simulation equipment is used to obtain the aging data of cement materials mixed with sludge particles under different temperature and pressure conditions, and the aging rate and the change trend of environmental parameters are analyzed. According to the change trend of the obtained aging data, a cement material mechanical property degradation model considering parameters such as temperature, pressure, and time is established to predict the performance changes of the material in the high temperature and high pressure oil well environment. The vibration test equipment is used to identify the resonance frequency of the cement material mixed with sludge particles in the high temperature and high pressure environment, and the accumulation law of the resonance energy at this frequency is analyzed. Combined with the prediction results of the mechanical property degradation model and the law of resonance energy accumulation, the cement material formula with energy absorption capacity is designed by optimizing the proportion and distribution of the damping components in the cement material, while taking into account the strength and durability requirements of the material. According to the optimized cement material formula, the support vector machine model is used to establish the mapping relationship between material properties and production process parameters. The input parameters include material components, environmental conditions and time, and the output parameters are the mechanical properties of the material and the resonance suppression effect. Through the prediction results of the support vector machine model, the optimal molding conditions and curing time of cement materials mixed with sludge particles under high temperature and high pressure environment are determined, and the production process parameters of cement materials are generated. According to the initial water content of the sludge, the preset threshold of the drying temperature is set, and the threshold is compared with the temperature parameter in the mechanical property degradation model of the oil well structure. If the drying temperature is higher than the preset threshold, the drying equipment parameters are adjusted to reduce the temperature to avoid surface sintering, and the material performance changes are predicted according to the mechanical property degradation model. If the curing temperature is lower than the preset threshold, the drying time is increased to reduce the water content, and the resonance frequency identification method is used to analyze the effect of extending the drying time on energy accumulation. Through the optimized cement material formula, the proportion and distribution of the damping components are adjusted to improve the energy absorption capacity of the material to meet the requirements of different drying temperatures and times. The support vector machine model is used to input parameters such as sludge water content, drying temperature, and time to predict the mechanical properties and resonance suppression effects of the material. Based on the prediction results of the support vector machine model, the mapping relationship between drying process parameters and material properties is established to optimize the production process of cement materials. Simulation tests are carried out in the actual oil well environment to verify the mechanical property stability and resonance suppression effect of cement materials mixed with sludge particles. Based on the performance data obtained from the test, the cement material formula is finally optimized to ensure its long-term stability and effective resonance suppression ability in the high temperature and high pressure oil well environment. Through simulation tests in the actual oil well environment, the performance data of cement materials under different drying conditions are obtained to verify the mechanical stability and resonance suppression effect of the material.According to the simulation test data, the cement material formula is finally optimized to ensure its long-term stability and effective resonance suppression ability at different drying temperatures and times. The optimized cement material formula and drying process parameters are applied to the actual oil well environment, the material performance and sludge drying effect are monitored, and continuous improvement is made based on the feedback data. Through continuous monitoring and optimization, the mechanical properties, durability and resource utilization of cement materials are continuously improved to achieve long-term stable operation in high temperature and high pressure oil well environment.

[0044] For example, first, according to the mechanical property degradation model of oil well structure under high temperature and high pressure environment, it is determined that the compressive strength of cement materials is not less than 100MPa, the tensile strength is not less than 10MPa, the elastic modulus is not less than 30GPa, and the durability index requires that the strength loss of cement materials after immersion at 200℃ for 180 days does not exceed 20%. Then, the support vector machine model is used, with the mass percentage of cement, fly ash, silica fume, admixture and other material components, environmental temperature, humidity and time as input parameters, and the mechanical properties and damping ratio of cement materials with different proportions under different environmental conditions after 180 days are predicted through Gaussian kernel function mapping and sequence minimum optimization algorithm training. The proportion of material components is optimized by genetic algorithm, and 10 groups of candidate formulas that meet the strength and durability requirements are obtained. A sine vibration load of 2g acceleration is applied to the 40mm×40mm×160mm cement test block prepared by the candidate formula at a resonance frequency of 20Hz, and the strain data is measured by strain gauge, and the damping ratio is calculated by logarithmic attenuation method. If the damping ratio is less than 0.5, the particle swarm optimization algorithm is used to further optimize the material formula and increase the damping ratio to above 0.8. The BET method is used to measure the specific surface area of ​​cement test blocks with 5% to 20% oil well sludge particles. Based on the Eshelby equivalent inclusion model and the Krieger-Dougherty equation, the quantitative relationship between the sludge particle content and the mechanical properties of the cement matrix is ​​established. The initial water content of the sludge is set to 20%, and the drying temperature threshold is set to 150℃. The temperature field and stress field distribution of the cement material during the drying process are analyzed by finite element simulation to predict the mechanical properties of the material after drying. If the simulation temperature exceeds the threshold, the drying temperature is reduced by 20℃, the drying time is extended by 2h, and the simulation is repeated. The cement test blocks with the optimized formula are hot-pressed at 180℃ and 20MPa for 30 days to test the compressive strength, tensile strength and elastic modulus, and evaluate the durability of the material. The formula parameters and process parameters are input into the support vector machine model to predict the performance of cement materials under different production conditions, determine the optimal parameter combination, and guide production practice.

[0045] What is disclosed above is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. A person skilled in the art can understand that all or part of the processes of the above embodiments and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for preparing an environmentally friendly mining and ore dressing sludge cement additive, characterized in that: include: Obtaining mining sludge, analyzing the chemical composition of the mining sludge, and determining an initial range of sludge addition amount according to the chemical composition; According to the initial water content of the sludge, a preset threshold value of the drying temperature is set, and the drying temperature and the drying time are adjusted according to the preset threshold value; Based on the adjusted drying temperature and the drying time, the sludge is processed by a drying device to obtain dried sludge particles, and the water content of the dried sludge particles is measured; Screening the dried sludge particles to obtain particle data of different particle size ranges, and analyzing the particle data to obtain particle size distribution; Determining a target particle size range according to the particle size distribution, and controlling the particle size of the dried sludge particles based on the target particle size range; Mixing the dried sludge particles with cement clinker in a preset ratio to obtain a mixture; Detecting the uniformity of the mixture, and adjusting the preset ratio or mixing time according to the uniformity; The compressive strength and specific surface area of ​​the cement test block mixed with the dried sludge particles are tested, and the sludge addition amount, the drying temperature, the drying time and the particle size are adjusted according to the compressive strength and the specific surface area data.

2. The method for preparing an environmentally friendly mining and ore dressing sludge cement additive according to claim 1, characterized in that: The analysis of the chemical composition of the mining sludge comprises: Performing element analysis on the sludge using an X-ray fluorescence spectrometer to obtain the content of each chemical element in the sludge; Energy dispersive X-ray fluorescence spectrometer was used to analyze the fly ash and obtain the content of each chemical element in the fly ash. According to the chemical element contents of the sludge and the fly ash, the similarity of the chemical composition of the sludge and the fly ash is calculated by using the Euclidean distance method; If the similarity is higher than the set value, the initial addition amount range of the sludge in the cement material is determined according to the addition ratio of fly ash in the cement material formula; The quantitative relationship model between sludge addition amount and material strength was established by using multiple linear regression analysis method, and the average relative error of cement material strength under different sludge addition amounts was predicted. Scanning electron microscopy was used to observe the microscopic morphology of cement samples with different sludge addition amounts, and the relationship between sludge addition amount and the structural density of cement hydration products was established.

3. The method for preparing an environmentally friendly mining and ore dressing sludge cement additive according to claim 1, characterized in that: The step of setting a preset threshold of the drying temperature according to the initial water content of the sludge comprises: Comparing the preset threshold with the temperature parameter in the oil well structure mechanical performance degradation model; If the drying temperature is higher than the preset threshold, the drying equipment parameters are adjusted to reduce the temperature to avoid surface sintering, and at the same time, the material performance change is predicted according to the mechanical property degradation model; If the drying temperature is lower than the preset threshold, the drying time is increased to reduce the water content, and a resonance frequency identification method is used to analyze the effect of extending the drying time on energy accumulation; By adjusting the proportion and distribution of damping components in the cement material formula, the energy absorption capacity of the material can be improved; The support vector machine model is used to input the sludge moisture content, drying temperature, and time parameters to predict the material mechanical properties and resonance suppression effect; According to the prediction results of the support vector machine model, the mapping relationship between drying process parameters and material properties is established.

4. The method for preparing an environmentally friendly mining and ore dressing sludge cement additive according to claim 1, characterized in that: The measuring of the moisture content of the dried sludge particles comprises: According to the process parameters of the drying equipment, the initial drying temperature and time are set to start the sludge drying process; The temperature and humidity data during the sludge drying process are collected through sensors inside the drying equipment and transmitted to a data processing module; Using the algorithm in the data processing module, the collected temperature and humidity data are analyzed to obtain the dynamic change trend of the sludge drying process; According to the dynamic change trend of the drying process obtained by analysis, combined with the preset target moisture content, the current drying effect is judged; If the drying effect does not reach the preset target value, the drying temperature and time parameters are adjusted by the control module according to the dynamic change trend of the drying process; The adjusted drying temperature and time parameters are transmitted to the drying equipment to restart the sludge drying process.

5. The method for preparing an environmentally friendly mining and ore dressing sludge cement additive according to claim 1, characterized in that: Determining the target particle size range according to the particle size distribution includes: The dried sludge particles are graded using a vibrating screen to obtain mass fractions of particles in different particle size ranges, and the particle size distribution is fitted using a Rosin-Rammler distribution function; The image processing technology is used to perform morphological analysis on the sieved sludge particles, and the sphericity and surface roughness of the particles are obtained through a contour extraction algorithm; A multivariate linear regression model between morphological parameters and cement fluidity and water separation rate was established to optimize the sphericity and surface roughness of particles; According to the optimized sludge particle characteristics, the rheological properties of cement paste are predicted by using Bingham rheological model, and the plastic viscosity and yield stress of cement paste are obtained by numerical simulation; A BP neural network model between rheological parameters and cement bleeding rate and sedimentation was established to optimize the water-cement ratio and water-reducing agent dosage of cement paste.

6. The method for preparing an environmentally friendly mining and ore dressing sludge cement additive according to claim 1, characterized in that: The detecting the uniformity of the mixture comprises: The sludge particles and the cement clinker are weighed by a weighing sensor, and the raw material quality data is recorded by a control system and transmitted to a database; The sludge particles and the cement clinker are conveyed to a mixer by a conveyor, and the mixing time and speed are set; An online monitoring system based on computer vision is used to collect images of the mixture, and an image segmentation algorithm is used to extract the pixel area of ​​the sludge particles, calculate their area ratio, and obtain the mixing uniformity data; The mixing effect is judged according to the preset uniformity threshold. When the uniformity is lower than the threshold, feedback control is triggered to automatically extend the mixing time or increase the speed. A manipulator is used to grab mixture samples from different positions and send them into a material testing machine to test the compressive strength and flexural strength and obtain mechanical property data.

7. The method for preparing an environmentally friendly mining and ore dressing sludge cement additive according to claim 1, characterized in that: The adjusting the sludge addition amount, the drying temperature, the drying time and the particle size according to the compressive strength and the specific surface area data comprises: Using cement strength testing equipment, measure the compressive strength of cement test blocks mixed with different proportions of sludge particles to obtain a set of strength data; Comparing the strength data with cement strength requirements to determine whether the cement material mixed with sludge particles meets the requirements; Obtaining aging data of the cement material mixed with the sludge particles by means of a high temperature and high pressure environment simulation device, and fitting the changing trend of the aging rate and the environmental parameters by means of the Arrhenius equation; Establish a cement material mechanical property degradation model that takes into account temperature, pressure, and time parameters to predict the material's compressive strength in an oil well environment; Vibration testing equipment is used to identify the resonance frequency of the cement material mixed with the sludge particles under a high temperature and high pressure environment, and an energy integration method is used to analyze the accumulation law of the resonance energy at the frequency.

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