5747 results about "Particle-size distribution" patented technology

The invention discloses a screening efficiency online monitoring method, device and system based on a vibrating screen, and relates to the technical field of vibrating screen efficiency monitoring, and the screening efficiency online monitoring method comprises the following steps: collecting working parameter data of the vibrating screen in real time through a sensor arranged on the vibrating screen; the accuracy and stability of screening efficiency are improved by monitoring key parameters such as humidity, particle size distribution and vibration frequency in real time and combining a screening effect model and a dynamic correction algorithm. The system automatically adjusts equipment parameters, equipment overload or screen damage is avoided, the material passing rate is optimized, and production losses are reduced. Meanwhile, manual intervention is reduced through an automatic control mechanism, the automation level of a production line is improved, human errors are reduced, the equipment fault and shutdown time is shortened, the production efficiency is improved, and the service life of the equipment is prolonged.

The invention relates to the field of particle size distribution measurement, in particular to a particle size distribution measurement method and system based on machine learning and a light scattering method.The method comprises the steps that a to-be-measured particle sample is irradiated through a light source, and meanwhile light scattering intensity data of different scattering angles and different wavelengths are collected through a multi-angle scattered light detector; constructing a multi-modal deep learning model, and forming an integrated learning architecture in combination with a support vector machine model and a random forest model; inputting the preprocessed multi-wavelength light scattering intensity data into the trained integrated learning architecture to obtain a preliminary particle size distribution result of the to-be-detected particle sample; based on the improved Mie scattering theory, a high-precision theoretical model considering the particle shape, the refractive index, the surface roughness and the internal structure is constructed; an improved Bayesian optimization algorithm is adopted, and the initial particle size distribution result is optimized and adjusted by minimizing the error between the theoretical light scattering intensity and the actually collected light scattering intensity, so that a corrected particle size distribution result is obtained.

The invention provides a battery monomer, a secondary battery and an electric device, and belongs to the field of batteries. The battery monomer comprises a positive electrode plate, the positive electrode plate comprises a positive electrode active material layer, the positive electrode active material layer comprises a positive electrode active material, and the volume particle size distribution Dv50 of the positive electrode active material is 0.3 [mu] m-3 [mu] m; the volume particle size distribution Dv10 is 100-800 nm, the volume particle size distribution Dv99 is less than 20 [mu] m, the positive electrode active material comprises M-doped lithium iron phosphate, M comprises Al and / or at least one transition metal element except Fe, and the mass content of M in the M-doped lithium iron phosphate is 200-1500 ppm. According to the present invention, the particle size of the positive electrode active material, the type of the doping element and the doping amount are simultaneously regulated and controlled in the above range, and the parameters are matched and co-act so as to provide the good cycle performance on the basis of the high power performance of the battery monomer.

The invention relates to the technical field of process optimization, and discloses an optimization method and system for a graphite sagger compression molding process. The method comprises the following steps: determining and analyzing particle size distribution, specific surface area, porosity and viscosity-temperature characteristics of graphite powder and a binder to obtain raw material characteristic parameter data; modeling is conducted on the migration behavior of the binder and the deformation characteristic of the graphite powder in the pressing process, a material rheological model is obtained, meanwhile, a multi-point pressure sensor system is arranged on the surface of a pressing mold, and target pressure data are collected; constructing a pressure distribution feature vector and a material flow state prediction result; performing adaptive adjustment on the initial pressing parameters to obtain a segmented pressure curve and a local pressure compensation strategy; and performing multi-objective optimization on the graphite sagger pressing path to obtain an optimal pressing process parameter combination. The method is combined with material flow state prediction, accurate control over the pressing process is achieved, and the intelligent level of the graphite sagger pressing forming technology is improved.

The invention discloses an intelligent regulation and control method applied to cement grinding particle gradation, and relates to the technical field of cement grinding control, and the method comprises the following steps: S1, collecting grinding system operation parameters in real time, including mill current, powder concentrator rotating speed, feeding amount, grinding sound signals and online particle size detection data; s2, calculating core characteristic parameters based on the operation parameters, wherein the core characteristic parameters comprise an effective crushing power index, a dynamic fine powder retention rate and an in-mill material filling state index; s3, inputting the core characteristic parameters into a grading prediction model, and outputting predicted particle size distribution; s4, generating an adjustment instruction through a hierarchical control strategy based on the predicted particle size distribution; and S5, outputting the adjustment instruction to an execution mechanism. The particle size distribution is accurately predicted by collecting and analyzing the operation parameters of the grinding system in real time and combining core characteristic parameters, closed-loop intelligent regulation and control are achieved, the grinding efficiency, stability and product quality are improved, and meanwhile the control precision is optimized.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention relates to an optimized method for medicament preparation. A machine learning model of a deep neural network is adopted to perform screening and combinatorial optimization on active ingredients and auxiliary materials of the medicament, and a relation model among the ingredients, the drug effect and the stability is established; a dynamic blending module is adopted to adjust the component proportion and dosage in real time according to different medicament preparation conditions including temperature and pH value; constructing a self-adaptive reaction condition adjusting system, and monitoring reaction conditions including temperature, pressure, pH value and stirring speed in the preparation process in real time; a dynamic mixing system is introduced, and a microfluidic technology is adopted to control metering and mixing of pharmaceutical components; a medicament preparation process is decomposed into a plurality of key steps including primary reaction, purification and concentration, and each step is independently optimized; a real-time quality monitoring system is constructed, and the physical properties, including particle size distribution, solubility and purity, of the medicament are monitored in real time through a sensor and imaging equipment; and optimizing the integration platform in the whole process.

The invention discloses an intelligent preparation and coating method for lithium battery electrode slurry in the technical field of information, which comprises the following steps: acquiring particle size distribution, specific surface area and chemical composition attribute parameters of a lithium battery electrode slurry raw material, and establishing a quantitative relation model among the slurry component proportion, rheological property and stability through an autonomous learning algorithm; adjusting the component proportion by adopting a dynamic optimization algorithm to obtain a slurry formula meeting a target attribute; feature extraction and classification are performed on the constructed data set, a decision tree algorithm is adopted to train and generate optimization rules and judgment logic of slurry preparation process parameters, a preparation process knowledge base is formed, and autonomous optimization of the process parameters is realized; aiming at the coating quality problem, slurry preparation process parameters are traced and analyzed, collaborative optimization of a slurry formula, a preparation process and coating parameters is realized through a human-computer interaction interface, and the production efficiency and the yield are improved while the electrode performance is ensured.

The invention discloses a dynamic pressure control airflow crushing system for improving crushing efficiency, and relates to the technical field of crushing control, the system is characterized in that the pressure change in a crushing cavity is monitored in real time through a pressure sensor, and the real-time data of the pressure in the crushing process is obtained; according to the characteristics and crushing requirements of the materials, airflow crushing cycle analysis is carried out, and initial control parameters are obtained; in the crushing control process, crushing parameters are collected and fed back; performing difference analysis according to the monitoring control parameter and the crushing monitoring parameter by taking the crushing requirement as a target, and identifying a compensation requirement parameter; and dynamically adjusting the initial control parameters to obtain self-adaptive control parameters. The technical problems that in the prior art, due to the fact that accurate pressure and airflow parameters in a smashing cavity are difficult to obtain in real time, smashing parameters cannot be flexibly adjusted, the smashing efficiency is low, and particle size distribution of products is uneven are solved, and the technical effect of improving the smashing efficiency and the uniformity of discharged particles is achieved.

The invention belongs to the technical field of water treatment, and particularly relates to a whole-process three-stage linkage control intelligent flocculation dosing method, which comprises the following steps: collection: collecting water quality and water quantity parameters of inlet water and outlet water in real time in the water treatment process; collecting a floc image in the reaction process, analyzing the particle size distribution and the breakage rate of the floc image, and collecting a torque parameter of a stirring device at the same time; according to the invention, multiple types of sensors are deployed in the water inlet area of the flocculation tank, the reaction area and the water outlet area of the sedimentation tank, water quality, water quantity and reaction process parameters are collected in real time, and edge calculation and cloud optimization technologies are combined, so that full-flow intelligent control from agent pre-feeding, reaction process regulation and control to water outlet effect refinement is realized; the method is suitable for scenes such as municipal sewage treatment plants and industrial wastewater treatment stations requiring precise control of flocculant addition, and is especially suitable for water treatment processes with frequent water quality fluctuation and sensitive agent cost, so that the treatment efficiency can be remarkably improved, and the operation cost can be reduced.

The invention belongs to the technical field of metal powder preparation, and discloses a method for preparing silver powder through reduction of hydrazine hydrate with the adjustable particle size. The preparation method comprises the following steps that a first dispersing agent is dissolved in deionized water, a silver-ammonia solution and a hydrazine hydrate solution are sequentially added, stirring reaction is conducted, and nano-silver seed crystal sol is obtained; uniformly mixing the obtained nano-silver seed crystal sol with a hydrazine hydrate solution and a second dispersing agent to obtain a reducing base solution; adding a surface modifier into absolute ethyl alcohol, and uniformly dissolving to obtain a surface modification coating solution; preparing a silver-ammonia solution, adding the prepared silver-ammonia solution into the reducing base solution, then adding the surface modification coating solution, and stirring for reaction to obtain silver powder slurry liquid; and after solid-liquid separation, a solid phase is taken, washed and dried, and silver powder is obtained. The preparation method is simple in process, the particle size of the silver powder can be adjusted through the adding amount of the seed crystal, and the prepared silver powder particles are uniform in particle size distribution, high in dispersity and suitable for the fields of conductive silver paste, electronic packaging and the like.

The invention belongs to the technical field of powder metallurgy, and particularly relates to high-performance tungsten carbide and a preparation method thereof. A preparation method of medium-coarse tungsten carbide comprises the following steps that tungsten oxide is subjected to high-temperature reduction through a reverse hydrogen reduction method, and tungsten powder is obtained; and the tungsten powder is subjected to carbon blending, uniform mixing and high-temperature carbonization in a reducing atmosphere, a product is pre-crushed and then subjected to airflow crushing, and the tungsten carbide is obtained. According to the preparation method of the medium-coarse tungsten carbide, the particle size of the prepared tungsten carbide is 1-3 microns, the total carbon content is 6.12-6.18%, the combined carbon content is larger than or equal to 6.10%, the free carbon content is smaller than or equal to 0.06%, the oxygen content is smaller than or equal to 0.12%, and the impurity content is smaller than 0.5%; the particle size distribution is narrow, the particles are uniform, and the performance of the tungsten carbide powder is greatly improved.

The invention provides an iron tailing-based geopolymer electromagnetic wave attenuation performance optimization control method and system, and relates to the technical field of electromagnetic wave attenuation, and the method comprises the steps: 1, setting a synthesis condition parameter set of an iron tailing-based geopolymer in a simulation environment, comprising virtual matching of raw materials, particle size distribution, concentration gradient of an alkali activator and curing temperature-time coupling parameters, and a forming process is deduced through an amorphous phase topological configuration and a pore network evolution rule of aluminosilicate gel in a geopolymer microstructure; and step 2, constructing an interface reaction kinetics process based on the synthesis condition parameter set, and generating an enhanced interface digital mapping system with periodically arranged aluminum oxide and silicon oxide regular tetrahedrons through virtual iteration of mineral admixture ratio and forming process parameter boundary conditions. According to the method, a strengthened interface is constructed through simulation synthesis, a loss layer database is established, and microstructure regulation and control and multi-band electromagnetic wave efficient attenuation are achieved.

Milling systems and methods for producing materials with a particular particle size distribution are disclosed herein. In some embodiments, a system for providing materials having a particular particle size includes a mill configured to produce grinded materials, a classifier positioned to receive the grinded materials and including a first classifier outlet and a second classifier outlet, and a screen positioned to receive the grinded materials from the second classifier outlet and including a first screen outlet and a second screen outlet. The classifier can be configured to separate the grinded materials based on a first threshold particle size. The screen can be configured to separate the grinded materials based on a second threshold particle size greater than the first threshold particle size.

The invention discloses an optimized preparation method of a high-strength flame-retardant polyamide-based composite material in the technical field of information, which comprises the following steps: in the preparation process of the composite material, acquiring online dispersed state monitoring data of a modified filler by using a laser particle size analyzer and an optical microscope, including particle size distribution, agglomeration degree and dispersion uniformity of the filler; according to online dispersion state monitoring data, parameter ranges of melt flow property characterization are determined, the parameter ranges comprise the shearing rate and the temperature, particle size distribution data are used for determining the shearing rate range, small particle size distribution corresponds to the high shearing rate range, and agglomeration degree data are used for determining the temperature range; the higher degree of agglomeration corresponds to a higher temperature range to promote the dispersion of the filler, and these parameters are directly used to guide the setting of the subsequent rheological test.

The invention relates to the technical field of rocket engine thrust chamber additive manufacturing, and discloses a metal powder recovery system for rocket engine thrust chamber additive manufacturing. Comprising a powder collection module, a screening classification module, a purification treatment module, a particle size adjustment module and an intelligent control module. The powder collecting module captures residual metal powder and collects data to generate a data set; the screening and grading module performs particle size separation by using a joint algorithm; the purification treatment module is used for constructing a multi-stage purification model to remove an oxide layer and decompose organic matters; the particle size adjustment module designs a composite regulation strategy to adjust particle size distribution; and the intelligent control module establishes an optimization model to generate a parameter adjustment instruction. In addition, the system is further provided with a powder characteristic analysis module, a circulation monitoring module and an exception handling module. The system can efficiently recover the metal powder, improve the quality and reutilization value of the metal powder, reduce the cost and guarantee the quality of additive manufacturing products, and is suitable for metal powder recovery treatment in the field of additive manufacturing of rocket engine thrust chambers.

The invention discloses a viscous coarse-grained soil permeability coefficient prediction method based on a physical constraint neural network, and the method comprises the following steps: carrying out an indoor viscous coarse-grained soil seepage test, and establishing a viscous coarse-grained soil permeability coefficient formula considering porosity and grain composition characteristics, further constructing a mixed model containing a physical driving item and a neural network data driving item, forming a complete data set through a numerical simulation technology and literature investigation on the basis of a seepage test, complementarily collecting porosity, grain composition characteristics and corresponding permeability coefficient data of the viscous coarse-grained soil sample, and dividing the complete data set into a training set and a test set; according to the method, optimal hyper-parameters are dynamically searched in combination with Bayesian optimization for model training, a loss function curve and permeability coefficients of the viscous coarse-grained soil under different porosity and grading characteristics are obtained, tests show that high-precision prediction of the permeability coefficients of the viscous coarse-grained soil is achieved, and the problems that a traditional method is insufficient in physical constraint and low in prediction precision are solved.

The invention provides a multi-layer soft soil foundation settlement prediction method, and relates to the field of settlement prediction, and the method comprises the steps: obtaining soft soil parameters through arranging a multi-source monitoring device, carrying out inversion to generate multi-dimensional feature vectors containing particle size distribution, void ratio, compression modulus gradient and effective stress, and dividing structure piece elements to construct a feature set; the Euclidean distance between the fragments is calculated and clustered to generate a structural similar region, and a judgment threshold is dynamically adjusted and combined based on the change rate of the structural information entropy to construct a dynamic structural map; newly added or cracked similar areas are identified, a structure sudden change strength index is generated, and when a threshold value is exceeded, a jump prediction model is constructed according to entropy change, pore pressure gradient and deformation rate to output discontinuous settlement increment; and generating an error field by using the difference value of the actually measured settlement and the predicted value, and correcting the structural information entropy and merging the threshold value after weighted averaging according to the similar region, thereby realizing the self-adaptive correction of the structural map and iterative optimization prediction.

The invention provides a positive pole piece, a secondary battery and an electric device. The positive pole piece comprises a current collector and a positive pole film layer located on at least one side of the current collector, the positive pole film layer comprises a first region and a second region, and the particle size distribution index of an active material in the first region is smaller than that of an active material in the second region; the particle size distribution index refers to the ratio of the standard deviation of the particle size of the active material to the average particle size, and the first region refers to a region where the vertical extension distance of the positive electrode film layer from the surface of one side close to the current collector to the direction of the positive electrode film layer is within h1; the second region is a region where the positive electrode film layer vertically extends from the surface of one side close to the current collector to the positive electrode film layer by a distance of h1 to H, H is the thickness of the positive electrode film layer, and h1 is smaller than H.

The invention provides a medicine crushing method and device, and relates to the technical field of medicine processing, and the technical scheme is characterized in that a target crushing state is set, and the target crushing state comprises a target particle size distribution range and a target effective component retention rate; acquiring real-time particle size distribution information and process parameter information in the traditional Chinese medicine crushing process, wherein the process parameter information comprises temperature, humidity and pressure in a crushing cavity; calculating the deviation between the current crushing state and the target crushing state according to the real-time particle size distribution information, the process parameter information and the target crushing state; based on the deviation and a preset traditional Chinese medicine characteristic model, a crushing parameter adjustment scheme is generated, and the crushing parameter adjustment scheme comprises adjustment values of the rotating speed of a crushing main machine, the pressure in a crushing cavity and the feeding speed; and according to the crushing parameter adjustment scheme, working parameters of the crushing equipment are automatically adjusted. The medicine smashing method and device have the advantages that the smashing efficiency is improved, and the smashing quality and the effective component retention rate are guaranteed.

The invention discloses a particle size measuring device based on combination of a dynamic light scattering method and a sedimentation method and a measuring method thereof. The particle size measuring device comprises a laser, a light intensity adjusting unit, a sample pool, a thermostatic bath, a PD detector, an optical fiber head, an APD detector, a data acquisition unit, a PC and a control unit. The laser emits incident laser, the incident laser passes through the light intensity adjusting unit, penetrates through the sample cell and a sample and then enters the PD detector, and a transmission light signal is collected through the data acquisition unit and transmitted to software for calculating the particle size and concentration of settled large particles; scattered light is received by an optical fiber head with a fixed angle of 90 degrees or 160-175 degrees and is converted into a digital signal by an APD detector, and the digital signal enters a data acquisition unit and is transmitted to software for calculating the particle size of particles in a non-settling nanometer range. According to the technical scheme provided by the invention, the particle size distribution information of the wide-distribution particle dispersion system in a nano-micron range can be measured in a testing process.

The invention provides an intelligent control method and system for a lithium carbonate preparation process, and the method comprises the steps: collecting the temperature field, ion concentration, feeding flow velocity and turbulence intensity data of a reaction kettle through a sensor array, and generating a state feature vector through multivariate analysis; inputting the feature vector into a depth prediction model to output a particle size distribution probability cloud picture and an impurity trend; inputting the probability cloud picture and the impurity trend into a multi-target optimizer to generate a temperature compensation value, a feeding correction coefficient and a stirring adjustment instruction; inputting the instruction into an anti-interference execution module and converting the instruction into an equipment driving signal; actual crystal data are obtained through an online particle size analyzer, and the prediction model is updated; and outputting a closed-loop optimization instruction to the execution terminal through the digital twin platform. The product consistency and the production stability are synchronously improved through full-process closed-loop intelligent control, and reliable technical support is provided for continuous production of battery-grade lithium carbonate.

The invention provides a sample preparation and determination method of an ore sample, and belongs to the field of process mineralogy, the sample preparation method of the ore sample comprises two times of wet screening to divide the sample into a sample 11, a sample 31 and a sample 41, the sample 11 and liquid glue are uniformly mixed, the mixture is heated and vibrated to remove bubbles, the mixture is heated and cured, side cutting, secondary inlaying and re-curing are performed, and a sample 12 is obtained; uniformly mixing the sample 31 with a thermosetting inlaying material, putting the mixture into an inlaying machine, and heating, pressurizing and curing to obtain a sample 32; uniformly mixing the sample 41 with resin particles, adding 3-5 mL of liquid glue at a low temperature, uniformly stirring and mixing, vacuumizing to remove bubbles, curing, adding the liquid glue again until the total volume is 10-15 mL, and curing to obtain a sample 42; and grinding, polishing and carbon spraying treatment are carried out on different samples respectively. Different sample preparation methods are selected according to different particle size distributions of the sample 11, the sample 31 and the sample 41, so that the samples with different particle size distributions are prepared more uniformly and have higher representativeness.

A sulfide solid electrolyte, which is able to adjust the morphology unavailable traditionally, or is readily adjusted so as to have a desired morphology, the sulfide solid electrolyte having a volume-based average particle diameter measured by laser diffraction particle size distribution measurement of 3 μm or more and a specific surface area measured by the BET method of 20 m2 / g or more; and a method of treating a sulfide solid electrolyte including the sulfide solid electrolyte being subjected to at least one mechanical treatment selected from disintegration and granulation.

The invention relates to the technical field of electrochemistry, and particularly discloses a positive pole piece, an electrochemical device comprising the positive pole piece and an electric device. The positive pole piece comprises a first active material with a particle size of 4-20 [mu] m and a second active material with a particle size of 200-600 nm, the first active material comprises lithium manganese iron phosphate with the manganese content of m%, the second active material comprises lithium manganese iron phosphate and / or lithium iron phosphate with the manganese content of n%, and m and n meet the condition that m-n is larger than or equal to 15 and smaller than or equal to 85. According to the invention, by controlling the particle size distribution in the positive electrode active material and the manganese content relationship, the electrochemical device containing the positive electrode plate has excellent fast charging performance and energy density.

The invention discloses an intelligent fluidized bed jet mill, and relates to the technical field of material crushing and grading equipment. The equipment comprises a hardware part and a control module, wherein the hardware part consists of a crushing area, a feeding area and a grading area. Low-temperature and high-pressure nozzles uniformly distributed on an annular gas distribution disc are adopted in the crushing area; the grading area is provided with a flow dividing cylinder and a conical flow guide plate for separating ascending airflow and falling coarse particles; the control module collects particle size data of powder in real time, predicts future particle size distribution in combination with an LSTM network, dynamically optimizes the rotating speed of a grading impeller through a particle swarm algorithm, and achieves cooperative control over smashing efficiency, particle size precision and energy consumption with the effective output per unit energy consumption as the optimization target. Through low-temperature embrittlement, an anti-pollution structure and an intelligent algorithm, the problems that traditional equipment is high in energy consumption, extensive in particle size control and prone to contaminating materials are solved, and the device is suitable for fine smashing of high-purity or high-hardness materials.

The invention discloses a landslide early warning method and system, and the method comprises the steps: carrying out the field testing of a sampling point through a multi-parameter soil rapid detection device, obtaining the index data of the water content, density, particle size distribution and the like of the soil, uploading the data to a cloud server in real time through a wireless transmission module, and achieving the comprehensive evaluation of the soil indexes; according to the soil evolution prediction result, combined with a preset landslide risk threshold value, the landslide risk grade is dynamically evaluated, if the predicted landslide possibility exceeds the threshold value, an early warning signal is generated, and the early warning signal is pushed to related departments and personnel in real time through a wireless transmission module; according to different geological conditions and landslide types, a plurality of soil property-environment factor coupling analysis sub-models are pre-established, and according to the geological features of the current sampling point, the optimal sub-model is dynamically selected to perform landslide risk pre-judgment, so that the applicability and accuracy of early warning are improved.

The invention relates to the technical field of chemical raw material processing, and discloses a petroleum coke particle size grading and batching system which comprises a raw material pretreatment module, a multi-stage dynamic screening module, a particle size real-time monitoring module, an intelligent batching decision module and a closed-loop control execution module. The classification definiteness of materials with different particle sizes is ensured; meanwhile, through a particle size monitoring feedback mechanism, the particle size distribution abnormal state in the screening process is recognized in real time, the material purity within the target particle size range is guaranteed, the particle size grading accuracy is improved, a closed-loop matching decision module is arranged, particle matching weight parameters are dynamically optimized in combination with the electrolytic aluminum process requirement, and the particle size grading accuracy is improved. The deviation degree between the ingredient proportion and the target performance can be judged in real time; the speed ratio of the conveying belt and the opening degree of the stock bin are automatically corrected through the executing mechanism, it is guaranteed that the mixing proportion of coarse particles, medium particles and fine particles is always in the optimal interval matched with electrolytic aluminum anode production, and the quality stability of anode carbon blocks is improved.

A positive electrode sheet, a secondary battery (4), and an electric device (5). The positive pole piece comprises a positive current collector; the bottom coating is positioned on at least one side of the positive current collector and comprises a conductive agent and a binder; the positive electrode active material layer is positioned on one side, far away from the positive electrode current collector, of the bottom coating layer and comprises a positive electrode active material; the thickness of the bottom coating layer is marked as L, and the thickness of the bottom coating layer meets the formula (I); wherein Dv50 is the corresponding particle size when the cumulative particle size distribution number of particles in the volume distribution of the positive electrode active material reaches 50%, and the unit is [mu] m; pD represents the compaction density of the positive pole piece, and the unit is g / cm < 3 >. # imgabs0 #

The invention provides a cement particle grading optimization method and system for minimizing porosity, and relates to the technical field of cement material science and engineering, and the method comprises the following steps: obtaining cement particles with different particle size distributions; taking the cement particles as spheres, and generating a sphere set according to the cement particles with different particle size distribution; in the sphere set of each particle size distribution, sampling according to a proportion, and constructing a three-dimensional simulation microstructure model by using the sampled spheres; simulating a sphere accumulation process in a three-dimensional box by using a three-dimensional simulation microstructure model, calculating the porosity in the three-dimensional box in the simulation process, establishing a loss function according to the overlapping volume between spheres and the collision volume between the spheres and the boundary, and obtaining the porosity of cement proportions with different particle size distributions by using a gradient descent method; and adjusting the sampling proportion of each particle size distribution by adopting an optimization algorithm so as to minimize a loss function and realize the grain composition of the cement powder with the minimum porosity and different distribution so as to optimize the strength of the cement.