3611 results about "Particle-size distribution" patented technology

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 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 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.

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 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 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.

The invention discloses an intelligent regulation and control method for a power battery positive electrode binder production process, and the method comprises the steps: synchronously collecting multi-dimensional process parameters such as temperature, viscosity and the like and performance indexes such as particle size distribution, bonding strength and the like, carrying out the normalization, denoising and time sequence alignment processing, and fusing hydrodynamic simulation and historical data to construct an initial parameter coupling model; identifying a parameter influence weight through an attention mechanism neural network, generating a decoupling factor matrix to reconstruct a parameter space, and establishing a virtual control channel; executing constrained gradient descent multi-objective optimization in the channel, generating a regulation and control instruction, and reversely mapping the regulation and control instruction into an equipment executable parameter; according to the method, performance indexes and optimization targets after regulation and control are continuously compared, model parameters are updated, retraining is triggered when deviation exceeds a threshold value, regulation and control precision and process stability are guaranteed, and the real-time performance of parameter regulation and control, the collaborative optimization capability and the quality consistency of the production process are remarkably improved.

The invention discloses a flocculation treatment device and method for removing high turbidity of a water sample. The method comprises the following steps: monitoring an inflow turbidity parameter in real time, continuously collecting original optical characteristic data of water through an optical sensor array, and calculating a turbidity fluctuation rate parameter after noise reduction treatment; carrying out graded rotational flow mixing treatment, forming a pressure gradient through three stages of tapered rotational flow pipes in sequence, activating an ultrasonic frequency field, and synchronously adding main and auxiliary flocculants; the method comprises the following steps: introducing a water sample into a multi-stage gradient flocculation reaction zone, and sequentially executing turbulent flow enhanced flocculation, slow stirring flocculation and pulsed electric field assisted flocculation to form a floc structure with different particle size distribution; running parameters of a sedimentation module are adjusted according to real-time effluent turbidity feedback, and the inclination angle of an inclined plate assembly and the opening degree of a mud discharging valve are dynamically adjusted; a parameter set is updated through a prediction model based on periodically collected system operation data, a closed-loop optimization control strategy is formed, the adaptability to extreme turbidity is enhanced, the solid-liquid separation efficiency is improved, the overall energy consumption is reduced, and the efficient and stable requirements of emergency treatment are met.

The invention provides a method for recovering a BHET monomer from a waste polyester textile alcoholysis solution, and is characterized in that the recovery method comprises the following steps: (1) filtering: mixing the waste polyester textile alcoholysis solution with water under a stirring condition, and filtering at 55-70 DEG C; and (2) cooling crystallization: under a stirring condition, carrying out primary cooling on the filtrate obtained by filtering in the step (1) from 55-70 DEG C to 45-55 DEG C, then adding a BHET seed crystal, then carrying out secondary cooling to 5-15 DEG C, and maintaining the temperature for 20-60 min to obtain the BHET monomer. The mixed solution cooling crystallization method is developed on the basis of complexity of components of the waste polyester textile fabric, the problems that in the conventional production process, a BHET crystal product is low in quality, prone to scaling, difficult to filter and dry and the like are solved, and the obtained BHET product is large in particle size, narrow in particle size distribution, high in purity, good in fluidity and high in stability.

The invention relates to the technical field of high-performance structural ceramic materials and corrosion resistance optimization thereof, in particular to a preparation method of high-performance silicon nitride ceramic, which realizes performance improvement through dynamic optimization of a composite sintering aid, closed-loop regulation and control of air pressure sintering and multi-process data linkage. In the raw material pretreatment stage, a laser particle size analyzer is adopted for monitoring particle size distribution in real time, surface modification parameters are adjusted in a linkage mode, and a uniform monomolecular coating layer is formed; the proportion of rare earth oxide and aluminum oxide in the composite sintering aid is dynamically adjusted according to the fluctuation of the mixing torque, and agglomeration and segregation of the aid are inhibited. And in the air pressure sintering stage, the argon pressure and the heating rate are dynamically adjusted based on the slope of a shrinkage curve, and directional arrangement of beta-phase grains is promoted by combining low-temperature nitrogen protection and high-temperature argon densification. And multi-dimensional process control synergistically optimizes grain boundary components, structure and density, so that the corrosion resistance and the service life of the silicon nitride ceramic in a sulfur-containing environment are remarkably improved and the service life of the silicon nitride ceramic in the sulfur-containing environment is remarkably prolonged.

The invention discloses a big data-based farmland soil quality intelligent evaluation method and system, and the method comprises the steps: collecting farmland data, carrying out the preprocessing, obtaining soil data and remote sensing data, carrying out the cooperative Kriging inversion through the remote sensing data and the soil data, obtaining surface topographic data, and carrying out the calculation of the surface topographic data. The method comprises the following steps: constructing a three-dimensional partitioning model through a hierarchical Bayesian interpolation method based on a partitioning model according to surface topographic data, combining the first and second three-dimensional partitioning models by using B-spline modeling to obtain a three-dimensional model, carrying out particle size distribution fractal to obtain fractal features, and calculating a soil quality index by using a random forest based on the fractal features and soil data. And inputting the fractal features, the soil data and the soil quality indexes into a fractal neural network for training to obtain a soil quality intelligent evaluation model. According to the method, fractal feature calculation and model training are utilized, and the accuracy of farmland soil quality evaluation and the model generalization ability are improved.

The invention provides a carbon powder particle distribution real-time analysis method based on image processing, and the method comprises the steps: applying an adjusted gray threshold parameter to the processing and analysis of a carbon powder particle image, and carrying out the edge extraction, geometric feature extraction and particle size distribution analysis of carbon powder particles again, the adjusted analysis result is used as a basis for regulating and controlling a carbon fiber recovery process; establishing a quantitative relationship between the particle size distribution of the carbon powder particles and the centrifugal rotation rate of the solution by adopting a mathematical statistical method according to a particle size distribution analysis result, and analyzing the influence of the centrifugal rotation rate on particle aggregation and particle size distribution; the carbon powder particle distribution in the carbon fiber centrifugation process is analyzed and monitored in real time, the centrifugation process parameters are dynamically regulated and controlled according to the real-time analysis result, and the online monitored data are used for optimizing the carbon fiber recycling process.

The invention discloses a mine semi-autogenous grinding process analog simulation method and system based on discrete elements, and relates to the field of analog simulation, and the method comprises the steps: according to a crushing characteristic label and a parameter mapping matrix, fusing Internet of Things sensor data, constructing a particle size distribution prediction model and an energy consumption optimization scheduling strategy, and obtaining an energy-saving operation plan; comparing the simulation data set with field production data, adjusting multi-phase coupling particle model parameters, generating a calibration parameter set, researching the influence law of semi-autogenous mill operation parameters by adopting a control variable method based on the calibration parameter set, obtaining a performance index database and an analysis report, and fusing multi-sensor data and laser radar data to obtain a semi-autogenous mill operation parameter analysis result. An ore grinding process sensing mechanism and optimization control logic are constructed, and a visual interface is generated; through multiphase modeling and data driving optimization, the simulation precision and the process suitability are comprehensively improved, and an efficient and economical solution is provided for the mine semi-autogenous grinding process.

The invention relates to the technical field of sewage detection, provides an intelligent detection method for suspended particles in coking sewage based on image deep learning, and aims to solve the problems of detection lag and low precision in the prior art. According to the technical scheme, the method comprises the following steps: synchronously acquiring sound wave and image data through a high-frequency ultrasonic transducer and an industrial camera, and extracting acoustic parameters and optical characteristic parameters; using a dual-channel convolutional neural network to fuse features, and combining a cross-modal attention mechanism to perform dynamic weighting; and outputting a particle concentration classification result and a particle size distribution prediction value through a multi-task learning model, and performing online learning to compensate environmental interference factors. The device is mainly used for realizing real-time and high-precision suspended particle monitoring and automatic process adjustment of coking sewage treatment.

The invention discloses a surface wear volume time-space distribution prediction method combining abrasive particle and vibration monitoring, which comprises the following steps: constructing an abrasive particle size distribution driven Markov process, representing a random wear process, and realizing dynamic and probability representation of real-time wear volume time evolution; a physically constrained double-branch mapping model is constructed, fuzzy mapping of the surface wear volume and the vibration time-frequency characteristics is established, and precise observation of wear volume space distribution is achieved; the two physical mechanisms are fused on the basis of a state space modeling framework, a digital twinborn model combining abrasive particle monitoring and vibration monitoring is constructed, and accurate prediction of the abrasion volume of the friction pair part in the time dimension and the space dimension is achieved. According to the method, the wear volume space-time distribution is predicted by fusing the wear particle information and the physical correlation between the vibration characteristics and the wear process, and the precise monitoring of the wear process is realized.

The invention relates to the technical field of water treatment agent preparation and environmental governance material processing, in particular to polymeric ferric sulfate and a preparation method thereof.The preparation method comprises the steps that steam condensation recycled water of a thermal power plant of an aluminum factory is mixed with ferrous sulfate and concentrated sulfuric acid to form a reaction solution, oxygen is introduced for an oxidative polymerization reaction, and a liquid intermediate is generated; gradient cooling is carried out on the liquid intermediate to control the molar ratio of ferric ions to hydroxyl, and a porous solid precursor is formed through concentration and closed aging; crushing the precursor, performing graded drying by adopting a rotary kiln and flash drying, and synchronously spraying a hydrophobic modifier to reduce the surface hydroxyl activity; the powdery product is secondarily dried in a nitrogen inert environment, and moisture absorption and agglomeration are inhibited. The invention solves the problems of mixed crystal forms, delayed release of effective components and secondary pollution in the traditional process. The obtained product is uniform in crystal form, concentrated in particle size distribution, remarkably improved in dissolution rate and storage stability, and suitable for black and odorous water body treatment and industrial sewage treatment.

The invention discloses a metal mine crushing and grinding process simulation and prediction method and system, and relates to the technical field of grinding control, and the method comprises the steps: collecting whole-size distribution data, ore component data and ore grade, transmitting the data to an edge calculation node for data cleaning, extracting key feature parameters, and generating an ore genetic map vector; the actual whole-size distribution data and the equipment energy consumption data are compared with operation parameters, and when any data exceeds a preset prediction threshold value, a time delay cause and effect graph is constructed, fault equipment is positioned, and an equipment health degree report is output; and according to the equipment health degree report, generating an adversarial sample by adopting a generative adversarial network, injecting the adversarial sample into the digital twinborn model, reconstructing grinding process parameters to complete self-calibration of the digital twinborn model, and updating a pre-stored typical ore sample. The working condition adaptability of the grinding process is improved through a dynamic gene matching mechanism.

The invention relates to the technical field of sewage treatment optimization, in particular to an intelligent sewage plant full-process management and control method based on digital twinning, which comprises the following steps: acquiring concentration data to calculate rate clustering fluctuation, setting a threshold value mark stable section to analyze sedimentation association, constructing dynamic threshold value output control information, and adjusting stirring to generate a sludge discharge instruction set. According to the method, through the time sequence clustering of the concentration change rate, the stable section in the sludge sedimentation process is identified, the concentration fluctuation identification precision is improved, the sedimentation hysteresis is judged based on the time difference between the sedimentation track and the standard curve, the particle layering identification capability is improved, and the dynamic threshold matrix is constructed by using the correlation degree and the particle size data; the concentration range, the settling velocity deviation and the particle size distribution dispersion are fused, the multidimensional property of a regulation and control basis is enhanced, the interface position movement trend is combined, the concentration range is matched, a stirring intervention instruction is output, real-time regulation and accurate control of the layered settling process are achieved, and the treatment stability and the reaction time efficiency are improved.

The invention discloses a power distribution station multi-point environment control system and method, and relates to the technical field of power system automation, and the method comprises the following steps: building a time-frequency phase-locked observation baseline based on the scattering relation between an infrared band and water vapor under the condition of sudden humidity increase, inverting aerosol particle size distribution and concentration distribution, and generating a multi-dimensional scattering phase residual field; according to the multi-dimensional scattering phase residual field, a real radiation field is reconstructed by adopting multi-path reverse ray variation solution and combining polarization dual-angle scanning, and a reflection saturation risk spectrum is generated. According to the method, a real radiation field is reconstructed through multi-dimensional observation and inversion, a consistency graph is generated by fusing multi-source data, a misjudgment boundary is accurately identified, a control margin and a switching condition are constructed based on residual playback and time correction, and a steady-state closed-loop control chain is formed in combination with a dynamic regulation and control mechanism. The self-adaptive precise regulation and control of the power distribution station environment and the stability guarantee of the thermal field are realized.

The invention discloses a method for efficiently separating and purifying plant exosomes and application, and belongs to the technical field of biological nanotechnology. The method comprises the following key steps: (1) performing sterile cleaning on plant raw materials, and then performing peeling or tissue slitting pretreatment; (2) carrying out mechanical crushing treatment by adopting a phosphate buffer solution (PBS), and filtering through a 200-mesh screen to obtain a primary extracting solution; (3) performing gradient centrifugal purification on the extracting solution, collecting supernate, and filtering the supernate through a 0.45 mu m microporous filter membrane; (4) adding 8-12% of polyethylene glycol for low-temperature phase separation and precipitation; (5) purifying by using an electrodialysis technology; and (6) carrying out final concentration by adopting a 100kDa ultrafiltration membrane to obtain the high-purity PELNs preparation. According to the method, the process, equipment and reagents are simple and easy to obtain, the final yield is high, the purity is high, particle size distribution is uniform, and an innovative solution is provided for large-scale industrial production of natural-source nano-drug carriers.

The invention relates to the technical field of mineral processing data processing, in particular to a preparation method of a high-calcium fluorite and barite paragenic ore combined inhibitor, which comprises the following steps: collecting multi-source process mineralogy data output by an X-ray fluorescence spectrometer, an automatic mineral analyzer and the like, converting the multi-source process mineralogy data into a structured format, and constructing a process mineralogy database; based on historical data of a database, a random forest algorithm is adopted to train a correlation model, the correlation rule of mineral characteristics such as calcite content, fluorite particle size distribution and barite monomer dissociation degree and the inhibitor ratio is learned, and parameters of the optimization model are fed back through a flotation test. The combined inhibitor solves the problem of selective separation caused by similar surface properties through a synergistic effect of covering active sites of fluorite calcium by modified water glass, regulating active sites of barite barium by citric acid and assisting in enhancing inhibition by modified starch, and solves the problem of low efficiency of multi-source data integration through structured data processing and model closed-loop optimization; the flotation separation efficiency and the concentrate grade are improved.

The invention provides a rotating electrode atomization powder preparation control system and a control method. A multivariable machine learning prediction model among the electrode rotating speed, the plasma arc power, the protective gas flow, the powder particle size and the sphericity degree is established; calculating the optimal electrode rotating speed, plasma arc power and protective gas flow by using the multivariable machine learning prediction model according to the target powder particle size and sphericity; the optimal technological parameters are applied to the powder making process, and a powder making system is operated; the electrode rotating speed, the electric arc power and the gas pressure are monitored in real time in the atomization process, and technological parameters are dynamically adjusted to compensate fluctuation; the image acquisition unit is used for acquiring images of flying powder particles, particle size distribution and sphericity are analyzed in real time through an AI visual model, and the electrode rotating speed, plasma arc power and protective gas flow are fed back and adjusted according to analysis results.

The invention discloses a transformer fault diagnosis method, system and equipment and a medium, and the method comprises the steps: through the linkage design of an oil pumping sampling module and a transparent sampling channel, combining high-speed camera shooting collection and synchronous light source illumination, and obtaining the image of particles in a transformer oil sample in real time; secondary pollution and personal errors possibly caused by traditional off-line sampling are avoided, and the detection efficiency and the objectivity of data are improved; the ResNet neural network is introduced to carry out feature extraction on the oil sample image, the particle number and particle size distribution information are automatically identified, the complex process of manually designing features in a traditional image processing method is avoided, and the accuracy and robustness of diagnosis are greatly improved; by setting judgment threshold values for indexes such as the particle number, the average particle size and the large particle size proportion, logic judgment is conducted in combination with the actual operation state, potential abrasion, pollution and other abnormalities can be recognized in the early stage, the capacity of quick response and advanced intervention is achieved, and the operation safety and the intelligent operation and maintenance level of the transformer are remarkably improved.

The invention discloses a high-oxygen-storage zirconium solid solution material and a preparation method, and belongs to the technical field of rare earth catalytic materials, and the method comprises the following steps: precursor preparation, ultrasonic dispersion, double-gradient precipitation, ultrasonic aging, solid-liquid separation, three-section roasting and furnace cooling. Through the synergistic effect of double-gradient precipitation and ultrasonic aging, coarse grains and non-uniform particle size distribution are avoided, and the material is not prone to abnormal growth of the grains and structural collapse in long-term high-temperature service. Meanwhile, according to the gradient roasting process, through step-by-step regulation and control of crystal densification and crystal lattice reconstruction, separation of a fluorite phase and a pyrochlore phase easily occurring in traditional low-temperature single-section roasting is reduced, a stable crystal phase structure is maintained, it is ensured that the material can still keep sufficient active surface and pore channel structures under the working conditions of high temperature and thermal shock alternation, and the performance of the material is improved. The oxygen storage capacity and the long-term durability of the catalyst carrier are obviously improved.

The invention belongs to a food and beverage sterile filling technology, and discloses a sterile nitrogen filling pressure-flow cooperative control system for particle-containing high-viscosity orange juice. Comprising a multi-source signal processing module used for synchronously obtaining a pressure numerical sequence, a temperature numerical sequence and a voiceprint spectrum vector of orange juice in a pipeline; the rheological state identification module is used for determining a particle density index and a particle size distribution probability of the orange juice flowing through the valve port area at present, and inverting an apparent viscosity value and a thixotropic state parameter of the orange juice so as to form a comprehensive state signature; the prediction collaborative decision module is used for generating a control target instruction set according to the maximum shear stress and further generating a pressure setting curve instruction and a displacement travel curve instruction; and the execution closed-loop control module is used for responding to the pressure setting curve instruction to adjust the pressure of the sterile nitrogen and driving the liquid filling valve to execute opening operation based on the displacement stroke curve instruction, so that dual-objective optimization of high-efficiency circulation and low-damage filling of orange juice is realized.

The invention belongs to the technical field of motor casing lost foam casting, and particularly relates to a special coating for a motor casing lost foam as well as a preparation method and application thereof. Through a quantitative formula design method, an aggregate proportion calculation formula based on thermal expansion coefficient matching and a grain size distribution correlation formula based on target air permeability, cooperative compounding of quartz powder and bauxite is achieved, and key functional components such as mullite, organic matter fibers and crystalline flake graphite are combined; the unification of high air permeability and excellent crack resistance of the motor shell lost foam coating is realized. The invention further provides a two-time coating gradient drying efficient process matched with the coating, and on the premise that the coating performance is guaranteed, the production efficiency is remarkably improved, and the energy consumption is reduced.

The invention discloses a lithium ion battery mixed slurry viscosity detection method based on lithium iron phosphate. The method comprises the steps of slurry pretreatment and parameter initialization, multi-modal data acquisition, dynamic viscosity compensation model construction, rheological property prediction, abnormality diagnosis and online feedback control. The temperature, the shearing rate, the solid content and the rheological curve of the slurry are acquired in real time through a multi-sensor fusion technology, a dynamic viscosity compensation model is constructed in combination with the characteristics of the lithium iron phosphate material, and a rheological prediction algorithm based on machine learning is introduced, so that high-precision and nondestructive online detection is realized. The influence of particle size distribution and temperature drift is eliminated through material characteristic modeling, and the measurement precision is further improved; and an ultrasonic wave and near infrared combined detection mode is adopted, so that the slurry structure is prevented from being damaged by a traditional sampling method, viscosity abnormity self-diagnosis and process parameter real-time feedback are realized, and the rejection rate is reduced.