2269 results about "Particle-size distribution" patented technology

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 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 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 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 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 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 application discloses a kind of derived biomass hard carbon material pulverization process, belong to biomass charcoal material processing technical field, first, pulverization process includes the following steps: after drying, the coconut shell derived hard carbon block is carried out primary crushing, while spraying first modifier, obtain the particle size 1~3mm of coarse crushing modified particle;Coarse crushing modified particle is carried out secondary precision crushing under argon atmosphere, while spraying second modifier, obtain the precision crushing modified particle of D 50 Particle size is 15 μm;Precision crushing modified particle is carried out roll grinding spheroidization, while spraying third modifier, obtain the spherical modified particle of D 50 Particle size is 10 μm;Spherical modified particle is carried out flash drying after cooling, then add magnesium stearate to it, after mixing, obtain derived biomass hard carbon material.Then, the application realizes breakthrough in tap density, particle size distribution, first efficiency and other core indexes by the coordination of three-stage gradient modification and progressive pulverization process.

The invention provides an ore particle granularity detection device and method based on a flexible vibration disk, and the device comprises the flexible vibration disk which is used for vibrating an ore material at a certain frequency and amplitude, so that the ore material is uniformly distributed and is in a proper posture; the image acquisition device is used for continuously shooting the ore materials on the flexible vibration disc to obtain ore images; the image processing device is used for carrying out preprocessing, segmentation, feature extraction and particle size analysis on the ore image and outputting the ore particle size distribution condition and an accumulative distribution curve; and the result output device is used for displaying the ore image and the granularity analysis result. According to the invention, while material accumulation is prevented, real-time, accurate and efficient detection of attitude distribution of ore materials is realized, and the method has the advantages of low cost of detection equipment, easy maintenance, strong interference resistance, high accuracy and stability of detection results, and the like.

The invention discloses a rock slag multi-dimensional intelligent identification and real-time early warning method for tunnel boring machine construction. The method comprises the steps that continuous rock slag images are acquired and preprocessed; inputting the preprocessed rock slag image into the trained multi-task network, and outputting a multi-task result; wherein the task network adopts a unified encoder and multi-head decoder architecture, the multi-head decoder comprises a segmentation head, a detection head, a regression head and an anomaly scoring head, the segmentation head outputs a pixel mask, the detection head outputs a rock slag frame and category, the regression head outputs a particle size distribution parameter, and the anomaly scoring head outputs a frame level anomaly score; synchronously carrying out sliding window statistics based on output results of the regression head and the abnormal score head; and performing multi-level early warning judgment based on a sliding window statistical result. According to the method, multiple functions of rock slag instance segmentation, target detection, particle size distribution regression, flow estimation, anomaly scoring and the like can be realized, and multi-stage early warning and parameter suggestion are realized through time sequence statistics.

The invention provides a method for detecting an external vesicle marker through high-flux nano plasma exciting light immune color development, and belongs to the technical field of human extracellular vesicles. After a serum sample is subjected to centrifugal treatment, the serum sample and a CD81 capture antibody substrate are incubated, and the particle size distribution of the vesicles is monitored in real time; a zwitterionic polymer modified gold nanoparticle LAM detection probe and a polyethylene glycol modified silver nanoparticle LprG detection probe are prepared to be specifically combined with a vesicle surface antigen, a chromogenic enhancement solution is adopted to induce a plasma resonance signal, and full-hole scanning imaging is carried out; and constructing a double-layer game optimization model to cooperatively optimize the detection sensitivity and the signal stability, carrying out weighted summation on normalized signals of the particle size subgroups to obtain comprehensive detection signal intensity, comparing the comprehensive detection signal intensity with a threshold value, and outputting a final judgment result. The technical problem that quantitative accuracy is affected by signal intensity deviation caused by vesicle particle size difference in outer vesicle marker detection is solved.

The invention provides an operation regulation and control method for a pulverizing unit matched with a coal-fired boiler during thermal power peak shaving, and belongs to the technical field of deep peak shaving boilers of coal-fired power plants. The operation regulation and control method comprises the following steps: determining a first typical particle size distribution interval when the coal-fired boiler is in a normal state and a second typical particle size distribution interval when the coal-fired boiler is in a peak regulation state, and establishing a mathematical intelligent model between the coal-fired particle size and the thermal power generation system performance; the number and working parameters of flour mills are configured; and when the thermal power generation system is in a normal state or a peak regulation state, according to the calculated fire coal granularity requirement, in combination with the real-time monitoring data and the optimization result of the mathematical intelligent model, determining and starting a corresponding number of flour mills, and dynamically adjusting the working parameters of the flour mills. The number of the flour mills is increased on the basis of one original flour mill, and the running states of the flour mills are finely regulated and controlled, so that the flour mills can efficiently run under different working conditions, and the requirements of a thermal power generation system are met.

The invention provides an optimization method of a magnesium slag sensible heat recovery system, which comprises the following steps: acquiring particle size distribution data of magnesium slag particles, and judging whether the problem of non-uniform particle size distribution exists or not according to a preset particle size threshold value; aiming at the problem of non-uniform particle size distribution, magnesium slag particles are classified by adopting a clustering algorithm, and the distribution areas of large particles and small particles are determined; according to the distribution area of the large particles, the rotating speed of the cooling roller is dynamically adjusted, and the accumulation probability of the large particles in the roller is reduced; the temperature change trend of the inner wall of the cooling roller is monitored in real time, and if it is detected that the local temperature abnormally rises, it is judged that local hot spots exist; when a local hot spot is detected, a spraying cooling system in the roller is started, and intensified cooling is conducted on a local area; pressure and temperature data of high-pressure and high-temperature water are collected in real time through a pressure sensor and a temperature sensor, and whether fluctuation exists or not is judged.

The invention relates to the technical field of preparation of new materials for coal mine tunnel construction, in particular to a mining ultrahigh-strength sprayed concrete active admixture and a preparation method of the mining ultrahigh-strength sprayed concrete active admixture. The prepared UHPC has excellent jet constructability, rapid hardening performance and long-term durability, realizes nearby high-valued utilization of coal mine solid waste, solves the problems of low strength, high rebound rate and long maintenance period of a traditional roadway spraying material, can replace traditional guniting concrete to be used for rapid support construction and repair of a coal mine roadway, and has a wide application prospect. And the method has remarkable technical economic benefits and social benefits.

Tantalum powder, comprising pure tantalum powder or tantalum alloy powder, wherein the particle size distribution of the tantalum powder has the following characteristics: (1) Dv90 < 5 μm; and (2) Dv10 > 0.5 μm. The tantalum powder has a narrow particle size distribution and does not contain nanoscale ultrafine particles, and has high stability and a high utilization rate. The present invention further relates to a method for preparing tantalum powder.

The invention discloses an impedance self-adaptive high-frequency probe, a probe card and a testing method, and belongs to the technical field of semiconductor high-frequency testing. When an existing high-frequency probe is used for a crimping test, a conductive probe body of the existing high-frequency probe generates mechanical deformation due to contact pressure, so that characteristic impedance is deviated, and uneven pressure in an array causes impedance dispersion. The problem is solved by coating the outer side of the conductive needle body with a composite material layer, the composite material layer is composed of an elastic insulating matrix and metal particles dispersed in the elastic insulating matrix, the metal particles have bimodal particle size distribution, and the total integral number is 30%-40%. When the probe is pressed, the composite material layer is subjected to reversible microscopic reconstruction, the effective dielectric constant of the composite material layer is increased along with the increase of the pressure, and the generated impedance change can counteract the impedance drop caused by the deformation of the conductive probe body, so that the self-adaptive stability of the characteristic impedance of the probe under the change of the pressure is realized. The impedance dispersion of the probe array can be remarkably reduced, the consistency and precision of high-frequency testing are improved, and the process compatibility is good.

The invention provides a cooling rate optimization control method and system for continuous cooling crystallization, and the method comprises the steps: collecting crystal slurry concentration data and temperature data of each stage of crystallization tank in real time; the crystal slurry concentration data, the temperature data and the feeding flow data are input into a deep learning model, and a prediction result is obtained; based on the prediction result and crystal mush concentration data, dynamically adjusting the cooling rate of each stage of crystallizing tank and the addition amount of a co-crystallizing solvent; and feeding back an outlet crystal mush concentration signal of the last-stage crystallizing tank to the solid-liquid separation unit, and adjusting the running rotating speed of the solid-liquid separation unit in a linkage manner. According to the cooling rate optimization control method and system for continuous cooling crystallization, prospective correction of the cooling rate is carried out by using the predicted supersaturation degree, and adjustment of the additive amount of the auxiliary crystallization solvent is carried out by using the predicted crystal particle size distribution; and finally, an outlet concentration signal of the last-stage crystallizing tank is fed back to the solid-liquid separation unit in real time to adjust the separation rotating speed in a linkage manner, so that the crystal particle size deviation is reduced, and the triazole crystallization recovery rate is improved.

The invention relates to a method for preparing MgAlON ceramic with high infrared transmittance by utilizing coarse powder pressureless sintering, and belongs to the field of ceramic preparation. A method for preparing MgAlON ceramic with high infrared transmittance by utilizing pressureless sintering of coarse powder comprises the steps that pure-phase MgAlON powder and a sintering aid are subjected to ball-milling mixing, drying and granulation to obtain mixed powder of MgAlON and the sintering aid, the mixed powder is formed and subjected to pressureless sintering in the nitrogen atmosphere to obtain the MgAlON ceramic, the atomic ratio of Mg to Al to O to N in the pure-phase MgAlON powder is 1: (10-20): (15-25): (2-4), and the atomic ratio of Mg to Al to O to N in the sintering aid is 1: (10-20): (15-25): (2-4). The D50 of the mixed powder of the MgAlON and the sintering aid is 1.1-2.0 [mu] m, and the particle size distribution range is 0.2-10 [mu] m. According to the method, the difficulty of a ball-milling process is small, the risk that impurities are introduced due to particle refinement can be effectively reduced, sintering equipment is easy to obtain, the heat preservation time in the pressureless sintering process is short, the obtained ceramic is high in infrared transmittance, good in energy-saving effect, high in efficiency, low in cost and suitable for preparation of large-size and complex-shape sample pieces, and industrialization is easy to achieve.

The invention discloses a sandstone hydraulic optimal path determination method and system, and belongs to the technical field of rock seepage, and the method comprises the steps: obtaining a sandstone surface particle size distribution two-dimensional image; constructing a micro-scale two-dimensional digital pipeline network according to the sandstone surface particle size distribution two-dimensional image; boundary conditions and node and pipeline constraints are added to the micro-scale two-dimensional digital pipeline network, and a two-dimensional pore network digital rock model is obtained; according to the two-dimensional pore network digital rock model, the water pressure difference between the two ends of each pipeline is obtained through calculation; and the water pressure difference between the two ends of the pipeline serves as the weight of the pipeline, and the seepage path with the maximum path weight is selected as the optimal sandstone hydraulic path. Accurate identification of the sandstone hydraulic optimal path is realized, and the technical problem that the sandstone hydraulic optimal path cannot be accurately determined at present is solved.

The invention belongs to the technical field of preparation processes of barium metaphosphate, and particularly relates to a preparation method of monodisperse micron-sized barium metaphosphate. The preparation method comprises the following steps: mixing purified barium carbonate slurry and phosphoric acid according to a certain molar ratio, reacting to obtain barium dihydrogen phosphate slurry, adding ethanol and a particle size regulating agent, uniformly mixing, and carrying out spray drying to obtain a crude product; the particle size regulating agent comprises ammonium citrate, polyalcohol and barium metaphosphate; and calcining the crude product to obtain the product monodisperse micron-sized barium metaphosphate. According to the preparation method, a small amount of particle size regulating agent and a certain amount of ethanol are added, a crude product with uniform particle size distribution is prepared under a spray drying process condition, and then calcination is performed to obtain the micron-sized barium metaphosphate with uniform particle size distribution.

The application provides a rotating electrode atomization powder production control system and a control method, a multivariate machine learning prediction model is established between an electrode rotating speed, a plasma arc power, a protective gas flow and powder particle size and sphericity, according to target powder particle size and sphericity, the multivariate machine learning prediction model is used to calculate optimal electrode rotating speed, plasma arc power and protective gas flow, the optimal process parameters are applied to a powder production process, and a powder production system is operated, the electrode rotating speed, the arc power and the gas pressure are monitored in real time in the atomization process, and the process parameters are dynamically adjusted to compensate for fluctuations, an image acquisition unit is used to obtain images of powder particles in flight, AI vision model is used to analyze particle size distribution and sphericity in real time, and the electrode rotating speed, the plasma arc power and the protective gas flow are adjusted according to the analysis results.

The invention provides a Bayesian network granularity prediction method and system based on physical constraints. The method comprises the following steps: collecting historical process parameters and corresponding granularity distribution indexes D10, D50 and D90 of a ternary hydroxide synthesis process; preprocessing the process data to generate an input feature matrix; a Bayesian attention neural network model is constructed, probability distribution and confidence intervals of particle size distribution parameters are output through Bayesian back propagation training, and model output layers correspond to D10, D50 and D90; and embedding physical constraint terms such as range, distribution width, proportion and dynamic process and / or PBE constraint terms in the loss function, and balancing prediction precision and physical rationality and quantifying uncertainty based on the comprehensive loss function. The system comprises a data acquisition module, a data processing module, a model construction module and a prediction module. According to the method, process data, physical constraint and data driving are fused, the accuracy, real-time performance and adaptability of particle size distribution prediction are improved, and intelligent manufacturing and process optimization of the ternary hydroxide precursor are supported.

The invention belongs to the technical field of electrochemistry, and particularly provides a lanthanum-cerium double-doped sodium ferric sulfate positive electrode material and a preparation method thereof. The material is composed of a matrix and a carbon coating layer coating the matrix, the matrix has a chemical expression of Na6Fe4-x-yLaxCey (SO4) 7, x is greater than or equal to 0.01 and less than or equal to 0.1, and y is greater than or equal to 0.01 and less than or equal to 0.1. The material is prepared through spray drying and high-temperature sintering treatment in sequence, the particle morphology is regular, and the particle size distribution is uniform; the electronic conductivity and Na < + > diffusion rate of the material are remarkably improved by rare earth doping, so that the material has more excellent low-temperature cycle performance, sodium storage performance and the like.

The invention belongs to the technical field of sintering production process control, and relates to an intelligent control method and device for a sinter crushing and granulating system, and the method comprises the following steps: building a size fraction target set according to a raw material structure and production load parameters; material size distribution information is collected, and deviation is calculated; the materials are guided to a pre-screening channel and a fine screening channel, and technological parameters are adjusted according to deviation; coarse grains larger than the screening limit are removed in the pre-screening process, and return mine, separated materials, finished products and a crushing path are formed in the fine screening process; and updating the target set and adjusting the process parameters when the raw materials or loads change. According to the technical scheme, by establishing the size fraction target set based on the raw material structure and the production load parameters and combining online size fraction detection and linkage control, dynamic coordination of crushing, screening and material distribution is achieved, the size distribution is stabilized in the target interval, self-adaptive adjustment of raw material and load changes is kept, and the product quality is improved. And the production requirements of timely particle size adjustment response, coordinated crushing and screening and high particle size distribution precision are met.

The present application relates to the technical field of kitchen air conditioner detection, and particularly relates to a cooking oil fume generating device and a generating method based on multi-modal fusion, which comprises: a multi-component oil fume generation simulation module, a multi-modal data acquisition module and an AI decision engine module. The present application can precisely reproduce the oil fume components generated by five cooking methods by means of accurate simulation and reproduction, and can precisely reproduce the transient oil fume characteristics of five Chinese cooking methods, namely, frying, frying, frying, stewing and stir-frying, to provide reliable oil fume gas for oil fume purification equipment such as kitchen air conditioners, range hoods, kitchen air purifiers and the like; a multi-modal fusion mechanism is adopted, an AI model of an improved CNN convolutional neural network model is used to monitor parameters such as the temperature, humidity and particle size distribution of oil fume gas in real time, has the ability to respond to environmental disturbances in real time, can automatically adjust PID control parameters according to external environmental changes, ensures the stability of the generated oil fume gas, and improves the identification precision of small smoke particles.

A charging roller comprises a conductive shaft, an elastic body layer, and a surface layer. The surface layer contains a binder resin and conductive particles. The binder resin contains only a thermoplastic resin. The thermoplastic resin has a water contact angle of not less than 40° but less than 180°. The conductive particles contain first conductive particles and second conductive particles. The first conductive particles are metal oxide particles. A content of the first conductive particles in the surface layer is not less than 15.0 parts by mass but not more than 200.0 parts by mass with respect to 100.0 parts by mass of the binder resin. A particle size distribution of the conductive particles has at least one peak within a range of not less than 0.01 μm but not more than 1.00 μm.

The invention provides an atmospheric environment particulate matter pollution detection method and device, and the method comprises the steps: carrying out the grading analysis of the scattered light intensity at each environment detection point in an environment pollution detection region, and obtaining the particle size distribution characteristics of air particulate matters at each environment detection point in a plurality of particle size intervals; determining a spatial distribution field of the particulate matter concentration in the air based on the particle size distribution characteristics; determining a dynamic correction factor of diffusion and aggregation of particulate matters in the air according to an atmospheric physical state, and further performing dynamic correction on the spatial distribution field based on the dynamic correction factor; performing spatial reconstruction on the spatial distribution field obtained by dynamic correction on a regular grid, and determining the distribution pattern of the air particulate matter concentration in the environmental pollution detection area according to a reconstruction result; and generating a particulate matter pollution index in the environmental pollution detection area according to the distribution pattern. By adopting the scheme of the invention, the reliability of atmospheric environment particulate matter pollution detection can be improved based on particulate matter concentration space reconstruction of multi-point dynamic correction.

The invention relates to the technical field of industrial data processing and intelligent control, and discloses an iron ore concentrate grinding medium intelligent regulation and control method and system based on real-time data processing, and the system comprises a sensing layer, an edge calculation layer, a cloud intelligent layer and an execution layer; according to the method, multi-dimensional data are obtained in real time and processed through a professional data preprocessing process, complex process characteristics are condensed into three core indexes including a particle size distribution index, a chemical component index and an energy consumption process index, and fuzzy worker experience is converted into an accurate numerical index; a reinforcement learning algorithm is introduced to enable the system to autonomously learn an optimal control strategy, so that the system has advanced intelligence of prediction, learning and global optimization; millisecond-level real-time response of index grading and medium regulation and control is achieved through the edge computing unit, timeliness of control is ensured, depth computing and model training are carried out based on cloud capacity, and automation and closed loop of the whole process from data collection to instruction execution are ensured.

The invention discloses zwitterion co-doped CeO2 abrasive particles and a preparation method and application thereof.The preparation method includes the steps that a cerium source and a soluble carbonate precipitator are subjected to an aging reaction to obtain a cerium-based precursor, and the cerium-based precursor, fused salt, metal ions and a fluorinating agent are mixed and then roasted to obtain the zwitterion co-doped CeO2 abrasive particles; the synthesis method is simple, the size is controllable, the particle size distribution is uniform, and the spherical-like zwitterion co-doped CeO2 abrasive particles are obtained; the co-doped CeO2 abrasive particles have excellent photo-assisted chemical mechanical polishing performance; when the polishing solution prepared from the sphere-like co-doped CeO2 abrasive particles synthesized by the method is used for processing the surface of quartz glass, the material removal rate reaches 988.69 nm / min, and a high material removal rate is obtained due to the fact that the co-doped CeO2 abrasive particles synthesized by the two-step method shorten the forbidden bandwidth and have high chemical activity, so that the material removal rate is favorably improved.

The invention discloses a construction waste treatment feeding control method and system, and the method comprises the following steps: carrying out the real-time material characteristic collection of entering construction waste, so as to obtain the material composition data, particle size distribution data and water content data of the construction waste; a construction waste feeding initial characteristic data set is generated based on the material composition data, the particle size distribution data and the water content data and transmitted to the central control unit for comparison, and a material-process matching scheme is generated; the control module is used for sending a control instruction based on a material-process matching scheme, realizing dynamic feeding adjustment parameter control of the construction waste, acquiring operation state data of the processing module in real time, and generating a feeding-operation state data set; and feeding back the feeding-operation state data set to the central control unit for dynamic correction, and adjusting operation parameters of a feeding adjustment execution mechanism to form a corresponding closed-loop feeding control flow. The feeding control efficiency in the construction waste treatment process can be improved.