55 results about "Physical chemical" patented technology

The invention discloses a machine learning guidance laser forming ultrahigh strength and toughness steel design method based on fusion of physical and chemical characteristics, which combines the physical and chemical characteristics with Shapley additive interpretation and analysis, and overcomes the limitation that a traditional pure data driving model lacks theoretical support. The quantitative analysis of the influence between the input characteristics and the output performance is realized, the internal association between the physical and chemical characteristics and the material strengthening and toughening theoretical mechanism is disclosed, the contribution degree and the interaction effect of each alloy element and the heat treatment process parameters are determined, the theoretical basis of variable screening is provided for multi-objective optimization, and the optimization efficiency is improved. Therefore, the efficiency of obtaining the non-dominated solution set is remarkably improved. According to the method, the physical and chemical characteristic combination can be conveniently and rapidly determined, the relation between the input characteristic and the output performance can be quantified, and optimal design of the laser forming ultrahigh strength and toughness steel alloy components and the heat treatment process is achieved.

The embodiment of the invention provides a catalyst activity analysis method and system based on material surface physical and chemical indexes, and relates to the technical field of water treatment. According to the method, a sample data set can be obtained firstly, after the sample data set is divided into a training set and a test set, the training set is used for training a plurality of alternative models, and then a prediction model is selected from the alternative models. And inputting data of a catalyst to be detected into the prediction model to obtain a predicted degradation rate output by the prediction model. Wherein the sample data set comprises surface physical property parameters such as pore depth gray difference of the catalyst and surface chemical property parameters such as functional group peak height ratio. According to the method, alternative model training can be carried out through Bayesian optimization, a prediction model is selected by combining evaluation indexes obtained by calculation of a test set, and a machine learning process can be enhanced by utilizing multi-mode fusion data in an environment of small sample machine learning data volume, so that the prediction precision and generalization ability of catalyst activity analysis are improved.

The invention discloses a material corrosion-oriented surface microenvironment multi-parameter sensing method and system, which realize real-time monitoring of a plurality of key physical and chemical parameters by arranging a micro sensing probe array on the surface of a material. And constructing a space-time parameter matrix by using the obtained data, calculating the time change rate of each parameter, and setting a disturbance threshold value to mark an abnormal point. A disturbance area is identified through spatial clustering of abnormal points, corrosion activity indexes are defined in combination with temperature, relative humidity, electrochemical driving force, local acidification degree and chloride ion concentration, and a continuous corrosion activity distribution diagram is generated. And tracking a spatial overlapping relation of the disturbance area to form an evolution chain, and predicting a future expansion trend. And finally generating a comprehensive report containing a corrosion activity space-time overview map, a key event timeline and a regional risk grading list. The accuracy and efficiency of corrosion monitoring are improved, and a scientific basis is provided for preventive maintenance.

The invention relates to the technical field of environment monitoring, in particular to a physical and chemical factor coupled nearshore denitrification process influence factor analysis method, which comprises the following steps: S1, collecting a nearshore environment sample, and obtaining a physical and chemical factor data set; s2, on the basis of the physical and chemical factor data set, calculating the coupling strength between the physical and chemical factors, and generating a coupling strength matrix; s3, performing dimension reduction processing on the coupling strength matrix by adopting a principal component analysis method, and determining a key influence factor list; s4, analyzing the contribution degree of each dominant factor to the denitrification rate based on the key influence factor list, and generating a contribution degree analysis result; s5, according to the contribution degree analysis result, an influence factor analysis report of the nearshore denitrification process is output, and the analysis report comprises key influence factor sorting and optimization suggestions. The method provides intelligent and data-driven auxiliary decision support for coastal ecological restoration and water quality improvement, and has good popularization and application prospects.

The application provides an amorphous alloy magnetocaloric performance prediction method and system based on interpretable machine learning, relates to the technical field of material science and engineering, and comprises the following steps: step 1, constructing an amorphous alloy magnetocaloric performance data set, wherein the data set comprises alloy composition data, physical and chemical descriptors, external conditions and structural characteristics of a plurality of amorphous alloy samples and corresponding experimental values of maximum magnetic entropy change and Curie temperature; step 2, screening and optimizing the features in the data set, eliminating the collinearity between the features and performing recursive feature elimination to determine the final feature subsets for predicting the maximum magnetic entropy change and the Curie temperature respectively; and step 3, establishing machine learning models for predicting the maximum magnetic entropy change and the Curie temperature respectively through the final feature subsets, and training and optimizing the models to obtain the trained prediction models. The application realizes a closed loop of high-precision performance prediction, physical mechanism analysis and directional composition design.

Coating materials such as paints, and / or coatings used to protect and improve the aesthetics of a material surface, develop the problem of degradation under environmental exposure. The degradation causes the coating to fail unexpectedly in its required applications. To keep this in check, the service life and change in material properties of a paint / coating need to be predicted before its use. Present disclosure provides system and method that implement a combined model to estimate the service lifetime and predict the chemical and physical changes in a coating material under various weathering conditions. The combined model captures the chemical modifications and physical modifications and affected properties like surface roughness, thickness loss, fracture toughness, gloss loss respectively. Chemical changes and physical changes are estimated correlated to help in estimating the service lifetime and degradation of the coating material.

The invention relates to the technical field of fluorescent gel, and discloses a method for predicting stimulation response performance of poly-eutectic fluorescent gel based on machine learning, which comprises the following steps of: acquiring, constructing and preprocessing characteristic data such as chemical composition and microstructure of the poly-eutectic fluorescent gel; and performing physical, chemical and biological stimulation response performance prediction on the co-melting fluorescent gel according to the pre-processed co-melting fluorescent gel training data set. Compared with a traditional method, the method has the advantages of low cost, safe experiment and fast effect output, and has the advantages of high precision of quantitative prediction and high-dimensional nonlinear problem processing.

The present application relates to the technical field of computer simulation, and particularly relates to a kind of loss stress simulation analysis method of electronic packaging curing agent.The method comprises the following steps: obtaining the physical and chemical parameters corresponding to electronic packaging curing agent and mechanical property data, to generate packaging curing agent basic performance data set;Based on the packaging curing agent basic performance data set, generate curing agent microstructure evolution data and stress source identification and packaging stress field distribution mapping, obtain the initial distribution data of packaging structure stress field;According to the initial distribution data of packaging structure stress field, stress evolution simulation under temperature cycle load is carried out, and the loss factor and stress relaxation coefficient corresponding to electronic packaging curing agent are calculated, to obtain curing agent loss stress characteristic parameter;Obtain humidity aging factor and carry out influence dynamic evaluation, while generating curing agent loss stress life evaluation report.The present application can realize the accurate simulation process of curing agent loss and stress distribution in electronic packaging process.

The present application relates to the technical field of testing or analyzing materials by means of determining chemical or physical properties of materials, in particular to an in-situ characterization method and system for shale reservoir micro-nanopore gas adsorption behavior, the method comprising using non-destructive shale samples, introducing gas in a high-pressure high-temperature reaction kettle simulating formation environment, synchronously collecting Raman spectrum and small-angle / ultra-small-angle X-ray scattering data, and realizing in-situ characterization through multi-scale data fusion analysis.The present application realizes in-situ, non-destructive, high-resolution and multi-component dynamic characterization of shale micro-nanopore gas adsorption behavior under real formation conditions, improves the accuracy of reservoir evaluation, and constructs a more accurate physical and chemical model.

The embodiment of the present application relates to the field of testing the permeability of materials, and particularly to a method for determining the sealing performance of a filler. The method simulates the ground stress, temperature, pore water pressure and water chemical environment in the near field of a high-level waste repository, and restores the geological environment of a rock sample in the near field of the high-level waste repository as much as possible, so that the sealing performance of the filler determined in the environment is more accurate; by monitoring parameters such as the normal displacement of the fracture, the pore water pressure and the axial pressure, in combination with the microstructure and porosity data of the filler, different stages of the sealing of the fracture by the filler can be clearly identified, and the parameters can be quantitatively analyzed, so as to obtain data such as the deformation amount of the filler, the variation law of the porosity and the mineral precipitation amount, which reflect the sealing performance, and the data can reveal the physical-chemical mechanism of the mineral reaction of the filler, and thus can assist in the long-term safety prediction of the repository.

The invention relates to a system unit of a field device (1) for determining and / or monitoring a physical, chemical or biological process parameter and / or the material properties of a medium, comprising: - at least two demountable subunits (Ui1, Ui2, ...); and - a label (100), wherein the label (100) includes at least one disassembly information for the system unit or a link that is configured to lead to the at least one disassembly information when the label (100) is read. Furthermore, the invention comprises a method for assisting the operating personnel in deciding whether the replacement and / or recycling of a field device (1) used in an automation system (5) for determining and / or monitoring a physical, chemical or biological process parameter and / or the material properties of a medium is indicated, wherein the method comprises the following process steps: - during the intended operation of the field device (1) at a deployment site, events are recorded that affect the service life of the field device (1), - At predetermined time intervals, a current recycling indicator is determined based on the recorded events, - the current recycling indicator is compared with a predefined limit value for the recycling indicator, - A message is issued to the operating personnel that the field device (1) must be replaced and / or recycled as soon as the current recycling parameter reaches the limit value for the recycling parameter.

The present application relates to catalytic technology, and aims to provide a method for evaluating the influence law of entropy increase on the OER performance of catalytic materials. The method comprises the following steps: selecting a plurality of physical and chemical properties of elements related to the OER performance by using existing data, obtaining corresponding values and forming a set, and then performing clustering classification; the corresponding entropy values of each attribute are calculated according to the category distribution of the sample, and the entropy values under different cluster numbers are combined as the characteristics of the sample; a new data set is constructed in this way; a regression fitting, hyperparameter tuning and testing are performed on the data set by using a machine learning algorithm to obtain a regression model; finally, SHAP analysis model is used to obtain the importance of each feature in the model and the influence of each feature on the OER overpotential predicted by the model. The present application firstly proposes to correlate the information entropy of the physical and chemical properties of elements with the OER performance of catalytic materials; the research method can save a large amount of resources and improve work efficiency; the accuracy is high and the error is small.

The invention discloses an alumen adding closed-loop control method and system based on alumen ustum recognition and sedimentation velocity inversion, and the method comprises the following steps: 1, calculating a target sedimentation velocity, and calculating the target sedimentation velocity according to the current inflow water quality parameter, inflow water flow and target post-sedimentation turbidity; calculating through a physical and chemical mechanism model to obtain a target settling velocity required for reaching the target post-settling turbidity; 2, feedforward prediction is carried out, and inflow water quality parameters, inflow water flow and target post-sedimentation turbidity are obtained; calculating a target settling velocity through a physical and chemical mechanism model according to the target post-settling turbidity; inputting the inlet water quality parameter, the inlet water flow and the target settling velocity into a pre-trained alum adding amount prediction model, predicting to obtain an initial alum adding amount, and implementing initial alum adding control; through the implementation of the invention, advanced prediction and real-time feedback compensation of the alum adding amount are realized, the water quality fluctuation is effectively responded, and the chemical consumption and the operation risk are obviously reduced while the effluent is ensured to reach the standard.

The invention discloses a sediment heavy metal pollution analysis and evaluation method, and belongs to the technical field of environmental statistical modeling. The method comprises the following steps: acquiring physicochemical property indexes of sediments, and calculating the effective adsorption capacity of each component by utilizing a preset saturation response relationship; inputting the physicochemical indexes into a Bayesian network constructed through hierarchical mechanism constraint, and deducing the adsorption affinity latent variable of the heavy metal; constructing a competition mapping function containing a vacancy constant term based on a competition Langmuir mechanism, and calculating a capacity occupancy rate under a multi-metal competition condition; and finally, calculating a heavy metal content predicted value according to the effective adsorption capacity and the occupancy rate. According to the method, a physical capacity upper limit and a multi-metal competition occupation mechanism are introduced into the Bayesian network, so that the problems of prediction distortion and lack of a competition mechanism of a traditional model under high load are solved, and accurate evaluation conforming to physical and chemical laws is realized.

The invention provides a performance evaluation method and system for a differential pressure plugging material for leakage control, and belongs to the field of petroleum engineering plugging materials.The method comprises the steps that the physical and chemical performance of the differential pressure plugging material is evaluated, particle size distribution is obtained, and density, acid solubility and dispersion stability tests are conducted; testing the mechanical performance of the differential pressure plugging material to obtain a laboratory test performance curve of stretching and friction; testing and evaluating the critical starting pressure in a laboratory; performing laboratory testing and mechanism analysis on the ultimate bearing pressure to obtain a performance evaluation result; according to the method, the evaluation results of laboratory testing, theoretical simulation and mechanism analysis of the plugging material on the performance of the plugging material are comprehensively considered, the plugging material is evaluated to have the characteristics of low-pressure activation, self-adaptive plugging and high-pressure resistance, the preparation process is simple and controllable, the method can be effectively applied to micro-leakage plugging of petroleum and natural gas downhole pipe columns, and the application prospect is wide. And a reliable technical scheme is provided for shaft integrity repair.

The invention discloses a method and a system for predicting occurrence forms of heavy metal elements in waste residues, a medium and a terminal. The method comprises the following steps: acquiring physical and chemical attribute data in to-be-predicted waste residues, and performing data preprocessing to obtain characteristic data; wherein the physical and chemical attribute data comprises one or more of pH value, conductivity, element content, waste residue type, organic matter content, phase content and detection method; and inputting the feature data as input into the constructed and trained adaptive model for predicting the occurrence form of the target heavy metal for prediction, and outputting to obtain the occurrence form of the target heavy metal element. According to the method, similar element occurrence form data with similar physicochemical properties in the same family, the same period and the diagonal position of a target heavy metal element in the periodic table of elements are introduced, and a model for predicting the occurrence form of the heavy metal in the waste residues, which is high in prediction precision and strong in generalization ability, is successfully established through a transfer learning method.

The application belongs to the technical field of CrMo high strength and toughness steel design, and discloses a design method of CrMo high strength and toughness steel based on atomic physical feature coupling machine learning and a preparation method thereof. By introducing atomic scale physical and chemical features (especially "weighted variance" features) as key inputs of machine learning, the application accurately designs a CrMo alloy steel which still maintains excellent plasticity under ultrahigh strength (>1400MPa) or maintains high strength under high toughness. The application first introduces atomic physical and chemical feature parameters such as "ionization energy variance" into steel design, reveals the strengthening mechanism at the electron level, avoids blind component trial and error, and significantly reduces the research and development cost.

TITLE OF THE INVENTION: SYSTEM AND METHOD FOR MONITORING AN AQUATIC FACILITY The aquatic facility monitoring system (100) comprises: - at least one physical / chemical sensor (110, 117, 159, 181, 182, 183 and / or 184), configured to provide a series of at least one detected value representative of a local physical / chemical parameter, and - at least one optical sensor (115, 118), configured to provide a graphical representation of the water in the aquatic facility and / or of the aquatic facility, and - a means for determining the state of the aquatic facility (120), comprising a computer device configured to receive and process at least one series of a local physical / chemical parameter and / or at least one graphical representation to determine a value representative of a state of the aquatic facility. Figure for the abstract: Figure 1:

The invention discloses rock core cutting and sampling equipment and method for mineral geological exploration, and the rock core cutting and sampling equipment comprises a cutting track, a cutting machine and a grating type clamping mechanism; the cutting machine is movably arranged on the cutting track and is configured to vertically cut the rock core in half along the axis direction of the rock core so as to obtain a rock core sample for determining the physicochemical properties of a reservoir in which the rock core is located; wherein the arc-shaped side walls of the two cut parts of the rock core are respectively a first surface and a second surface; the multiple grating type clamping mechanisms are arranged on the outer side of the rock core at intervals in the axial direction of the rock core and can provide clamping force in the length direction of the first face and the second face, and any grating type clamping mechanism can disperse the clamping force to the width direction of the corresponding arc-shaped side wall. Clamping force can be dispersed to the side wall of the rock core, single-point-position clamping on the side wall of the rock core is avoided, and rock core fragmentation in the cutting process is avoided.

According to the method for measuring the true density of the coal rock, provided by the invention, different evolution stages experienced by the coal rock in a complex geological history can be efficiently and accurately identified. The stages cover the initial diagenesis to the later metamorphism, and each stage is accompanied by significant changes of physical and chemical properties of the coal rock. Besides, the method also has excellent distinguishing capability, the carbonaceous mudstone can be clearly and effectively distinguished from other types of coal rocks, and the carbonaceous mudstone is used as a special sedimentary rock, has complex components, often contains relatively high organic matters, and has significant difference from conventional coal rocks in multiple aspects. Therefore, the method not only contributes to deepening the understanding of the formation cause and the evolution process of the coal rock, but also provides important technical support and scientific basis for the fields of exploration and development of coal resources, environmental protection and the like.

The application relates to a method for analyzing a geological body by means of physical and chemical properties of the geological body, and particularly relates to a method for determining a hydrothermal uranium ore prospective area, which comprises the following steps: determining a first region in an exploration area, the first region being a region in which at least one element group corresponding to an abnormal region is distributed; determining a second region in the exploration area, the second region being a region in which a metallogenic siliceous vein is located; determining a third region in the exploration area, the third region being a region in which the content of uranium in surface rock in the exploration area is greater than a preset value, and a marker in the surface rock simultaneously exists rigid deformation and plastic deformation; and determining the hydrothermal uranium ore prospective area based on one or more of the first region, the second region and the third region.

The invention discloses an extracting solution low-temperature stage concentration and division technology and system, and belongs to the technical field of material concentration and separation. The process comprises the following steps: predicting and setting initial process parameters based on an improved gradient boosting decision tree model according to input physical and chemical attribute vectors of an extracting solution, predicting real-time temperature time sequence data in a first-stage concentration and division tank based on an improved LSTM (Long Short Term Memory) model, and calculating the temperature of the first-stage concentration and division tank according to the predicted real-time temperature time sequence data. The rotating speed of a scraper blade is adaptively controlled according to a prediction result to induce ice crystal nucleation and regulate and control growth of the ice crystal, and the target temperature of the secondary concentration and division tank is dynamically and finely adjusted based on the total solid content of the material and the concentration of target active ingredients monitored in real time by near infrared spectroscopy, so that quality-oriented closed-loop control is realized; and carrying out solid-liquid separation on the thick slurry output by the secondary concentration and division tank to obtain a final concentrated solution and ice crystals, and reusing the melted ice crystals as a solvent in the previous process. According to the invention, thermosensitive components can be protected to the greatest extent, energy conservation and consumption reduction are realized, and integration of concentration and component division is realized.

The invention discloses a hafnium oxide-based ferroelectric material performance prediction method based on machine learning. The method comprises the following steps: acquiring physicochemical property data and process parameters of each doping element of a target hafnium oxide-based ferroelectric material; calculating an average factor and a variance factor of each physicochemical property of each doped element based on the physicochemical property data of each doped element; inputting the average factor, the variance factor and the process parameters into a pre-trained target performance prediction model, and outputting a performance prediction result for the target hafnium oxide-based ferroelectric material; wherein the target performance prediction model is constructed based on an automatic machine learning framework and is obtained by adopting a pre-constructed target data set to carry out supervised training. According to the method, the process parameter characteristics of the hafnium oxide-based ferroelectric material and the physicochemical property characteristics of the doped elements are jointly input into the model, so that the model can learn the complex nonlinear relationship between the physical intrinsic attribute and external process regulation at the same time, and the prediction precision and generalization ability of the model are improved.

The application relates to a method for analyzing a geological body by means of physical and chemical properties of the geological body, and particularly relates to a method for determining distribution of a hydrothermal uranium deposit based on an iron valence ratio, which comprises the following steps: collecting ore samples at different depths in an exploration area; fitting a functional relationship between the iron valence ratio of the ore samples and the depths of the ore samples; determining a vertical variation trend of the iron valence ratio of the hydrothermal uranium deposit in the exploration area based on the functional relationship; determining the iron valence ratio at a point to be explored in the exploration area; and determining the vertical distribution of the hydrothermal uranium deposit at the point to be explored based on the iron valence ratio at the point to be explored and the vertical variation trend. The method provided in the application embodiment can effectively predict the vertical distribution of the hydrothermal uranium deposit, so that the exploration efficiency of the hydrothermal uranium deposit is improved and the cost is reduced.

A method for operating a chemical plant (1) implemented to operate a batch process (BP) for producing chemical products, where the batch process (BP) comprises a plurality of process steps (P1 to PN), the method comprising the steps of: receiving (S1) sensor data (SDQ) associated with at least one process step (PQ) of the batch process (BP); processing (S2) said sensor data (SDQ) according to a data driven model (DDCM) to generate a compressed digital representation (SDQ ') of the sensor data (SDQ); determining (S3) a quality metric indicative of a physicochemical quality of a chemical product to be produced by the plant (1) and / or a performance indicator of the batch process from the digital representation (SDQ ') of the received sensor data (SDQ); and generating (S4) planned operation data (CD) indicative of the operation of the chemical plant (1) as a function of the determined quality metric. A system (1) for producing chemical products comprises a control unit (3) and controllable facility units (6, 7, 10) associated with batch process steps (P1 to PN), where the control unit (3) is implemented to carry out the method.

Methods of predicting physicochemical properties of a chemical system using a family of surrogate or reduced order models, trained on first principle simulation results. The models are created using machine learning techniques. The chemical system can be a complex multicomponent and multiphase system such as produced water.

The invention discloses a method for accurately measuring interaction force among mineral particles based on anisotropy of a mineral crystal face, and belongs to the field of measurement of physical and chemical interaction of mineral surfaces. According to the invention, the piezoelectric sensor drives the colloid probe on the AFM needle-point-free cantilever to get close to the surface of a single mineral crystal face and get away from the single mineral crystal face, so that the interaction process of two mineral particles is simulated, and accurate measurement of the interaction force between the particle surfaces is realized in combination with an EDLVO theoretical model. Compared with the traditional characterization means, the method has the advantages that the theoretical fitting error is greatly reduced, the interaction force in the particle aggregation process and the like can be measured more directly, microcosmically and accurately, the evolution rule of the anisotropy of the mineral crystal face is further clarified, and the crystal face dependence of the interaction force among the mineral particles is revealed.

The application discloses a kind of based on organic matter interference evaluation cadmium isotope standard substance stability verification method, belongs to environmental monitoring and geology reference material analysis technical field, the method first from the mixed to be evaluated Cd standard reference material large sample Randomly equal amount takes two samples;Two samples are respectively subjected to acid digestion and Cd separation and purification, then MC-ICP-MS measurement is carried out, obtains the δ 114 / 110 Cd value of each sample;Finally, the difference of the δ 114 / 110 Cd value of two samples is calculated and compared with threshold value to judge the homogeneity and stability of the Cd isotope composition of the standard substance, and the effectiveness of the method is verified by the fact that the measurement results of the control sample are within the known demonstration interval. The verification method directly separates the sample when taking the solid powder sample, classifies and evaluates it based on its inherent physical and chemical properties, and provides a reliable reference standard substance screening method for Cd isotope research of environmental samples.

The embodiment of the invention relates to the field of testing the permeability of materials, in particular to a method for determining the plugging performance of filler. According to the method, by simulating the ground stress, temperature, pore water pressure and water chemical environment of the near field of the high-level radioactive waste repository, the geological environment of a rock sample in the near field of the high-level radioactive waste repository is reduced as much as possible, so that the plugging performance of the filler determined in the environment is more accurate; by monitoring parameters such as fracture normal displacement, pore water pressure and axial pressure and combining with the microstructure and porosity data of the filler, different stages of the filler blocking the fracture can be clearly identified, and the parameters can be quantitatively analyzed to obtain data reflecting the blocking performance such as filler deformation, porosity change rule and mineral precipitation amount. The data can reveal the physical-chemical mechanism of the filler mineral reaction, so that the long-term safety prediction of the repository can be assisted.

The invention provides a material data processing method and device, a storage medium, electronic equipment and a computer program product, and relates to the technical field of material analysis. The method comprises the following steps: determining a material physical and chemical descriptor space of target substance properties; determining a first machine learning model of the target substance property; and determining a first subspace of the material physicochemical descriptor space based on the first machine learning model, the first machine learning model having a minimum prediction error in the first subspace. The invention provides a standard process to find a group of proper descriptors, and the physical property of an object can be accurately and properly described.