79122 results about "Raw material" patented technology

The present invention relates to the technical field of carbon footprint calculation. Disclosed are a method and system for calculating a carbon footprint of a high-energy-consuming product on the basis of electro-carbon coupling. The method comprises: collecting data of electricity consumption and data of a carbon emission factor for electricity generation in the production process of a high-energy-consuming product; constructing an electro-carbon coupling model for describing the relationship between electricity consumption and carbon emission, and performing iterative optimization; using the electro-carbon coupling model combined with the collected electricity consumption data and the carbon emission factor for electricity within a corresponding time period to calculate carbon emissions of energy directly consumed for electricity generation during each phase; and analyzing and calculating indirect carbon emissions of raw materials and energy consumed during the other indirect stages in the production of the product, and verifying the result. Thus, the relationship between electricity consumption and carbon emission is more accurately reflected, the impact of time and place changes on carbon emission factors can be taken into consideration, and a method for comprehensively evaluating an overall carbon footprint of a product is provided, thereby facilitating more comprehensive understanding and management of the environmental impact of the product.

The invention discloses a method of preparing aluminum oxide-silicon carbide compound porous ceramic by aluminum-silicon industrial waste residues. The primary raw materials comprise industrial waste residues, carbon powder, a pore forming substance and an adhesive. The aluminum oxide-silicon carbide compound porous ceramic is prepared by the following steps of mixing, drying, forming and sintering to obtain a final product. Treatment and high additional value utilization of the industrial waste residues are achieved. The prepared porous ceramic is remarkable in performance, and can be applied to the fields of filtration, sound isolation, heat isolation, fire fighting and the like. The method is simple in process flow and convenient for industrialized promotion; and the cost is good to control and the method has extreme economical value.

The invention discloses a health food production line control method and system based on deep management, and relates to the technical field of food production control. A raw material feature database is established, a first analysis result is output as a raw material deviation evaluation reference, data of a current batch of raw materials are collected, differences are compared, and a raw material deviation result is output; the method comprises the following steps: collecting process data, analyzing whether the process data is abnormal or not by combining with raw material deviation, generating an adjustment suggestion, executing formula and parameter adjustment, triggering a self-repairing mechanism when the process data is abnormal, identifying an intermediate product state, evaluating a quality risk level and triggering early warning. Dynamic optimization of a formula and process parameters is achieved, meanwhile, an image and spectrum fusion early warning technology is introduced, the quality control ability of intermediate products is improved, finally, optimization parameter configuration is generated for the next batch through whole-process data chain construction and causal reasoning analysis, and self-adaptive control and quality steady-state improvement in a production closed loop are achieved.

The invention relates to a preparation method of phenylmethylamine. The technical scheme adopted by the invention for mainly solving the problems of high catalyst loss, low production efficiency and high intermittent operation labor intensity is as follows: the preparation method of the phenylmethylamine comprises the following steps: mixing the raw material of cyanobenzene with a solvent material or at least one of one part of product materials in a mixing unit, introducing the mixture into a fixed bed hydrogenation reactor to be in contact with hydrogen and a catalyst and carry out multi-phase continuous hydrogenation reaction to obtain a product material containing phenylmethylamine, separating the product material to obtain a phenylmethylamine product, wherein the catalyst is a metal loaded catalyst; and the metal is selected from at least one of Pd, Pt, Cu, Ni, Mo and Fe. The preparation method can be used for preparing phenylmethylamine.

The invention provides a management method and system for coal preparation quality intelligent control, and the method comprises the steps: firstly obtaining raw material quality, equipment operation and environment state index sets of a target production line, then carrying out the dynamic association processing of the index sets, and generating a technological process feature set; then calling a preset optimization decision model to carry out parameter prediction on the technological process feature set to obtain a process regulation parameter set, generating a quality optimization instruction set based on the process regulation parameter set, transmitting the quality optimization instruction set to a production control unit to execute dynamic regulation operation, and finally, according to a quality feedback index set after regulation execution, carrying out dynamic regulation operation. And the parameter weight distribution of the optimization decision model is updated, intelligent control and continuous optimization of the coal dressing quality are realized, and the stability of coal dressing production and the product quality are improved.

The invention discloses a fiber foamed concrete and a preparation method thereof. The fiber foamed concrete comprises the following raw materials in parts by weight: 5290 parts of cement, 4965 parts of water, 1.361 part of foaming solution, 1.2964 part of fly ash and 10 parts of fiber. The preparation method comprises the following steps: proportionally weighing the components; sequentially adding the cement and fly ash into a stirrer, proportionally weighing the water, uniformly stirring the mixture by the stirrer, and adding the fiber; preparing and adding the foaming solution; gradually adding the foam into the mixture, and continuously stirring until all the components are uniformly mixed; pouring the mixture into a mold on which lubricating oil is brushed, and curing in a curing chamber; after 24 hours, removing the mold; and respectively curing for 3 days, 7 days and 28 days for experimental detection. The fiber foamed concrete has the advantages of favorable thermal insulation property, favorable compression resistance and favorable bending resistance, and is applicable to a railway bed strengthening layer.

The present invention relates to an intelligent material distribution and material taking device and method for solid fermentation of powdery and granular fertilizers, comprising at least one production line, the production line comprising: a longitudinal conveyor belt parallel to the length direction of the fermentation tank; a transverse conveyor belt arranged above the fermentation tank along the width direction of the fermentation tank; a discharge vehicle, including a longitudinal discharge vehicle and a transverse discharge vehicle; a material distribution conveyor belt for receiving the fermentation raw materials scattered by the longitudinal discharge vehicle; a longitudinal receiving hopper; a mobile spiral material taking machine, which is longitudinally slidably arranged in the fermentation tank to collect the intermediate products in the fermentation tank; a controller, which controls the intelligent material distribution and material taking device for solid fermentation of powdery and granular fertilizers to switch between the two working states of distribution and material taking, and controls the transverse conveyor belt and the longitudinal discharge vehicle to keep synchronous longitudinal movement. The controller is used to switch the working state of the material distribution and material taking device, so as to realize intelligent and automatic material distribution and material taking, so as to meet the needs of solid fermentation of powdery and granular fertilizers.

Provided is a concrete full lifecycle green quality control method based on Internet of Things and big data, comprising the following steps: step 1, on the basis of an information platform, selecting raw materials required by concrete, so as to achieve the control of the selection of the raw materials of concrete; step 2, on the basis of the information platform, performing engineering concrete mix ratio intelligent design, and obtaining the mix ratio of the raw materials required by concrete corresponding to a project under construction; step 3, on the basis of the information platform, taking into account the obtained concrete mix ratio to mix and prepare concrete by means of an intelligent concrete factory, and automatically testing and controlling the quality of the prepared concrete at the same time; and step 4, on the basis of the information platform, using the mixed and prepared concrete to perform intelligent construction. Automatic, intelligent and green control of concrete throughout the full lifecycle from raw materials, mix ratio design, production, and construction to the service life of the structure are achieved by controlling each stage of the full lifecycle of the concrete.

The invention relates to the technical field of automobile part manufacturing, and discloses an automobile injection molding part production process control system and method, and the system comprises the following modules: a data collection module which is used for collecting technological parameters, molds, raw materials and equipment operation original data, attaching timestamps, and storing the data in a database; the process parameter prediction module is used for reading original data to construct a time sequence data set, inputting the time sequence data set into a TFT model to obtain a pre-training model, and predicting a short-term process parameter fluctuation range in combination with current production working condition parameters; and the quality risk index acquisition module is used for inputting the process parameter data and the mold data into a quality risk index calculation formula to obtain a quality risk index. Through the system, data-driven comprehensive production optimization is realized, the process control accuracy and adaptability are improved, the quality control scientificity and reliability are enhanced, the intelligent level of the production process is improved, the production efficiency is effectively improved, the defective rate is reduced, and the product quality is stabilized. The problem that process control lacks system intelligence is solved.

The invention provides a solid waste filling material ratio optimization method of a magnesium slag-steel slag composite cementing material, and relates to the technical field of solid waste filling. The method comprises the following steps: mixing magnesium slag, steel slag, desulfurized gypsum and mineral slag to prepare a cementing material, and carrying out pretreatment and physicochemical property analysis on raw materials by taking tailings and waste rocks as filling aggregates; then designing a cementing material proportioning scheme and carrying out strength test to obtain an optimal ratio of the cementing material; then considering different slurry concentrations and plastic-to-bone ratios, and carrying out an all-solid waste filling slurry rheological test and an all-solid waste cementitious body strength test; and finally, according to the two test results, establishing a multi-objective optimization model taking the on-way resistance and the filling material cost as optimization objectives and taking the strengths of cementitious bodies at different ages as constraint conditions, and determining optimal parameters of the full-solid waste filling material. According to the invention, resource utilization of the metallurgical solid waste and the mine solid waste is realized, the bottlenecks of high carbon emission and high cost of a cement-based filling material are broken through, and a solution is provided for co-processing of the mine solid waste.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention provides a flow-state solidified soil mixing construction method based on automatic adjustment of proportioning parameters, belongs to the technical field of flow-state solidified soil, and realizes intelligent control of a flow-state solidified soil mixing process by establishing a three-section type precise screening and stirring system and a high-precision automatic control system. According to the method, firstly, target performance parameters are input, and an optimal matching scheme is automatically selected through a multi-target optimization algorithm; starting an original soil treatment unit to perform soil material dispersion treatment and measuring the physical characteristics of the soil material; adding the raw materials according to the accurate measurement, and fully mixing; when it is detected that the performance of the mixture deviates from a target value, the system automatically triggers a parameter compensation mechanism for adjustment; and meanwhile, a material ratio fluctuation early warning system is established, so that uneven mixture quality caused by starting and stopping of equipment or material conveying delay is avoided, and the technical problem that in the prior art, matching parameters are difficult to accurately regulate and control in real time in the mixing process of the flow-state solidified soil is solved.

The present invention relates to the technical field of carbon footprint accounting. Disclosed are a carbon footprint accounting method and system for a power grid material product based on life cycle assessment (LCA). The method comprises: on the basis of an LCA method, building a product carbon footprint model for a power grid material product; collecting power grid material product data, and on the basis of the collected data, accounting the carbon emission of the power grid material product from the raw material acquisition stage to the scrap recovery stage; during carbon footprint accounting, selecting power carbon emission factors of different grades in each stage, and accounting the power carbon emission; and on the basis of the accounted data, performing high-precision real-time carbon footprint accounting of the power grid material product in the whole life cycle. The environmental impacts of the product are assessed more accurately. Moreover, real-time accounting can reflect any change or improvement effect in time. By means of the comprehensive carbon footprint analysis, detailed carbon footprint accounting can reveal high-emission activities, provide data support for optimization of the product design and production process, and reduce the overall carbon emission.

The invention provides an industrial park soil and groundwater pollution early warning grade evaluation method and system.The method comprises the steps that production raw material components of pollution source enterprises, waste discharge data and soil heavy metal concentration evolution data in historical pollution events are integrated, optical sensing data of earth surface and underground monitoring points are combined, and the early warning grade of soil and groundwater pollution is evaluated; constructing a dynamic characteristic factor library reflecting pollution source characteristics; carrying out spatial concentration gradient calculation on the optical sensing data based on the characteristic factor library, and generating a dynamic heat map containing heavy metal distribution and migration trends at the same time; performing cross-modal analysis on the pollution source characteristics and the dynamic heat map, and establishing a spatio-temporal evolution trajectory of a pollution diffusion path; according to the diffusion rate and direction in the track, differentiated pollution risk early warning intervals are divided, and a targeted grading prevention and control strategy is generated. According to the invention, the timeliness of pollution early warning and the targeting of prevention and control measures are improved.

The invention relates to the technical field of visual management and control, in particular to an essential oil production digital visual management and control system and method based on data analysis. The method comprises the following steps: obtaining essential oil raw material data and carrying out component content identification, removing abnormal values to generate standard raw material components, obtaining historical process data, matching the standard raw material components according to the data, obtaining a reference process, carrying out production simulation to generate essential oil parameters, and carrying out multi-dimensional quality evaluation on simulation process parameters. The method comprises the following steps: optimizing a matching reference process, forming a quality compensation essential oil process, carrying out scene simulation on the process, evaluating process risks, implementing safety compensation treatment, obtaining a safety compensation essential oil process, carrying out production control adjustment on standard raw material components based on the process, carrying out visual rendering, and constructing a digital visual view of essential oil production. According to the invention, a more efficient and safer digital visual management and control method for essential oil production is realized.

The invention relates to the technical field of polyamide materials, in particular to a high-heat-resistance bio-based polyamide material and a preparation method thereof, and the high-heat-resistance bio-based polyamide material comprises the following raw materials in parts by weight: 45-50 parts of PA56, 7-11 parts of a balance multi-effect agent doped with a filler modifier, 5-8 parts of a flame retardant, 4-7 parts of a silane coupling agent, 3-5 parts of an antioxidant and 2-4 parts of a lubricant. According to the bio-based polyamide material, the bio-based PA56 is adopted as a matrix, meanwhile, the flame retardant, the silane coupling agent, the antioxidant and the lubricant are added to serve as functional auxiliaries, the balance multi-effect agent doped with the filler modifier is added to be matched with the stable supplement agent, and the raw materials are blended, coordinated and jointly synergized, so that the flame retardant property of the bio-based polyamide material is improved, and the flame retardant property of the bio-based polyamide material is improved. The obtained bio-based polyamide material is harmoniously improved in dielectric loss, flame retardance and wear resistance, and meanwhile, the product is remarkable in weather resistance and high heat stability resistance.

The invention relates to the technical field of control systems, in particular to a sorbitol-based production parameter intelligent regulation and control system, which comprises a real-time feedback regulation and control module for continuously acquiring data, calculating real-time deviation and generating real-time deviation data based on real-time monitoring of temperature, pressure and concentration in a current reaction kettle. According to the invention, through real-time monitoring and dynamic calculation of parameters in the production process, variable deviations such as temperature, pressure and concentration are obtained and adjusted, and quick response processing of multi-dimensional production data is realized. By optimizing a dynamic adjustment strategy of PID controller parameters, proportion, integral and differential parameters are optimized according to real-time feedback, and the problem of adjustment lag caused by fixed parameters is solved. The current reaction condition and the raw material consumption rate are comprehensively considered in dynamic adjustment of the feeding flow, and meanwhile, the flow value matched with the current reaction demand is calculated and obtained, so that the condition that feeding and demand are disjointed in traditional regulation and control is avoided.

The invention provides a fertilizer production energy consumption real-time optimization method, and particularly relates to the technical field of industrial process optimization control based on artificial intelligence, and the method comprises the steps: constructing a mechanism sub-model based on a chemical reaction kinetic equation, and outputting a theoretical energy consumption constraint value; and meanwhile, a data driving model is used for receiving temperature, pressure and raw material ratio data collected by a real-time sensor and outputting a real-time energy consumption predicted value. And inputting the theoretical energy consumption constraint value and the real-time energy consumption predicted value into a dynamic weight distribution module to generate a fused energy consumption predicted value. And generating a production parameter adjustment instruction through a rolling optimization algorithm according to the fused energy consumption predicted value, and sending the production parameter adjustment instruction to a production execution system. And if the deviation between the data acquired after the adjustment instruction is executed and the fused energy consumption predicted value exceeds a preset threshold value, performing online fine adjustment on the data-driven model parameters. The technical problems that real-time energy consumption optimization lags behind due to multivariable dynamic coupling in the fertilizer production process, and an optimal production parameter combination cannot be rapidly generated are solved, and the fertilizer production energy consumption is remarkably reduced.

The invention discloses an intelligent smelting process parameter optimization management method and system, and relates to the technical field of smelting process data processing, and the method comprises the steps: determining the initial time lag of a process parameter according to the input of the process parameter and the output of a smelting raw material, and considering the time lag characteristic of the process parameter during the adjustment. And the smelting state of the smelting equipment is defined, and the working state of the smelting equipment is defined, so that a reliable basis is provided for optimization of subsequent process parameters. The nonlinear relation between process parameter input and smelting raw material output and the coupling relation between process parameters are determined, the nonlinear relation is analyzed by considering the process parameter input, the smelting state and the smelting raw material output, and description is carried out through a polynomial regression mode. And the initial time lag is adjusted in combination with the nonlinear relation and the coupling relation. The process parameters are stably, rapidly and reasonably adjusted, the accuracy and adaptability of optimization and adjustment of the process parameters are improved, and it is guaranteed that the smelting process is efficiently and safely carried out.

The invention discloses a catalyst used for preparing acrylic acid and butyl ester by the dehydration of butyl lactate and a preparation method thereof. The catalyst is a composite catalyst, comprising a main catalyst A and auxiliary catalysts B, C, D and E, wherein, A is calcium sulphate, B is sulphuric acid, C is phosphate, D is alkaline matter and E is promoter. The preparation method comprises the steps as follows: five compositions A, B, C, D and E are sufficiently mixed, baked for 2-10 hours at the temperature of 200-800DEG C and formed for standby. The catalyst has high catalytic activation; under the catalysis of the catalyst, the molar yield reaches 75.1 percent and the molar selectivity of the acrylic acid and butyl ester reaches 71.5 percent. The preparation method has the advantages that the crystal structure of the main catalyst can be improved so as to be beneficial for carrying out the reaction; and the introduction of different auxiliary catalysts and quantities is helpful to improve the conversion ratio of the raw material and the product selectivity and is beneficial for improving the strength of the catalyst.

The invention discloses a concrete mix proportion optimization method and system based on machine learning, and particularly relates to the technical field of intelligent proportioning of building materials, and the method comprises the following steps: constructing a mapping model of target performance indexes by using raw material performance parameters and historical trial matching data, cross-station mix proportion optimization is carried out based on real-time sensing data of a plurality of mixing stations, the resonance relation between the moisture content of raw materials and the iteration frequency of a model is monitored in the dynamic construction process, and a parameter smoothing and disturbance suppression mechanism is triggered to correct a mix proportion scheme. The corrected mix proportion adjustment scheme is applied to raw material controlled feeding of multiple mixing stations; according to the method, the quality of training samples is improved through unified data processing, a multi-dimensional mapping model is constructed to realize cross-mixing-station performance consistency optimization, and a resonance detection and disturbance suppression mechanism is introduced to ensure the stability and anti-interference capability of mix proportion adjustment under a dynamic construction condition; therefore, high-performance, low-cost and high-robustness concrete intelligent optimization control is realized.

The invention discloses a kit raw material quality detection method based on multi-modal data fusion, and relates to the technical field of automatic quality detection of chemical reagent raw materials. Comprising the following steps: S1, synchronously acquiring spectral data, microscopic image data and chemical detection data of a kit raw material through a spectral sensor, microscopic imaging equipment and a chemical sensor; s2, inputting the multi-modal data into a self-adaptive spatial-temporal feature calibration network, and analyzing local detail features and global time sequence features of the modal data through a multi-scale spatial-temporal feature extraction module; according to the kit raw material quality detection method based on multi-modal data fusion, by constructing a collaborative fusion system of an adaptive spatial-temporal characteristic calibration network and a layered information entropy screening mechanism, the core technical problems of misalignment of multi-modal data dynamic alignment and redundant interference accumulation are solved.

The invention discloses a bio-organic fertilizer for repairing heavy metal cadmium in soil and a preparation method of the bio-organic fertilizer, and belongs to the technical field of organic fertilizers. The organic fertilizer is prepared from the following raw materials in parts by weight: 7-12 parts of a compound microbial agent, 30-40 parts of modified charcoal, 3-8 parts of sodium alginate, 6-8 parts of a chelating agent and 60-80 parts of decomposed edible fungus residues. The compound microbial agent is prepared from potassium-solubilizing paenibacillus mucilaginosus MSSW02, pseudomonas plecoglossicida and bacillus aryabhattai; the chelating agent is composed of calcium humate and nano-hydroxyapatite according to the mass ratio of 100: (3-5). The multi-component synergistic effect is achieved, the content of heavy metal in soil is effectively reduced, especially the content of heavy metal Cd, meanwhile, rich organic matter is contained, the physical and chemical properties of the soil can be remarkably improved, the water and fertilizer retention capacity of the soil is improved, and crop growth is promoted; the remediation effect on medium-light cadmium polluted farmland soil is good, and the growth promoting effect on crops is remarkable.

The invention discloses a batch circulation method for a tobacco primary processing workshop, and the method comprises the steps: generating a unique batch code based on raw material warehousing information, binding a two-dimensional code with an RFID dual identifier, and enabling the batch code to comprise a date, a production line code, a raw material batch number and a sequence number; the tobacco shred making process is divided into the working procedures of raw material moisture regaining, slicing / tip cutting, feeding and leaf moistening, shredding, tobacco shred drying, blending and perfuming and tobacco shred storing, and each working procedure is provided with an independent operation unit and an electronic lock. Intelligent temporary storage bins are arranged at a cut tobacco drying outlet and a blending and perfuming inlet, inter-process residence time is monitored through an MES system, early warning is triggered when time is out, and batches are frozen; when the batch enters each process, the preset process parameter range is automatically loaded, and the equipment is only allowed to operate in the parameter compliance range; process data and quality inspection results of all procedures are collected in real time, and raw material batches and finished silk boxes are associated to form a forward / reverse tracing chain; and starting freezing, reviewing and reworking / scrapping processes for the batches which are detected to be abnormal, and synchronously triggering an equipment self-cleaning program.

The invention discloses a high-solid-content single-walled carbon nanotube aqueous conductive slurry formula composition and a preparation method thereof, belonging to the technical field of conductive slurry. The raw materials include single-walled carbon nanotube powder, dispersant, dispersing aid, pH regulator and deionized water. The invention innovatively proposes to shorten the single-walled carbon nanotube powder with the aid of a three-roll grinder, grind the single-walled carbon nanotube powder, the dispersant, the dispersing aid and the like into fibers of less than 10 μm under the forward shear force of the three-roll grinder, and the van der Waals force between the shorter single-walled carbon nanotube bundles is relatively weak and easier to be opened, so that the homogenization pressure of the subsequent high-pressure microfluidizer is ≤1000 bar, and the single-walled carbon nanotubes are uniformly dispersed under a relatively low homogenization pressure. While opening the single-walled carbon nanotube bundles, the problem of damage to the length size of the carbon nanotubes under a higher homogenization pressure is avoided, thereby ensuring the conductivity of the high-solid-content single-walled carbon nanotube aqueous conductive slurry formula composition.

The invention provides an AI-enhanced large mine water-doped concrete material performance prediction and mix proportion design integrated system, aiming at the key problem of large mine water-doped concrete material temperature control, the importance of different influence factors such as raw material-structure-environment and the like is comprehensively and quantitatively evaluated; aiming at a complex raw material system of the large mine water-doped industrial concrete, main features of a performance prediction model such as feature dimension reduction and weight calculation are optimized, and meanwhile, a machine learning algorithm combined with a self-attention mechanism is constructed so as to reduce interference of features weakly related to target performance, and the performance regulation and control capability of the large mine water-doped industrial concrete is enhanced. Furthermore, a multi / super multi-target optimization algorithm containing a constraint system is developed and integrated aiming at targets such as material key performance, carbon emission and cost in the preparation process, and a comprehensive and flexible data-driven solution is provided for temperature control, influence factor evaluation, performance regulation and control and proportion optimization of the large-mine water-doped concrete material. Therefore, the overall decision-making and design process is optimized.

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 relates to the field of coating material production, and discloses a coating formula calculation method and system based on self-learning feedback parameter correction, and the method comprises the steps: obtaining raw material basic parameters, target performance requirements and process feedback data of historical production batches, combining a material screening mechanism and an environment working condition correction strategy, and calculating a coating formula; constructing a coating formula initial input matrix; performing multi-dimensional variable normalization processing on the initial input matrix of the coating formula, introducing a feature sensitivity analysis model, and extracting a key variable group influencing the coating performance in the feature sensitivity analysis model; based on the performance response relation mapping model, analyzing error distribution between model prediction output and actual detection data by using a prediction deviation recognition mechanism, and extracting learning error features; a self-learning feedback updating mechanism is introduced, and dynamic weight optimization is conducted on the parameter correction factor set; and performing sample test and performance verification on the corrected candidate formula set. The method has the advantage that the coating formula precision is improved.

The invention relates to the technical field of intelligent regulation and control, in particular to a method for predicting and adjusting the moisture content of raw materials for shaving board production. According to the method, the raw material drying section disturbance parameter set and the moisture content detection sequence record in the shaving board production process are obtained, symbol matching, amplitude sorting and disturbance direction and influence intensity marking are carried out in combination with the prediction sequence and the disturbance characteristic sequence, so that the raw material moisture content prediction has the dynamic trend judgment capability; tracing correlation can be carried out on a disturbance source, prediction sequence amplitude adjustment is carried out through a disturbance direction and an amplitude mark, prediction correction of disturbance response and trend inversion abnormal point detection are achieved, and meanwhile local residual analysis is introduced to construct a trend residual sorting table; the abnormal inflection point is taken as the core to carry out weight sorting of the correction result and guide scaling and direction adjustment of the moisture content correction trend, and finally, adjustment instructions of the hot air temperature, the heating source power and the drying channel humidity are generated according to the deviation degree of the prediction result and the target set value.

To provide a steam distillation system for producing clean water by purifying water.SOLUTION: A fluid steam distillation system is provided, the system comprises: a control system for controlling the fluid steam distillation device, the control system comprising, a release valve, a raw material flow controller for controlling a raw material flow rate valve, and a discharge level sensor connected to the release controller and the raw material flow rate controller. The discharge level sensor transmits a signal related to a discharge level to the discharge controller and the raw material flow rate controller which indicates the discharge level, the raw material flow rate controller drives the raw material flow rate valve on the basis of at least the discharge level sensor signal. The discharge controller drives the discharge valve on the basis of at least the discharge level sensor signal, and the discharge level and the raw material flow rate level are held by using the discharge level sensor signal as input.SELECTED DRAWING: Figure 75