584 results about "Chemical process" patented technology

The invention discloses a chemical process fault diagnosis method and system, and relates to the technical field of chemical process fault diagnos.The scheme aims at the diagnosis bottleneck of catalyst inactivation type progressive faults, fine parameter offset is captured in real time through a dynamic reference model, weak signals are accumulated and amplified in combination with an attenuation weighting mechanism, and the fault diagnosis accuracy is improved. The problem that a traditional method is not sensitive to slow drifting, and consequently report omission is caused is solved, and a fault recognition window is remarkably advanced. The dynamic threshold value is updated in real time based on mobile statistics, and raw material fluctuation and sensor noise can be self-adapted; during working condition switching, the model is automatically reset and the threshold value is relaxed, so that false alarm triggered by parameter mutation is avoided, and the stability of production scheduling is guaranteed; the design that fault half-life period weight and moving window length are associated with an inactivation period is introduced, so that the model autonomously adapts to different catalyst characteristics.

The invention discloses a deep resource fluidized mining disturbance effect evaluation model under a multi-field coupling effect, and the method specifically comprises the following steps: constructing a multi-field coupling parameter system: collecting geologic body basic parameters, covering rock mass mechanical parameters, thermal physical parameters, fluid parameters and chemical parameters in deep three-high, high ground stress, high ground temperature and high karst water pressure environments, and establishing a multi-field coupling parameter system; the method comprises the following steps: establishing a dynamic parameter database, realizing real-time updating of parameters along with a mining process, establishing a heat-flow-force-chemical full-coupling mathematical model, introducing the influence of a rock plastic damage correction term quantification temperature on heat conduction efficiency, associating a dynamic mapping relation between mineral reaction and rock physical property parameters, and establishing a dynamic parameter database. Simplification errors of a traditional model on multi-field effects are thoroughly eliminated, chained interaction of temperature, fluid, stress and chemical processes in a deep three-high environment is accurately described, a model prediction result better fits an actual mining working condition, and prediction deviation is greatly reduced.

The invention discloses a biomass-based reinforcing agent for paper and application of the biomass-based reinforcing agent. The reinforcing agent comprises a biomass-based material, the biomass-based material comprises a micron-sized structure and a nano-sized structure distributed on the micron-sized structure. The reinforcing agent prepared by the invention has abundant nanostructures, is beneficial to physical crosslinking such as entanglement and bridging of fibers in paper pulp, and improves the wet strength of paper; the preparation of the reinforcing agent is a simple physical process, and a complex synthesis chemical process for synthesizing a polymer in a traditional organic reinforcing agent is avoided; the reinforcing agent is prepared by a dissolution regeneration method, so that the energy consumption is low, the solvent can be recycled and reused, no chemicals are consumed, and the reinforcing agent is environment-friendly; the reinforcing agent prepared by the invention can achieve a satisfactory wet strength effect at a lower content.

The invention belongs to the technical field of chemical process intelligent control, and discloses an iron phosphate synthesis parameter self-adaptive control system based on an intelligent algorithm. The system is composed of a target quality sensing module, a reaction data acquisition and preprocessing module, a sensitive section identification module, a process boundary modeling module, a multi-strategy control model library module, a strategy evaluation and structure evolution module and a control execution and feedback module. According to the method, a multi-module cooperation mechanism including target scoring modeling, dynamic sensitive segment identification and reaction boundary modeling is constructed, so that whole-process online identification and segmented precise control of the crystal form, the particle size and the crystallinity of a product in the iron phosphate synthesis process are realized; compared with a traditional control system depending on experience setting and univariate feedback, the system has the advantages of being higher in state sensing depth, more accurate in crystal form conversion process recognition, finer in reaction stage regulation and control and the like, and the problems of crystal form deviation, particle size drift and inter-batch quality fluctuation are effectively solved.

The invention relates to the technical field of computer application, and particularly discloses a chemical industry park safety risk assessment method based on an agent model. The method comprises the following steps: constructing a multi-agent collaborative perception framework, and fusing multi-source heterogeneous data of equipment, environment, materials and personnel to form a park panoramic view under a unified space-time reference; establishing an agent behavior model library embedded with the chemical process mechanism and the safety interlocking logic; an intelligent agent decision strategy is optimized through deep reinforcement learning, and multi-target training is carried out by taking a safety index as a reward function; executing dynamic risk deduction and accident chain simulation in a digital twin environment, and quantifying risk probability and consequences in combination with Monte Carlo sampling; and finally, a visual thermodynamic diagram and an interpretable risk assessment report are generated, weak links are identified, and intervention suggestions are provided. According to the technical scheme, dynamic, accurate and prospective evaluation of the safety risk of the chemical industry park can be realized, and the early warning timeliness and the evaluation reliability are remarkably improved.

A system for online-coupled atmospheric chemical transport modeling and data assimilation is built using online coupling and secondary parallelization. In the prototype code of a nested air quality forecasting model system, a parallel data assimilation framework routine is introduced. The system includes observation modules, model modules, and assimilation modules. The observation module is responsible for flexible access and preprocessing of various component-type observation data. The model module handles the model integration of initial fields, involving calculations of physical and chemical processes. The assimilation module performs analysis assimilation of model state variables. This system meets the requirements for coordinated assimilation of multiple chemical component variables, simultaneous introduction and flexible combination of various types of observation data, and effective handling of non-linear and non-Gaussian distribution issues in chemical assimilation.

The invention relates to a chemical production anomaly detection method based on a space-time diagram variational encoder. The method comprises the following steps: selecting public chemical normal working condition data D; an anomaly monitoring model ST-GVAE is constructed, a loss function L (.) is constructed, D serves as input, an Adam optimizer is adopted to train ST-GVAE, model parameters are reversely updated through gradient descent, and trained ST-GVAE'is obtained; a to-be-detected target variable a is selected, normal working condition historical data corresponding to a are input into ST-GVAE ', and an anomaly detection score corresponding to a is obtained and serves as an anomaly detection threshold value of the target variable a; inputting the current chemical working condition data of a into ST-GVAE'to obtain an anomaly detection score corresponding to the current state of a, and if the score is greater than a threshold value, triggering an alarm; and then calculating a reconstruction deviation score B of each node in the PID, taking the node corresponding to the B greater than a deviation threshold as an abnormal node, and outputting the abnormal node. By using the method provided by the invention, the variable abnormal condition of the existing chemical process can be accurately detected and positioned.

The invention discloses a chemical process multi-parameter intelligent monitoring method based on deep learning, and the method comprises the following steps: collecting and preprocessing the data of a chemical production process, and obtaining a standardized input sequence; according to the chemical process flow diagram and the equipment topological relation, a standardized process structure matrix is obtained; inputting the standardized input sequence and the standardized process structure matrix into a constraint decoupling echo state network model, and outputting a hidden state sequence; performing multi-scale gating fusion on the hidden state sequence, and outputting a parameter prediction result; calculating a residual error based on a parameter prediction result and an actual observation value, and outputting an anomaly detection result and channel-level attribution information; triggering a self-adaptive correction process to form a monitoring model after self-adaptive correction; and outputting a parameter prediction result, an anomaly detection result and channel-level attribution information based on the monitoring model after adaptive correction. According to the invention, the constraint decoupling echo state network is adopted, and multi-parameter intelligent monitoring of the chemical process is realized.

The invention discloses an internal-acid and external-alkali type organic-inorganic hybrid molecular sieve catalyst as well as a preparation method and application thereof, and belongs to the technical field of heterogeneous catalytic materials and chemical processes. According to the catalyst, a hydrogen type Beta molecular sieve is used as an acidic inner core, polyvinylpyrrolidone (PVP) is used as an organic shell, and a unique inner-acid outer-alkali structure is formed. The preparation method comprises the following steps: hydrothermally synthesizing a hydrogen type Beta molecular sieve with a specific silica-alumina ratio, and then coating PVP through surface adsorption. The catalyst is used for catalyzing the condensation of methylal and trioxymethylene under mild conditions (40-70 DEG C and normal pressure) to prepare polyoxymethylene dimethyl ethers (DMMn). The PVP shell covers acidic sites on the outer surface of the molecular sieve to form an external neutral / weak base and internal strong acid microenvironment, so that the secondary side reaction of the product is effectively inhibited, the trioxymethylene conversion rate of more than 98% and the DMMn selectivity of more than 96% can be obtained in the reaction at 50 DEG C and normal pressure, the product distribution can be regulated and controlled, and the traditional Anderson-Schulz-Flory distribution limitation is broken through. The catalyst disclosed by the invention has the advantages of high activity, high selectivity, mild conditions and easiness in separation, and has a good industrial application prospect.

The invention discloses a chemical process anomaly type identification method based on a multi-dimensional causal association index, and belongs to the technical field of chemical process monitoring and fault diagnosis. The method comprises the following steps: firstly, carrying out offline modeling: acquiring normal working condition data, constructing a monitoring model by utilizing a trend slow feature analysis algorithm, and determining a statistic control limit; during online application, statistics of real-time data are calculated through a model, and an alarm is given and a type identification process is triggered if the statistics exceed a limit. A multi-dimensional causal association index (MCAI) is constructed by analyzing whether a quality variable deviates or not and further performing comprehensive analysis on a root variable causing an anomaly from four dimensions of a position (CPI) of the root variable in a process, an attribute (CAI) of the root variable in a causal network and an influence propagation capability (CPD) of the root variable. The problem that a traditional anomaly detection method cannot distinguish process faults from new working conditions is solved, false alarm can be effectively avoided, and a decision basis is provided for accurate maintenance.

The invention relates to the technical field of chemical process control, in particular to an HCl oxidation fluidized bed reactor control method and device and computer equipment, and the method comprises the steps: obtaining first real-time state information and second real-time state information of an HCl oxidation fluidized bed reactor; obtaining a target temperature of the HCl oxidation fluidized bed reactor according to the first real-time state information and a preset temperature optimization model; according to the target temperature and the second real-time state information, the HCl raw gas temperature set value, the oxygen temperature set value and the recycle gas preheating temperature set value of the HCl oxidation fluidized bed reactor are adjusted. According to the method, key operation parameters, such as the temperature of each part, the gas flow and the preheating temperature, the use number of cooling pipes, the catalyst operation time and the like, of the reactor are collected in real time, and an intelligent temperature optimization model is combined, so that the state of the reactor is dynamically sensed and accurately regulated and controlled, and the automation degree and the response speed of a control system are remarkably improved.

A method, system, and computer program product are described capable of controlling an industrial based chemical process through accessing sensor data, pre-processing accessed sensor data through an automated process, forming an initial prediction model of the chemical process, and automatically determining linearity or non-linearity of the initial prediction model. As a function of quantitatively measured linearity, being non-linear, quasilinear, or linear, automatically train the initial prediction model, then deploy the trained prediction model in a manner that controls the subject chemical process at the industrial plant, including optimizing consumption of a certain resource. Data-driven modeling of non-linear, continuous-like industrial or chemical processes results.

The invention discloses an HAZOP analysis method and system based on digital delivery, and relates to the technical field of chemical safety risk analysis, and the method comprises the steps: obtaining to-be-analyzed digital delivery data; carrying out digital delivery model integration and dynamic verification by adopting a topological correlation theory and a rule engine method; performing multi-source process data standardization processing and risk element mapping by adopting a statistical learning and semantic matching method; performing model-driven deviation intelligent generation and causal chain analysis by adopting a topological reasoning and analogue simulation method; carrying out cross-professional collaborative optimization and measure closed-loop tracking by adopting a collaborative filtering and closed-loop control method; and carrying out full-life-cycle data linkage and iterative optimization by adopting a feedback learning and knowledge iteration method to generate a standardized report. According to the method and the system, full-process digitization, intelligentization and full-life-cycle management and control of chemical process risk analysis can be realized, and the precision and the efficiency of HAZOP analysis are improved.

The invention provides a chemical process safe and stable operation method and system combined with a DCS, and relates to the technical field of chemical process control, and the method comprises the steps: firstly collecting multi-dimensional operation data of each operation link of a chemical process, and forming a disturbance trigger signal set through multi-feature correlation analysis; constructing an operation element disturbance diffusion dynamic logic map based on the chemical process network; evaluating the response capability of the DCS control node according to the disturbance trigger signal set and the running element disturbance diffusion dynamic logic map, dividing multiple response stages and determining a core operation range; generating a control node composite operation sequence containing a conventional sequence and an emergency sequence; issuing a control node composite operation sequence and performing real-time monitoring to form a running state dynamic tracking record; and on the basis of the running state dynamic tracking record verification correction atlas and logic, a self-adaptive dynamic response mechanism is formed through multi-round iteration, and safe and stable running of the chemical process is guaranteed.

The invention relates to the technical field of chemical production safety, in particular to a chemical production safety risk intelligent reasoning method and system and a storage medium, and the method comprises the steps: building a multi-level flow model according to a chemical production flow chart, and constructing a fault tree analysis qualitative model for the multi-level flow model through a danger and operability analysis technology; fusing and correcting the multiple groups of evidence sources according to the D-S evidence theory to obtain the initial abnormal probability of each basic event; and establishing a dynamic Bayesian network quantitative model in combination with a logic gate rule, and performing forward reasoning and backward reasoning. According to the invention, forward reasoning and backward reasoning can be carried out on abnormal event nodes in the chemical production process, causal traceability is completed, an abnormal propagation path is explored, and an effective management means is provided for safe production in the chemical process.

The invention discloses a boiler blending combustion biomass fuel coupling chemical looping combustion system, and particularly relates to the technical field of fuel control, the system comprises an operation monitoring module, through deploying a multi-type sensor network at a key position, real-time collecting whole flow operation data, constructing a space-time monitoring data set covering the physical and chemical process of a combustion system, and determining a combustion state of the combustion system; the reliability of data acquisition is guaranteed through signal filtering and abnormal value correction; according to the method, a multi-dimensional cooperative control framework is constructed, the six-dimensional influence coefficients are fused into the comprehensive distribution index, the distribution strategy of the fuel between the main combustion area and the chemical looping reactor is dynamically optimized, traditional single control dimension is expanded to multi-dimensional cooperative control, and the adaptability of the system to complex fuel characteristics is improved.

The application provides a MEMS differential thermal analysis sensor and a DTA / DSC testing method, the sensor comprising a detection thermocouple, a reference thermocouple, an ambient resistance and a shielding ring, wherein the detection thermocouple and the reference thermocouple each comprise a single crystal silicon substrate, a heat insulation cavity, a plurality of single crystal silicon thermocouple pairs and a supporting film, the heat insulation cavity is located in the single crystal silicon substrate, the plurality of single crystal silicon thermocouple pairs are connected in series and are supported by the supporting film to be suspended above the heat insulation cavity to achieve thermal isolation, and the single crystal silicon thermocouple pair comprises an N-type single crystal silicon thermocouple and a P-type single crystal silicon thermocouple connected in series, the temperature and power sensitivity of the sensor are significantly improved, reach 28 mV / K and 100 V / W, and the noise equivalent temperature difference and the noise equivalent power reach 0.5 mK or 0.2 muW. Based on the output signal of the sensor, differential thermal analysis testing and differential scanning calorimetry testing on physical or chemical processes of material heat absorption and release can be realized, the sample consumption is small, and the testing precision is high.

The invention relates to a method for extracting lithium from brine based on a multistage CCIX combined system, and belongs to the technical field of chemical processes. An adsorption section procedure for extracting lithium from brine is divided into three areas A, B and C, and each area adopts a CCIX continuous ion exchange system, so that efficient lithium extraction is realized. The area A is used for adsorbing pretreated raw halogen, the area B is used for secondary adsorption of tail liquid of the area A, and the area C is used for extracting lithium and removing impurities from desorption liquid of the area A and the area B. The technological process comprises the steps of pretreatment, adsorption, leaching, desorption and tail brine top water treatment, the lithium yield is increased through the aluminum-based lithium adsorbent, and meanwhile impurity ions are reduced. The final product can be lithium carbonate or crude lithium chloride. The method is suitable for lithium extraction of salt lake brine and ore leaching brine. And by optimizing the process, the cost of rear-end membrane coupling and resin impurity removal is reduced, and the environmental protection pressure is reduced.

The invention belongs to the field of real-time monitoring, particularly relates to a chemical process real-time monitoring method and system based on digital twinning, and aims to solve the technical problem that an existing method lacks an online model correction and updating mechanism. The monitoring method comprises the following steps: S1, constructing a state snapshot data set, and synchronously collecting real-time measurement data; s2, identifying a process fluctuation interval, and selecting to-be-selected state snapshots to form an optimal snapshot matrix; s3, calculating a reduced-order truncation error energy ratio, and constructing a reduced-order model based on a reduced-order basis function; s4, in the real-time monitoring stage, if the norm of the prediction residual error is smaller than a monitoring threshold value, a Kalman filtering algorithm is adopted to correct the state coefficient of the reduced-order model; otherwise, calculating the projection error of the prediction residual on each primary function in the standby primary function library; and outputting a full-order state vector reconstructed based on the corrected or updated reduced-order model. The monitoring method provided by the invention ensures the continuous effectiveness and accuracy of monitoring when the chemical process changes.

The invention discloses a phosphorus chemical industry production equipment on-line state monitoring and intelligent diagnosis system based on multi-parameter fusion, and relates to the technical field of chemical industry equipment intelligent monitoring and diagnosis. Functional modules such as data input and preprocessing, equipment wear evaluation, real-time data analysis, fault prediction, personnel risk analysis and comprehensive diagnosis are deeply integrated, and the system accurately adapts to non-stationary characteristics of the phosphorus chemical process by dynamically adjusting a time window and a self-attention weight. The comprehensive diagnosis module innovatively constructs a causal diagram based on self-attention features, generates a structured diagnosis report by using a Transform decoder, and optimizes the diagnosis probability in combination with Bayesian reasoning, so that the accuracy, comprehensiveness and intelligence level of monitoring diagnosis are improved, accurate early warning can be realized, deep insight and optimization operation and maintenance decisions can be provided, and the system has a wide application prospect. And the production risk and economic loss are effectively reduced.

The invention discloses a multi-parameter online monitoring and fault early warning method for phosphorus chemical production equipment, which relates to the technical field of chemical process automatic monitoring and safety early warning, integrates visible light, infrared and ultraviolet multi-mode sensing and multi-physical quantity sensing of temperature, smoke, vibration and the like, and adopts a complex numerical value optical convolution and lightweight neural network. Real-time identification and early warning of high-precision and multi-category faults are realized, the system has the operational level computing power of millions of times per second, supports multi-path parallel and dynamic convolution kernel weight updating, can automatically store multi-dimensional historical data and an AI judgment process in multiple minutes before and after alarming, facilitates fault evolution analysis and model retraining, and is high in reliability and high in reliability. The system can greatly improve the industrial production safety and intelligent level, reduces false alarm and missing alarm, supports large-scale deployment and production environment dynamic adaptation, and has a wide industrial application prospect.

A system selecting a process key factor in a commercial chemical process, includes: a data extraction unit that extracts tag data in units of a set period; an outlier discrimination unit that discriminates and aggregates outliers by tag by using an outlier extraction reference master; an outlier processing unit that generates an input mart draft excluding the outliers; a derived variable generation unit that generates derived variables for each tag, and generates an advanced input mart having the derived variable added thereto; a yield calculation unit that backs up the result of calculation of a yield by realizing a target value via exclusion and correction of the outliers; and a key factor extraction unit that extracts a yield key factor by calculating importance of each tag, and backs up importance data for each tag.

The invention belongs to the technical field of in-situ analysis of water environment interface nutrient elements ammonia nitrogen and nitrate nitrogen, and particularly relates to a zeolite and S110MH mixed adsorption film and a preparation method and application thereof. The zeolite and S110MH mixed adsorption film provided by the invention comprises agar hydrogel and adsorbent particles dispersed in the agar hydrogel, and the adsorbent particles comprise zeolite particles and anion exchange resin particles. The invention provides a preparation method of a zeolite and S110MH mixed adsorption film, functional parameters of the mixed adsorption film and a DGT device installed on the mixed adsorption film, and a testing method of DGT in various environmental media. Experiments show that the DGT device provided with the zeolite and S110MH mixed adsorption film can accurately test the time average concentration or diffusion flux of ammonia nitrogen and nitrate nitrogen at rhizosphere of a water body, sediment or aquatic plants, and reflect the bio-availability, geochemical process and mobility of inorganic nitrogen.

The application provides a method for synthesizing an eplerenone intermediate N-1 by one-pot method, and belongs to the technical field of organic synthesis. The method is characterized in that: the eplerenone intermediate N-4 is dissolved in dichloromethane, a potassium carbonate aqueous solution and dibromohydantoin are added to perform an opening ring reaction; after the reaction is completed, hydrochloric acid is directly added to perform a rearrangement reaction; then water is separated and methanol is added in the organic layer to perform an ozonation reaction; then sodium sulfite is added to perform a reduction reaction; then hydrochloric acid is added to perform a methyl esterification reaction, so as to obtain the eplerenone intermediate N-1 crude product, and finally the eplerenone intermediate N-1 fine product is obtained through refining. The method realizes the one-pot synthesis of the eplerenone intermediate N-1 from the eplerenone intermediate N-4 through the continuous reactions of opening ring, rearrangement, ozonation, reduction and methyl esterification, and can avoid column chromatography purification, so that the method becomes a green chemical process route with greatly reduced organic solvent consumption and wastewater.

The present application relates to the technical field of chemical process digital twinning, and discloses a physical property mixing solving method, device, equipment and medium applied to digital twinning, comprising: obtaining physical property data of a digital twinning system; establishing a state equation and a state equation coefficient corresponding to the digital twinning system according to a mixing rule and the physical property data; calculating a discriminant by using the state equation coefficient, and determining that the state equation has three real number solutions or a single real number solution by using the discriminant; when the state equation has three real number solutions, calculating a compression factor of a target phase state by using an analytical method; when the state equation has a single real number solution, calculating the compression factor of the target phase state by using an iterative method; and simulating and optimizing the digital twinning system by using the compression factor to obtain an optimized target digital twinning system. The present application improves the solving speed of the state equation on the basis of meeting the robustness requirement of state equation solving, and significantly improves the overall performance of the calculation speed of the chemical process digital twinning system.

The application discloses a boiler blending biomass fuel coupling chemical chain combustion system and concretely relates to the fuel control technical field.The boiler blending biomass fuel coupling chemical chain combustion system comprises an operation monitoring module, a plurality of types of sensor networks are arranged at key positions, whole-process operation data are collected in real time, a time-space monitoring data set covering physical and chemical processes of the combustion system is constructed, and the reliability of data collection is ensured through signal filtering and abnormal value correction; through the construction of a multi-dimensional collaborative control architecture, six-dimensional influence coefficients are fused into a comprehensive distribution index, the distribution strategy of fuel between the main combustion zone and the chemical chain reactor is dynamically optimized, the traditional single control dimension is expanded to multi-dimensional collaborative regulation, and the adaptability of the system to complex fuel characteristics is improved.

This utility model relates to a fluid nozzle, particularly a high-efficiency entrainment-type fluid mixing jet nozzle. It includes a nozzle body, characterized by a double-layer structure comprising an outer circular tube and a central guide cone arranged concentrically; both the outer circular tube and the central guide cone have ejector holes and baffles at their fluid outlet ends. The structure is simple and can be used not only for gas entrainment mixing but also for liquid entrainment mixing, such as the mixing of wastewater and pharmaceuticals, and acid-base neutralization in chemical processes, thus having a wide range of applications. The addition of ejector holes to the nozzle creates a vacuum region inside the ejector hole due to the high-speed flow of the fluid, allowing fluid from outside the nozzle to enter the nozzle, increasing the mixing performance and creating premixing within the tube. Experiments have verified that this structure significantly increases the mixing performance of the two fluids, improves the uniformity of mixing, effectively avoids flame root quenching, and ensures stable combustion.

The invention provides an anti-interference control method for the concentration of a main reactant in a continuous stirring reaction kettle process, belongs to the technical field of chemical process control, and solves the problem that in the chemical process of producing cyclopentanol by catalyzing electrophilic water with cyclopentadienoic acid, the concentration of the main reactant is not influenced. The technical problem that the concentration of a main reactant is difficult to track a set value without steady-state errors under the conditions of fluctuation of feeding concentration, uncertainty and external disturbance of the continuous stirring reaction kettle is solved. According to the technical scheme, the method comprises the steps of constructing a model pre-estimation compensator and an anti-interference control structure, designing a four-dimensional decision model, carrying out parameter collaborative optimization by adopting a multi-target genetic optimization algorithm, and carrying out optimal solution selection based on a TOPSIS method. Under the condition of feed concentration fluctuation, the dynamic response speed and the anti-interference capability of the system can be remarkably improved, and faster adjustment time, smaller overshoot and stronger robustness can be realized in linear and nonlinear models.

The application discloses an ultrasonic reinforced multi-stage atomizing nozzle device and an application method and an atomizing treatment system. The application adopts micro-nano aeration and ultrasonic dissolved air technology, two-stage ultrasonic spiral nozzle atomizing technology, bubble atomizing technology and air jet amplification technology, realizes a micro-nano atomizing process under low power consumption and maintenance-free conditions, and has the characteristics of a large nozzle inner hole, good atomizing effect (micro-nano level), no scaling and no blockage during operation, and maintenance-free during operation, and is especially suitable for application in micro-nano physical and chemical processes of high-viscosity fluids. The application is not only suitable for common atomizing fields, but also suitable for seawater desalination, desulfurization and denitrification atomizing, high-salt wastewater evaporation, coal water slurry atomizing and other fields.