121 results about "Reaction model" patented technology

The invention relates to a multi-cell control method for hydrogen production alkaline electrolytic cells based on nonlinear model predictive control, which belongs to the technical field of hydrogen production and comprises the following steps of: performing data acquisition on key positions of a hydrogen production alkaline electrolytic cell system in real time; based on the dynamically determined priority of each electrolytic cell, dynamic load distribution of each electrolytic cell is carried out, and a multi-dimensional cooperative regulation strategy is implemented to obtain a hydrogen yield target value of each electrolytic cell; constructing a nonlinear model comprising an electrolytic reaction model, a heat transfer model and an inter-tank coupling model, and performing parameter identification and verification; and designing a nonlinear model predictive control algorithm, performing discretization processing on the nonlinear model, introducing Kalman filtering to process the uncertainty of the model, taking a corresponding hydrogen yield target value reached by each electrolytic cell on the premise of meeting the constraint as a final control target, constructing an optimized target function, and performing solving to obtain an optimal control scheme. Compared with the prior art, the control precision and the system stability can be effectively improved.

The invention discloses a safety assessment method for a heating surface of a coal-fired unit boiler based on a field control signal. The method comprises the following steps: S1, collecting a monitoring signal through a field control end; s2, modifying the monitoring signal to obtain a fault monitoring signal under a simulation condition; s3, establishing a simulation structure model and a reaction model, and obtaining working condition data under simulation conditions; and S4, calculating the predicted residual life of the heating surface under the simulation condition. According to the method, by monitoring the control signal of the control end and combining simulation means for simulation analysis, the problem that monitoring cannot be carried out without a measuring point position in a conventional method can be avoided, and system danger can be early warned as soon as possible by utilizing signal analysis of the control end; the method is high in repeatability and safer to implement, can qualitatively analyze the working state of the boiler system under the abnormal field control signals, does not need to actually adjust parameter tests in the boiler system or collect signals of fixed measuring points at the rear end for safety evaluation, and is relatively low in cost.

The invention relates to a molecular simulation method for inhibiting gas and coal dust composite explosion microreaction by a powder explosion suppressant. The molecular simulation method comprises the following steps: constructing an initial model of a gas / solid mixed reaction system; training to obtain a machine learning force field; a machine learning force field optimization initial model is utilized to obtain a balanced and uniform reaction model, and de novo calculation molecular dynamics simulation is carried out under the conditions of an NVT or NPT ensemble, specific temperature and pressure; performing data post-processing and calculation on a simulation result, analyzing a pyrolysis evolution process, product types and structure information through a ChemTraYzer method, constructing a reaction trajectory network, extracting key primitive reactions and free radicals, performing sensitivity analysis through a Chemkin post-processor, and calculating the sensitivity of the product. Weak interaction and electronic structure and potential distribution in the molecular dynamics evolution process are calculated based on DFT and TST theories, and the inhibition mechanism of the selected powder explosion suppressant and the evolution rule of the powder explosion suppressant for inhibiting gas / coal dust burning explosion are disclosed.

The invention discloses a multi-dimensional multi-physics field coupling analysis platform of a hydrogen fuel cell based on artificial intelligence, and relates to the technical field of hydrogen fuel cells, the platform comprises a user interaction interface module, a multi-physics field model construction module, a data acquisition and preprocessing module, an integration module and an analysis and result output module; an electrochemical reaction model, a temperature field model and a gas and liquid flow model are respectively built through a multi-physical field model building module, the coupling strength among different physical fields is quantified, and the electrochemical reaction model, the temperature field model and the gas and liquid flow model are integrated into a multi-physical field model; physical action parameters such as electrochemical reaction rate, temperature value and the like are associated with time change, spatial distribution and fault conditions, so that the platform can simulate the multi-physical field coupling effect of the hydrogen fuel cell under complex working conditions, and the performance of the fuel cell is analyzed; and a reliable basis is provided for the geometric structure, material selection and operation condition optimization of the fuel cell.

The invention discloses a lithium ion battery expansion modeling method coupling gas generation and mechanical deformation, and relates to the technical field of lithium ion battery model construction and simulation methods.The method comprises the following specific steps that 1, a lithium ion battery module is selected, and battery electrochemical parameters, material physical property parameters and battery geometric characteristic parameters are obtained; (2) establishing a three-dimensional geometric model and dividing grids according to the initial parameters and the simulated working conditions, and setting boundary conditions; (3) solving an electrochemical reaction model and a pressure change model in the battery, and calculating the internal pressure of the battery; (4) taking the calculated battery temperature and internal pressure parameters as dynamic boundary conditions, and applying the dynamic boundary conditions to the physical boundary of the inner surface of the battery shell; and (5) solving the finite element-based mechanical model to obtain the stress and strain of the battery, and simulating the expansion behavior of the battery in thermal runaway. According to the method, the expansion behavior of the lithium ion battery caused by internal pressure rise in the thermal runaway process can be calculated, meanwhile, the thermal runaway key parameters can be accurately predicted, and a basis can be provided for safety early warning design of a lithium ion battery module and an energy storage system.

The invention relates to the technical field of computer numerical simulation, discloses a multiphase flow coupling analogue simulation method and device, computer equipment and a storage medium, and is applied to a process for preparing methanol through CO2 hydrogenation by a liquid phase method. The method comprises the following steps: constructing an initial multiphase flow model of a gas-liquid-solid three-phase system based on a gaseous phase state parameter, a liquid phase state parameter and a particle phase state parameter in an initial reaction stage; combining and correcting the liquid phase state parameters and the particle phase state parameters so as to simplify the initial multiphase flow model into an equivalent multiphase flow model of a gas-slurry phase two-phase system; constructing a component transport model based on the equivalent state parameters of the equivalent multiphase flow model; and constructing a coupling simulation model by using the equivalent multiphase flow model, the component transport model and the reaction model, and carrying out iterative solution on the coupling simulation model to obtain an analogue simulation result of multiphase flow coupling. Therefore, high-efficiency and high-precision simulation of the stirring reaction process for preparing methanol through CO2 hydrogenation by the liquid phase method is realized.

The invention discloses a method for intelligently controlling RNA (Ribonucleic Acid) enzyme reaction by applying Raman, and particularly relates to the fields of molecular biology and biological reaction engineering. Comprising the following specific preparation steps: S1, establishing a reaction model; S2, finding a characteristic peak which is most suitable for tracking; S3, tracking the change of the characteristic peak along with time; S4, comparing a result with a model which is independently calculated by chemometrics; according to the present invention, the enzyme reaction conversion rate can be monitored by using Raman, the reaction process can be subjected to in-situ, real-time, online and non-destructive composition analysis monitoring, and the previous HPLC off-line test is not required; the content of four nucleotides and the reaction conversion rate can be quickly reflected in real time; meanwhile, the reaction can be controlled to a certain degree, the device is communicated with an ultrafiltration device, the enzyme utilization rate is increased, the total RNA reaction amount is increased, the conversion rate is increased, the production cost is saved, and efficient intelligent control is achieved.

The invention relates to a modeling method for energy conversion and acting of a launcher in a launching channel, and belongs to the field of insensitive charge design and evaluation. Comprising the following steps: establishing a function for constraining the internal energy of a charging gas product in the charging system, a function for constraining the external work of the charging system and a function for constraining the mass increase rate of the gas product; establishing an energy and work function in the launching channel, and obtaining an energy conversion and work model and a charge reactivity model in the launching channel based on the function of the internal energy of the charge gas product, the function of constraining the external work of the charge system, the function of the mass increase rate of the gas product and the energy and work function in the launching channel. According to the method, a function of internal energy of a charging gas product, a function of constraining external work of a charging system and a function of mass increase rate of the gas product are substituted into an energy and work function in a launching channel to obtain an energy conversion and work model in the launching channel; the problem that at present, a modeling method for an energy conversion and external work applying model in a launching channel is lacked is solved.

The invention relates to the technical field of chemical production regulation and control, and discloses a system for thickening agent addition and on-line viscosity regulation and control in a post-regulation stage of a polyacrylic acid emulsion. The system comprises an overview construction module, a coefficient generation module, a rate optimization module and an execution control module. The overview construction module creates a production overview based on user settings, wherein the production overview comprises a reaction tank body connection relationship and thickening agent injection node parameters; the coefficient generation module is used for collecting real-time stirring state data and tank reaction data, respectively constructing a stirring state model and a tank reaction model, and outputting a stirring state coefficient and a tank reaction coefficient; the rate optimization module obtains the difference value between the current viscosity measurement value and the target viscosity, constructs a rate optimization model in combination with the tank reaction coefficient, the stirring state coefficient and the viscosity difference value, and outputs the target thickening agent injection rate; and the execution control module drives the thickening agent injection device according to the target thickening agent injection rate. According to the system, the accuracy and the stability of viscosity regulation and control are improved.

The invention discloses a Bayesian estimation method for longitudinal multistage and multilevel item reaction model parameters, and belongs to the technical field of model parameter estimation. The method comprises the following steps: S1, establishing longitudinal multi-level and multi-level project reaction models, including a level 1-level reaction model, a level 2 longitudinal individual growth model and a level 3 multi-level model; s2, based on an improved Bayesian parameter estimation method, estimating all parameters of the project reaction model proposed in the step S1 by introducing auxiliary variables; and S3, verifying the effect of the improved Bayesian parameter estimation method, and comparing the improved Bayesian parameter estimation method with the traditional Bayesian parameter estimation method. According to the Bayesian estimation method for the longitudinal multi-level and multi-level item reaction model parameters, on the basis that estimation precision is guaranteed, the operation time of the QMCMC is better than that of a traditional MCMC, and the operation speed and the estimation efficiency of a longitudinal multi-level and multi-level scoring item reaction theoretical model are improved.

The invention discloses a lithium battery thermal runaway internal temperature estimation method based on an electrochemical thermal coupling model, and the method comprises the steps: S1, selecting a to-be-measured lithium battery, and obtaining corresponding electrochemical parameters and physical parameters of the to-be-measured lithium battery; s2, carrying out a basic charging and discharging experiment and an extreme working condition experiment on the battery, and collecting related actual measurement data; s3, establishing a battery electrochemical model; s4, establishing a battery thermal model and a battery side reaction model, and obtaining an electrochemical-thermal coupling model; s5, performing numerical simulation on the electrochemical-thermal coupling model to obtain temperature data of each point in the battery at corresponding moments from the early stage to the last stage of thermal runaway of the lithium battery; and S6, using the data in the step S5 and the actually measured data in the step S2 for training a neural network, establishing a neural network model, and predicting the internal temperature of the lithium battery in real time. According to the invention, the advantages of the mechanism model and the data-driven neural network model are combined, and the speed and precision of temperature prediction are improved.

The invention discloses an explanation method for damage threshold improvement of a fused quartz material in a femtosecond laser processing / welding process. By establishing a fused quartz electron-lattice heat transfer model under femtosecond laser irradiation, a dissociation model of environment molecules and interstitial molecules under the action of femtosecond laser and a defect-containing fused quartz molecule dynamic reaction model based on a ReaxFF reaction force field, atomic structure changes in a system before and after a reaction are analyzed; and finally, obtaining a chemical reaction mechanism of the fused quartz containing the defects under the action of the femtosecond laser, and explaining the phenomenon that the damage threshold of the fused quartz is increased after the fused quartz is irradiated by the low-flux femtosecond laser. According to the method, the research in the current fused quartz femtosecond laser processing / welding process is perfected, and meanwhile, the theoretical blank that the damage threshold lifting mechanism is not clear in the fused quartz glass femtosecond laser processing / welding process is filled up.

The invention provides a method and device for determining temperature field distribution in a coke tower reactor, and the method comprises the steps: carrying out the component analysis and thermal reaction analysis of target heavy oil, so as to determine the lumped component and lumped reaction model of the target heavy oil; establishing a mutual conversion equation group and a heat change equation group among all lumped components according to the lumped reaction model; determining the yield result of each component in the reaction process through a steady-state calculation method; component composition and temperature values are selected from the yields of different components in the reaction process; and by taking the selected component composition and the temperature value as initial values, determining temperature field distribution at different positions in the coke tower in a predetermined reactor by adopting computational fluid mechanics. By means of the scheme, the technical effect that time-space distribution prediction and calculation of the internal temperature field in the whole-cycle production process of the coke tower reactor in the delayed coking technology are established, and therefore the production process can be precisely regulated and controlled can be achieved.

The invention discloses a rotary kiln multi-physical phenomenon numerical simulation method based on artificial intelligence reasoning, and relates to the technical field of rotary kiln multi-physical phenomenon simulation. On the basis of an Euler multiphase flow equation, a heat transfer model, a chemical kinetic reaction model, a particle growth model, a chemical reaction model and the like are added; establishing a three-dimensional numerical model capable of accurately simulating multiple physical and chemical phenomena such as gas-solid two-phase flow movement, combustion, NOx generation, clinker calcination and particle growth in the rotary kiln; a long-short-term memory neural network is introduced to build an AI model, the AI model is used for predicting the working condition of the second half section of the model, the simulation speed is increased, and long-time simulation of the rotary kiln is achieved.

An embodiment represents composition of molecules in a feedstock as a combination of individual molecule representations and molecular attribute representations. A representation of chemistry of a chemical reaction of the feedstock in a chemical reactor is then formulated based on the representations. Then, a simulation of the chemical reaction of the feedstock in the reactor is performed using the representations to determine composition of products of the reaction. A first subset of the products are represented as individual molecule represented products, and a second subset of the products are represented as attribute represented products. In turn, the attribute represented products of the second subset are sampled to determine individual molecule representations of the attribute represented products. As a consequence of the sampling, individual molecule representations of the first and second subsets of the products of the chemical reaction of the feedstock in the chemical reactor result.

The invention provides a method and a system for autonomously and intelligently adjusting the pH value of a desulfurization device of a thermal power plant. The method comprises the following steps: collecting inlet flue gas parameters, pH values of multiple points in an absorption tower and slurry physical property parameters in real time; transmitting the data to a control system, and dynamically predicting the optimal pH value range required by the current working condition by using a built-in desulfurization reaction model; comparing the actual pH value with a prediction range, generating a desulfurizing agent adding basic instruction, and performing dynamic correction by combining the liquid level and density of the slurry to obtain a final instruction; a high-precision metering pump and a regulating valve are controlled to execute precise adding; meanwhile, the model is self-optimized according to performance index feedback such as desulfurization efficiency and outlet SO2 concentration. Through combination of multi-parameter perception, model prediction feedforward and physical property parameter dynamic correction, the pH value is converted from passive and lagged adjustment to active and accurate intelligent adjustment, the desulfurization efficiency and the system stability are effectively improved, the operation cost is reduced, and environment-friendly up-to-standard emission is ensured.

The invention discloses a rapid modeling method for a material pyrolysis kinetic model, and solves the problems that prediction of'kinetic three factors' of an existing pyrolysis reaction is suitable for a single-step reaction, calculation errors may be large, or prediction of'kinetic three factors' of the existing pyrolysis reaction is suitable for a complex multi-step reaction, but pre-exponential factors and a reaction mechanism function cannot be directly obtained. According to the method, physical information and an operator neural network are fused, a double-branch network architecture, namely the physical information enabling operator neural network, is constructed, and meanwhile, a physical constraint module containing a kinetic equation is introduced to enhance the interpretability of a neural network model. In practical application, thermogravimetric data measured by experiments and corresponding heating rates can be respectively used as input of a branch network and a backbone network of the neural network model, and then kinetic parameters of the pyrolysis problem multi-step reaction model are predicted. A numerical result shows that the method effectively improves the precision of parameter estimation by fusing physical priori knowledge.

The invention relates to the technical field of waste gas monitoring, in particular to a waste gas component monitoring method for organic waste gas treatment, which comprises the following steps: capturing chemical reaction data of a laboratory and a site, modeling volatile organic compounds, calculating parameters of reactants and products, and generating a volatile organic compound reaction model. According to the method, the volatile organic compound reaction model and the waste gas conversion path diagram are constructed, conversion of chemical components in waste gas treatment is accurately mapped, accurate recognition of key chemical reactions and catalyst monitoring points is achieved, and operation parameters such as temperature, pressure and reaction time of waste gas treatment equipment can be accurately adjusted; the equipment performance and the treatment efficiency are optimized, the optimization enhances the quality control of waste gas components, and through regular quality inspection and a comprehensive monitoring mechanism, the safety and the environmental protection effect of waste gas treatment are improved, the environmental pollution and the health risk are reduced, and the realization of effective execution of a sustainable development policy is facilitated.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a retrosynthesis tree for a target molecule In one aspect, there is provided a method comprising: selecting a leaf node that is included in one or more retrosynthesis trees in a collection of retrosynthesis trees; processing data defining the respective molecule represented by the selected leaf node, using a reaction modeling neural network, to generate a score distribution over a set of chemical reactions that produce the respective molecule represented by the selected leaf node; selecting one or more chemical reactions from the set of chemical reactions using the score distribution over the set of chemical reactions; and generating one or more new retrosynthesis trees that each extend a respective retrosynthesis tree that includes the selected leaf node based on the one or more chemical reactions selected from the set of chemical reactions.

The invention discloses a construction and performance analysis method for a low-pressure performance prediction model of a fuel cell, which is applied to the technical field of proton exchange membrane hydrogen-air fuel cells, and comprises the following steps: establishing an air pressure and density model changing along with altitude based on an empirical formula; according to a Nernst equation and a fuel cell polarization equation, obtaining a fuel cell monomer voltage; establishing a stack cathode and anode reaction model by using a mass conservation principle and electrochemical characteristics; and an air compressor model based on the MAP and a cathode outlet pipeline model based on the nonlinear nozzle equation are established. A simulation environment is completed by using Simulink based on the model, and simulation result data is obtained and used for predicting the performance of the fuel cell in a low-pressure environment. According to the invention, waste of manpower and material resources caused by blind low-pressure test and irreversible damage possibly caused to the fuel cell system are avoided, theoretical guidance and predictive analysis basis are provided for subsequent test research, and powerful support is provided for low-pressure performance analysis and design of the fuel cell.

The invention discloses a method and system for determining the in-situ limit exploitation depth of oil shale, and the method comprises the steps: measuring the permeability under different depths according to an oil shale sample of an oil shale in-situ region to be evaluated; performing a pyrolysis experiment on the oil shale sample to obtain the composition of each simulated product and the components of each simulated product changing along with the temperature, and fitting a pre-constructed kerogen pyrolysis-based multi-step total reaction model on the basis of the composition and the components, so as to obtain fitted model coefficients and reaction kinetic parameters; predicting a formation temperature field formed on the basis of a heating condition of high-temperature fluid injection in the current formation, and predicting a yield change curve on the basis of the formation temperature field in combination with the permeability at different depths, the model coefficients and the reaction kinetic parameters; and establishing an income prediction model related to the to-be-evaluated oil shale in-situ area, and obtaining the limit mining depth when the income value is zero according to the yield change curve.

The invention provides a coal-fired unit boiler abnormal air volume evaluation method and a hearth safety evaluation method. The method comprises the steps that an analysis structure model is constructed with a combustion area of a coal-fired unit boiler as a computational domain; constructing a reaction model based on the analysis structure model so as to obtain an air volume analysis model; inputting normal induced air volume, operating the air volume analysis model, and obtaining a stable state by adopting a steady-state calculation method; inputting the to-be-evaluated induced air volume, taking a stable state as an initial condition, operating the air volume analysis model, and adopting a transient calculation method to obtain a transient hearth state to judge whether the to-be-evaluated induced air volume is an abnormal air volume or not; the transient hearth state comprises a transient high-temperature flame state of each time step length and transient hearth internal pressure of each time step length. According to the evaluation method, through a simulation means, an actual parameter adjustment test in a boiler system is not needed, the cost for evaluating whether the induced air volume to be evaluated is the abnormal air volume or not and evaluating the safety of the hearth can be greatly reduced, and the repeatability is high.

The present application discloses a method, device and storage medium for optimizing crude oil reaction kinetic parameters, the method comprising: performing a thermal analysis experiment on the crude oil to be tested by a TG / DSC instrument to obtain a first weight loss curve and a first reaction exothermic curve at a first heating rate; constructing an oil drop reaction model identical to the thermal analysis experiment; simulating the oil drop reaction model by a finite element method based on the working parameters of the TG / DSC instrument to obtain a second weight loss curve and a second reaction exothermic curve under an instantaneous state; adjusting the reaction activation energy and pre-exponential factor parameters of the oil drop reaction model to make the second weight loss curve coincide with the first weight loss curve; adjusting the reaction enthalpy parameters of the oil drop reaction model to integrate the second reaction exothermic curve and the first reaction exothermic curve to make the second reaction exothermic curve and the first reaction exothermic curve have the same integral amount; thereby obtaining optimized reaction kinetic parameters. In this way, the accuracy and reliability of the kinetic parameters can be improved.

The invention discloses an interactive learning model training method and system based on machine learning, and the method comprises the steps: building a neural network reaction model which comprises an input layer, a hidden layer and an output layer; the neural network reaction model receives interaction data, the interaction data is imported into the input layer, and the input layer extracts at least one keyword of the interaction data. According to the invention, the central processing library is arranged to evaluate the matching degree of the execution command and the interaction data, scoring is carried out according to the matching degree, and the neural network reaction model with low rating is optimized; a one-way flow path of a hidden layer and an output layer in the neural network reaction model is corrected or output neurons in the output layer are added, so that the re-optimized neural network reaction model can return an execution command with a high matching degree for input data; the problem that an existing optimization method is difficult to optimize a neural network reaction model is solved.

This invention discloses a method, system, equipment, and medium for controlling pollutant concentration in wastewater pipe networks, relating to the field of drainage system technology. The method includes: obtaining the mass flushing rate of pollutants under pipe scouring; collecting the amount of components generated by chemical reactions within the pipe; constructing a pollutant reaction model; and, based on convection diffusion, obtaining the concentration changes of sediments following wastewater, constructing a convection diffusion reaction model. The mass flushing rate, pollutant reaction model, and convection diffusion reaction model are coupled to obtain a pollutant concentration prediction coupling model. The pollutant concentration in the wastewater pipe network is predicted based on the pollutant concentration prediction coupling model, and the pollutant concentration is divided into multiple levels. Different flushing methods are selected within different levels to control the pollutant concentration in the wastewater pipe network. This invention can adopt different methods according to different actual scenarios, thereby controlling the pollutant concentration within a reasonable range.

The application discloses a rotary kiln multi-physical phenomenon numerical simulation method based on artificial intelligence reasoning, relates to the technical field of rotary kiln multi-physical phenomenon simulation, and is based on the Euler multiphase flow equation, adds a heat transfer model, a chemical dynamics reaction model, a particle growth model, a chemical reaction model and the like, establishes a three-dimensional numerical model capable of accurately simulating the multi-physical and chemical phenomena such as the gas-solid two-phase flow movement, combustion, NOx generation, clinker calcination and particle growth in the rotary kiln, introduces a long short-term memory neural network to build an AI model, uses the AI model to predict the latter half of the working condition, accelerates the simulation speed, and realizes long-time simulation of the rotary kiln.

A method and medium for simulating a hydrogen absorption / desorption process of a hydrogen storage device relates to the technical field of energy system modeling and simulation, and the method comprises the following steps: obtaining a hydrogen absorption / desorption reaction model of a hydrogen storage material, the hydrogen storage material being a metal hydride for hydrogen storage and desorption in the hydrogen storage device; based on the reaction model, a user-defined function is generated, and the user-defined function is used for representing the hydrogen absorption / desorption process of the hydrogen storage material; determining a user-defined scalar based on the conversion rate of the hydrogen storage material; and based on the user-defined function and the user-defined scalar, simulating the hydrogen absorption / desorption process of the hydrogen storage device by using simulation software so as to realize coupling calculation of hydrogen absorption / desorption reaction and flow, heat transfer and mass transfer.

The present invention provides a method for predicting the production capacity of in-situ steam injection mining of oil shale, which belongs to the technical field of oil and gas field development. The method for predicting the production capacity of in-situ steam injection mining of oil shale uses the steam front evolution theory, combined with the basic characteristics of the pyrolysis reaction of kerogen in oil shale, to divide the entire oil shale reservoir during the steam injection in-situ mining process into different areas, establish a mathematical model for the in-situ steam injection mining of oil shale, and use it to predict its production capacity, while reflecting the different development stages of the oil shale reservoir; the production capacity prediction method belongs to the field of oil and gas field development, and can solve the problems of existing production capacity prediction methods such as numerous calculation parameters and poor calculation ability, which greatly increase the difficulty of solving the model and the model is prone to non-convergence, so that the production capacity settlement result cannot be given; at the same time, it can also solve the problems of existing methods such as difficulty in correcting the reaction model according to actual conditions, involving multiple experimental tests and theoretical calculations, and complex actual operations resulting in poor practicality.

The invention relates to a boiler waste gas blending combustion control method and device, a storage medium and electronic equipment, and the method comprises the steps that the air inlet amount and the waste gas blending combustion amount of a boiler are input into a boiler reaction model obtained through pre-training, and first boiler reaction parameters output by the boiler reaction model are obtained; under the condition that the first boiler reaction parameter does not meet the pollutant emission control condition, the position of an exhaust gas blending combustion inlet in the boiler reaction model is adjusted, and the air inlet amount and the exhaust gas blending combustion amount are adjusted; the adjusted air inlet amount and the adjusted waste gas blending combustion amount are input into the adjusted boiler reaction model, and second boiler reaction parameters output by the boiler reaction model are obtained; and under the condition that the second boiler reaction parameter meets the pollutant emission control condition, the air inlet amount and the waste gas blending combustion amount flowing into the boiler are controlled according to the adjusted waste gas blending combustion inlet position, the air inlet amount and the waste gas blending combustion amount.

The invention discloses a hydrogen-based shaft furnace one-dimensional multi-phase field modeling method based on fuzzy neural network optimization. The method comprises the following steps: firstly, constructing a multi-phase field one-dimensional preliminary model of internal temperature, pressure and concentration of the hydrogen-based shaft furnace by using a fluid mechanics equation; secondly, constructing a reaction model based on an additive reaction time law, endowing each characteristic time item with a coefficient multiplier, and fully exciting the model through random values of the coefficient multipliers to obtain multi-phase field model simulation data; then constructing an agent model and optimizing a coefficient multiplier by utilizing a fuzzy neural network and combining a non-dominated sorting genetic algorithm II (NSGA-II); and finally, inputting the optimized coefficient multiplier and newly collected shaft furnace feeding parameters into the multi-phase field model to obtain an optimized hydrogen-based shaft furnace multi-phase field simulation result. Through cooperative driving of a mechanism and data, precise modeling of a multi-phase field in the hydrogen-based shaft furnace is achieved, and high simulation precision is achieved for different smelting conditions.