Gasification slag separation fluidization resource utilization method and system

Abstract

The invention discloses a gasification slag separation fluidization resource utilization method and system, and relates to the field of solid waste treatment. The method comprises the following steps: fitting gasification slag particle size distribution through a particle size separation model, and calculating particle size distribution characteristic parameters; calculating kinetic parameters of the particles according to sensor data; calculating an energy relationship between the airflow and the solid particles based on the particle size distribution characteristic parameters, the particle kinetic parameters and the sensor data; an NOx emission optimization model is introduced to optimize NOx emission; a leaching kinetic model is introduced, and the leaching rate and the leaching solution concentration of the materials are calculated; classifying the sorted materials by combining a support vector machine algorithm; calculating a NOx reduction rate using the catalytic model; the exhaust emission trend is predicted, and the operation parameters are optimized in real time according to the prediction result. Through precise particle size separation, heat exchange optimization combustion and chemical leaching dynamic modeling, the separation precision of the gasified slag, the energy utilization rate and the resource recovery efficiency are improved, and efficient resource utilization is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of solid waste treatment, in particular to a gasification slag sorting fluidization resource utilization method and system. BACKGROUND

[0002] Gasification slag is a solid byproduct in the coal gasification process, mainly composed of unburned carbon, minerals and trace heavy metals. Its particle composition is complex, with a wide range of particle size distribution. Traditional treatment methods such as landfill, backfill and building material utilization have low resource utilization rate and high pollution risk. In order to improve the resource utilization rate, researchers have made optimization and exploration in the aspects of fluidized bed combustion, particle sorting and catalytic reduction in recent years.

[0003] Fluidized bed technology is widely used in gasification slag combustion, sorting and purification due to its excellent heat and mass transfer characteristics. However, the gas-solid two-phase interaction is complex, and the particle motion law is difficult to accurately describe, which affects heat transfer and chemical reaction rate. In addition, NOx emission is the main source of air pollution in the gasification process, and the existing catalytic reduction technology has limited efficiency under complex working conditions. How to optimize the combustion conditions to reduce NOx emission is a key problem.

[0004] To solve the above problems, the present application proposes a gasification slag sorting resource utilization method based on fluidized bed. The method calculates the particle size characteristics through a particle size sorting model, and optimizes the sorting process combined with a particle motion model. The combustion efficiency is improved by using thermodynamic optimization method, and the pollution is reduced by introducing NOx emission optimization model. The available substances are recovered by combining with leaching kinetics model, and intelligent classification is carried out by using support vector machine. In addition, the LSTM time series prediction model is used to predict the waste gas emission trend, and real-time optimization control is realized. This method integrates particle sorting, combustion optimization, pollution control and intelligent prediction technology, improves the resource utilization efficiency of gasification slag, and provides a new scheme for green environmental protection. SUMMARY

[0005] Based on the shortcomings of the prior art described above, the purpose of the present application is to provide a gasification slag sorting fluidization resource utilization method and system to solve the above technical problems.

[0006] To achieve the above purpose, the present application provides the following technical scheme: a gasification slag sorting fluidization resource utilization method, comprising: fitting the particle size distribution of the gasification slag by a particle size sorting model, and calculating the particle size distribution characteristic parameters; combining the particle size distribution characteristic parameters with sensor data, simulating the particle motion process by a particle motion model, and calculating the particle velocity, contact force and drag force dynamics parameters of the particles; Based on the particle size distribution characteristic parameters, particle dynamics parameters and sensor data, including the chemical composition data of the gasification slag, the fluidized bed operation parameters, the heat exchange between the gas flow and the solid particles, the reaction rate and the energy transfer are calculated; the NOx emission optimization model based on the reaction rate is introduced to optimize the NOx emission; The leaching kinetics model based on the diffusion and reaction constants is introduced to simulate the interaction of chemical reaction and diffusion, calculate the leaching rate of the material and the concentration of the leaching liquid; the support vector machine algorithm is combined to classify the sorted materials to recover the available materials; According to the characteristics of the recovered materials and the composition data of the exhaust gas and the operation conditions in the reactor, the NOx reduction rate is calculated using the catalytic model; based on the LSTM time series prediction model, the exhaust emission trend is predicted, and the operation parameters are optimized in real time according to the prediction results to adjust the reaction conditions to reduce the NOx emission and improve the catalytic reaction efficiency.

[0007] The application further provides that the particle size distribution of the gasification slag is fitted by the particle size sorting model, and the particle size distribution characteristic parameters are calculated, including: , wherein, is the cumulative distribution function of the particle size distribution, is the particle size of the particle, is the characteristic particle size, is the shape parameter, is the correction factor, is the correction index.

[0008] The application further provides that the particle motion model is used to simulate the particle motion process, and the particle velocity, contact force and drag force dynamics parameters of the particle are calculated, and the calculation logic is: , wherein, is the mass of the particle, is the particle velocity, is the contact force between the particles, is the drag force of the particle, is the gas flow velocity, is the gas density, is the particle volume, is the particle aggregation constant, is the gas flow influence parameter.

[0009] The application further provides that the heat exchange between the gas flow and the solid particles, the reaction rate and the energy transfer are calculated, and the energy conservation equation considers the heat transfer and energy conversion efficiency between the phases, , wherein, is the volume fraction of the phase, is the density of the phase, is the specific energy of the phase, is the velocity of phase, is the thermal conductivity, is the temperature, is the combustion released heat flow, is the combustion efficiency adjustment factor, is the bed temperature, is the temperature influence factor.

[0010] The application is further provided that the discharge optimization model, the calculation logic of which is: wherein, is the thermal efficiency, is the combustion released heat flow, is the recovered heat flow, is concentration, is the reference concentration, is discharge penalty factor.

[0011] The application is further provided that the leaching kinetics model, which simulates the interaction of chemical reaction and diffusion, the calculation logic of which is: wherein, is the chemical concentration, is the effective diffusion coefficient, is the reaction rate constant, is the equilibrium concentration, is the reference temperature, is the reaction order, is the temperature dependence factor.

[0012] The application is further provided that the combined support vector machine algorithm classifies the sorted materials to recover the available materials, and the decision function of the support vector machine considers the nonlinear mapping of the multidimensional feature space and the extended kernel function: wherein, is the input feature vector, including particle size, density, concentration, is the Lagrange multiplier, is the class label, is the bias term, is the kernel function width parameter, which processes the particle size, density, and concentration features through the support vector machine to determine whether the material is suitable for recovery and improve the recovery rate.

[0013] The application is further provided that the NOx reduction rate is calculated using the catalytic model, and a new adsorption constant and temperature dependence relationship are introduced, and the calculation logic is: wherein, is the NOx reduction rate, is the catalyst reaction rate constant, , For and the partial pressure of ammonia, , For and the adsorption equilibrium constant of ammonia, is the adsorption effect correction factor, is the temperature effect factor, is the reference temperature.

[0014] The application is further provided, the exhaust emission trend is predicted based on the LSTM time sequence prediction model, and the operation parameters are optimized in real time according to the prediction result, the reaction condition is adjusted to reduce NOx emission and improve catalytic reaction efficiency, including: Data input and feature construction, input variables include main factors affecting NOx generation in combustion and catalytic process, define input data vector: , is the reaction temperature, is the system pressure, is the gas-solid phase flow rate, is the gas , Concentration, is the catalyst surface reaction rate, is the catalyst activity attenuation factor at time t, output target variable: , is the NOx emission concentration, is the catalytic reaction efficiency; LSTM network predicts future NOx emission value through multiple time step history data; Based on the prediction result, the key parameters of combustion and catalytic reaction are optimized to minimize NOx emission and improve catalytic efficiency.

[0015] The application also provides a gasification slag sorting fluidized resource utilization system, the system comprises: Particle size sorting and data fitting module: the particle size distribution of gasification slag is fitted through the particle size sorting model, and the particle size distribution characteristic parameters are calculated; Particle motion and dynamics simulation module: combine the particle size distribution characteristic parameters with the sensor data, simulate the particle motion process through the particle motion model, calculate the particle velocity, contact force and drag force dynamics parameters of the particle; Fluidized bed blending and thermodynamic optimization module: based on particle size distribution characteristic parameters, particle dynamics parameters and sensor data, including chemical composition data of gasification slag, fluidized bed operation parameters, calculate the heat exchange, reaction rate and energy transfer between gas flow and solid particles; Introduce the NOx emission optimization model based on reaction rate to optimize NOx emission; Chemical leaching and support vector sorting module: introduce the leaching kinetics model established according to the diffusion and reaction constant, simulate the interaction of chemical reaction and diffusion, calculate the leaching rate and leaching liquid concentration of the material; combine the support vector machine algorithm to classify the sorted material to recover the available material; Exhaust gas catalytic purification and real-time optimization control module: according to the characteristics of the recovered material and the exhaust gas composition data and the operating conditions in the reactor, use the catalytic model to calculate the NOx reduction rate; based on the LSTM time series prediction model, predict the exhaust emission trend, and according to the prediction result, optimize the operating parameters in real time, adjust the reaction conditions to reduce NOx emission and improve the catalytic reaction efficiency.

[0016] The present application provides a kind of gasification slag sorting fluidization resource utilization method and system, the method is fitted to gasification slag particle size distribution by particle size sorting model, calculates particle size distribution characteristic parameter;Particle size distribution characteristic parameter is combined with sensor data, the particle motion model is simulated, the particle velocity, contact force and drag dynamics parameters of particle are calculated;Based on particle size distribution characteristic parameter, particle dynamics parameters and sensor data, including the chemical composition data of gasification slag, fluidized bed operating parameters, the heat exchange between gas flow and solid particle, reaction rate and energy transfer are calculated;Introduce the NOx emission optimization model based on reaction rate to optimize NOx emission;Introduce the leaching kinetics model established according to the diffusion and reaction constant, simulate the interaction of chemical reaction and diffusion, calculate the leaching rate and leaching liquid concentration of the material;Combine the support vector machine algorithm to classify the sorted material to recover the available material;According to the characteristics of the recovered material and the exhaust gas composition data and the operating conditions in the reactor, use the catalytic model to calculate the NOx reduction rate;Based on the LSTM time series prediction model, predict the exhaust emission trend, and according to the prediction result, optimize the operating parameters in real time, adjust the reaction conditions to reduce NOx emission and improve the catalytic reaction efficiency, the beneficial effects include: 1, accurately fitting gasification slag particle size distribution, improve sorting accuracy: the particle size distribution of gasification slag is fitted by particle size sorting model, so that the particle size distribution characteristic parameter is more consistent with the actual particle distribution, and the accuracy of particle sorting is improved; 2, based on heat exchange and reaction rate optimization fluidized bed combustion, improve energy utilization rate: adopt multiphase flow energy conservation equation, comprehensively consider the heat exchange, reaction rate and combustion efficiency between phases, optimize the heat transfer between gas flow and solid particle, improve the energy utilization rate of system; 3, chemical leaching kinetics modeling, improve recovery efficiency: considering the influence of chemical reaction rate, diffusion and temperature on leaching rate, improve the extraction efficiency of valuable components in gasification slag. Through the regulation of reaction order, effective diffusion coefficient and temperature dependence factor, the leaching process is more controllable, and the accurate resource recovery of different component gasification slag is realized.

[0017] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort. In the drawings: Figure 1 A flow chart of a gasification slag sorting fluidized resource utilization method is shown for an exemplary embodiment of the present application; Figure 2 A structural schematic diagram of a gasification slag sorting fluidized resource utilization system is shown for an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiments of the present application will be described below with reference to the drawings and preferred embodiments, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. The present application can also be implemented or applied by different specific embodiments, and the details in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, but not for limiting the protection scope of the present application.

[0020] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and the diagrams only show the components related to the present application, but not the number, shape and size of the components when actually implemented. The actual implementation of each component may be randomly changed in shape, number and proportion, and the layout pattern of the components may be more complex.

[0021] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application, however, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details, and in other embodiments, the known structures and devices are shown in the form of block diagrams rather than in the form of details, so as not to make the embodiments of the present application difficult to understand.

[0022] Embodiment one A gasification slag sorting fluidized resource utilization method, such as Figure 1The application is shown, comprising: The particle size distribution of the gasification slag is fitted by a particle size sorting model to calculate the particle size distribution characteristic parameters; The particle size distribution characteristic parameters are combined with the sensor data to simulate the particle motion process by a particle motion model, and the particle velocity, contact force and drag force dynamics parameters of the particles are calculated; Based on the particle size distribution characteristic parameters, the particle dynamics parameters and the sensor data, including the chemical composition data of the gasification slag, the fluidized bed operation parameters, the heat exchange between the gas flow and the solid particles, the reaction rate and the energy transfer are calculated; the NOx emission optimization model based on the reaction rate is introduced to optimize the NOx emission; The leaching kinetics model established according to the diffusion and reaction constants is introduced to simulate the interaction of chemical reaction and diffusion, and the leaching rate and leaching liquid concentration of the material are calculated; the support vector machine algorithm is combined to classify the sorted material to recover the available material; According to the characteristics of the recovered material and the composition data of the exhaust gas and the operation conditions in the reactor, the NOx reduction rate is calculated using the catalytic model; based on the LSTM time series prediction model, the exhaust emission trend is predicted, and the operation parameters are optimized in real time according to the prediction results to adjust the reaction conditions to reduce the NOx emission and improve the catalytic reaction efficiency.

[0023] The application is further provided, wherein the particle size distribution of the gasification slag is fitted by a particle size sorting model to calculate the particle size distribution characteristic parameters, comprising: , wherein is the cumulative distribution function of the particle size distribution, is the particle size of the particle, is the characteristic particle size, is the shape parameter, is the correction factor, is the correction index, specifically, the particle size sorting model fits the particle size distribution of the gasification slag, and uses the cumulative distribution function to describe the particle size distribution of the particles. The cumulative distribution function gives the cumulative proportion of particles with a particle size less than or equal to a certain particle size Therefore, the relative number of particles of different particle sizes in the sample can be reflected, so as to evaluate the distribution characteristics of the particles, the model can accurately describe the distribution of the particles by accurately fitting the particle size distribution of the gasification slag, and further realize higher precision of particle sorting, optimize the resource recovery process, accurately control the sorting operation by calculating the proportion of particles of different particle sizes, so that the separation of the particles is more accurate, avoids the misclassification of unsuitable particles, reduces the waste of resources, and accurately masters the particle distribution characteristics, which can better predict which particles are more suitable for recovery and which particles should be excluded, and improves the resource recovery efficiency of waste.

[0024] The present invention is further configured such that, by simulating the particle motion process using a particle motion model, the particle velocity, contact force, and drag force dynamic parameters are calculated, and the calculation logic is as follows: ,in, For the mass of the particles, For particle velocity, The contact force between particles, For the drag force of the particles, For airflow velocity, For gas density, For particle volume, The particle aggregation constant, Specifically, this particle motion model aims to simulate the dynamic behavior of gasified slag particles in a fluidized bed, using parameters related to airflow influence. The goal of the equations is to derive the particle motion process by analyzing the mass, velocity changes, and external forces acting on each particle. This simulation provides accurate predictions of particle behavior in the fluidized bed, thereby affecting operating conditions, material sorting, and energy transfer. The model precisely simulates particle motion in the airflow, including particle velocity, acceleration, and their interaction with the airflow, further improving the control precision of the fluidized bed system. By accurately calculating the particle motion process, particle sorting efficiency within the fluidized bed can be optimized, enhancing material separation and resource recovery. Accurate simulation of particle dynamics optimizes airflow velocity, particle aggregation, and reaction conditions, improving the energy utilization efficiency of the fluidized bed and reducing NOx emissions.

[0025] The present invention is further configured such that the energy conservation equation for calculating the heat exchange, reaction rate, and energy transfer between the airflow and solid particles takes into account the heat transfer and energy conversion efficiency between each phase. ,in, The volume fraction of the phase. For the density of the phase, For the specific energy of phase, For the velocity of phase, Thermal conductivity, For temperature, To release heat during combustion, As a combustion efficiency adjustment factor, For bed temperature, Specifically, to account for temperature influencing factors, the heat transfer process between different phases is described using an energy conservation equation through precise calculations of heat exchange, reaction rates, and energy transfer between the airflow and solid particles. This energy conservation equation considers factors such as the mass, specific energy, velocity, and thermal conductivity of each phase, aiming to accurately simulate energy conversion and transfer in a fluidized bed. This further optimizes the system's thermal management and reaction efficiency. By accurately calculating the heat exchange process between the airflow and particles, the energy transfer efficiency in the fluidized bed can be improved, thermal management optimized, and energy waste reduced. By introducing combustion efficiency adjustment factors and combustion heat flow models, the heat release during combustion can be precisely adjusted, achieving more efficient energy recovery. Furthermore, by comprehensively modeling the energy transfer and reaction rates between each phase, reaction conditions can be optimized, resulting in more efficient reaction rates and lower NOx emissions.

[0026] The present invention is further configured such that the calculation logic of the emission optimization model is as follows: ,in, For thermal efficiency, To release heat during combustion, To recover heat flow, for concentration, For reference concentration, for Emission penalty factor, specifically, thermal efficiency model Thermal efficiency is used to calculate the thermal efficiency of the combustion process by comparing the ratio of heat flow released during combustion to heat flow recovered. Higher thermal efficiency indicates that the system utilizes the generated heat more effectively. Emission optimization models are used to calculate The cumulative amount of emissions, based on actual The ratio of concentration to reference concentration, along with an emission penalty factor, is used to assess emissions. This model allows for optimization of the combustion process and reduction of emissions. To optimize emissions and achieve emission reduction goals, thermal efficiency models help optimize heat recovery and utilization, reduce energy waste, and improve the overall energy efficiency of the system. Emissions optimization models effectively reduce emissions through real-time monitoring and penalty mechanisms. The emissions help comply with environmental protection regulations and reduce air pollution. By optimizing thermal management and emission control, this invention can achieve more efficient operation of systems such as fluidized beds, while reducing environmental impact and meeting the requirements of green and sustainable development.

[0027] The present invention is further configured such that the leaching kinetic model simulates the interaction between chemical reaction and diffusion, and its calculation logic is as follows: ,in, For chemical substance concentration, is the effective diffusion coefficient, is the reaction rate constant, is the equilibrium concentration, is the reference temperature, is the reaction order, is the temperature-dependent factor, the leaching kinetics model used mainly simulates the interaction between chemical reaction and diffusion, especially in the resource utilization process of gasification slag, by calculating the concentration change of chemical substances, reaction rate and diffusion behavior to optimize the leaching process, represents the concentration of chemical substances the rate of change with time t, that is, the increase and decrease of the concentration of substances in the leaching process with time, is the diffusion term, which represents the diffusion process of substances in the medium. is the effective diffusion coefficient, is the spatial second derivative of concentration, which describes the distribution of concentration in space. This term is used to represent the diffusion process of substances through the medium, is the reaction rate term, which describes the rate of chemical reaction. is the reaction rate constant, describes the nonlinear relationship between reaction rate and chemical substance concentration deviating from the equilibrium concentration . is the reaction order, which represents the degree of dependence of reaction rate on concentration difference. The reaction rate is usually a function that increases with the increase of concentration difference, is the temperature-dependent term, which represents the influence of temperature on reaction rate. Temperature has a strong influence on the rate of chemical reaction, and generally, the higher the temperature, the faster the reaction rate. is the current temperature, is the reference temperature, is the temperature-dependent factor, which is used to quantify the influence of temperature change on reaction rate, and reflects the nonlinear relationship between temperature and reaction rate. By establishing an accurate leaching kinetics model, the interaction between chemical reaction and diffusion can be simulated, the leaching rate can be accurately calculated, and the recovery efficiency of valuable components in gasification slag can be effectively improved. The model provides accurate numerical simulation for chemical reaction and diffusion process, which can be used for optimization prediction in practical application, and improve the controllability and stability of the leaching process.

[0028] The application further provides that the combined support vector machine algorithm is used to classify the sorted materials to recover the available materials, and the decision function of the support vector machine considers the nonlinear mapping of the multi-dimensional feature space and the extended kernel function: wherein, is the input feature vector, including particle size, density and concentration, is the Lagrange multiplier, is the class label, is a bias term, is a kernel function width parameter, which is used to process the granularity, density and concentration features through a support vector machine (SVM) to determine whether the material is suitable for recycling and improve the recycling rate, is a decision function of the SVM, which represents the classification decision result made by the SVM model on the input feature vector . The output of the SVM is +1 or -1, representing whether the material is suitable for recycling, is the core part of the decision function, which represents the weighted sum of each support vector . The contribution of each support vector to the decision boundary is determined by its corresponding Lagrange multiplier and the class label , represents the Euclidean distance between the input feature vector and the support vector , which is used to calculate their similarity. By classifying the multi-dimensional features of the material such as granularity, density and concentration, the SVM can accurately determine which materials are suitable for recycling and which are not, thereby improving the recycling efficiency and recycling rate. The SVM uses a kernel function to map the data to a high-dimensional feature space, which can effectively handle complex nonlinear problems. Especially when the relationship between the material features is complex, better classification results can be obtained. By adjusting the parameters of the kernel function, the SVM can adaptively adjust the classification decision according to different material features, making the recycling process more flexible and efficient.

[0029] The application further provides that the NOx reduction rate is calculated using a catalytic model, and a new adsorption constant and temperature dependence relationship is introduced, and the calculation logic is: wherein, is the NOx reduction rate, representing the rate of NOx reduction per unit time, is the catalyst reaction rate constant, representing the efficiency of the catalyst in catalyzing the NOx reduction reaction, , is and the partial pressure of ammonia, , is and the adsorption equilibrium constant of ammonia, is the adsorption effect correction factor, is the temperature effect factor, For reference temperature, the catalytic reaction rate refers to the rate at which the catalyst catalyzes the reaction under given conditions. For the NOx reduction reaction, the catalyst accelerates the reduction of NOx by reducing the activation energy of the reaction, and the model can more accurately describe the rate of the NOx reduction reaction by introducing a new adsorption constant and temperature dependence, thereby improving the NOx reduction efficiency, reducing environmental pollution, and optimizing the use efficiency of the catalyst. By introducing temperature-dependent factors and adsorption effect correction factors, the model can adapt to changes in different reaction temperatures and catalyst surface adsorption conditions, and optimize the use efficiency of the catalyst. The various parameters in the model, such as adsorption constants, reaction rate constants and temperature factors, can be adjusted according to different reaction conditions, thereby improving the controllability of the reaction process and optimizing the overall efficiency of the reaction.

[0030] The application further provides that the LSTM time series prediction model predicts the exhaust emission trend, and optimizes the operating parameters in real time according to the prediction results to adjust the reaction conditions to reduce NOx emissions and improve the catalytic reaction efficiency, including: Data input and feature construction, input variables include the main factors affecting NOx generation in the combustion and catalytic process, and the input data vector is defined: , For reaction temperature, For system pressure, For gas-solid phase flow rate, For gas phase , Concentration, For catalyst surface reaction rate, For catalyst activity decay factor at time t, output target variable: , For NOx emission concentration, For catalytic reaction efficiency; The LSTM network predicts the future NOx emission value through multiple time steps of historical data; Based on the prediction results, the key parameters of the combustion and catalytic reaction are optimized to minimize NOx emissions and improve catalytic efficiency.

[0031] Example two Please refer to Figure 2 , the exemplary gasification slag sorting fluidized resource utilization system includes: Particle size sorting and data fitting module: fitting the particle size distribution of gasification slag by particle size sorting model, calculating the particle size distribution characteristic parameters; Particle motion and dynamics simulation module: combine particle size distribution characteristic parameters with sensor data, simulate particle motion process by particle motion model, calculate particle velocity, contact force and drag dynamics parameters of particles; Fluidized bed blending and thermodynamic optimization module: based on particle size distribution characteristic parameters, particle dynamics parameters and sensor data, including gasification slag chemical composition data, fluidized bed operation parameters, calculate the heat exchange between gas flow and solid particles, reaction rate and energy transfer; introduce a NOx emission optimization model based on reaction rate to optimize NOx emission; Chemical leaching and support vector sorting module: introduce a leaching kinetics model based on diffusion and reaction constants to simulate the interaction of chemical reaction and diffusion, calculate the leaching rate of materials and the concentration of leaching solution; combine support vector machine algorithm to classify sorted materials to recover useful materials; Waste gas catalytic purification and real-time optimization control module: according to the characteristics of recovered materials and waste gas composition data and the operation conditions in the reactor, use the catalytic model to calculate the NOx reduction rate; based on the LSTM time series prediction model to predict the trend of waste gas emission, and according to the prediction results, real-time optimization of operation parameters, adjust the reaction conditions to reduce NOx emission and improve the efficiency of catalytic reaction.

[0032] It should be noted that the gasification slag sorting fluidized resource utilization system provided by the above embodiment and the gasification slag sorting fluidized resource utilization method provided by the above embodiment belong to the same concept, wherein the specific way of each module and unit to perform operation has been described in detail in the method embodiment, which will not be repeated here. The gasification slag sorting fluidized resource utilization system provided by the above embodiment can complete the above described all or part of functions by different functional modules according to the needs in the actual application, that is, the internal structure of the system is divided into different functional modules to complete the above described all or part of functions, which is not limited here.

[0033] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server, or data center to another via wired (for example, infrared, wireless, microwave, etc.) or wireless means. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, or the like, which includes one or a set of available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0034] It should be understood that the term "and / or" herein merely describes an association relationship of associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after it, but it can also represent an "and / or" relationship. The specific meaning can be understood according to the context before and after it.

[0035] In this application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0036] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined according to their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0037] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0038] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0039] In several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0040] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0041] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0042] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0043] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for fluidized resource utilization of gasification slag sorting, characterized in that, The method comprises the following steps: Fitting the particle size distribution of the gasification slag by a particle size sorting model to calculate the particle size distribution characteristic parameters; Combining the particle size distribution characteristic parameters with the sensor data, simulating the particle motion process by a particle motion model, and calculating the particle velocity, contact force and drag force dynamics parameters of the particles; Based on the particle size distribution characteristic parameters, particle dynamics parameters and sensor data, including the chemical composition data of the gasification slag and the fluidized bed operating parameters, the heat exchange, reaction rate and energy transfer between the gas flow and the solid particles are calculated; a NOx emission optimization model based on the reaction rate is introduced to optimize the NOx emission; A leaching kinetics model based on diffusion and reaction constants is introduced to simulate the interaction of chemical reaction and diffusion, calculate the leaching rate of the material and the concentration of the leaching liquid; support vector machine algorithm is combined to classify the sorted materials to recover the available materials; According to the characteristics of the recovered materials, the composition data of the exhaust gas and the operating conditions in the reactor, the NOx reduction rate is calculated using a catalytic model; based on the LSTM time series prediction model, the exhaust emission trend is predicted, and the operating parameters are optimized in real time according to the prediction results to adjust the reaction conditions to reduce NOx emission and improve the efficiency of catalytic reaction.

2. The method according to claim 1, wherein the method is characterized by, The particle size distribution of the gasification slag is fitted by the particle size sorting model, and the particle size distribution characteristic parameters are calculated, including the calculation logic of the particle size sorting model: wherein, is a cumulative distribution function of the particle size distribution, is a particle size of the particle, is a characteristic particle size, is a shape parameter, is a correction factor, is a correction index.

3. A method for sorting fluidized resource utilization of gasification slag according to claim 2, characterized in that, The particle motion model is used to simulate the particle motion process, calculate the particle velocity, contact force and drag force dynamics parameters, and the calculation logic is as follows: Wherein, is the mass of the particle, is the particle velocity, is the contact force between particles, is the drag force of the particle, is the gas flow velocity, is the gas density, is the particle volume, is the particle aggregation constant, is the gas flow influence parameter.

4. A method for sorting fluidized resource utilization of gasification slag according to claim 2, 3, characterized in that, The heat exchange between the gas flow and the solid particles, the reaction rate and the energy transfer, whose energy conservation equation considers the heat transfer between phases and the energy conversion efficiency, wherein, is the volume fraction of the phase, is the density of the phase, is the specific energy of the phase, is the velocity of the phase, is the thermal conductivity, is the temperature, is the combustion released heat flow, is the combustion efficiency adjustment factor, is the bed temperature, is the temperature influence factor.

5. A method for sorting fluidized resource utilization of gasification slag according to claim 4, characterized in that, The emission optimization model, whose calculation logic is: wherein, is the thermal efficiency, is the combustion released heat flow, is the recovered heat flow, is the concentration, is the reference concentration, is the emission penalty factor.

6. The method of claim 1, wherein the method further comprises: The leaching kinetics model simulates the interaction of chemical reactions and diffusion, and the calculation logic is: wherein, is the concentration of the chemical substance, is the effective diffusion coefficient, is the reaction rate constant, is the equilibrium concentration, is the reference temperature, is the reaction order, is the temperature dependence factor.

7. The method according to claim 1, wherein the method is characterized by, The combined support vector machine algorithm classifies the sorted materials to recover the available materials, and the decision function of the support vector machine considers the nonlinear mapping of the multidimensional feature space and the extended kernel function: wherein, is the input feature vector, including granularity, density, concentration, is the Lagrange multiplier, is the class label, is the bias term, is the kernel function width parameter, and the granularity, density and concentration features are processed by the support vector machine to determine whether the material is suitable for recycling, thereby improving the recycling rate.

8. The method according to claim 7, wherein the fluidized resource utilization method of sorting and utilizing the gasification slag is characterized by, The NOx reduction rate is calculated using a catalytic model, and a new adsorption constant and temperature dependence are introduced, and the calculation logic is as follows: wherein, is the NOx reduction rate, is the catalyst reaction rate constant, , is and the partial pressure of ammonia, , is and the adsorption equilibrium constant of ammonia, is the adsorption effect correction factor, is the temperature effect factor, is the reference temperature.

9. The method of claim 1, wherein the method further comprises: The LSTM time series prediction model predicts the exhaust emission trend, and optimizes the operating parameters in real time according to the prediction results to adjust the reaction conditions to reduce NOx emission and improve the efficiency of catalytic reaction, which comprises: Data input and feature construction, input variables include the main factors affecting the generation of NOx in the combustion and catalytic process, define the input data vector: , is the reaction temperature, is the system pressure, is the gas-solid phase flow rate, is the gas phase , concentration, is the catalyst surface reaction rate, is the catalyst activity decay factor at time t, the output target variable: , is the NOx emission concentration, is the catalytic reaction efficiency; The LSTM network predicts the future NOx emission value through multiple time steps of historical data; Based on the prediction results, the key parameters of combustion and catalytic reaction are optimized to minimize NOx emission and improve catalytic efficiency.

10. A gasification slag sorting fluidized resource utilization system for realizing the gasification slag sorting fluidized resource utilization method according to any one of claims 1-9, characterized in that, The method comprises the following steps: Particle size sorting and data fitting module: fitting the particle size distribution of the gasification slag by a particle size sorting model to calculate the particle size distribution characteristic parameters; Particle motion and dynamics simulation module: combining the particle size distribution characteristic parameters with the sensor data, simulating the particle motion process by a particle motion model, and calculating the particle velocity, contact force and drag force dynamics parameters of the particles; Fluidized bed blending and thermodynamic optimization module: based on the particle size distribution characteristic parameters, particle dynamics parameters and sensor data, including the chemical composition data of the gasification slag and the fluidized bed operating parameters, the heat exchange, reaction rate and energy transfer between the gas flow and the solid particles are calculated; a NOx emission optimization model based on the reaction rate is introduced to optimize the NOx emission; Chemical leaching and support vector sorting module: introducing a leaching kinetics model based on diffusion and reaction constants to simulate the interaction of chemical reaction and diffusion, calculate the leaching rate of the material and the concentration of the leaching liquid; support vector machine algorithm is combined to classify the sorted materials to recover the available materials; Exhaust gas catalytic purification and real-time optimization control module: according to the characteristics of the recovered materials, the composition data of the exhaust gas and the operating conditions in the reactor, the NOx reduction rate is calculated using a catalytic model; based on the LSTM time series prediction model, the exhaust emission trend is predicted, and the operating parameters are optimized in real time according to the prediction results to adjust the reaction conditions to reduce NOx emission and improve the efficiency of catalytic reaction.

Images