Urban solid waste incineration process tail gas emission modeling analysis system and method

Abstract

This invention provides a system and method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes. The system includes a module for constructing a multi-software coupled full-process numerical simulation model under baseline operating conditions, a module for acquiring simulation mechanism data under multiple operating conditions, a module for constructing an exhaust gas emission model based on MIMO-LRDT, and a module for analyzing exhaust gas emissions based on single / dual factors. The module for constructing the multi-software coupled full-process numerical simulation model under baseline operating conditions is connected to the module for acquiring simulation mechanism data under multiple operating conditions. The module for acquiring simulation mechanism data under multiple operating conditions is connected to the module for constructing the exhaust gas emission model based on MIMO-LRDT, and the module for constructing the exhaust gas emission model based on MIMO-LRDT is connected to the module for analyzing exhaust gas emissions based on single / dual factors. The system and method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes provided by this invention can achieve exhaust gas emission modeling and analysis during urban solid waste incineration processes.

Description

Technical Field

[0001] This invention relates to the field of urban solid waste incineration technology, and in particular to a system and method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes. Background Technology

[0002] The generation of municipal solid waste (MSW) is gradually increasing with economic development and accelerated urbanization. MSW incineration (MSWI) technology transforms waste into energy (WTE) through processes such as fermentation, combustion, heat exchange, and purification, and has been widely used due to its advantages of harmlessness, volume reduction, and resource recovery. Currently, in actual industrial sites, domain experts combine multimodal information such as distributed control systems (DCS), flame video images, and shift work records, and rely on expert experience to manually set the "air and fuel distribution" operating variable parameters to ensure the safe and stable operation of the MSWI process, addressing issues such as abnormal furnace temperature and excessive exhaust emissions. This manual operation mode suffers from serious randomness, lag, limited human capacity, and differences in expert experience, making it difficult to guarantee that MSWI power plants maintain stable operating conditions in the long term. Therefore, it is necessary to study the mapping relationship between the "air and fuel distribution" operating variables and exhaust gas emissions to support the control strategy of the MSWI process.

[0003] MSWI processes, typical of process industries, involve complex physicochemical reactions with numerous and interdependent variables, making it difficult to construct accurate mathematical models. Currently, numerical simulation software such as FLIC, Computational Fluid Dynamics (CFD), and Aspen Plus have become effective tools for analyzing MSWI processes due to their efficiency, economy, and ease of use. However, simulations using single numerical simulation software are insufficient for effectively analyzing the complex mapping relationship between "air distribution and material distribution" variables and exhaust gas emission concentrations. Therefore, it is essential to design a modeling and analysis system and method for exhaust gas emissions from urban solid waste incineration processes. Summary of the Invention

[0004] The purpose of this invention is to provide a system and method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes, which can realize the modeling and analysis of exhaust gas emissions from urban solid waste incineration processes.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A system for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes includes: a multi-software coupled full-process numerical simulation model construction module under baseline operating conditions, a multi-operating-condition simulation mechanism data acquisition module, a MIMO-LRDT-based exhaust gas emission model construction module, and a single / dual-factor-based exhaust gas emission analysis module. The multi-software coupled full-process numerical simulation model construction module under baseline operating conditions is connected to the multi-operating-condition simulation mechanism data acquisition module, the multi-operating-condition simulation mechanism data acquisition module is connected to the MIMO-LRDT-based exhaust gas emission model construction module, and the MIMO-LRDT-based exhaust gas emission model construction module is connected to the single / dual-factor-based exhaust gas emission analysis module.

[0007] The multi-software coupled full-process numerical simulation model construction module under the benchmark working condition is used to construct a full-process numerical simulation model that can fit the benchmark working condition of the industrial site.

[0008] The multi-operational-condition simulation mechanism data acquisition module is used to acquire simulation mechanism data under multiple operating conditions from the full-process data simulation model.

[0009] The exhaust emission model construction module based on MIMO-LRDT is used to construct an exhaust emission model based on the MIMO-LRDT algorithm.

[0010] The exhaust emission analysis module based on single / two factors is used to perform single / two-factor analysis on the mapping relationship between the manipulated variables and the exhaust emission concentration based on the exhaust emission model.

[0011] This invention also provides a method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes, applied to the aforementioned urban solid waste incineration exhaust gas emission modeling and analysis system, comprising the following steps:

[0012] Step 1: Construct a full-process numerical simulation model that can fit the benchmark working conditions of the industrial site based on the multi-software coupled full-process numerical simulation model construction module under the benchmark working conditions;

[0013] Step 2: The multi-operating-condition simulation mechanism data acquisition module acquires simulation mechanism data under multiple operating conditions through a full-process data simulation model;

[0014] Step 3: Construct an exhaust emission model based on the MIMO-LRDT algorithm using the exhaust emission model construction module based on MIMO-LRDT;

[0015] Step 4: Using the exhaust emission analysis module based on single / two factors, perform single / two-factor analysis on the mapping relationship between the manipulated variables and the exhaust emission concentration based on the exhaust emission model.

[0016] Optionally, in step 1, a full-process numerical simulation model capable of fitting the benchmark working conditions of the industrial site is constructed based on the multi-software coupled full-process numerical simulation model construction module under the benchmark working conditions. This specifically includes the following steps:

[0017] Step 101: Solid-phase combustion process on the grate based on FLIC simulation;

[0018] Step 102: Gas-phase combustion process in the furnace based on Fluent simulation;

[0019] Step 103: Waste heat exchange and other flue gas purification processes based on Aspen Plus simulation.

[0020] Optionally, in step 101, the solid-phase combustion process on the grate based on FLIC simulation specifically includes:

[0021] The solid-phase combustion process on the grate was simulated using FLIC, including the solid-phase MSW combustion model, the basic conservation equations, and the equations for component transport and thermal radiation conversion.

[0022] The solid-phase MSW combustion process includes stages of moisture evaporation, volatile matter release, volatile matter combustion, and coke oxidation. The MSW is pushed to the grate by the feeder, and after being subjected to high-temperature heat radiation from the furnace interior and walls, the moisture gradually evaporates at the following rate:

[0023]

[0024] In the formula, R evp S represents the rate of water evaporation. a h is the particle surface area. s C is the convective mass transfer coefficient. w,s and C w,g Q represents the moisture content in MSW and the mixed gas, respectively. cr H absorbs heat during radiation and convection heat transfer processes. evp T is the amount of heat absorbed for water evaporation. s MSW temperature: After complete evaporation of moisture, the MSW reaches the temperature at which volatiles are released. The release process of the main volatiles is as follows:

[0025] MSW→Volatile(C m H n ,CO,CO2,H2O)+Char (2)

[0026] When volatile gases mix with air and burn, the corresponding combustion reaction is as follows:

[0027] C m H n +(m / 2+n / 4)O2→mCO+n / 2H2O (3)

[0028] CO + 1 / 2O₂ → CO₂ (4)

[0029] Its combustion rate is the minimum of the kinetic rate and the mixing rate. MSW gradually turns into coke as volatiles are released, and it further reacts with air to produce CO and CO2, as follows:

[0030] C+αO2→2(1-α)CO+(2α-1)CO2 (5)

[0031] In the formula, α is the stoichiometric coefficient, and 0.5≤α≤1.

[0032] Optionally, in step 102, the in-furnace gas-phase combustion process based on Fluent simulation specifically includes:

[0033] The FLIC output is treated as the boundary condition for the Fluent numerical simulation. In Fluent, the combustion component CmHn is replaced with CH4. The second-order upwind scheme and SIMPLE algorithm are used to discretize and solve the gas-phase combustion equation. The thermal radiation DO model is as follows:

[0034]

[0035] In the formula, I represents the radiation intensity. and These are the position vector and direction vector, respectively, where a is the absorption coefficient and σ is the position vector and direction vector. s Let ρ be the diffusion coefficient, n be the refractive index, σ be the Boltzmann constant, Φ be the phase function, and Ω′ be the fixed angle. The standard k-ε model for solving turbulent gas flow is:

[0036]

[0037]

[0038] In the formula, ρ is the gas density, k is the turbulent kinetic energy, and u i For velocity, x i and x j Given a coordinate system and μ as the turbulent viscosity, the conceptual model for eddy dissipation in the interaction between gas flow and combustion chemical reaction is as follows:

[0039]

[0040] In the formula, Y i Let i be the mass fraction of substance i. R is the diffusion flux of matter. i S is the net production rate. iTo generate additional rates, the thermal radiation distribution data after the Fluent simulation is completed is used as the input of FLIC for iterative coupling. After the temperature difference between the two outputs meets the set error, the visualized furnace temperature field, velocity field and concentration field are output.

[0041] Optionally, in step 103, the waste heat exchange and other flue gas purification processes based on Aspen Plus simulation are as follows:

[0042] Using the flue gas temperature and composition output from FLIC, the furnace temperature output from Fluent, the MSW ash content obtained from laboratory analysis, and the secondary air temperature, secondary air flow rate, and urea solution dosage collected from the actual industrial site as inputs, the flue gas components include H2O, N2, O2, H2, CO, CO2, CH4, and S. The combustion process in the furnace is simulated using a Gibbs reactor RGibbs, and the denitrification reaction is simulated using a stoichiometric reactor Rstoic1. The reactions involved are as follows:

[0043]

[0044]

[0045] In the waste heat boiler heat exchange process stage, a simulation of the heat exchange process between high-temperature flue gas and the superheater and economizer is used with two stream heat exchanger modules to obtain the cooled furnace outlet flue gas G1. In the flue gas treatment process stage, a stoichiometric reactor is used to simulate the acid removal process of flue gas G1.

[0046]

[0047] A mixer module simulates the adsorption process of heavy metals and dioxins in flue gas by activated carbon. A component separator module simulates a bag filter. Finally, a distributor module simulates the process of fly ash entering the ash silo, resulting in treated flue gas G2. During the flue gas emission stage, a compressor module simulates the emission of flue gas G3 into the atmosphere.

[0048] Optionally, in step 2, the multi-operating-condition simulation mechanism data acquisition module acquires simulation mechanism data under multiple operating conditions through a full-process data simulation model, specifically as follows:

[0049] A four-level orthogonal experimental design was conducted using one non-operating variable and eleven operating variables. The non-operating variable was the MSW component, and the operating variables included feed rate, grate speed, primary air temperature 1, primary air temperature 2, primary air temperature 3, primary air flow rate 1, primary air flow rate 2, primary air flow rate 3, primary air flow rate 4, secondary air temperature, and secondary air flow rate. Based on the orthogonal experimental design, simulation mechanism data under multiple operating conditions were obtained through a full-process data simulation model.

[0050] Optionally, in step 3, an exhaust emission model based on the MIMO-LRDT algorithm is constructed using the MIMO-LRDT-based exhaust emission model construction module, specifically as follows:

[0051] Based on the simulation mechanism data under the above multiple operating conditions, a tail gas emission model including CO, CO2, O2, SO2, and NOx emissions is established using a multi-input multi-output linear regression decision tree algorithm. The obtained simulation mechanism data is denoted as...

[0052] The mean square error of the target value is calculated by traversing the simulation mechanism data:

[0053]

[0054] In the formula, Let (n,i) represent the loss function value of MSE, and (n,i) represent the i-th feature value of the n-th sample in the k-th iteration.

[0055] Based on the calculation results, the minimum MSE is selected as the first non-leaf node. As a splitting variable, it is:

[0056]

[0057] Repeat the above process to obtain (T / 2-1) intermediate nodes based on the minimum sample size θ set empirically.

[0058] The predicted output of the CART leaf nodes is calculated using the linear regression method, as follows:

[0059]

[0060] In the formula, and Let be the output, input, and weight matrix of the t-th leaf node, respectively;

[0061] Obtaining the weight vector is reduced to solving a class of overdetermined matrix equations, and a regularized least squares loss function is used, as follows:

[0062]

[0063] In the formula, λ represents the regularization coefficient, and λ≥0. The above loss function is applied to... The gradient is expressed as:

[0064]

[0065] make have to:

[0066]

[0067] Based on the obtained weight vector, the predicted values ​​for the leaf nodes are calculated. Considering all leaf nodes, the MIMO-LRDT model is represented as:

[0068]

[0069] According to specific embodiments provided by the present invention, the following technical effects are disclosed: The present invention provides a system and method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes. The system includes a module for constructing a multi-software coupled full-process numerical simulation model under baseline conditions, a module for acquiring simulation mechanism data under multiple operating conditions, a module for constructing an exhaust gas emission model based on MIMO-LRDT, and a module for analyzing exhaust gas emissions based on single / dual factors. The method includes constructing a full-process numerical simulation model capable of fitting the baseline operating conditions of the industrial site based on the multi-software coupled full-process numerical simulation model construction module; acquiring simulation mechanism data under multiple operating conditions through the full-process data simulation model based on the multi-operating conditions simulation mechanism data acquisition module; constructing an exhaust gas emission model based on the MIMO-LRDT algorithm through the exhaust gas emission model construction module based on MIMO-LRDT; and analyzing exhaust gas emissions based on single / dual factors. The emission analysis module performs single / two-factor analysis on the mapping relationship between manipulated variables and exhaust emission concentrations based on the exhaust emission model. A full-process numerical simulation model lays the foundation for analyzing the relationship between the "air and material distribution" manipulated variable and exhaust emissions. Based on the full-process numerical simulation model, a twelve-factor, four-level orthogonal experiment was designed and implemented to obtain simulation mechanism data under multiple operating conditions, providing support for building a data-driven model. The MIMO-LRDT algorithm is used to establish a multi-input, multi-output data-driven model with the "air and material distribution" manipulated variable as input and CO, CO2, O2, SO2, and NOx as outputs. Compared with the comparative algorithm, this model has certain advantages in model fit and model structure. Based on the established MIMO-LRDT data-driven model, a single / two-factor strategy is used to analyze the relationship between manipulated variables and exhaust emissions, providing corresponding guidance for the optimization of MSWI process operation. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 A schematic diagram of the urban solid waste incineration process;

[0072] Figure 2 This is a schematic diagram of the structure of the exhaust gas emission modeling and analysis system for urban solid waste incineration process according to an embodiment of the present invention;

[0073] Figure 3 This is a schematic diagram showing the gas composition distribution during the combustion process on the grate.

[0074] Figure 4 This is a schematic diagram of the simulation results of the combustion process inside the furnace;

[0075] Figure 5a This is a schematic diagram of CO emissions obtained from simulation.

[0076] Figure 5b This is a schematic diagram of CO2 emissions obtained from simulation.

[0077] Figure 5c This is a schematic diagram of O2 emissions obtained from simulation.

[0078] Figure 5d This is a schematic diagram of SO2 emissions obtained from simulation.

[0079] Figure 5e This is a schematic diagram of NOx emissions obtained from simulation.

[0080] Figure 6a This is a schematic diagram of the feed rate analysis;

[0081] Figure 6b This is a schematic diagram of the primary air temperature analysis.

[0082] Figure 7a This is a schematic diagram illustrating the analysis of feed rate and grate speed.

[0083] Figure 7b Schematic diagram for analysis of feed rate and primary air temperature;

[0084] Figure 8 This is a schematic diagram of the process for modeling and analyzing exhaust gas emissions from urban solid waste incineration, as described in an embodiment of the present invention. Detailed Implementation

[0085] The purpose of this invention is to provide a system and method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes, which can realize the modeling and analysis of exhaust gas emissions from urban solid waste incineration processes.

[0086] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0087] Urban solid waste incineration process such as Figure 1 As shown.

[0088] Based on the current process and control status of a certain MSWI plant, this invention uses feed rate, primary air temperature, primary air flow rate, secondary air temperature, and secondary air flow rate as key operating variables to construct a full-process numerical simulation model and a data-driven model.

[0089] like Figure 2 As shown in the figure, the urban solid waste incineration process exhaust gas emission modeling and analysis system provided in this embodiment of the invention includes a multi-software coupled full-process numerical simulation model construction module under benchmark conditions, a multi-operating condition simulation mechanism data acquisition module, a MIMO-LRDT-based exhaust gas emission model construction module, and a single / dual-factor-based exhaust gas emission analysis module. The multi-software coupled full-process numerical simulation model construction module under benchmark conditions is connected to the multi-operating condition simulation mechanism data acquisition module, the multi-operating condition simulation mechanism data acquisition module is connected to the MIMO-LRDT-based exhaust gas emission model construction module, and the MIMO-LRDT-based exhaust gas emission model construction module is connected to the single / dual-factor-based exhaust gas emission analysis module.

[0090] The multi-software coupled full-process numerical simulation model construction module under the benchmark working condition: uses FLIC to simulate the solid-phase combustion process on the grate, Fluent to simulate the gas-phase combustion process in the furnace, and Aspen Plus to simulate the waste heat exchange and flue gas purification stages, to construct a full-process numerical simulation model that can fit the benchmark working condition of the industrial site.

[0091] The multi-operational condition simulation mechanism data acquisition module: conducts orthogonal experimental design for the "partial air and material" operation variable, and obtains simulation mechanism data under multiple operating conditions based on the numerical simulation model under the above benchmark conditions;

[0092] The exhaust emission model construction module based on MIMO-LRDT: constructs an exhaust emission model based on the MIMO-LRDT algorithm using the "air distribution and material distribution" operation variable as input;

[0093] The exhaust gas emission analysis module based on single / dual factors: Based on the above exhaust gas emission model, it performs single / dual factor analysis on the mapping relationship between the manipulated variables and the exhaust gas emission concentration to support the control strategy in the industrial field.

[0094] like Figure 8 As shown, the present invention also provides a method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes, applied to the aforementioned urban solid waste incineration process exhaust gas emission modeling and analysis system, comprising the following steps:

[0095] Step 1: Construct a full-process numerical simulation model that can fit the benchmark working conditions of the industrial site based on the multi-software coupled full-process numerical simulation model construction module under the benchmark working conditions;

[0096] Step 2: The multi-operating-condition simulation mechanism data acquisition module acquires simulation mechanism data under multiple operating conditions through a full-process data simulation model;

[0097] Step 3: Construct an exhaust emission model based on the MIMO-LRDT algorithm using the exhaust emission model construction module based on MIMO-LRDT;

[0098] Step 4: Using the exhaust emission analysis module based on single / two factors, perform single / two-factor analysis on the mapping relationship between the manipulated variables and the exhaust emission concentration based on the exhaust emission model.

[0099] In step 1, a full-process numerical simulation model capable of fitting the benchmark working conditions of the industrial site is constructed based on the multi-software coupled full-process numerical simulation model construction module under the benchmark working conditions. This specifically includes the following steps:

[0100] Step 101: Solid-phase combustion process on the grate based on FLIC simulation;

[0101] Step 102: Gas-phase combustion process in the furnace based on Fluent simulation;

[0102] Step 103: Waste heat exchange and other flue gas purification processes based on Aspen Plus simulation.

[0103] In step 101, the solid-phase combustion process on the grate based on FLIC simulation is specifically as follows:

[0104] Assuming that the fuel on the grate is a homogeneous porous medium with constant porosity during combustion, and disregarding the flow of particulate matter within the furnace, the grate bed is considered to move forward at a constant velocity. The MSW is composed of moisture, volatile matter, fixed carbon, and ash. The gas phase components are only considered as CO, CO2, CH4, H2, H2O, NO, HCN, NH3, N2, and O2. The solid-phase combustion process on the grate is simulated using FLIC, including a solid-phase MSW combustion model, basic conservation equations, and component transport and thermal radiation conversion equations.

[0105] The solid-phase MSW combustion process includes stages of moisture evaporation, volatile matter release, volatile matter combustion, and coke oxidation. The MSW is pushed to the grate by the feeder, and after being subjected to high-temperature heat radiation from the furnace interior and walls, the moisture gradually evaporates at the following rate:

[0106]

[0107] In the formula, R evp S represents the rate of water evaporation. a h is the particle surface area. s C is the convective mass transfer coefficient. w,s and C w,gQ represents the moisture content in MSW and the mixed gas, respectively. cr H absorbs heat during radiation and convection heat transfer processes. evp T is the amount of heat absorbed for water evaporation. s MSW temperature: After complete evaporation of moisture, the MSW reaches the temperature at which volatiles are released. The release process of the main volatiles is as follows:

[0108] MSW→Volatile(C m H n ,CO,CO2,H2O)+Char (2)

[0109] When volatile gases mix with air and burn, the corresponding combustion reaction is as follows:

[0110] C m H n +(m / 2+n / 4)O2→mCO+n / 2H2O (3)

[0111] CO + 1 / 2O₂ → CO₂ (4)

[0112] Its combustion rate is the minimum of the kinetic rate and the mixing rate. MSW gradually turns into coke as volatiles are released, and it further reacts with air to produce CO and CO2, as follows:

[0113] C+αO2→2(1-α)CO+(2α-1)CO2 (5)

[0114] In the formula, α is the stoichiometric coefficient, and 0.5≤α≤1.

[0115] In step 102, the gas-phase combustion process in the furnace based on Fluent simulation is specifically as follows:

[0116] The FLIC output is treated as the boundary condition for the Fluent numerical simulation. In Fluent, the combustion component CmHn is replaced with CH4. The second-order upwind scheme and SIMPLE algorithm are used to discretize and solve the gas-phase combustion equation. The thermal radiation DO model is as follows:

[0117]

[0118] In the formula, I represents the radiation intensity. and These are the position vector and direction vector, respectively, where a is the absorption coefficient and σ is the position vector and direction vector. s Let ρ be the diffusion coefficient, n be the refractive index, σ be the Boltzmann constant, Φ be the phase function, and Ω′ be the fixed angle. The standard k-ε model for solving turbulent gas flow is:

[0119]

[0120]

[0121] In the formula, ρ is the gas density, k is the turbulent kinetic energy, and u i For velocity, x i and x j Given a coordinate system and μ as the turbulent viscosity, the conceptual model for eddy dissipation in the interaction between gas flow and combustion chemical reaction is as follows:

[0122]

[0123] In the formula, Y i Let i be the mass fraction of substance i. R is the diffusion flux of matter. i S is the net production rate. i To generate additional rates, the thermal radiation distribution data after the Fluent simulation is completed is used as the input of FLIC for iterative coupling. After the temperature difference between the two outputs meets the set error, the visualized furnace temperature field, velocity field and concentration field are output.

[0124] In step 103, the waste heat exchange and other flue gas purification processes based on Aspen Plus simulation are as follows:

[0125] Assuming that all reactions occurring in the furnace can reach equilibrium, the furnace temperature and pressure are constant, MSW ash is an inert component that does not participate in any reaction, the influence of MSW particle size on the combustion reaction is ignored, and pressure and gas losses and leaks are not considered, the flue gas temperature and composition obtained from FLIC simulation, the furnace temperature obtained from Fluent simulation, and process data from actual industrial sites (MSW ash content, secondary air flow rate, secondary air temperature, urea solution, economizer feedwater, calcium hydroxide solution, activated carbon, and reclaimed water) are input into Aspen Plus to simulate MSW storage and incineration, waste heat boiler heat exchange, flue gas treatment, and flue gas emissions, etc.

[0126] In the MSW storage and incineration process, using the flue gas temperature and composition (including H2O, N2, O2, H2, CO, CO2, CH4, and S) output from the FLIC, the furnace temperature output from Fluent, the MSW ash content obtained from laboratory analysis, and the secondary air temperature, secondary air flow rate, and urea solution dosage collected from the actual industrial site as inputs, the combustion process in the furnace is simulated using a Gibbs reactor RGibbs, and the denitrification reaction is simulated using a stoichiometric reactor Rstoic1. The reactions involved are as follows:

[0127]

[0128]

[0129] In the waste heat boiler heat exchange process stage, a simulation of the heat exchange process between high-temperature flue gas and the superheater and economizer is used with two stream heat exchanger modules to obtain the cooled furnace outlet flue gas G1. In the flue gas treatment process stage, a stoichiometric reactor is used to simulate the acid removal process of flue gas G1.

[0130]

[0131] A mixer module is used to simulate the adsorption process of pollutants such as heavy metals and dioxins in flue gas by activated carbon. A component separator module is used to simulate a bag filter. Finally, a distributor module is used to simulate the process of fly ash entering the ash silo, thus obtaining the treated flue gas G2. In the flue gas emission stage, a compressor module is used to simulate the flue gas G3 entering the atmosphere.

[0132] In step 2, the multi-operating-condition simulation mechanism data acquisition module acquires simulation mechanism data under multiple operating conditions through a full-process data simulation model, specifically as follows:

[0133] Based on the above multi-software coupled simulation strategy, and combined with the process of a MSWI plant in Beijing, a four-level orthogonal experimental design was carried out with 12 factors, including 1 non-operating variable (MSW component) and 11 operating variables (feed rate, grate speed, primary air temperature 1, primary air temperature 2, primary air temperature 3, primary air flow rate 1, primary air flow rate 2, primary air flow rate 3, primary air flow rate 4, secondary air temperature and secondary air flow rate). The values ​​of each factor level are shown in Table 1.

[0134] Table 1. Factor Levels in Experimental Design

[0135]

[0136]

[0137] Based on the above experimental design, 64 experimental schemes were obtained. The experiments were carried out using a multi-software coupled full-process numerical simulation model to obtain simulation mechanism data under multiple operating conditions.

[0138] In step 3, an exhaust emission model based on the MIMO-LRDT algorithm is constructed using the MIMO-LRDT-based exhaust emission model construction module, specifically as follows:

[0139] Based on the simulation mechanism data under the above multiple operating conditions, a tail gas emission model including CO, CO2, O2, SO2, and NOx emissions is established using a multi-input multi-output linear regression decision tree algorithm. The obtained simulation mechanism data is denoted as...

[0140] The mean square error of the target value is calculated by traversing the simulation mechanism data:

[0141]

[0142] In the formula, Let (n,i) represent the loss function value of MSE, and (n,i) represent the i-th feature value of the n-th sample in the k-th iteration.

[0143] Based on the calculation results, the minimum MSE is selected as the first non-leaf node. As a splitting variable, it is:

[0144]

[0145] Repeat the above process to obtain (T / 2-1) intermediate nodes based on the minimum sample size θ set empirically.

[0146] The predicted output of the CART leaf nodes is calculated using the linear regression method, as follows:

[0147]

[0148] In the formula, and Let be the output, input, and weight matrix of the t-th leaf node, respectively;

[0149] Obtaining the weight vector is reduced to solving a class of overdetermined matrix equations, and a regularized least squares loss function is used, as follows:

[0150]

[0151] In the formula, λ represents the regularization coefficient, and λ≥0. The above loss function is applied to... The gradient is expressed as:

[0152]

[0153] make have to:

[0154]

[0155] Based on the obtained weight vector, the predicted values ​​for the leaf nodes are calculated. Considering all leaf nodes, the MIMO-LRDT model is represented as:

[0156]

[0157] To investigate the effects of manipulated variables such as feed rate, grate velocity, primary air temperature 1, primary air temperature 2, primary air temperature 3, primary air flow rate 1, primary air flow rate 2, primary air flow rate 3, primary air flow rate 4, secondary air temperature, and secondary air flow rate on CO, CO2, O2, SO2, and NOx in exhaust gas emissions, single / two-factor analyses were conducted on the above manipulated variables based on the constructed MIMO-LRDT exhaust gas emission model.

[0158] This invention provides an embodiment for simulation verification. Numerical simulation is performed based on a certain MSWI power plant. The MSW component parameters and incinerator operating parameters under the baseline operating conditions are shown in Table 2 and Table 3, respectively.

[0159] Table 2 MSW component parameters

[0160]

[0161] Table 3. Incinerator operating parameters under baseline conditions

[0162]

[0163]

[0164] Results and discussion of the construction of a full-process numerical simulation model with multi-software coupling under baseline conditions:

[0165] Gas component distribution during combustion on the grate based on FLIC simulation is as follows: Figure 3 As shown, by Figure 3 It can be seen that as the MSW gradually moves on the grate, the rate of water evaporation increases due to the high-temperature thermal radiation, causing the mass fraction of H2O in the flue gas to rise continuously before 2.0m, reach a peak, and then briefly decrease. The reason for the brief increase in H2O between 4.0m and 5.0m is that the primary air carries some H2O. Finally, as the combustion process continues, it drops to 0 near 8.0m. The mass fractions of CO, CmHn, and H2 increase with the increase of the volatile matter release rate. CO2 is produced by the combustion of volatile matter and coke mixed with O2, so the increase in the mass fraction of CO2 is accompanied by the decrease in the mass fraction of O2.

[0166] Temperature distribution map and mass fraction cloud maps of O2 and CO2 in the furnace combustion process based on Fluent simulation are shown below. Figure 4 As shown, by Figure 4 It is known that the combustion process consumes a large amount of O2, thereby generating CO2. Therefore, the temperature and CO2 mass fraction are the highest in the middle of the furnace, while the O2 mass fraction is the lowest. As the flue gas gradually flows along the furnace wall to the furnace outlet, the temperature gradually decreases compared to the middle of the furnace. At the same time, combustion is basically completed during the flue gas flow, which leads to an increase in the O2 mass fraction and a decrease in the CO2 mass fraction.

[0167] The numerical simulation results of waste heat exchange and flue gas purification processes based on Aspen Plus simulation are shown in Table 4.

[0168] Table 4. Numerical simulation results using Aspen Plus

[0169]

[0170] As shown in Table 4, with the physicochemical reactions that occur during the flue gas treatment stage, the O2 and CO2 concentrations of flue gas G3 can reach the national standard emission levels.

[0171] Multi-operational-condition simulation mechanism data acquisition results and discussion: Orthogonal experiments were conducted based on a multi-software coupled full-process numerical simulation model under baseline conditions, resulting in 64 sets of experimental data. The obtained data were then converted based on actual industrial field units to finally obtain the exhaust gas emission results obtained from the simulation. Figures 5a-5e As shown, the combustion mechanism reveals that CO2 is produced by the combustion of volatile matter and coke mixed with O2. The CO2 emission concentration is inversely proportional to the O2 and CO emission concentrations. Figure 5a , Figure 5b and Figure 5c The mechanism is reflected in all of these studies, thus verifying the effectiveness of the multi-software coupled full-process numerical simulation model established in this invention under the benchmark working condition and the designed orthogonal experiment. However, the obtained simulation mechanism data... Figure 5c The O2 emission concentrations at sampling points 14 and 16 exceeded the standard value (21%) for oxygen content in the air, therefore the data was identified as abnormal and needed to be removed during the modeling process.

[0172] Results and Discussion of Exhaust Gas Emission Model Construction Based on MIMO-LRDT: Based on the results of the multi-operating condition simulation mechanism data acquisition module mentioned above, 62 sets of data were obtained after preprocessing to build the MIMO-LRDT model. To verify the effectiveness of the model built in this invention, a multi-input single-output model was built using backpropagation neural network (BPNN), decision tree (CART), and random forest (RF) for comparative experiments. The parameters of the MIMO-LRDT model were set as follows: minimum number of samples 5, regularization coefficient 0.5; the parameters of the CART model were set as follows: minimum number of samples 5; the parameters of the RF model were set as follows: minimum number of samples 5, number of decision trees 100; and the parameters of the GBDT model were set as follows: minimum number of samples 5, number of iterations 5, and learning rate 0.5.

[0173] The root mean square error (RMSE) metric is used to evaluate the performance of the above model, and the calculation formula is as follows:

[0174]

[0175] In the formula, and y i These represent the model's predicted value and the true value, respectively; N represents the number of samples.

[0176] The statistical results of RMSE on the model test set are shown in Table 5.

[0177] Table 5. RMSE statistics for the model test set

[0178]

[0179] As shown in Table 5, there are differences in the statistical results of the outputs of different model test sets. Among them, the MIMO-LRDT model performs best in the statistical results of NOx output and average value; the CART model performs best in the statistical results of CO output; the RF model performs best in the statistical results of CO2 output; and the GBDT model performs best in the statistical results of O2 and SO2 output. The exhaust emission model established in this invention has the smallest average error.

[0180] The MIMO-LRDT model proposed in this invention is an improvement upon the CART model. It converts the leaf node mean output into weights, improving model accuracy and making the output smoother, thus making it more suitable for controlled object models. Furthermore, compared to the multi-input single-output models established by comparative methods, the MIMO-LRDT model proposed in this invention shows certain advantages in statistical results, verifying its effectiveness.

[0181] Results and Discussion of Exhaust Gas Emission Analysis Based on Single / Dual Factors: For the constructed MIMO-LRDT model, with other operating variables fixed as baseline values, single-factor analysis was conducted by varying the feed rate and primary air temperature within a set range. The curves showing the changes in different gas concentrations in the exhaust gas are as follows: Figures 6a-6b As shown;

[0182] Depend on Figure 6a It can be seen that as the feed rate gradually increases, the emission concentrations of O2 and NOx gradually decrease, while the emission concentration of CO first decreases and then increases. Overall, both CO2 and SO2 show an upward trend. Figure 6b It can be seen that as the primary air temperature gradually increases, the emission concentrations of CO, CO2 and SO2 first decrease and then increase. At this time, O2 and NOx show a trend of first increasing and then decreasing. In summary, the trend of emission concentration changes in exhaust gas is basically consistent with the understanding of the MSWI process mechanism.

[0183] Similarly, a two-factor analysis was conducted by simultaneously varying the grate speed and feed rate, as well as the primary air temperature and feed rate within a set range. The CO emission concentration change curve in the exhaust gas is shown in the figure below. Figures 7a-7b As shown;

[0184] As shown in Figure 7, compared with the feed rate, the grate speed and primary air temperature have a smaller impact on the CO emission concentration in the exhaust gas. Therefore, the feed rate needs to be reasonably selected to ensure that the exhaust gas emissions meet the standards while increasing the economic benefits of the MSWI power plant.

[0185] This invention provides a system and method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes. The system includes a module for constructing a full-process numerical simulation model under baseline conditions using multi-software coupling, a module for acquiring simulation mechanism data under multiple operating conditions, a module for constructing an exhaust gas emission model based on MIMO-LRDT, and a module for analyzing exhaust gas emissions based on single / dual factors. The method includes: constructing a full-process numerical simulation model capable of fitting the baseline operating conditions of an industrial site using the module for constructing a full-process numerical simulation model under baseline conditions; acquiring simulation mechanism data under multiple operating conditions through the module for acquiring simulation mechanism data under multiple operating conditions using the full-process data simulation model; constructing an exhaust gas emission model based on the MIMO-LRDT algorithm using the module for constructing an exhaust gas emission model using the module for constructing an exhaust gas emission model based on MIMO-LRDT; and analyzing exhaust gas emissions based on the exhaust gas emission model using the module for analyzing exhaust gas emissions based on single / dual factors. Single / two-factor analysis was conducted to investigate the mapping relationship between manipulated variables and exhaust gas emission concentrations. A full-process numerical simulation model was used to lay the foundation for analyzing the relationship between the "air and material distribution" manipulated variable and exhaust gas emissions. Based on the full-process numerical simulation model, a twelve-factor, four-level orthogonal experiment was designed and implemented to obtain simulation mechanism data under multiple operating conditions, providing support for the construction of a data-driven model. A multi-input, multi-output data-driven model was established using the MIMO-LRDT algorithm, with the "air and material distribution" manipulated variable as input and CO, CO2, O2, SO2, and NOx as outputs. Compared with the comparative algorithm, this model has certain advantages in model fit and model structure. Based on the established MIMO-LRDT data-driven model, a single / two-factor strategy was used to analyze the relationship between manipulated variables and exhaust gas emissions, providing corresponding guidance for the optimization of MSWI process operation.

[0186] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes, applied to an urban solid waste incineration exhaust gas emission modeling and analysis system, characterized in that... Includes the following steps: Step 1: Construct a full-process numerical simulation model that can fit the benchmark working conditions of the industrial site based on the multi-software coupled full-process numerical simulation model construction module under the benchmark working conditions; Step 2: The multi-operating-condition simulation mechanism data acquisition module acquires simulation mechanism data under multiple operating conditions through a full-process data simulation model; Step 3: Construct an exhaust emission model based on the MIMO-LRDT algorithm using the exhaust emission model construction module based on MIMO-LRDT; Step 4: Using the exhaust emission analysis module based on single / two factors, perform single / two-factor analysis on the mapping relationship between the manipulated variables and the exhaust emission concentration based on the exhaust emission model; In step 3, an exhaust emission model based on the MIMO-LRDT algorithm is constructed using the MIMO-LRDT-based exhaust emission model construction module, specifically as follows: Based on the simulation mechanism data under the above multiple operating conditions, a multi-input multi-output linear regression decision tree algorithm was used to establish a system including CO, CO2, O2, SO2, and NO. x The exhaust gas emission model includes exhaust gases, where the obtained simulation mechanism data is denoted as... ; The mean square error of the target value is calculated by traversing the simulation mechanism data: （13） In the formula, This represents the loss function value of MSE; Based on the calculation results, the minimum MSE is selected as the first non-leaf node. As a splitting variable, it is: （14） Repeat the above process, setting a minimum sample size based on experience. thereby obtaining intermediate nodes ; The predicted output of the CART leaf nodes is calculated using the linear regression method, as follows: （15） In the formula, , and The first The output, input, and weight matrices of each leaf node; Obtaining the weight vector is reduced to solving a class of overdetermined matrix equations, and a regularized least squares loss function is used, as follows: （16） In the formula, The value represents the coefficient of the regularization term, and The above loss function is for The gradient is expressed as: （17） make ,have to: （18） Based on the obtained weight vector, the predicted values ​​for the leaf nodes are calculated. Considering all leaf nodes, the MIMO-LRDT model is represented as: (19).

2. The method for modeling and analyzing exhaust gas emissions from urban solid waste incineration as described in claim 1, characterized in that, In step 1, a full-process numerical simulation model capable of fitting the benchmark working conditions of the industrial site is constructed based on the multi-software coupled full-process numerical simulation model construction module under the benchmark working conditions. This specifically includes the following steps: Step 101: Solid-phase combustion process on the grate based on FLIC simulation; Step 102: Gas-phase combustion process in the furnace based on Fluent simulation; Step 103: Waste heat exchange and other flue gas purification processes based on Aspen Plus simulation.

3. The method for modeling and analyzing exhaust gas emissions from urban solid waste incineration as described in claim 2, characterized in that, In step 101, the solid-phase combustion process on the grate based on FLIC simulation is specifically as follows: The solid-phase combustion process on the grate was simulated using FLIC, including the solid-phase MSW combustion model, the basic conservation equations, and the equations for component transport and thermal radiation conversion. The solid-phase MSW combustion process includes stages of moisture evaporation, volatile matter release, volatile matter combustion, and coke oxidation. The MSW is pushed to the grate by the feeder, and after being subjected to high-temperature heat radiation from the furnace interior and furnace walls, the moisture gradually evaporates at the following rate: （1） In the formula, The rate of water evaporation. For particle surface area, The convective mass transfer coefficient, and These are the moisture contents in the MSW and the mixed gas, respectively. To absorb heat during radiation and convection heat transfer processes, It absorbs heat to meet the needs of water evaporation. MSW temperature: After complete evaporation of moisture, the MSW reaches the temperature at which volatiles are released. The release process of the main volatiles is as follows: （2） When volatile gases mix with air and burn, the corresponding combustion reaction is as follows: （3） （4） Its combustion rate is the minimum of the kinetic rate and the mixing rate. MSW gradually turns into coke as volatiles are released, and it further reacts with air to produce CO and CO2, as follows: （5） In the formula, It is the stoichiometric coefficient, and .

4. The method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes according to claim 3, characterized in that, In step 102, the gas-phase combustion process in the furnace based on Fluent simulation is specifically as follows: The FLIC output is treated as the boundary condition for the Fluent numerical simulation. In Fluent, the combustion components C... m H n The gas phase combustion equations were discretized and solved using a second-order upwind scheme and the SIMPLE algorithm, with the thermal radiation DO model as follows: （6） In the formula, Radiation intensity, and These are the position vector and the direction vector, respectively. The absorption coefficient is... Where is the diffusion coefficient. For refractive index, Boltzmann's constant, Let be the phase function. For a fixed angle, the standard k-ε model for solving turbulent gas flow is: （7） （8） In the formula, For gas density, For turbulent kinetic energy, For speed, and coordinate system Given the turbulent viscosity, the conceptual model for eddy dissipation in the interaction between gas flow and combustion chemical reaction is as follows: （9） In the formula, For matter mass fraction, For the diffusion flux of matter, Net production rate, To generate additional rates, the thermal radiation distribution data after the Fluent simulation is completed is used as the input of FLIC for iterative coupling. After the temperature difference between the two outputs meets the set error, the visualized furnace temperature field, velocity field and concentration field are output.

5. The method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes according to claim 4, characterized in that, In step 103, the waste heat exchange and other flue gas purification processes based on Aspen Plus simulation are as follows: Using the flue gas temperature and composition output from FLIC, the furnace temperature output from Fluent, the MSW ash content obtained from laboratory analysis, and the secondary air temperature, secondary air flow rate, and urea solution dosage collected from the actual industrial site as inputs, the flue gas components include H2O, N2, O2, H2, CO, CO2, CH4, and S. The combustion process in the furnace is simulated using a Gibbs reactor RGibbs, and the denitrification reaction is simulated using a stoichiometric reactor Rstoic1. The reactions involved are as follows: （10） （11） In the waste heat boiler heat exchange process stage, a simulation of the heat exchange process between high-temperature flue gas and the superheater and economizer is used with two stream heat exchanger modules to obtain the cooled furnace outlet flue gas G1. In the flue gas treatment process stage, a stoichiometric reactor is used to simulate the acid removal process of flue gas G1. （12） A mixer module is used to simulate the adsorption process of heavy metals and dioxins in flue gas by activated carbon, a component separator module is used to simulate a bag filter, and finally, a distributor module is used to simulate the process of fly ash entering the ash bin to obtain the treated flue gas G2. In the flue gas emission stage, a compressor module is used to simulate the flue gas G3 entering the atmosphere.

6. The method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes according to claim 5, characterized in that, In step 2, the multi-operating-condition simulation mechanism data acquisition module acquires simulation mechanism data under multiple operating conditions through a full-process data simulation model, specifically as follows: A four-level orthogonal experimental design was conducted using one non-operating variable and eleven operating variables. The non-operating variable was the MSW component, and the operating variables included feed rate, grate speed, primary air temperature 1, primary air temperature 2, primary air temperature 3, primary air flow rate 1, primary air flow rate 2, primary air flow rate 3, primary air flow rate 4, secondary air temperature, and secondary air flow rate. Based on the orthogonal experimental design, simulation mechanism data under multiple operating conditions were obtained through a full-process data simulation model.

7. A system for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes, characterized in that, The method for modeling and analyzing exhaust gas emissions from urban solid waste incineration processes according to any one of claims 1 to 6, wherein the urban solid waste incineration process exhaust gas emission modeling and analysis system comprises: a multi-software coupled full-process numerical simulation model construction module under baseline operating conditions, a multi-operating-condition simulation mechanism data acquisition module, a MIMO-LRDT-based exhaust gas emission model construction module, and a single / dual-factor-based exhaust gas emission analysis module, wherein the multi-software coupled full-process numerical simulation model construction module under baseline operating conditions is connected to the multi-operating-condition simulation mechanism data acquisition module, the multi-operating-condition simulation mechanism data acquisition module is connected to the MIMO-LRDT-based exhaust gas emission model construction module, and the MIMO-LRDT-based exhaust gas emission model construction module is connected to the single / dual-factor-based exhaust gas emission analysis module; The multi-software coupled full-process numerical simulation model construction module under the benchmark working condition is used to construct a full-process numerical simulation model that can fit the benchmark working condition of the industrial site. The multi-operational-condition simulation mechanism data acquisition module is used to acquire simulation mechanism data under multiple operating conditions from the full-process data simulation model. The exhaust emission model construction module based on MIMO-LRDT is used to construct an exhaust emission model based on the MIMO-LRDT algorithm. The exhaust emission analysis module based on single / two factors is used to perform single / two-factor analysis on the mapping relationship between the manipulated variables and the exhaust emission concentration based on the exhaust emission model.

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