Gradient catalytic biomass tar cracking reactor and control method thereof

Abstract

The invention relates to the technical field of biomass energy conversion, in particular to a gradient catalytic biomass tar cracking reactor and a control method thereof.The gradient catalytic biomass tar cracking reactor comprises a multilevel component characterization module, a functional group reaction activity evaluation module, a multi-scale reaction network coupling module, an oxygen content gradient regulation module, a self-adaptive product directional regulation module and the like; the system is used for finely representing tar components, improving complicated composition understanding and processing capability, calculating a functional group reaction activity index, constructing a three-level reaction network, optimizing reaction path selectivity, and generating an oxygen content gradient index and a catalyst distribution scheme, so that gradient reduction of oxygen content and oriented conversion of products in a reactor are realized; the multi-section fixed bed reactor is designed, different catalysts are filled in each reaction area, the reaction requirements are met, the system intelligently regulates and controls reaction operation parameters, the biomass tar cracking efficiency and the product directionality are remarkably improved, and innovation and breakthrough are brought to the technical field of biomass energy conversion.

Description

Technical Field

[0001] The present invention relates to the technical field of biomass energy conversion, and particularly relates to a gradient catalytic biomass tar cracking reactor and its control method, which are used for efficiently treating the tar generated in the biomass pyrolysis process and improving its conversion efficiency and product selectivity. Background Art

[0002] Biomass tar is a liquid product generated in the biomass pyrolysis process, containing a large amount of oxygen-containing compounds, and the oxygen content is usually 15 - 45 wt%. These oxygen-containing compounds reduce the calorific value of the tar, increase its corrosiveness, and are unstable, prone to polymerization reactions resulting in coking, which limits the possibility of directly using it as a fuel or a chemical raw material. Therefore, converting biomass tar into liquid fuels or high-value-added chemicals with low oxygen content and high stability is an important link in biomass energy utilization.

[0003] Currently, catalytic cracking is one of the main methods for treating biomass tar. For example, CN 119416538A discloses a dynamic simulation system for a heavy oil high-efficiency catalytic cracking reactor, which, through a lumped reaction kinetic model and in combination with a calculation method for the properties of the raw oil group composition, can real-time correct the lumped properties participating in the reaction, thereby calculating reaction results and state values such as temperature and pressure that are close to the actual situation.

[0004] However, Comparative Document 1 has the following technical deficiencies:

[0005] 1) This system is aimed at the catalytic cracking of petroleum heavy oil and does not consider the special reaction behavior of the high oxygen-containing compounds in biomass tar;

[0006] 2) It uses a unified hydrocarbon correlation factor to characterize the influence of different hydrocarbons on each reaction product and does not consider the special reaction behavior of oxygen functional groups;

[0007] 3) It only considers the reaction network at the lumped level and lacks information on microscopic reaction steps and mesoscopic reaction types;

[0008] 4) It does not consider the influence of the oxygen content in the raw material on the reaction path selectivity and lacks a gradient regulation mechanism for the oxygen content;

[0009] 5) It mainly focuses on the simulation accuracy and lacks a product orientation regulation mechanism.

[0010] Therefore, there is an urgent need to develop a gradient catalytic cracking reactor for biomass tar and its control method to improve the conversion efficiency and product selectivity of biomass tar. Summary of the Invention

[0011] The object of the present invention is to provide a gradient catalytic biomass tar cracking reactor and its control method, which can achieve efficient conversion of biomass tar and product-oriented regulation through key technologies such as multi-level biomass tar component characterization, quantitative evaluation of functional group reaction activity, multi-scale reaction network coupling, oxygen content gradient regulation, and adaptive product-oriented regulation.

[0012] The present invention proposes a gradient catalytic biomass tar cracking reactor, including:

[0013] A multi-level biomass tar component characterization module, used to construct a three-dimensional component characterization matrix, characterize the biomass tar components from the dimensions of carbon skeleton structure, oxygen functional group, and molecular weight distribution, and generate component characterization data;

[0014] A functional group reaction activity quantitative evaluation module, connected to the multi-level biomass tar component characterization module, used to receive the component characterization data and calculate the functional group reaction activity index;

[0015] A multi-scale reaction network coupling module, connected to the functional group reaction activity quantitative evaluation module, used to receive the functional group reaction activity index and construct a three-level reaction network structure including the micro level, meso level, and macro level;

[0016] An oxygen content gradient regulation module, connected to the multi-scale reaction network coupling module, used to receive the three-level reaction network structure, establish a quantitative relationship between the oxygen content gradient index and the reaction path selectivity, and generate a catalyst distribution scheme;

[0017] An adaptive product-oriented regulation module, connected to the oxygen content gradient regulation module, used to receive the catalyst distribution scheme and product analysis data and optimize the reaction operation parameters;

[0018] A gradient catalytic reactor, including a multi-stage fixed bed structure, axially divided into multiple reaction zones, and each reaction zone is filled with catalysts with different functions to achieve a gradient reduction of oxygen content and product-oriented conversion during the reaction process.

[0019] Preferably, the multi-level biomass tar component characterization module includes:

[0020] A component data acquisition unit, used to acquire high performance liquid chromatography data, gas chromatography-mass spectrometry data, and Fourier transform ion cyclotron resonance mass spectrometry data of biomass tar;

[0021] A multi-source data fusion unit, used to perform fusion processing on the acquired multi-source data and identify characteristic peaks;

[0022] A three-dimensional matrix construction unit for constructing a three-dimensional component characterization matrix including a carbon skeleton structure dimension, an oxygen functional group dimension, and a molecular weight distribution dimension based on the characteristic peak recognition result.

[0023] Preferably, the functional group reaction activity quantitative evaluation module includes:

[0024] An activity index calculation unit for calculating the functional group reaction activity index according to the following formula:

[0025] FGRI(i,j,k) = k0(i,j,k) × exp(-E a (i,j,k) / RT) × S(i,j,k) × D(i,j,k),

[0026] where FGRI(i,j,k) is the functional group reaction activity index, k0(i,j,k) is the frequency factor, E a (i,j,k) is the activation energy, R is the gas constant, T is the reaction temperature, S(i,j,k) is the steric hindrance factor, D(i,j,k) is the electronic effect factor, and i, j, k are the indices of the three-dimensional matrix respectively;

[0027] A catalyst-functional group interaction evaluation unit for constructing an interaction model between the acidic sites of the catalyst and the oxygen functional groups;

[0028] A heteroatom influence evaluation unit for calculating the influence of heteroatoms such as nitrogen and sulfur on the catalyst activity.

[0029] Preferably, the multi-scale reaction network coupling module includes:

[0030] A micro reaction network unit for constructing a primitive reaction step network based on quantum chemical calculations;

[0031] A meso reaction network unit for constructing a meso reaction network including typical reaction types such as deoxygenation, decarboxylation, dehydration, cyclization, and rearrangement;

[0032] A macro reaction network unit for constructing a transformation network between product lumps;

[0033] A scale parameter transfer unit for realizing the transfer and mapping of reaction parameters at the micro, meso, and macro levels.

[0034] Preferably, the oxygen content gradient regulation module includes:

[0035] An oxygen content gradient index calculation unit for calculating the oxygen content gradient index according to the following formula:

[0036] OGI = ∑ i [w i ×C i ×Oi ,

[0037] wherein, OGI is the oxygen content gradient index, w i is the weight coefficient of component i, C i is the concentration of component i, and O i is the oxygen content of component i;

[0038] The selective correlation analysis unit is used to establish the quantitative relationship between the oxygen content gradient index and the selectivity of each reaction path;

[0039] The catalyst gradient design unit is used to design the axial catalyst acidity gradient distribution.

[0040] Preferably, the adaptive product orientation regulation module includes:

[0041] The product distribution prediction unit is used to construct a product distribution prediction model based on a deep neural network;

[0042] The multi-objective optimization unit is used to design a multi-objective optimization function to balance the target product yield, energy utilization efficiency and system stability;

[0043] The closed-loop feedback control unit is used to monitor the change of product composition in real time and dynamically adjust the prediction model parameters.

[0044] Preferably, the multiple reaction zones of the gradient catalytic reactor are, from top to bottom in sequence:

[0045] The deoxidation catalytic zone is filled with a composite catalyst of HZSM-5 molecular sieve and MgO, and the working temperature is 350 - 400 °C;

[0046] The primary cracking zone is filled with a composite catalyst of USY molecular sieve and Fe2O3, and the working temperature is 400 - 450 °C;

[0047] The deep cracking zone is filled with a composite catalyst of Beta molecular sieve and ZnO, and the working temperature is 425 - 475 °C;

[0048] The selective aromatization zone is filled with a composite catalyst of ZSM-5 molecular sieve and Ga2O3, and the working temperature is 400 - 450 °C.

[0049] Preferably, the system further includes:

[0050] The on-line analysis and detection module is used to collect samples at different positions of the reactor and perform gas chromatography and near-infrared spectroscopy analysis;

[0051] The data management module is used to store and manage various types of data during the operation of the system;

[0052] An abnormal condition identification module, which is used to identify abnormal states during system operation and generate countermeasures.

[0053] Preferably, the system realizes linkage through the following data transfer process:

[0054] The component characterization data generated by the multi-level biomass tar component characterization module is transmitted to the functional group reaction activity quantitative evaluation module;

[0055] The functional group reaction activity index generated by the functional group reaction activity quantitative evaluation module is transmitted to the multi-scale reaction network coupling module;

[0056] The three-level reaction network structure generated by the multi-scale reaction network coupling module is transmitted to the oxygen content gradient regulation module;

[0057] The catalyst distribution scheme generated by the oxygen content gradient regulation module is transmitted to the adaptive product orientation regulation module and the gradient catalytic reactor;

[0058] The operation parameter optimization scheme generated by the adaptive product orientation regulation module is transmitted to the gradient catalytic reactor.

[0059] A control method for a gradient catalytic biomass tar cracking reactor, comprising the following steps:

[0060] Construct a three-dimensional component characterization matrix of biomass tar, and characterize the biomass tar components from the dimensions of carbon skeleton structure, oxygen functional group, and molecular weight distribution;

[0061] Calculate the functional group reaction activity index to quantify the reaction activities of different functional groups;

[0062] Construct a reaction network structure including three levels: micro, meso, and macro;

[0063] Establish a quantitative relationship between the oxygen content gradient index and the reaction path selectivity, and generate a catalyst distribution scheme;

[0064] Optimize the reaction operation parameters according to the catalyst distribution scheme and product analysis data;

[0065] Through the multi-stage fixed bed structure and catalysts with different functions in the gradient catalytic reactor, realize the gradient reduction of oxygen content and the directional conversion of products during the reaction process.

[0066] The beneficial effects of the present invention include:

[0067] 1) Fine characterization of biomass tar through a three-dimensional component characterization matrix (3D-CRCM) improves the understanding and processing ability of the complex composition of biomass tar;

[0068] 2) By quantifying the reactivity of different functional groups through the Functional Group Reactivity Index (FGRI), an accurate description of the reaction behavior of each component in biomass tar was achieved;

[0069] 3) By connecting microscopic reaction steps, mesoscopic reaction types, and macroscopic lumping conversions through a three-level reaction network structure, the accuracy and predictive ability of the reaction network model were improved;

[0070] 4) Through the correlation analysis between the Oxygen Content Gradient Index (OGI) and reaction path selectivity, gradient regulation of the oxygen content was achieved, reducing the occurrence of side reactions;

[0071] 5) Through a deep neural network and a multi-objective optimization decision-making system, directional regulation of the product distribution was achieved, improving the selectivity and yield of target products.

[0072] Compared with the prior art, the present invention can effectively process biomass tar with a high oxygen content, significantly improve the quality of liquid products, significantly reduce energy consumption and the catalyst deactivation rate, and has significant economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a schematic diagram of the overall framework of the gradient catalytic biomass tar cracking reactor and its control system of the present invention;

[0074] Figure 2 is a schematic diagram of the structure of the multi-level biomass tar component characterization module of the present invention;

[0075] Figure 3 is a schematic diagram of the structure of the functional group reactivity quantitative evaluation module of the present invention;

[0076] Figure 4 is a schematic diagram of the structure of the multi-scale reaction network coupling module of the present invention;

[0077] Figure 5 is a schematic diagram of the structure of the oxygen content gradient regulation module of the present invention;

[0078] Figure 6 is a schematic diagram of the structure of the adaptive product directional regulation module of the present invention;

[0079] Figure 7 is a schematic diagram of the structure of the gradient catalytic reactor of the present invention;

[0080] Figure 8 is a schematic diagram of the oxygen content gradient change of the present invention;

[0081] Figure 9 is a graph showing the variation of product selectivity with control parameters of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0082] Please refer to the appendix Figures 1-9 , and the specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0083] Example 1: Overall architecture of a gradient-catalyzed biomass tar cracking reactor and its control system

[0084] As Figure 1 shown, the gradient-catalyzed biomass tar cracking reactor and its control system provided by the present invention include a multi-level biomass tar component characterization module 1, a functional group reaction activity quantitative evaluation module 2, a multi-scale reaction network coupling module 3, an oxygen content gradient regulation module 4, an adaptive product orientation regulation module 5, and a gradient catalytic reactor 6.

[0085] The multi-level biomass tar component characterization module 1 is used to construct a three-dimensional component characterization matrix, characterize the biomass tar components from the dimensions of carbon skeleton structure, oxygen functional group, and molecular weight distribution, and generate component characterization data. The multi-level biomass tar component characterization module 1 is connected to the functional group reaction activity quantitative evaluation module 2 through a data transmission line.

[0086] The functional group reaction activity quantitative evaluation module 2 is used to receive the component characterization data transmitted by the multi-level biomass tar component characterization module 1 and calculate the functional group reaction activity index. The functional group reaction activity quantitative evaluation module 2 is connected to the multi-scale reaction network coupling module 3 through a data transmission line.

[0087] The multi-scale reaction network coupling module 3 is used to receive the functional group reaction activity index transmitted by the functional group reaction activity quantitative evaluation module 2 and construct a three-level reaction network structure including the micro level, meso level, and macro level. The multi-scale reaction network coupling module 3 is connected to the oxygen content gradient regulation module 4 through a data transmission line.

[0088] The oxygen content gradient regulation module 4 is used to receive the three-level reaction network structure transmitted by the multi-scale reaction network coupling module 3, establish a quantitative relationship between the oxygen content gradient index and the reaction path selectivity, and generate a catalyst distribution plan. The oxygen content gradient regulation module 4 is connected to the adaptive product orientation regulation module 5 and the gradient catalytic reactor 6 through a data transmission line.

[0089] The adaptive product orientation regulation module 5 is used to receive the catalyst distribution plan and product analysis data transmitted by the oxygen content gradient regulation module 4 and optimize the reaction operation parameters. The adaptive product orientation regulation module 5 is connected to the gradient catalytic reactor 6 through a control line.

[0090] The gradient catalytic reactor 6 includes a multi-stage fixed bed structure, which is axially divided into multiple reaction zones, and each reaction zone is filled with catalysts with different functions to achieve a gradient reduction of oxygen content and product-directed conversion during the reaction process.

[0091] The data flow process of the entire system is as follows: Biomass tar is first characterized by the multi-level biomass tar component characterization module 1, and the generated component characterization data is transmitted to the functional group reaction activity quantitative evaluation module 2; the functional group reaction activity index calculated by the functional group reaction activity quantitative evaluation module 2 is transmitted to the multi-scale reaction network coupling module 3; the three-level reaction network structure constructed by the multi-scale reaction network coupling module 3 is transmitted to the oxygen content gradient regulation module 4; the catalyst distribution scheme generated by the oxygen content gradient regulation module 4 is transmitted to the adaptive product orientation regulation module 5 and the gradient catalytic reactor 6; the operation parameter optimization scheme generated by the adaptive product orientation regulation module 5 is transmitted to the gradient catalytic reactor 6 to achieve the coordinated operation of the entire system.

[0092] Example 2: Specific structure of the multi-level biomass tar component characterization module

[0093] As Figure 2 shown, the multi-level biomass tar component characterization module 1 includes a component data acquisition unit 11, a multi-source data fusion unit 12, and a three-dimensional matrix construction unit 13.

[0094] The component data acquisition unit 11 is used to collect high performance liquid chromatography (HPLC) data, gas chromatography-mass spectrometry (GC-MS) data, and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) data of biomass tar. Preferably, HPLC is mainly used to analyze medium and high molecular weight oxygen-containing compounds, GC-MS is mainly used to analyze low molecular weight volatile compounds, and FT-ICR-MS is mainly used for high-precision molecular formula identification.

[0095] The multi-source data fusion unit 12 is used to perform fusion processing on the multi-source data collected by the component data acquisition unit 11 to identify characteristic peaks. The multi-source data fusion algorithm can be expressed as:

[0096] F(m / z,RT,I) = α·f HPLC (RT,I) + β·f GC-MS (m / z,RT) + γ·f FT-ICR-MS (m / z),

[0097] where F(m / z,RT,I) is the fused characteristic function, m / z is the mass-to-charge ratio, RT is the retention time, I is the signal intensity, and f HPLC 、f GC-MS and f FT-ICR-MS are the characteristic functions of the three analysis methods respectively, and α, β, γ are weight coefficients satisfying ɑ + β + γ = 1. The weight coefficients can be dynamically adjusted according to the characteristics of different components. For macromolecular components, the value of ɑ is increased; for volatile components, the value of β is increased; for components with complex molecular structures, the value of γ is increased.

[0098] The three-dimensional matrix construction unit 13 is used to construct a three-dimensional component representation matrix (3D-CRCM) including the carbon skeleton structure dimension, the oxygen functional group dimension, and the molecular weight distribution dimension based on the characteristic peak recognition result. The 3D-CRCM can be expressed as a three-dimensional tensor:

[0099] CRCM i,j,k ={concentration,reactivity-index,conversion-vector},

[0100] where i ∈ [1, S number of categories] represents the carbon skeleton structure dimension, including aromatic ring structure (type A), aliphatic chain structure (type P), cyclic non-aromatic structure (type C), etc.; j ∈ [1, O number of categories] represents the oxygen functional group dimension, including phenolic hydroxyl group (P-OH), alcoholic hydroxyl group (A-OH), carboxyl group (COOH), ether bond (C-O-C), carbonyl group (C=O), etc.; k ∈ [1, M number of categories] represents the molecular weight distribution dimension, including light components (<150 Da), medium components (150 - 300 Da), heavy components (>300 Da), etc. Each element contains information such as concentration value, reactivity index, and conversion vector.

[0101] The concentration value (concentration) refers to the substance concentration under the specific carbon skeleton structure i, oxygen functional group j, and molecular weight k;

[0102] The reactivity index (reactivity_index) refers to the tendency or rate of this component to participate in chemical reactions, measured or calculated under the specific carbon skeleton structure i, oxygen functional group j, and molecular weight k;

[0103] The conversion vector (conversion_vector) refers to the probability or proportion of this component to be converted into other substances under a series of reaction conditions, defined under the specific carbon skeleton structure i, oxygen functional group j, and molecular weight k.

[0104] Through this three-dimensional characterization method, the composition characteristics of biomass tar can be comprehensively captured, providing basic data for subsequent reaction activity evaluation and reaction network construction.

[0105] Example 3: Specific structure of the functional group reaction activity quantitative evaluation module

[0106] As Figure 3 shown, the functional group reaction activity quantitative evaluation module 2 includes an activity index calculation unit 21, a catalyst-functional group interaction evaluation unit 22, and a heteroatom influence evaluation unit 23.

[0107] The activity index calculation unit 21 is used to calculate the functional group reaction activity index according to the following formula:

[0108] FGRI(i,j,k) = k0(i,j,k) × exp(-E a (i,j,k) / RT) × S(i,j,k) × D(i,j,k),

[0109] where FGRI(i,j,k) is the functional group reaction activity index, k0(i,j,k) is the frequency factor, E a (i,j,k) is the activation energy, R is the gas constant, T is the reaction temperature, S(i,j,k) is the steric hindrance factor, D(i,j,k) is the electronic effect factor, and i, j, k are the indices of the three-dimensional matrix respectively.

[0110] The frequency factor k0(i,j,k) is related to the molecular vibration frequency. The steric hindrance factor S(i,j,k) characterizes the influence of the molecular configuration on the reaction, and the electronic effect factor D(i,j,k) characterizes the influence of the electron cloud distribution on the reaction activity. These parameters can be obtained through molecular simulation or empirical formulas. Preferably, the activation energy E a (i,j,k) is obtained by calculation using density functional theory (DFT), and the range of the reaction temperature T is 350 - 475 °C.

[0111] The catalyst-functional group interaction evaluation unit 22 is used to construct an interaction model between the acidic sites of the catalyst and the oxygen functional groups. This model considers factors such as the acidic strength, acidic site density, and pore structure of the catalyst, and quantifies the interaction strength between the catalyst and different oxygen functional groups. Preferably, for common zeolite catalysts such as HZSM-5, USY, and Beta, the interaction potential energy surfaces between the acidic sites and functional groups such as hydroxyl groups, carboxyl groups, and ether bonds are established, and the interaction energy and activation energy reduction effect are calculated.

[0112] The heteroatom influence evaluation unit 23 is used to calculate the influence of heteroatoms such as nitrogen and sulfur on the catalyst activity. Biomass tar may contain a small amount of heteroatoms such as nitrogen and sulfur, and these heteroatoms may poison the active sites of the catalyst or change its electronic properties. The heteroatom influence evaluation unit 23 introduces a nitrogen heteroatom correction term (N-FMC) and a sulfur heteroatom correction term (S-FMC) to quantify the influence of heteroatoms on the catalytic process. Preferably, the value ranges of N-FMC and S-FMC are 0.5 - 1.5 and 0.3 - 1.2 respectively, and the smaller the value, the stronger the poisoning effect.

[0113] Through the functional group reaction activity index, the reaction activities of each component in biomass tar can be accurately evaluated, providing basic data for the construction of a multi-scale reaction network.

[0114] Example 4: Specific structure of the multi-scale reaction network coupling module

[0115] As Figure 4As shown, the multi-scale reaction network coupling module 3 includes a micro reaction network unit 31, a meso reaction network unit 32, a macro reaction network unit 33, and a scale parameter transfer unit 34.

[0116] The micro reaction network unit 31 is used to construct an elementary reaction step network based on quantum chemical calculations. This network includes basic reaction steps such as the C-C bond and C-O bond cleavage mechanisms, and the hydrogen transfer mechanism. Preferably, density functional theory (DFT) is used to calculate the activation energy and reaction heat of different reaction paths, and to determine the main reaction paths and rate-determining steps. For the deoxygenation reaction of phenolic hydroxyl groups, the activation energy range for C-O bond cleavage is 230 - 280 kJ / mol, while the activation energy range for C-O bond cleavage of alcoholic hydroxyl groups is 190 - 240 kJ / mol.

[0117] The meso reaction network unit 32 is used to construct a meso reaction network including typical reaction types such as deoxygenation, decarboxylation, dehydration, cyclization, and rearrangement. This network combines micro elementary reaction steps into chemically meaningful reaction types, and altogether includes 22 typical reaction types. Preferably, for the catalytic deoxygenation reaction of phenols, it mainly includes paths such as direct dehydroxylation, hydrodeoxygenation, and demethylation-cyclization; for structures containing ether bonds, it mainly includes paths such as bond cleavage-rearrangement and hydrogenolysis.

[0118] The macro reaction network unit 33 is used to construct a transformation network between product lumps. This network maps the results of meso reaction types to the product lump level, and contains 56 transformation paths between 15 product lumps. Product lumps include oxygenated macromolecule lumps, oxygenated medium molecule lumps, oxygenated small molecule lumps, aromatic hydrocarbon lumps, olefin lumps, alkane lumps, etc. Preferably, the transformation from oxygenated macromolecules to oxygenated medium molecules is mainly achieved through C-C bond cleavage, and the transformation from oxygenated molecules to hydrocarbons is mainly achieved through deoxygenation reactions.

[0119] The scale parameter transfer unit 34 is used to realize the transfer and mapping of reaction parameters at the micro, meso, and macro levels. The transfer function of micro reaction parameters to meso reaction type parameters is:

[0120] k mid (j) = ∑ i [w micro (i,j) × k micro (i),

[0121] where, k mid (j) is the rate constant of meso reaction j, k micro (i) is the rate constant of micro reaction i, and w micro (i,j) is the contribution weight of micro reaction i to meso reaction j.

[0122] The mapping function of meso reaction type parameters to macro lump parameters is:

[0123] k macro (m,n) = ∑ j [w mid (j,m,n) × k mid (j)],

[0124] where k macro (m,n) is the conversion rate constant from lumping m to lumping n, and w mid (j,m,n) is the contribution weight of mesoscopic reaction j to the conversion from lumping m to lumping n.

[0125] Through this multi-scale reaction network coupling method, the results of microscopic quantum chemistry calculations can be seamlessly connected to the macroscopic reaction engineering model, improving the accuracy and predictive ability of the reaction network model.

[0126] Example 5: Specific Structure of the Oxygen Content Gradient Regulation Module

[0127] As Figure 5 shown, the oxygen content gradient regulation module 4 includes an oxygen content gradient index calculation unit 41, a selective correlation analysis unit 42, and a catalyst gradient design unit 43.

[0128] The oxygen content gradient index calculation unit 41 is used to calculate the oxygen content gradient index according to the following formula:

[0129] OGI = ∑ i [w i × C i × O i ,

[0130] where OGI is the oxygen content gradient index, w i is the weight coefficient of component i, C i is the concentration of component i, and O i is the oxygen content of component i. The weight coefficient w i can be set according to the reaction activity of the component. The higher the reaction activity, the greater the weight. Preferably, the value range of the oxygen content gradient index is 0 - 100. The larger the value, the higher the oxygen content and the greater the difficulty of deoxidation.

[0131] The selective correlation analysis unit 42 is used to establish a quantitative relationship between the oxygen content gradient index and the selectivity of each reaction path. This relationship can be expressed as:

[0132] S(j) = a(j) × OGI b(j) × exp(c(j) × OGI),

[0133] Among them, S(j) is the selectivity of reaction path j, and a(j), b(j), and c(j) are fitting parameters. Through this relationship, the selectivity of each reaction path under different oxygen content conditions can be predicted, providing a basis for the catalyst gradient design. Preferably, for the aromatization path, a = 0.05 - 0.15, b = 0.8 - 1.2, c = (-0.02) - (-0.01); for the deoxygenation path, a = 1 - 3, b = 0.2 - 0.5, c = (-0.01) - (-0.005).

[0134] The catalyst gradient design unit 43 is used to design the axial catalyst acidity gradient distribution. This distribution can be expressed as:

[0135] A(z) = A0 × (1 - z / L) n +A m ,

[0136] where A(z) is the acidity at position z, A0 is the inlet acidity, L is the reactor length, n is the gradient coefficient, and A m is the minimum acidity. Preferably, the range of the inlet acidity A0 is 0.5 - 1.5 mmol / g, the range of the gradient coefficient n is 1 - 3, and the range of the minimum acidity A m is 0.1 - 0.3 mmol / g.

[0137] The catalyst gradient design unit 43 also designs the basic site distribution function to achieve the cooperative catalysis of hydrogen transfer and C - O bond cleavage. Preferably, in the front section area of the reactor, the ratio of acidic sites to basic sites is 3:1 to 5:1, mainly promoting the deoxygenation reaction; in the rear section area of the reactor, the ratio of acidic sites to basic sites is 8:1 to 12:1, mainly promoting the aromatization reaction.

[0138] Through the regulation of the oxygen content gradient, the step - by - step reduction of the oxygen content in biomass tar can be achieved, reducing the occurrence of side reactions and improving the selectivity of target products.

[0139] Example 6: Specific structure of the adaptive product orientation regulation module

[0140] As Figure 6 shown, the adaptive product orientation regulation module 5 includes a product distribution prediction unit 51, a multi - objective optimization unit 52, and a closed - loop feedback control unit 53.

[0141] The product distribution prediction unit 51 is used to construct a product distribution prediction model based on a deep neural network. This model can be expressed as:

[0142] Y = DNN(X,θ),

[0143] Among them, Y is the product distribution vector, X is the operating parameter vector, and θ is the model parameter. The deep neural network contains 3 - 5 hidden layers, each layer contains 20 - 50 neurons, and the ReLU activation function is adopted. Preferably, the operating parameter vector X includes parameters such as reaction temperature, pressure, space velocity, and catalyst acidity, and the product distribution vector Y includes the yields of components such as aromatics, olefins, alkanes, and oxygenates.

[0144] The multi - objective optimization unit 52 is used to design a multi - objective optimization function to balance the target product yield, energy utilization efficiency, and system stability. The optimization function can be expressed as:

[0145] f = w1×Y target +w2×E + w3×S,

[0146] Among them, Y target is the target product yield, E is the energy utilization efficiency, S is the system stability index, and w1, w2, w3 are weight coefficients, satisfying w1 + w2 + w3 = 1. Preferably, when the target product is aromatics, w1 = 0.5 - 0.7, w2 = 0.2 - 0.3, w3 = 0.1 - 0.2; when the target product is olefins, w1 = 0.6 - 0.8, w2 = 0.1 - 0.2, w3 = 0.1 - 0.2.

[0147] The closed - loop feedback control unit 53 is used to monitor the change of product composition in real time and dynamically adjust the prediction model parameters. This unit collects real - time data during the reaction process, compares it with the prediction results, and updates the prediction model parameters. Preferably, the parameter update frequency is once every 1 - 2 hours, and the batch gradient descent method is used to update the parameters, and the learning rate is set to 0.01 - 0.05.

[0148] The closed - loop feedback control unit 53 also includes an abnormal condition identification and response strategy. By monitoring the abnormal changes of key parameters, the operating conditions are adjusted in a timely manner to improve the system robustness. Preferably, when the catalyst activity drops by more than 20%, the reaction temperature is increased by 10 - 20°C; when the aromatics selectivity drops by more than 15%, the space velocity is reduced by 20 - 30%.

[0149] Through adaptive product orientation regulation, the target product distribution can be flexibly adjusted according to market demand, improving the economic efficiency and adaptability of the system.

[0150] Example 7: Specific structure of the gradient catalytic reactor

[0151] As Figure 7 shown, the gradient catalytic reactor 6 includes a multi - stage fixed - bed structure, which is axially divided into four reaction zones: a deoxygenation catalytic zone 61, a primary cracking zone 62, a deep cracking zone 63, and a selective aromatization zone 64.

[0152] The deoxidation catalytic zone 61 is filled with a composite catalyst of HZSM-5 molecular sieve and MgO, and the working temperature is 350-400 °C. The silica-alumina ratio of the HZSM-5 molecular sieve is 50, and the addition amount of MgO is 5-15 wt%. This zone mainly realizes preliminary deoxidation, reduces the oxygen content of tar, and alleviates the burden on subsequent catalysts.

[0153] The primary cracking zone 62 is filled with a composite catalyst of USY molecular sieve and Fe2O3, and the working temperature is 400-450 °C. The silica-alumina ratio of the USY molecular sieve is 30, and the addition amount of Fe2O3 is 3-8 wt%. This zone mainly promotes the breaking of C-C bonds and cracks macromolecules into medium and small molecules.

[0154] The deep cracking zone 63 is filled with a composite catalyst of Beta molecular sieve and ZnO, and the working temperature is 425-475 °C. The silica-alumina ratio of the Beta molecular sieve is 25, and the addition amount of ZnO is 3-8 wt%. This zone mainly promotes the hydrogen transfer reaction, stabilizes reaction intermediates, and reduces coke formation.

[0155] The selective aromatization zone 64 is filled with a composite catalyst of ZSM-5 molecular sieve and Ga2O3, and the working temperature is 400-450 °C. The silica-alumina ratio of the ZSM-5 molecular sieve is 40, and the addition amount of Ga2O3 is 2-5 wt%. This zone mainly promotes the aromatization reaction and improves the selectivity of aromatic hydrocarbon products.

[0156] The gradient catalytic reactor 6 is internally provided with a multi-point feeding system, which can inject hydrogen or steam at different positions to adjust the reaction atmosphere. The reactor wall is provided with a multi-point temperature control system to achieve precise temperature gradient control. The catalyst particle size is controlled at 1-3 mm, and the bed pressure drop is controlled at 0.05-0.1 MPa.

[0157] Through this gradient catalytic reactor design, it is possible to achieve a gradual reduction in the oxygen content of biomass tar and the directional conversion of products, improving the catalytic efficiency and product selectivity.

[0158] Example 8: Specific structure of the system auxiliary module

[0159] This embodiment provides an auxiliary module of the system, including an on-line analysis and detection module, a data management module, and an abnormal condition identification module.

[0160] The on-line analysis and detection module is used to collect samples at different positions of the reactor and perform gas chromatography and near-infrared spectroscopy analysis. The gas chromatography is equipped with FID and TCD detectors for analyzing the composition of reaction products; the near-infrared spectroscopy monitors the reaction process in real time through changes in characteristic absorption peaks. Preferably, a sampling point is set in each of the four regions of the reactor, and the sampling frequency is once every 15-30 minutes.

[0161] The data management module is used to store and manage various types of data during the operation of the system, including raw material composition data, reaction condition data, product analysis data, etc. This module adopts a relational database structure and supports data query, statistics, and visualization functions. Preferably, the data storage period is 3 - 6 months, and the data compression ratio is 5:1 to 10:1.

[0162] The abnormal working condition identification module is used to identify abnormal states during system operation and generate countermeasures. Based on the multivariate statistical process control (MSPC) method, this module sets normal ranges for key parameters such as temperature, pressure, and component change rate. When the parameters exceed the normal range, an alarm is issued and countermeasures are generated. Preferably, a temperature fluctuation exceeding ±10°C, a pressure fluctuation exceeding ±0.2 MPa, or a component change rate exceeding ±20% is defined as an abnormal working condition.

[0163] These auxiliary modules work together with the main functional modules to improve the reliability, maintainability, and safety of the system.

[0164] Example 9: System data flow process

[0165] This example details the data flow process of the system, demonstrating the data transfer and information exchange between modules.

[0166] The component characterization data generated by the multi-level biomass tar component characterization module 1 is transmitted into the functional group reaction activity quantitative evaluation module 2 through a data transmission line. The component characterization data includes a three-dimensional component characterization matrix (3D - CRCM), and each element in the matrix contains the concentration value, molecular structure characteristics, and reaction tendency information of the component.

[0167] The functional group reaction activity index generated by the functional group reaction activity quantitative evaluation module 2 is transmitted into the multi-scale reaction network coupling module 3 through a data transmission line. The functional group reaction activity index (FGRI) quantifies the reaction activity of different functional groups, considering factors such as frequency factor, activation energy, steric hindrance, and electronic effect.

[0168] The three-level reaction network structure generated by the multi-scale reaction network coupling module 3 is transmitted into the oxygen content gradient regulation module 4 through a data transmission line. The three-level reaction network structure includes a microscopic elementary reaction network, a mesoscopic reaction type network, and a macroscopic lumping conversion network, as well as the parameter transfer relationship between the three levels.

[0169] The catalyst distribution scheme generated by the oxygen content gradient regulation module 4 is transmitted into the adaptive product orientation regulation module 5 and the gradient catalytic reactor 6 through a data transmission line. The catalyst distribution scheme includes the catalyst type, composition, and acid-base distribution in each reaction zone to achieve a gradient reduction in oxygen content.

[0170] The operation parameter optimization scheme generated by the adaptive product orientation regulation module 5 is transmitted into the gradient catalytic reactor 6 through the control circuit. The operation parameter optimization scheme includes the parameter settings such as the temperature, pressure, and space velocity of each reaction zone to achieve the orientation regulation of the product distribution.

[0171] Meanwhile, the product analysis data of the gradient catalytic reactor 6 is transmitted back to the adaptive product orientation regulation module 5 through the data feedback circuit to form a closed-loop control. The product analysis data is provided by the on-line analysis and detection module and includes information such as the product composition, yield, and selectivity.

[0172] Through this data flow process, each module of the system works together to achieve the full-process automatic control from raw material characterization to product orientation regulation.

[0173] Example 10: Control method for gradient catalytic biomass tar cracking reactor

[0174] This example provides a control method for a gradient catalytic biomass tar cracking reactor, including the following steps:

[0175] 1. Construct a three-dimensional component characterization matrix of biomass tar to characterize the components of biomass tar from the dimensions of carbon skeleton structure, oxygen functional group, and molecular weight distribution. This step collects HPLC, GC-MS, and FT-ICR-MS data, applies a multi-source data fusion algorithm, identifies characteristic peaks, and constructs a three-dimensional component characterization matrix (3D-CRCM).

[0176] 2. Calculate the functional group reaction activity index to quantify the reaction activity of different functional groups. This step calculates the functional group reaction activity index (FGRI) by considering factors such as frequency factor, activation energy, steric hindrance, and electronic effect, and evaluates the reaction tendency of different functional groups.

[0177] 3. Construct a reaction network structure including three levels: micro, meso, and macro. This step constructs a network of elementary reaction steps through quantum chemical calculations, combines them to form a meso-scale reaction type network, and maps it to a macro-scale lumped conversion network to achieve parameter transfer between the three levels.

[0178] 4. Establish a quantitative relationship between the oxygen content gradient index and the reaction path selectivity, and generate a catalyst distribution scheme. This step calculates the oxygen content gradient index (OGI), analyzes its relationship with the selectivity of each reaction path, and designs an axial catalyst acidity gradient distribution to achieve gradient regulation of the oxygen content.

[0179] 5. Optimize the reaction operation parameters according to the catalyst distribution scheme and product analysis data. This step constructs a product distribution prediction model through a deep neural network and designs a multi-objective optimization function to achieve the orientation regulation of the product distribution.

[0180] 6. Through the multi-stage fixed-bed structure and catalysts with different functions in the gradient catalytic reactor, a gradient reduction of the oxygen content and a directional conversion of the product are achieved during the reaction process. This step includes the startup, steady-state operation, parameter adjustment, and shutdown processes of the reactor.

[0181] Preferably, the startup process of the reactor is as follows:

[0182] a) The catalyst bed is preheated to 300 °C;

[0183] b) Nitrogen is introduced to displace the air in the system;

[0184] c) The temperature is gradually increased to the operating temperature, and the temperature rise rate is controlled at 2 - 5 °C / min;

[0185] d) Biomass tar is introduced according to the set parameters, and the temperatures of each zone are adjusted to establish a temperature gradient.

[0186] Preferably, the operating parameter ranges during the steady-state operation are as follows:

[0187] a) Temperature of the upper catalytic zone: 350 - 400 °C;

[0188] b) Temperature of the upper-middle catalytic zone: 400 - 450 °C;

[0189] c) Temperature of the middle-lower catalytic zone: 425 - 475 °C;

[0190] d) Temperature of the lower catalytic zone: 400 - 450 °C;

[0191] e) System operating pressure: 0.5 - 2.0 MPa;

[0192] f) Liquid hourly space velocity (LHSV): 0.5 - 2.0 h -1 ;

[0193] g) Hydrogen-oil ratio (H2 / oil): 50 - 200 Nm 3 / m 3 ;

[0194] h) Steam / oil ratio: 0.1 - 0.5 kg / kg.

[0195] Through this control method, the efficient conversion of biomass tar and the directional regulation of the product can be achieved, improving the economic and environmental benefits of the system.

[0196] Example 11: Industrial application case

[0197] This example demonstrates an industrial application case, verifying the feasibility and effectiveness of the present invention.

[0198] Using pine biomass tar as the raw material, its initial oxygen content is 32.5 wt%, the main oxygen-containing functional groups include phenolic hydroxyl groups, carboxyl groups and ether bonds, and the molecular weight distribution is in the range of 150 - 350 Da. The gradient catalytic biomass tar cracking reactor and its control system of the present invention are used for a continuous 320-hour operation test.

[0199] The system first conducts three-dimensional component characterization of the raw material, identifies 32 main components, calculates the functional group reactivity index, and constructs a three-layer reaction network structure. Based on the oxygen content gradient index analysis, a four-stage catalytic bed is designed, and HZSM-5 / MgO, USY / Fe2O3, Beta / ZnO and ZSM-5 / Ga2O3 catalysts are filled from top to bottom respectively.

[0200] The operating conditions are set as follows: the temperature of the upper catalytic zone is 380 °C, the temperature of the upper-middle catalytic zone is 425 °C, the temperature of the middle-lower catalytic zone is 450 °C, the temperature of the lower catalytic zone is 430 °C, the system pressure is 1.2 MPa, the liquid hourly space velocity is 1.0 h -1 and the hydrogen-oil ratio is 100 Nm 3 / m 3 , and the steam / oil ratio is 0.2 kg / kg.

[0201] During the operation of the system, the adaptive product orientation regulation module dynamically adjusts the operating parameters according to the product analysis data to achieve precise control of the product distribution. When the target product is set as aromatics, the aromatics selectivity can reach 65.3%; when the target product is set as olefins, the olefins selectivity can reach 42.7%.

[0202] The oxygen content of the liquid product drops to 1.5 wt%, and the reduction rate reaches 95.4%. Compared with the traditional catalytic cracking technology, the system of the present invention has significant advantages in the following aspects: the product quality is improved by 45 - 60%, the energy consumption is reduced by 25 - 30%, the catalyst life is extended by 2.5 - 3 times, and the system stability is improved by 60 - 70%.

[0203] The economic benefit analysis shows that when using the system of the present invention to process biomass tar, compared with the traditional technology, the fixed asset investment is reduced by 15%, the operating cost is reduced by 30%, the product value is increased by 40%, and the investment recovery period is shortened by 35%.

[0204] The environmental benefit analysis shows that the system of the present invention reduces the generation of waste water and waste gas, reduces the energy consumption, and conforms to the development concept of green chemical industry and circular economy.

[0205] This application case proves that the gradient catalytic biomass tar cracking reactor and its control system of the present invention have good industrial application prospects and significant economic and environmental benefits.

[0206] The gradient catalytic biomass tar cracking reactor and its control system provided by the present invention have good industrial practicability, which is mainly reflected in the following aspects:

[0207] 1. Technical feasibility: The technologies adopted by the system, such as multi-level component characterization, functional group reaction activity evaluation, multi-scale reaction network coupling, oxygen content gradient regulation, and adaptive product orientation regulation, are all based on mature scientific theories and engineering practices, and have a reliable technical foundation.

[0208] 2. Equipment implementation: The gradient catalytic reactor adopts a multi-stage fixed bed structure, with different functional catalysts loaded in each reaction zone, and a multi-point feeding and temperature control system is set up. These equipment and operation methods have been widely used in the chemical industry and are easy to implement.

[0209] 3. Economic rationality: By optimizing the catalyst configuration and operation parameters, the system improves the selectivity and yield of target products, reduces energy consumption and the catalyst deactivation rate, and has significant economic benefits.

[0210] 4. Operational flexibility: The system can flexibly adjust the target product distribution according to market demands, and adapt to biomass tars with different sources and compositions, with strong adaptability and flexibility.

[0211] 5. Environmental friendliness: The system reduces the generation of waste through efficient catalytic conversion and reduces energy consumption, meeting the development concepts of green chemistry and circular economy.

[0212] Therefore, the present invention has good industrial practicability and application prospects.

[0213] The present invention provides a gradient catalytic biomass tar cracking reactor and its control method. Through innovative technologies such as multi-level biomass tar component characterization, quantitative evaluation of functional group reaction activity, multi-scale reaction network coupling, oxygen content gradient regulation, and adaptive product orientation regulation, the efficient conversion of biomass tar and product orientation regulation are achieved. Compared with the prior art, the present invention can effectively process biomass tar with high oxygen content, greatly improve the quality of liquid products, significantly reduce energy consumption and the catalyst deactivation rate, and has significant economic and environmental benefits.

[0214] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. Gradient catalytic biomass tar cracking reactor, characterized in that, Comprising: A multi-level biomass tar component characterization module for constructing a three-dimensional component characterization matrix to characterize biomass tar components from the dimensions of carbon skeleton structure, oxygen functional group, and molecular weight distribution, and generating component characterization data; A functional group reaction activity quantitative evaluation module connected to the multi-level biomass tar component characterization module for receiving the component characterization data and calculating the functional group reaction activity index; A multi-scale reaction network coupling module connected to the functional group reaction activity quantitative evaluation module for receiving the functional group reaction activity index and constructing a three-level reaction network structure including the micro level, meso level, and macro level; An oxygen content gradient regulation module connected to the multi-scale reaction network coupling module for receiving the three-level reaction network structure, establishing a quantitative relationship between the oxygen content gradient index and the reaction path selectivity, and generating a catalyst distribution scheme; An adaptive product orientation regulation module connected to the oxygen content gradient regulation module for receiving the catalyst distribution scheme and product analysis data and optimizing the reaction operation parameters; A gradient catalytic reactor including a multi-stage fixed bed structure axially divided into multiple reaction zones, each reaction zone filled with catalysts with different functions, for realizing the gradient reduction of oxygen content and product orientation conversion during the reaction process.

2. The gradient catalytic biomass tar cracking reactor according to claim 1, wherein, The multi-level biomass tar component characterization module includes: A component data acquisition unit for acquiring high performance liquid chromatography data, gas chromatography-mass spectrometry data, and Fourier transform ion cyclotron resonance mass spectrometry data of biomass tar; A multi-source data fusion unit for performing fusion processing on the acquired multi-source data and identifying characteristic peaks; A three-dimensional matrix construction unit for constructing a three-dimensional component characterization matrix including the carbon skeleton structure dimension, oxygen functional group dimension, and molecular weight distribution dimension based on the characteristic peak identification result.

3. The gradient catalytic biomass tar cracking reactor according to claim 1, wherein, The functional group reaction activity quantitative evaluation module includes: An activity index calculation unit for calculating the functional group reaction activity index according to the following formula: FGRI(i,j,k) = k0(i,j,k) × exp(-E a (i,j,k) / RT) × S(i,j,k) × D(i,j,k), Among them, FGRI(i, j, k) is the functional group reaction activity index, k0(i, j, k) is the frequency factor, E a (i, j, k) is the activation energy, R is the gas constant, T is the reaction temperature, S(i, j, k) is the steric hindrance factor, D(i, j, k) is the electronic effect factor, and i, j, k are the indices of the three-dimensional matrix respectively; A catalyst-functional group interaction evaluation unit for constructing an interaction model between the acidic sites of the catalyst and the oxygen functional group; A heteroatom influence evaluation unit for calculating the influence of heteroatoms such as nitrogen and sulfur on the catalyst activity.

4. The gradient catalytic biomass tar cracking reactor according to claim 1, wherein The multi-scale reaction network coupling module includes: A micro reaction network unit for constructing a network of elementary reaction steps based on quantum chemistry calculations; A meso reaction network unit for constructing a meso reaction network including typical reaction types such as deoxygenation, decarboxylation, dehydration, cyclization, and rearrangement; A macro reaction network unit for constructing a transformation network between product lumps; A scale parameter transfer unit for realizing the transfer and mapping of reaction parameters at the micro, meso, and macro levels.

5. The gradient catalytic biomass tar cracking reactor according to claim 1, characterized in that, The oxygen content gradient regulation module includes: An oxygen content gradient index calculation unit for calculating the oxygen content gradient index according to the following formula: OGI = ∑ i [w i × C i × O i ​ Among them, OGI is the oxygen content gradient index, w i is the weight coefficient of component i, C i is the concentration of component i, O i is the oxygen content of component i; A selectivity correlation analysis unit for establishing a quantitative relationship between the oxygen content gradient index and the selectivity of each reaction path; A catalyst gradient design unit for designing the axial catalyst acidity gradient distribution.

6. The gradient catalytic biomass tar cracking reactor according to claim 1, characterized in that, The adaptive product orientation regulation module includes: A product distribution prediction unit for constructing a product distribution prediction model based on a deep neural network; A multi-objective optimization unit for designing a multi-objective optimization function to balance the yield of target products, energy utilization efficiency, and system stability; A closed-loop feedback control unit for real-time monitoring of product composition changes and dynamically adjusting the parameters of the prediction model.

7. The gradient catalytic biomass tar cracking reactor according to claim 1, characterized in that, The multiple reaction zones of the gradient catalytic reactor are, from top to bottom in sequence: A deoxygenation catalytic zone filled with a composite catalyst of HZSM-5 molecular sieve and MgO, with an operating temperature of 350 - 400 °C; A primary cracking zone filled with a composite catalyst of USY molecular sieve and Fe2O3, with an operating temperature of 400 - 450 °C; A deep cracking zone filled with a composite catalyst of Beta molecular sieve and ZnO, with an operating temperature of 425 - 475 °C; A selective aromatization zone filled with a composite catalyst of ZSM-5 molecular sieve and Ga2O3, with an operating temperature of 400 - 450 °C.

8. The gradient catalytic biomass tar cracking reactor according to claim 1, wherein The system further includes: An on-line analysis and detection module for collecting samples at different positions of the reactor and performing gas chromatography and near-infrared spectroscopy analysis; A data management module for storing and managing various types of data during the operation of the system; An abnormal condition identification module for identifying abnormal states during the operation of the system and generating countermeasures.

9. The gradient catalytic biomass tar cracking reactor according to claim 1, wherein, The system realizes linkage through the following data transfer process: The component characterization data generated by the multi-level biomass tar component characterization module is transmitted to the functional group reaction activity quantitative evaluation module; The functional group reaction activity index generated by the functional group reaction activity quantitative evaluation module is transmitted to the multi-scale reaction network coupling module; The three-level reaction network structure generated by the multi-scale reaction network coupling module is transmitted to the oxygen content gradient regulation module; The catalyst distribution scheme generated by the oxygen content gradient regulation module is transmitted to the adaptive product orientation regulation module and the gradient catalytic reactor; The operation parameter optimization scheme generated by the adaptive product orientation regulation module is transmitted to the gradient catalytic reactor.

10. The control method of the gradient catalytic biomass tar cracking reactor according to any one of claims 1-9, characterized in that, Including the following steps: Construct a three-dimensional component characterization matrix of biomass tar to characterize the components of biomass tar from the dimensions of carbon skeleton structure, oxygen functional group, and molecular weight distribution; Calculate the functional group reaction activity index to quantify the reaction activities of different functional groups; Construct a reaction network structure including three levels: micro, meso, and macro; Establish a quantitative relationship between the oxygen content gradient index and the reaction path selectivity to generate a catalyst distribution scheme; Optimize the reaction operation parameters according to the catalyst distribution scheme and product analysis data; Through the multi-stage fixed-bed structure and catalysts with different functions in the gradient catalytic reactor, realize the gradient reduction of oxygen content and the directional conversion of products during the reaction process.

Images