Biomass gasification process twinborn optimization system and method thereof

Abstract

The invention relates to the field of industrial process control and digital twinning, in particular to a biomass gasification process twinning optimization system and a biomass gasification process twinning optimization method. Five modules, namely a multi-source data intelligent sensing module, a hybrid evolutionary algorithm self-optimization module, a knowledge graph self-adaptive reconstruction module, a multi-time-scale collaborative calibration and prediction module and a closed-loop feedback iterative symbiosis module, are integrated; the system constructs a multi-dimensional deviation feature vector through the intelligent sensing module, and accurately recognizes model deviation; the parameter self-optimization module performs self-adaptive optimization on model parameters by using a particle swarm and differential evolution algorithm; if the optimization does not reach the precision, the knowledge graph module is started, and the model is reconstructed based on the graph neural network; the multi-time scale module ensures that the model is accurately calibrated and predicted under different time scales; the closed-loop feedback module realizes bidirectional information flow between the model and the entity, continuous iterative optimization is realized, and long-term consistency and adaptability of the model are improved; the system realizes conversion from passive early warning to active optimization.

Description

Technical Field

[0001] The present invention relates to the fields of industrial process control and digital twin, and particularly to a twin optimization system and method for biomass gasification process, which are used to realize digital modeling, intelligent monitoring, adaptive optimization and closed-loop control of the biomass gasification process. Background Art

[0002] Biomass gasification is a thermochemical conversion technology that converts biomass raw materials into clean gas, which has important value in the fields of renewable energy utilization and environmental protection. However, due to the diversity and complexity of biomass raw materials, there are problems such as large parameter fluctuations, low control accuracy, and unstable efficiency in the gasification process, which pose challenges to process optimization and safe operation.

[0003] In the prior art, for example, the invention patent with the patent number CN115074158B discloses a process safety early warning system for coal gasification technology based on digital twin. The system includes a digital twin module, a safety early warning module and a DCS control module, and can simulate and early warn the coal gasification process. However, the system has the following deficiencies:

[0004] 1. Using a static digital twin model, lacking the ability of model adaptive evolution, resulting in a gradual decrease in the consistency between the model and the entity during long-term use;

[0005] 2. Focusing on the safety early warning function, lacking the ability to actively optimize process parameters and process conditions;

[0006] 3. Model update depends on manual intervention and cannot automatically adjust the model structure and parameters according to the actual operation situation;

[0007] 4. Not considering the collaborative optimization problem of different time scales, it is difficult to balance short-term accuracy and long-term stability at the same time;

[0008] 5. Lacking a complete closed-loop feedback mechanism, the interaction between the digital model and the physical entity is insufficient.

[0009] In addition, traditional process control methods mainly rely on preset control models and empirical parameters, and it is difficult to adapt to the changing working conditions and complex reaction mechanisms in the biomass gasification process. With the development of digital twin technology and artificial intelligence algorithms, it has become possible to build a digital twin system with the ability of adaptive evolution, but there is still a lack of a dedicated solution for the characteristics of the biomass gasification process. Summary of the Invention

[0010] The purpose of the present invention is to provide a twin optimization system and method for biomass gasification process, solve the technical problems of poor long-term consistency and insufficient adaptability of the digital twin system in the prior art, and realize a fundamental transformation from static mapping to dynamic evolution.

[0011] The present invention proposes a twin optimization system for the biomass gasification process, including:

[0012] A multi-source data-driven model deviation intelligent perception module, which is used to construct a multi-dimensional feature vector including physical deviation, time deviation and operating condition deviation, calculate the deviation sensitivity matrix, monitor the deviation accumulation function, and generate deviation feature data;

[0013] A parameter self-optimization module driven by a hybrid evolutionary algorithm, which is connected to the model deviation intelligent perception module, is used to receive the deviation feature data, classify the model parameters according to the influence degree, and adaptively optimize the model parameters by using a hybrid algorithm of particle swarm optimization and differential evolution, and generate optimized parameter data;

[0014] A model structure adaptive reconstruction module driven by a knowledge graph, which is connected to the parameter self-optimization module, is used to construct a reaction mechanism knowledge graph based on the optimized parameter data when the parameter optimization cannot meet the accuracy requirements, evaluate the adaptability of the model structure, use a graph neural network to reconstruct the model, and generate reconstructed model data;

[0015] A multi-time scale collaborative calibration and prediction module, which is connected to the model structure adaptive reconstruction module, is used to receive the reconstructed model data, perform collaborative calibration based on a hierarchical time scale framework and a multi-scale information transfer mechanism, and perform prediction by using an adaptive time step algorithm and a timeliness weight allocation method, and generate calibrated model data;

[0016] A model-entity iterative symbiosis module with closed-loop feedback, which is connected to the multi-time scale collaborative calibration and prediction module, is used to receive the calibrated model data, construct a two-way information flow system, realize a decision-execution-validation cycle, execute an incremental learning and forgetting mechanism, generate control instructions, and transmit entity feedback data to the model deviation intelligent perception module to form a closed-loop feedback.

[0017] Preferably, the model deviation intelligent perception module includes:

[0018] A deviation feature vector construction unit, which is used to construct a multi-dimensional feature vector including physical deviation, time deviation and operating condition deviation;

[0019] A deviation sensitivity analysis unit, which is connected to the deviation feature vector construction unit, is used to calculate the deviation sensitivity matrix and determine the key influencing factors;

[0020] A deviation accumulation monitoring unit, which is connected to the deviation sensitivity analysis unit, is used to calculate the deviation integral function changing with time, and generate a trigger signal when the deviation accumulation exceeds the threshold;

[0021] A deviation mapping table construction unit, connected to the deviation accumulation monitoring unit, is used to construct a three-layer deviation mapping relationship table for the parameter layer, module layer, and system layer.

[0022] Preferably, the parameter self-optimization module driven by the hybrid evolutionary algorithm includes:

[0023] A parameter sensitivity grading unit, which is used to classify model parameters into three levels of α, β, and γ according to the degree of influence;

[0024] A hybrid evolutionary algorithm execution unit, connected to the parameter sensitivity grading unit, is used to perform parameter optimization by combining the particle swarm optimization and differential evolution algorithms;

[0025] An optimized parameter dynamic adjustment unit, connected to the hybrid evolutionary algorithm execution unit, is used to adaptively adjust the learning rate and mutation rate according to the deviation change trend;

[0026] A parameter update constraint unit, connected to the optimized parameter dynamic adjustment unit, is used to conduct a physical meaning rationality test on the optimized parameters to ensure that the optimization results conform to the principles of entropy increase, energy conservation, and kinetic rationality.

[0027] Preferably, the model structure adaptive reconstruction module driven by the knowledge graph includes:

[0028] A reaction mechanism knowledge graph construction unit, which is used to construct a knowledge graph including substance nodes, reaction nodes, and condition nodes;

[0029] A model structure adaptability evaluation unit, connected to the reaction mechanism knowledge graph construction unit, is used to calculate the weighted scores of accuracy, sensitivity, and robustness;

[0030] A graph neural network model reconstruction unit, connected to the model structure adaptability evaluation unit, is used to encode the model structure into a graph representation and evaluate the importance of subgraphs based on the graph convolutional network;

[0031] A modular reconstruction technology unit, connected to the graph neural network model reconstruction unit, is used to define the basic component library and combination rules of the model and execute the automatic assembly algorithm.

[0032] Preferably, the multi-time scale collaborative calibration and prediction module includes:

[0033] A hierarchical time scale framework unit, which is used to construct a framework covering micro, meso, macro, and long-term time scales;

[0034] A multi-scale information transfer unit, connected to the hierarchical time scale framework unit, is used to achieve upward information transfer and downward constraint between different scales;

[0035] An adaptive time step algorithm unit, connected to the multi-scale information transfer unit, for calculating the local truncation error and dynamically adjusting the time step;

[0036] A timeliness adaptive weight allocation unit, connected to the adaptive time step algorithm unit, for allocating weights to data at different times based on a time correlation function.

[0037] Preferably, the closed-loop feedback model-entity iterative symbiosis module includes:

[0038] A two-way information flow system unit, for establishing two-way information channels from entity to model and from model to entity;

[0039] A decision-execution-validation loop unit, connected to the two-way information flow system unit, for generating a decision set, executing control commands, and validating the execution effect;

[0040] An incremental learning and forgetting mechanism unit, connected to the decision-execution-validation loop unit, for performing incremental learning of new knowledge, decay of obsolete knowledge, and assessment of knowledge importance;

[0041] An autonomous evolution decision unit, connected to the incremental learning and forgetting mechanism unit, for constructing a policy network based on reinforcement learning and achieving exploration-exploitation balance under safety constraints.

[0042] Preferably, the connection relationship between the multi-source data-driven model deviation intelligent perception module and the hybrid evolutionary algorithm-driven parameter self-optimization module is:

[0043] The model deviation intelligent perception module transmits a deviation feature vector and a key parameter sensitivity matrix to the parameter self-optimization module;

[0044] When the norm of the deviation feature vector exceeds a preset threshold, the parameter self-optimization module is triggered to perform parameter optimization operations;

[0045] The parameter self-optimization module feeds back the optimized parameter results to the model deviation intelligent perception module for verification.

[0046] Preferably, the connection relationship between the parameter self-optimization module and the model structure adaptive reconstruction module is:

[0047] When parameter optimization reaches the limit but still does not meet the accuracy requirements, the parameter self-optimization module sends a reconstruction trigger signal and optimization limit data to the model structure adaptive reconstruction module;

[0048] edThe model structure adaptive reconstruction module shares the optimization objective function and constraint conditions of the parameter self-optimization module;

[0049] The model structure adaptive reconstruction module sends the reconstructed model structure to the parameter self-optimization module for initial parameter setting.

[0050] Preferably, the system forms three feedback loops with different frequencies:

[0051] Fast parameter adjustment loop, with a period of seconds level, and the path is entity data → deviation perception → parameter optimization → model update → entity control;

[0052] Medium-speed structure optimization loop, with a period of hours level, and the path is performance evaluation → structure reconstruction → model reconstruction → performance evaluation;

[0053] Long-term evolution loop, with a period of days / months level, and the path is long-term data accumulation → knowledge graph update → model evolution → long-term optimization.

[0054] The twin optimization method for biomass gasification process includes:

[0055] Construct a multi-dimensional feature vector including physical deviation, time deviation and operating condition deviation, calculate the deviation sensitivity matrix, monitor the deviation accumulation function, and generate deviation feature data;

[0056] Receive the deviation feature data, classify the model parameters according to the influence degree, and use the hybrid algorithm of particle swarm optimization and differential evolution to adaptively optimize the model parameters, and generate optimized parameter data;

[0057] When the parameter optimization cannot meet the accuracy requirements, based on the optimized parameter data, construct a reaction mechanism knowledge graph, evaluate the adaptability of the model structure, use the graph neural network to reconstruct the model, and generate reconstructed model data;

[0058] Receive the reconstructed model data, perform collaborative calibration based on the hierarchical time scale framework and multi-scale information transfer mechanism, use the adaptive time step algorithm and timeliness weight allocation method for prediction, and generate calibrated model data;

[0059] Receive the calibrated model data, construct a two-way information flow system, realize the decision-execution-validation loop, execute the incremental learning and forgetting mechanism, generate control instructions, and transfer the entity feedback data to the deviation feature vector construction step to form a closed-loop feedback.

[0060] Through the construction of the "digital twin adaptive evolution engine" as the core innovation, the present invention realizes a complete technical system from model deviation perception, parameter self-optimization, structure adaptive reconstruction, multi-time scale coordination to closed-loop feedback symbiosis, and has the following beneficial effects:

[0061] 1. Improve the long-term consistency and adaptability of the digital twin model, enabling the model to continuously self-update with environmental changes;

[0062] 2. Achieved the transformation from passive early warning to active optimization, and actively optimized process parameters on the basis of safety guarantee;

[0063] 3. Solved the process adaptability problem brought by biomass diversity, and the system can automatically adapt to different working conditions;

[0064] 4. Achieved collaborative optimization on multiple time scales, taking into account short-term accuracy and long-term stability;

[0065] 5. Constructed a model-entity symbiotic ecosystem, achieving deep integration of the digital and physical worlds.

[0066] Experimental data show that compared with traditional methods, the model prediction accuracy of this system has increased by 65-85%, the system response speed has increased by 4-6 times, the optimization of process parameters has increased the energy efficiency by 8-15%, the product quality fluctuation has been reduced by 70%, and the fault early warning accuracy rate has been increased to over 92%. Brief Description of the Drawings

[0067] Figure 1 is the overall architecture diagram of the twin optimization system for the biomass gasification process of the present invention;

[0068] Figure 2 is the structural schematic diagram of the intelligent perception module for model deviation driven by multi-source data of the present invention;

[0069] Figure 3 is the structural schematic diagram of the parameter self-optimization module driven by the hybrid evolutionary algorithm of the present invention;

[0070] Figure 4 is the structural schematic diagram of the model structure adaptive reconstruction module driven by the knowledge graph of the present invention;

[0071] Figure 5 is the structural schematic diagram of the multi-time scale collaborative calibration and prediction module of the present invention;

[0072] Figure 6 is the structural schematic diagram of the closed-loop feedback model-entity iterative symbiosis module of the present invention;

[0073] Figure 7 is the schematic diagram of the data flow relationship between the modules of the present invention;

[0074] Figure 8 is the schematic diagram of three different frequency feedback loops of the present invention;

[0075] Figure 9 is the schematic diagram of the process of the present invention;

[0076] Figure 10 is the comparison chart of the application case results of the present invention in a certain biomass gasification plant. Detailed Embodiments

[0077] Please refer to the attached Figures 1-10 , and the specific implementation manners of the present invention will be further described in detail below with reference to the accompanying drawings.

[0078] Embodiment 1: Overall System Architecture

[0079] As Figure 1 shown, the twin optimization system for biomass gasification process provided by the present invention includes: a multi-source data-driven model deviation intelligent perception module 1, a hybrid evolutionary algorithm-driven parameter self-optimization module 2, a knowledge graph-driven model structure adaptive reconstruction module 3, a multi-time scale collaborative calibration and prediction module 4, and a closed-loop feedback model-entity iterative symbiosis module 5.

[0080] The multi-source data-driven model deviation intelligent perception module 1 is used to construct a multi-dimensional feature vector including physical deviation, time deviation, and operating condition deviation, calculate the deviation sensitivity matrix, monitor the deviation accumulation function, and generate deviation feature data. This module can accurately quantify the difference between the digital model and the physical entity, providing an accurate basis for subsequent model updates.

[0081] The hybrid evolutionary algorithm-driven parameter self-optimization module 2 is connected to the model deviation intelligent perception module 1, and is used to receive the deviation feature data, classify the model parameters according to the influence degree, and adaptively optimize the model parameters by using a particle swarm optimization and differential evolution hybrid algorithm, and generate optimized parameter data. This module automatically adjusts the model parameters through intelligent algorithms to improve the accuracy and adaptability of the model.

[0082] The knowledge graph-driven model structure adaptive reconstruction module 3 is connected to the parameter self-optimization module 2, and is used to construct a reaction mechanism knowledge graph based on the optimized parameter data when the parameter optimization cannot meet the accuracy requirements, evaluate the adaptability of the model structure, use a graph neural network for model reconstruction, and generate reconstructed model data. This module can automatically adjust the model structure according to actual needs to solve problems that cannot be solved by parameter optimization.

[0083] The multi-time scale collaborative calibration and prediction module 4 is connected to the model structure adaptive reconstruction module 3, and is used to receive the reconstructed model data, perform collaborative calibration based on a hierarchical time scale framework and a multi-scale information transfer mechanism, and perform prediction by using an adaptive time step algorithm and a timeliness weight allocation method, and generate calibrated model data. This module realizes model collaboration at different time scales, taking into account short-term accuracy and long-term stability.

[0084] The model-entity iterative symbiosis module 5 with closed-loop feedback is connected to the multi-time scale collaborative calibration and prediction module 4, used to receive calibration model data, construct a two-way information flow system, implement a decision-execution-validation cycle, execute an incremental learning and forgetting mechanism, generate control instructions, and transfer entity feedback data to the model deviation intelligent perception module 1 to form a closed-loop feedback. This module realizes the deep integration of the digital model and the physical entity, promoting the overall self-optimization and evolution of the system.

[0085] During the operation of the system, a closed-loop information flow is formed among the modules: the model deviation intelligent perception module 1 perceives the deviation and transfers it to the parameter self-optimization module 2; the parameter self-optimization module 2 attempts to optimize the model by adjusting parameters. If the effect is not good, it triggers the model structure adaptive reconstruction module 3; the model structure adaptive reconstruction module 3 reconstructs the model and transfers it to the multi-time scale collaborative calibration and prediction module 4; the multi-time scale collaborative calibration and prediction module 4 calibrates and predicts and transfers it to the model-entity iterative symbiosis module 5 with closed-loop feedback; the model-entity iterative symbiosis module 5 with closed-loop feedback generates control instructions for execution and feeds back the data to the model deviation intelligent perception module 1 to form a complete closed loop.

[0086] This architecture design endows the system with the characteristics of self-organization, self-adaptation, self-learning and self-evolution, and can effectively solve the complex problems in the modeling and optimization of the biomass gasification process.

[0087] Embodiment 2: Model deviation intelligent perception module

[0088] As Figure 2 shown, the model deviation intelligent perception module 1 includes a deviation feature vector construction unit 11, a deviation sensitivity analysis unit 12, a deviation cumulative monitoring unit 13, and a deviation mapping table construction unit 14.

[0089] The deviation feature vector construction unit 11 is used to construct a multi-dimensional feature vector including physical deviation, time deviation and operating condition deviation. Specifically, the multi-dimensional feature vector ΔS is defined as:

[0090] ΔS = [Δp, Δt, Δw],

[0091] where, Δp is the physical deviation, representing the difference matrix of physical quantities such as temperature, pressure, and flow rate between the entity and the model; Δt is the time deviation, representing the difference function between the model response lag time and the actual system response time; Δw is the operating condition deviation, representing the change curve of the model prediction accuracy under different operating conditions.

[0092] Preferably, the physical deviation Δp can be further expressed as:

[0093]

[0094] Among them, T represents temperature, P represents pressure, Q represents flow rate, the subscript real represents the entity measurement value, and pred represents the model prediction value.

[0095] The deviation sensitivity analysis unit 12 is connected to the deviation feature vector construction unit 11, and is used to calculate the deviation sensitivity matrix and determine the key influencing factors. This unit develops a variable importance evaluation algorithm based on the sensitivity matrix S, and the sensitivity matrix S is defined as:

[0096]

[0097] Among them, y i is the model output parameter, and x j is the input parameter. By calculating the eigenvalues and eigenvectors of the S matrix, it is possible to determine which parameters have the greatest impact on the model deviation, thereby providing a direction for subsequent parameter optimization.

[0098] The deviation accumulation monitoring unit 13 is connected to the deviation sensitivity analysis unit 12, and is used to calculate the deviation integral function that changes with time, and generate a trigger signal when the deviation accumulation exceeds the threshold. This unit designs the deviation integral function D(t):

[0099]

[0100] Among them, w(τ) is the time weight function, t is the time variable, and ||ΔS(t)|| is the norm of the eigenvector AS. When D(t) exceeds the preset threshold θ, the model update process is triggered.

[0101] Preferably, the time weight function w(t) can be set as an exponential decay function:

[0102] w(τ) = e -λ(t-τ) ,

[0103] Among them, λ is the decay coefficient, and t is the current time. This design makes the weight of recent deviations larger, and can better reflect the current accuracy state of the model.

[0104] The deviation mapping table construction unit 14 is connected to the deviation accumulation monitoring unit 13, and is used to construct a three-layer deviation mapping relationship table of the parameter layer, module layer, and system layer. This unit organizes the deviation information into a hierarchical structure. The parameter layer maps the deviations of specific parameters, the module layer maps the overall deviations of functional modules, and the system layer maps the comprehensive deviations of the entire system. This hierarchical structure helps to accurately locate the source of deviations and improve the pertinence and efficiency of model updates.

[0105] Through the collaborative work of the above four units, this module realizes the accurate perception and quantification of model deviations, provides a solid data basis for subsequent model updates, and solves the problem that deviations in traditional digital twin systems are difficult to accurately quantify.

[0106] Example 3: Parameter Self-Optimization Module Driven by Hybrid Evolutionary Algorithm

[0107] As Figure 3 shown, the parameter self-optimization module 2 driven by the hybrid evolutionary algorithm includes a parameter sensitivity grading unit 21, a hybrid evolutionary algorithm execution unit 22, an optimized parameter dynamic adjustment unit 23, and a parameter update constraint unit 24.

[0108] The parameter sensitivity grading unit 21 is used to classify model parameters into three levels: α, β, and γ according to the degree of influence. Among them, α-level parameters are key parameters that directly affect system stability, such as reaction activation energy, heat transfer coefficient, etc.; β-level parameters are secondary parameters that affect prediction accuracy, such as boundary conditions, material physical properties, etc.; γ-level parameters are auxiliary parameters that affect efficiency but do not affect accuracy. Different levels of parameters adopt different optimization strategies and weights in the optimization process to improve optimization efficiency and accuracy.

[0109] The hybrid evolutionary algorithm execution unit 22 is connected to the parameter sensitivity grading unit 21 and is used to perform parameter optimization by combining the particle swarm optimization and differential evolution algorithms. This unit innovatively integrates the advantages of the two algorithms. The velocity and position update equations of the particle swarm optimization part are:

[0110] v i,g+1 = w·v i,g + c1·r1·(p i,g - x i,g ) + c2·r2·(p g,g - x i,g ),

[0111] x i,g+1 = x i,g + v i,g+1 ,

[0112] where v is the particle velocity, x is the particle position, w is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers in the interval [0,1], p i is the historical optimal position of the particle, p g is the global optimal position, the subscript i represents the particle number, and g represents the iteration number.

[0113] The mutation and crossover operations of the differential evolution part are:

[0114] u i,j,g = x r3,j,g + F·(x r1,j,g - x r2,j,g ),

[0115] where u is the trial vector, x is the target vector, F is the scaling factor, r1, r2, and r3 are randomly selected different individual indices, and the subscript j represents the dimension.

[0116] The two algorithms are combined through adaptive weights, and the fitness function F is designed as the weighted deviation between the model prediction value and the measured value:

[0117]

[0118] where y pred is the model prediction value, y real is the entity measurement value, w i is the weight coefficient, and n is the number of data points.

[0119] The optimization parameter dynamic adjustment unit 23 is connected to the hybrid evolutionary algorithm execution unit 22 and is used to adaptively adjust the learning rate and mutation rate according to the deviation change trend. This unit dynamically adjusts the algorithm parameters according to the change of the fitness function during the optimization process:

[0120] η(t + 1) = η(t)·(1 - λ·ΔF / F),

[0121] μ(t + 1) = μ ｂａｓｅ + Δμ·sigmoid(k·ΔS),

[0122] where η is the learning rate, μ is the mutation rate, λ and k are adjustment coefficients, ΔF is the fitness change, F is the current fitness, ΔS is the deviation change, μ base is the base mutation rate, and Δμ is the mutation rate adjustment amplitude.

[0123] This dynamic adjustment mechanism can maintain a large exploration ability in the early stage of optimization, improve the fine adjustment ability when approaching the optimal solution, and significantly improve the optimization efficiency and accuracy.

[0124] The parameter update constraint unit 24 is connected to the optimization parameter dynamic adjustment unit 23 and is used to conduct a physical meaning rationality test on the optimization parameters to ensure that the optimization results conform to the principle of entropy increase, energy conservation, and kinetic rationality. This unit sets three types of constraint conditions:

[0125] Entropy increase principle test: ΔS ≥ 0,

[0126] Energy conservation test: |ΔE| < ε,

[0127] Kinetic rationality: k > 0, E a > 0,

[0128] where ΔS is the entropy change, ΔE is the energy balance deviation, ε is the allowable error, k is the reaction rate constant, and E a is the activation energy.

[0129] Through these constraints, the system can avoid generating parameter combinations that are mathematically feasible but physically unreasonable, ensuring the physical correctness of the model.

[0130] Through the collaborative work of the above four units, this module realizes the intelligent adaptive optimization of model parameters, greatly improves the ability of the model to adapt to environmental changes, and provides a basis for the reconstruction of the model structure.

[0131] Example 4: Knowledge Graph-Driven Model Structure Adaptive Reconstruction Module

[0132] As Figure 4 shown, the knowledge graph-driven model structure adaptive reconstruction module 3 includes a reaction mechanism knowledge graph construction unit 31, a model structure adaptability evaluation unit 32, a graph neural network model reconstruction unit 33, and a modular reconstruction technology unit 34.

[0133] The reaction mechanism knowledge graph construction unit 31 is used to construct a knowledge graph including substance nodes, reaction nodes, and condition nodes. This unit first defines three types of nodes: the substance node (M) represents the substances participating in the reaction, such as biomass, gas components, etc.; the reaction node (R) represents chemical reactions or physical processes, such as pyrolysis, gasification, etc.; the condition node (C) represents the conditions affecting the reaction, such as temperature, pressure, etc. The nodes are connected by three types of edges: the participation relationship (→) indicates that the substance participates in the reaction; the influence relationship indicates that the condition affects the reaction; the inhibition relationship indicates that the condition or substance inhibits the reaction.

[0134] Preferably, the weight W of the edge ij is determined based on the analysis of the importance of the reaction path:

[0135]

[0136] where ΔG is the change in Gibbs free energy of the reaction, representing the thermodynamic driving force of the reaction path, and f is the reaction frequency, representing the kinetic importance of the reaction path.

[0137] The model structure adaptability evaluation unit 32 is connected to the reaction mechanism knowledge graph construction unit 31 and is used to calculate the weighted scores of accuracy, sensitivity, and robustness. This unit designs a structure fitness scoring system S adapt :

[0138] S adapt = α·P accuracy + β·P sensitivity + γ·P robustness ,

[0139] where P accuracy is the accuracy score, reflecting the accuracy of the model prediction; Psensitivity is the sensitivity score, reflecting the sensitivity of the model to parameter changes; P robustness is the robustness score, reflecting the stability of the model under different working conditions; α, β, and γ are weight coefficients, and α + β + γ = 1.

[0140] The graph neural network model reconstruction unit 33 is connected to the model structure adaptability evaluation unit 32, and is used to encode the model structure into a graph representation and evaluate the importance of subgraphs based on the graph convolutional network. This unit first encodes the model structure into a graph representation G(V, E), where V is the set of nodes and E is the set of edges. Then, it uses the graph convolutional network to calculate the importance of subgraphs. The graph convolutional operation is defined as:

[0141]

[0142] where represents the feature representation of node i in the l-th layer, N i represents the neighbor set of node i, α ij represents the attention coefficient, W (l) is the weight matrix, and σ is the activation function. By stacking multiple layers of graph convolutional operations, the system can capture the high-order features and relationships of the model structure, providing a basis for structure optimization.

[0143] The modular reconstruction technology unit 34 is connected to the graph neural network model reconstruction unit 33, and is used to define the basic component library and combination rules of the model, and execute the automatic assembly algorithm. This unit defines the basic component library of the model B = {b1, b2,..., b n}, which contains various basic model units, such as heat transfer modules, reaction kinetics modules, etc. At the same time, it defines the module combination rules R = {r1, r2,..., r m}, which stipulate the effective combination methods of different modules. Based on these definitions, the unit implements the automatic assembly algorithm, which can automatically select appropriate modules according to requirements and assemble them into a new model structure according to the rules.

[0144] Through the collaborative work of the above four units, this module realizes the intelligent adaptive reconstruction of the model structure, enabling the system to have the ability of self-transformation, dynamically adjusting the model structure according to the actual situation, and solving the limitation of the fixed structure of traditional digital twin systems.

[0145] Example 5: Multi-time scale collaborative calibration and prediction module

[0146] As Figure 5 shown, the multi-time scale collaborative calibration and prediction module 4 includes a hierarchical time scale framework unit 41, a multi-scale information transfer unit 42, an adaptive time step algorithm unit 43, and a timeliness adaptive weight allocation unit 44.

[0147] The hierarchical time-scale framework unit 41 is used to construct a framework covering micro, meso, macro, and long-term time scales. This unit divides the time scale into four levels: the micro time scale (μs-ms) corresponds to the molecular reaction kinetics model, which describes gas-phase reactions and reactions inside particles; the meso time scale (s-min) corresponds to the heat and mass transfer model, which describes the heat and mass transfer processes in the bed; the macro time scale (h-day) corresponds to the process parameter variation model, which describes the impact of operating parameter changes on the system; the long-term time scale (month-year) corresponds to the equipment aging and catalyst deactivation model, which describes the long-term evolution law of the system.

[0148] The multi-scale information transfer unit 42 is connected to the hierarchical time-scale framework unit 41 and is used to achieve upward information transfer and downward constraint between different scales. This unit designs an upward transfer function U(·) and a downward constraint function D(·):

[0149] U(data_micro,t)=Fu p( data_micro,context,t),

[0150] D(data_macro,t)=F down (data_macro,boundary,t),

[0151] where data_micro is micro information, data_macro is macro information, context is context information, boundary is boundary condition, t is time, and F up and F down are function mappings for upward transfer and downward constraint respectively. Through this mechanism, models at different time scales can achieve information exchange and collaborative optimization.

[0152] The adaptive time step algorithm unit 43 is connected to the multi-scale information transfer unit 42 and is used to calculate the local truncation error and dynamically adjust the time step. This unit first calculates the local truncation error:

[0153]

[0154] where y i+1 is the calculated value of the current time step, is the reference value at a finer time step. Then, it dynamically adjusts the time step according to the error:

[0155]

[0156] Among them, Δt is the time step, ∈ is the target error, and p is the control parameter. Through this mechanism, the system can achieve a balance between computational accuracy and efficiency, automatically refine the time step at critical transition points, and use larger steps in the steady stage to improve computational efficiency.

[0157] The timeliness adaptive weight allocation unit 44 is connected to the adaptive time step algorithm unit 43 and is used to allocate weights to data at different times based on the time correlation function. This unit defines the time correlation function:

[0158] R(t) = e -λτ ,

[0159] where τ is the time difference and λ is the decay coefficient. Based on this function, weights are allocated to data at different times:

[0160]

[0161] where t0 is the current time and t ｉ is the historical time point. This method gives higher weights to recent data while not completely ignoring the influence of historical data, which helps to improve the accuracy and stability of predictions.

[0162] Through the collaborative work of the above four units, this module realizes the collaborative calibration and prediction of the model at different time scales, solves the problem of model consistency at different time scales, and achieves the unity of short-term accurate calibration and long-term stable prediction.

[0163] Example 6: Model-entity iterative symbiosis module with closed-loop feedback

[0164] As Figure 6 shown, the model-entity iterative symbiosis module 5 with closed-loop feedback includes a two-way information flow system unit 51, a decision-execution-validation loop unit 52, an incremental learning and forgetting mechanism unit 53, and an autonomous evolution decision unit 54.

[0165] The two-way information flow system unit 51 is used to establish two-way information channels from the entity to the model and from the model to the entity. This unit realizes information flow in two directions: the entity→model direction transmits sensing data, operating status, and fault information; the model→entity direction transmits optimization suggestions, prediction warnings, and decision support. To avoid interference from redundant data, the unit designs an information flow adaptive control mechanism to dynamically adjust the data transmission frequency and content according to the system state and information importance.

[0166] The decision-execution-validation loop unit 52 is connected to the two-way information flow system unit 51 and is used to generate a set of decisions, execute control commands, and verify the execution effects. This unit realizes a complete closed-loop control process: first, based on model predictions, a set of multiple possible decision schemes D = {d1, d2, …, dk}; Then convert the selected decision-making solution into a control command \(C = T(d i ) and execute it in the entity system; Finally, compare the expected effect with the actual effect, and calculate the effect deviation \(\Delta E=\vert E pred - E real \vert\); Finally, update the model according to the verification result to complete a complete optimization closed-loop.

[0167] The incremental learning and forgetting mechanism unit 53 is connected to the decision-execution-verification loop unit 52, and is used to perform new knowledge incremental learning, obsolete knowledge attenuation, and knowledge importance evaluation. This unit designs a new knowledge incremental learning function:

[0168] L new =\(\eta\cdot f(data\_new)\),

[0169] where \(\eta\) is the learning rate, \(f(\cdot)\) is the knowledge extraction function, and \(data\_new\) is the new data. At the same time, an obsolete knowledge attenuation function is designed:

[0170] L old = L old \cdot(1 - \delta\cdot t)\),

[0171] where \(\delta\) is the attenuation coefficient and \(t\) is the time variable. In addition, the unit also implements a knowledge importance evaluation mechanism:

[0172] I(k)=f(frequemcy,impact,recency),

[0173] where \(frequency\) is the knowledge usage frequency, \(impact\) is the knowledge impact degree, and \(recency\) is the knowledge update time. Through these mechanisms, the system can continuously learn new knowledge, while eliminating obsolete or low-value knowledge, and maintaining the conciseness and efficiency of the knowledge base.

[0174] The autonomous evolution decision-making unit 54 is connected to the incremental learning and forgetting mechanism unit 53, and is used to construct a policy network based on reinforcement learning and achieve the exploration-exploitation balance under safety constraints. This unit constructs a policy network \(\pi(s|\theta)\) based on reinforcement learning, where \(s\) is the system state and \(\theta\) is the network parameter. The policy network continuously optimizes the decision-making strategy by interacting with the environment, and at the same time establishes a dual evaluation mechanism to comprehensively evaluate the decision-making quality by combining model evaluation and entity feedback. To ensure system safety, the unit designs an exploration-exploitation balance strategy under safety constraints to explore and verify new strategies on the premise of ensuring system safety.

[0175] Through the collaborative work of the above four units, this module achieves the deep integration and co-evolution of the model and the entity, making the overall system present the characteristics of "self-organization, self-adaptation, self-learning, and self-evolution", forming a truly digital twin adaptive evolutionary system.

[0176] Example 7: Connection between the model deviation intelligent perception module and the parameter self-optimization module

[0177] like Figure 7 As shown, a close connection is established between the model deviation intelligent perception module 1 driven by multi-source data and the parameter self-optimization module 2 driven by the hybrid evolutionary algorithm to realize the data transmission and triggering mechanism.

[0178] Specifically, the model deviation intelligent perception module 1 transmits the deviation characteristic vector ΔS and the key parameter sensitivity matrix S to the parameter self-optimization module 2. The deviation characteristic vector ΔS contains information about physical deviation, time deviation, and operating condition deviation, providing a target for parameter optimization; the key parameter sensitivity matrix S indicates which parameters have the greatest impact on model accuracy, providing direction for parameter optimization.

[0179] Regarding the trigger mechanism, when the norm of the deviation feature vector, ||ΔS||, exceeds a preset threshold θ, the Model Deviation Intelligent Perception Module 1 sends a trigger signal to the Parameter Self-Optimization Module 2, initiating the parameter optimization process. The preset threshold θ can be adjusted based on different application scenarios and accuracy requirements, and is typically set between 0.05 and 0.15. A lower threshold results in more frequent optimization operations, improving model accuracy but increasing the computational burden. A higher threshold reduces the optimization frequency, reducing the computational burden but potentially affecting model accuracy.

[0180] After parameter optimization is complete, the parameter self-optimization module 2 feeds the optimized parameter results back to the model deviation intelligent perception module 1 for verification. The verification process involves updating the model with the new parameters, calculating the deviation characteristic vector ΔS′ of the updated model, and comparing it with the original deviation ΔS. If IIΔS′||<||ΔS′||<θ, the parameter optimization is considered successful and the new parameters are adopted. Otherwise, a determination is made as to whether ||ΔS′||<||ΔS|| holds. If so, the new parameters are adopted despite not meeting the threshold, and a reconstruction request is sent to the model structure adaptive reconstruction module 3. If not, the optimization results are discarded, and a reconstruction request is sent to the model structure adaptive reconstruction module 3.

[0181] This connection relationship design enables the system to automatically decide whether parameter optimization is needed based on the deviation situation, and automatically switch to model structure reconstruction when the parameter optimization effect is not good, realizing the system's adaptive optimization capability.

[0182] Example 8: Connection Relationship between Parameter Self-Optimization Module and Model Structure Adaptive Reconfiguration Module

[0183] As Figure 7 shown, a collaborative working connection relationship is established between the parameter self-optimization module 2 driven by the hybrid evolutionary algorithm and the model structure adaptive reconfiguration module 3 driven by the knowledge graph.

[0184] When the parameter optimization reaches the limit but still does not meet the accuracy requirements, that is, the deviation ||ΔS'|| after parameter optimization is less than the original deviation ||ΔS|| but still greater than the threshold θ, and further optimization cannot significantly reduce the deviation, the parameter self-optimization module 2 will send a reconstruction trigger signal and optimization limit data to the model structure adaptive reconfiguration module 3. The optimization limit data includes: the optimal parameter set P best ={p1, p2, …, p n} that can be achieved under the current model structure, the corresponding minimum deviation ||ΔS min ||, and the sensitivity analysis result, indicating which functional modules may be the accuracy bottlenecks.

[0185] During the model structure reconstruction process, the model structure adaptive reconfiguration module 3 and the parameter self-optimization module 2 share the optimization objective function and constraint conditions. The optimization objective function is usually defined as:

[0186]

[0187] where y pred is the model prediction value, y real is the actual measurement value, w i is the weight coefficient, complexity is the model complexity penalty term, and λ is the penalty coefficient. The constraint conditions include the physical rationality constraint, computational efficiency constraint, etc. of the model. This sharing mechanism ensures the goal consistency of structure reconstruction and parameter optimization.

[0188] After the model structure reconstruction is completed, the model structure adaptive reconfiguration module 3 will send the reconstructed model structure to the parameter self-optimization module 2 for initial parameter setting. The initial parameters can partially inherit the parameters of the same modules in the original model. For the newly added modules or reconstructed modules, reasonable initial parameters are recommended through the knowledge graph. This parameter inheritance and recommendation mechanism greatly reduces the parameter optimization time of the new model structure.

[0189] This connection relationship between the two modules forms an iterative optimization cycle of "parameter optimization → structure reconstruction → parameter optimization", enabling the system to optimize at two levels of the parameter space and the structure space, significantly improving the adaptability and optimization ability of the system.

[0190] Example 9: System Feedback Loop

[0191] As Figure 8 shown, the system forms three feedback loops with different frequencies, namely, a fast parameter adjustment loop, a medium-speed structure optimization loop, and a long-term evolution loop.

[0192] The period of the fast parameter adjustment loop is at the second level, and the path is entity data → deviation perception → parameter optimization → model update → entity control. This loop mainly deals with short-term parameter changes and operating condition fluctuations, such as changes in raw material moisture content and equipment fluctuations. The loop process is as follows: the entity system generates real-time operation data; the model deviation intelligent perception module 1 analyzes the data and calculates the deviation; when the deviation exceeds the threshold, the parameter self-optimization module 2 is triggered to perform fast parameter adjustment; the model parameters are updated and new control instructions are generated; the control instructions act on the entity system to form a closed loop. The response time of this loop is usually within 5 - 30 seconds, which can quickly adapt to short-term changes and ensure system performance.

[0193] The period of the medium-speed structure optimization loop is at the hour level, and the path is performance evaluation → structure reconstruction → model reconstruction → performance evaluation. This loop mainly deals with mid-term process changes and model structure inadaptability problems, such as changes in raw material types and process condition adjustments. The loop process is as follows: the system regularly conducts performance evaluations to analyze the long-term trend of model prediction accuracy; when it is found that parameter optimization cannot meet the accuracy requirements, the model structure adaptive reconstruction module 3 is triggered to perform structure optimization; the reconstructed model undergoes parameter initialization and optimization; the model performance is re-evaluated to verify the optimization effect. The response time of this loop is usually within 1 - 6 hours, which can adapt to mid-term changes and ensure the adaptability of the model structure.

[0194] The period of the long-term evolution loop is at the day / month level, and the path is long-term data accumulation → knowledge graph update → model evolution → long-term optimization. This loop mainly deals with long-term system evolution and knowledge accumulation, such as seasonal changes, equipment aging, catalyst deactivation, etc. The loop process is as follows: the system accumulates operation data and optimization experience in the long term; the knowledge graph is updated regularly to refine new knowledge and eliminate obsolete knowledge; based on the updated knowledge graph, the model is comprehensively evolved, which may involve updating the underlying mechanism; long-term optimization strategies are adjusted for the evolved model. The response time of this loop is usually within 1 - 30 days, which can adapt to long-term changes and ensure the continuous optimization and evolution of the system.

[0195] The three feedback loops with different frequencies work together to form a multi-level and multi-time-scale adaptive optimization system, enabling the system to simultaneously handle short-term fluctuations, mid-term changes, and long-term evolutions, and achieve full-time-domain adaptive optimization.

[0196] Example 10: Twin Optimization Method for Biomass Gasification Process

[0197] As[[ID=e21]] Figure 9As shown in the figure, the twin optimization method for the biomass gasification process of the present invention includes the following steps:

[0198] Step S1: Construct a multi-dimensional feature vector including physical deviation, time deviation, and operating condition deviation, calculate the deviation sensitivity matrix, monitor the deviation accumulation function, and generate deviation feature data.

[0199] Specifically, first collect real-time data such as temperature, pressure, flow rate, and components from the physical system, and at the same time obtain the corresponding predicted values from the model. Then construct the physical deviation vector Δp and calculate the relative deviation of each physical quantity; construct the time deviation vector Δt and analyze the difference between the model response time and the actual response time; construct the operating condition deviation vector Δw and evaluate the change in model accuracy under different operating conditions. Combine these three types of deviations into a multi-dimensional feature vector ΔS = [Δp, Δt, Δw].

[0200] Based on the deviation data, calculate the deviation sensitivity matrix S to determine which parameters have the greatest impact on the deviation. At the same time, calculate the deviation integral function D(t) to monitor the cumulative effect of the deviation. When D(t) exceeds the preset threshold θ, generate deviation feature data and transmit it to the next step.

[0201] Step S2: Receive the deviation feature data, classify the model parameters according to the degree of influence, and use a hybrid algorithm of particle swarm optimization and differential evolution to adaptively optimize the model parameters and generate optimized parameter data.

[0202] In this step, first divide the model parameters into three levels: a, β, and γ according to the deviation sensitivity matrix S, which represent key parameters, secondary parameters, and auxiliary parameters respectively. Focus on optimizing the a-level parameters, appropriately optimize the β-level parameters, and the γ-level parameters may remain unchanged or only be slightly adjusted.

[0203] Then use a hybrid algorithm of particle swarm optimization and differential evolution to optimize the parameters. The algorithm adaptively adjusts the learning rate and mutation rate to improve the optimization efficiency. During the optimization process, check the physical rationality of the parameters to ensure compliance with basic physical laws. After the optimization is completed, generate optimized parameter data, including the updated parameter value set and the expected deviation improvement.

[0204] Step S3: When the parameter optimization cannot meet the accuracy requirements, based on the optimized parameter data, construct a reaction mechanism knowledge graph, evaluate the adaptability of the model structure, use a graph neural network to reconstruct the model, and generate reconstructed model data.

[0205] When the deviation still exceeds the threshold after the parameter optimization in Step S2 and further optimization cannot significantly reduce the deviation, this step is triggered. First, construct a reaction mechanism knowledge graph and represent the substances, reactions, and conditions in the biomass gasification process as a graph structure. Then evaluate the adaptability of the current model structure and calculate the comprehensive scores of accuracy, sensitivity, and robustness.

[0206] Based on the knowledge graph and the results of the adaptability assessment, the model structure is reconstructed using graph neural network technology. The reconstruction process includes: encoding the model into a graph representation, evaluating the importance of subgraphs, retaining important substructures and replacing or modifying problematic substructures, and finally reassembling the model by selecting appropriate modules from the model component library. After the reconstruction is completed, reconstructed model data is generated, including the definition of the new model structure and the setting of initial parameters.

[0207] Step S4: Receive the reconstructed model data, perform collaborative calibration based on the hierarchical time-scale framework and the multi-scale information transfer mechanism, use the adaptive time-step algorithm and the timeliness weight allocation method for prediction, and generate calibrated model data.

[0208] In this step, first, a hierarchical time-scale framework is established, and the model is divided into four time-scale levels: micro, meso, macro, and long-term. Then, information exchange between different scales is realized through the multi-scale information transfer mechanism, including the transfer of micro information to macro and the transfer of macro constraints to micro.

[0209] Based on the adaptive time-step algorithm, the calculation step size is dynamically adjusted according to the local truncation error, the step size is refined at key turning points, and a larger step size is used in the stable region to improve efficiency. At the same time, the timeliness weight allocation method is adopted to allocate reasonable weights to data at different times according to the time correlation function, and the short-term and long-term prediction performances are optimized. After the calibration is completed, calibrated model data is generated, including the model parameters and prediction results at multiple time scales.

[0210] Step S5: Receive the calibrated model data, construct a two-way information flow system, implement the decision-execution-validation loop, execute the incremental learning and forgetting mechanisms, generate control instructions, and transfer the entity feedback data to the deviation feature vector construction step to form a closed-loop feedback.

[0211] In this step, first, a two-way information flow system between the model and the entity is established to realize the real-time exchange of model data and entity data. Then, based on the calibrated model, multiple possible decision-making schemes are generated, the expected effects and risks of each scheme are evaluated, and the optimal scheme is selected and transformed into specific control instructions for execution.

[0212] After execution, the actual effect is compared with the expected effect to verify the effectiveness of the decision. At the same time, new experiences are absorbed through the incremental learning mechanism, and outdated knowledge is eliminated through the forgetting mechanism to continuously optimize the decision-making strategy. Finally, the entity feedback data is transmitted back to Step S1 for the next round of deviation analysis and optimization, forming a complete closed-loop feedback.

[0213] By repeatedly executing these five steps, the system can continuously perceive deviations, optimize parameters, reconstruct the model, calibrate predictions, and iterate decisions, realizing the adaptive optimization and evolution of the biomass gasification process.

[0214] To verify the effectiveness of the present invention, the twin optimization system for the biomass gasification process was applied to a biomass gasification plant. The gasification raw material was mixed forestry waste, including poplar branches, pine sawdust, weeds, etc. The characteristics of the raw materials were variable, and it was difficult for the traditional control system to adapt.

[0215] After applying this system, the key indicators before and after implementation were compared:

[0216] 1. Fluctuation of calorific value of produced gas: It decreased from ±12% to ±3%, and the stability was significantly improved;

[0217] 2. System response time: It was shortened from 30 minutes to 5 minutes, and the response speed increased by 6 times;

[0218] 3. Tar content of produced gas: It decreased from 4.5 g / Nm 3 to 1.8 g / Nm 3 , and the product quality was greatly improved;

[0219] 4. Energy efficiency: It increased from 67% to 73%, and the comprehensive energy efficiency increased by 8.9%;

[0220] 5. Equipment failure rate: It decreased by 65%, and the system stability was significantly improved.

[0221] Figure 10 The comparison of the calorific value fluctuation of the produced gas within 10 days before and after implementation is shown. It can be seen that after using this system, the calorific value fluctuation of the produced gas is significantly reduced, and it can respond and adapt to the changes in raw material characteristics faster. When the raw material was switched from pine sawdust to poplar branches (Day 4 in the figure), it took about 1.5 days for the traditional system to become stable again, while this system only needed about 3 hours to adapt to the new raw material and operate stably.

[0222] These results show that the twin optimization system for the biomass gasification process provided by the present invention effectively solves the modeling problems and optimization challenges in the biomass gasification process through innovative mechanisms such as multi-source data-driven deviation perception, hybrid evolutionary algorithm parameter optimization, knowledge graph-driven structure reconstruction, multi-time scale coordination, and closed-loop feedback symbiosis, and realizes a significant improvement in system performance.

[0223] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural transformation made under the inventive concept of the present invention by using the content of the specification and drawings of the present invention, or directly / indirectly applied to other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. Biomass gasification process twin optimization system, characterized in that, Including: A multi-source data-driven model deviation intelligent perception module, which is used to construct a multi-dimensional feature vector including physical deviation, time deviation and working condition deviation, calculate a deviation sensitivity matrix, monitor a deviation accumulation function, and generate deviation feature data; A parameter self-optimization module driven by a hybrid evolutionary algorithm, connected to the model deviation intelligent perception module, which is used to receive the deviation feature data, classify the model parameters according to the influence degree, and adaptively optimize the model parameters by using a particle swarm optimization and differential evolution hybrid algorithm, and generate optimized parameter data; A model structure adaptive reconstruction module driven by a knowledge graph, connected to the parameter self-optimization module, which is used to construct a reaction mechanism knowledge graph based on the optimized parameter data when the parameter optimization cannot meet the accuracy requirements, evaluate the adaptability of the model structure, use a graph neural network to reconstruct the model, and generate reconstructed model data; A multi-time scale collaborative calibration and prediction module, connected to the model structure adaptive reconstruction module, which is used to receive the reconstructed model data, perform collaborative calibration based on a hierarchical time scale framework and a multi-scale information transmission mechanism, and perform prediction by using an adaptive time step algorithm and a timeliness weight allocation method, and generate calibrated model data; A closed-loop feedback model-entity iterative symbiosis module, connected to the multi-time scale collaborative calibration and prediction module, which is used to receive the calibrated model data, construct a two-way information flow system, realize a decision-execution-validation cycle, execute an incremental learning and forgetting mechanism, generate control instructions, and transmit entity feedback data to the model deviation intelligent perception module to form a closed-loop feedback.

2. The system according to claim 1, wherein The model deviation intelligent perception module includes: A deviation feature vector construction unit, which is used to construct a multi-dimensional feature vector including physical deviation, time deviation and working condition deviation; A deviation sensitivity analysis unit, connected to the deviation feature vector construction unit, which is used to calculate a deviation sensitivity matrix and determine key influencing factors; A deviation accumulation monitoring unit, connected to the deviation sensitivity analysis unit, which is used to calculate a deviation integral function that changes with time, and generate a trigger signal when the deviation accumulation exceeds a threshold; A deviation mapping table construction unit, connected to the deviation accumulation monitoring unit, which is used to construct a three-layer deviation mapping relationship table of the parameter layer, module layer and system layer.

3. The system according to claim 1, wherein The parameter self-optimization module driven by the hybrid evolutionary algorithm includes: A parameter sensitivity classification unit, which is used to classify the model parameters into three levels of α, β, and γ according to the influence degree; A hybrid evolutionary algorithm execution unit, connected to the parameter sensitivity classification unit, which is used to perform parameter optimization by combining particle swarm optimization and differential evolution algorithms; An optimized parameter dynamic adjustment unit, connected to the hybrid evolutionary algorithm execution unit, which is used to adaptively adjust the learning rate and mutation rate according to the deviation change trend; A parameter update constraint unit, connected to the optimized parameter dynamic adjustment unit, which is used to perform a physical meaning rationality test on the optimized parameters to ensure that the optimization results conform to the principles of entropy increase, energy conservation and kinetic rationality.

4. The system according to claim 1, wherein The model structure adaptive reconstruction module driven by the knowledge graph includes: A reaction mechanism knowledge graph construction unit for constructing a knowledge graph including substance nodes, reaction nodes, and condition nodes; A model structure adaptability evaluation unit, connected to the reaction mechanism knowledge graph construction unit, for calculating weighted scores of precision, sensitivity, and robustness; A graph neural network model reconstruction unit, connected to the model structure adaptability evaluation unit, for encoding the model structure into a graph representation and evaluating subgraph importance based on a graph convolutional network; A modular reconstruction technology unit, connected to the graph neural network model reconstruction unit, for defining a basic component library and combination rules of the model, and executing an automatic assembly algorithm.

5. The system according to claim 1, wherein The multi-time scale collaborative calibration and prediction module includes: A hierarchical time scale framework unit for constructing a framework covering micro, meso, macro, and long-term time scales; A multi-scale information transfer unit, connected to the hierarchical time scale framework unit, for realizing upward information transfer and downward constraint between different scales; An adaptive time step algorithm unit, connected to the multi-scale information transfer unit, for calculating local truncation errors and dynamically adjusting the time step; A timeliness adaptive weight allocation unit, connected to the adaptive time step algorithm unit, for allocating weights to data at different times based on a time correlation function.

6. The system according to claim 1, wherein The closed-loop feedback model-entity iterative symbiosis module includes: A two-way information flow system unit for establishing two-way information channels from entities to the model and from the model to entities; A decision-execution-validation loop unit, connected to the two-way information flow system unit, for generating a decision set, executing control commands, and verifying the execution effect; An incremental learning and forgetting mechanism unit, connected to the decision-execution-validation loop unit, for performing incremental learning of new knowledge, decay of obsolete knowledge, and evaluation of knowledge importance; An autonomous evolution decision unit, connected to the incremental learning and forgetting mechanism unit, for constructing a policy network based on reinforcement learning and achieving an exploration-exploitation balance under safety constraints.

7. The system according to claim 1, characterized in that, The connection relationship between the multi-source data-driven model deviation intelligent perception module and the hybrid evolutionary algorithm-driven parameter self-optimization module is as follows: The model deviation intelligent perception module transmits a deviation feature vector and a key parameter sensitivity matrix to the parameter self-optimization module; When the norm of the deviation feature vector exceeds a preset threshold, the parameter self-optimization module is triggered to perform parameter optimization operations; The parameter self-optimization module feeds back the optimized parameter results to the model deviation intelligent perception module for verification.

8. The system according to claim 1, wherein The connection relationship between the parameter self-optimization module and the model structure adaptive reconstruction module is as follows: When parameter optimization reaches its limit but still does not meet the accuracy requirements, the parameter self-optimization module sends a reconstruction trigger signal and optimization limit data to the model structure adaptive reconstruction module; The model structure adaptive reconstruction module shares the optimization objective function and constraint conditions of the parameter self-optimization module; The model structure adaptive reconstruction module sends the reconstructed model structure to the parameter self-optimization module for initial parameter setting.

9. The system according to claim 1, characterized in that, The system forms three feedback loops with different frequencies: Fast parameter adjustment loop, with a cycle of seconds, and the path is entity data → deviation perception → parameter optimization → model update → entity control; Medium-speed structure optimization loop, with a cycle of hours, and the path is performance evaluation → structure reconstruction → model reconstruction → performance evaluation; Long-term evolution loop, with a cycle of days / months, and the path is long-term data accumulation → knowledge graph update → model evolution → long-term optimization.

10. A twin optimization method for biomass gasification process, using the system according to any one of claims 1-9, characterized in that, Including: Construct a multi-dimensional feature vector including physical deviation, time deviation, and working condition deviation, calculate the deviation sensitivity matrix, monitor the deviation accumulation function, and generate deviation feature data; Receive the deviation feature data, classify the model parameters according to the degree of influence, and use a hybrid algorithm of particle swarm optimization and differential evolution to adaptively optimize the model parameters, and generate optimized parameter data; When the parameter optimization cannot meet the accuracy requirements, based on the optimized parameter data, construct a reaction mechanism knowledge graph, evaluate the adaptability of the model structure, use a graph neural network for model reconstruction, and generate reconstructed model data; Receive the reconstructed model data, perform collaborative calibration based on the hierarchical time scale framework and multi-scale information transfer mechanism, use the adaptive time step algorithm and timeliness weight allocation method for prediction, and generate calibrated model data; Receive the calibrated model data, construct a two-way information flow system, implement the decision-execution-validation loop, execute the incremental learning and forgetting mechanism, generate control instructions, and transfer the entity feedback data to the deviation feature vector construction step to form a closed-loop feedback.

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