Coal and biomass gasification gas blending optimization method based on dynamic modeling

Abstract

This invention discloses an optimization method for co-firing coal and biomass gasification gas based on dynamic modeling, including S1, data acquisition and preprocessing; S2, dynamic characteristic modeling; S3, online updating of model parameters; S4, setting optimization objectives and constraints; S5, multi-objective dynamic optimization solution; S6, generation and issuance of optimization instructions; S7, real-time adjustment of actuators; S8, closed-loop feedback monitoring; and S9, periodic verification of the model and strategy. By establishing and updating a dynamic mathematical model online, this invention can accurately capture the dynamic response of the combustion system as fuel characteristics and load change, achieving real-time and forward-looking optimization of the co-firing ratio. Furthermore, this method effectively overcomes the poor adaptability of static models, significantly improving the overall thermal efficiency of the co-firing process while ensuring that pollutant emissions are stably controlled at a low level.

Description

Technical Field

[0001] This invention relates to the fields of thermal power generation and biomass energy utilization, specifically to an optimized method for co-firing coal and biomass gasification gas based on dynamic modeling. Background Technology

[0002] Currently, coal-fired power plants face increasing pressure from stringent environmental regulations and limited coal resources. Biomass gasification gas, as a renewable and clean energy source, is an effective way to reduce pollution emissions when co-fired with coal. However, most existing co-firing technologies rely on static or semi-static models for proportioning control, making it difficult to adapt in real-time to dynamic factors such as fluctuations in biomass gasification gas composition, changes in boiler load, and differences in coal quality. This leads to unstable combustion efficiency, inaccurate pollutant emission control, and even affects the safe operation of the boiler.

[0003] Therefore, this application proposes an optimization method for co-firing coal and biomass gasification gas based on dynamic modeling. Summary of the Invention

[0004] To address this issue, the present invention provides an optimization method for co-firing coal and biomass gasification gas based on dynamic modeling. This method solves the problem that most existing co-firing technologies are based on static or semi-static models for proportion control, which makes it difficult to adapt in real time to dynamic factors such as fluctuations in biomass gasification gas composition, changes in boiler load, and differences in coal quality. This results in unstable combustion efficiency, inaccurate control of pollutant emissions, and even affects the safe operation of the boiler.

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

[0006] The optimization method for co-firing coal and biomass gasification gas based on dynamic modeling includes the following steps:

[0007] S1. Data Acquisition and Preprocessing: Real-time acquisition of coal characteristic parameters, biomass gasification gas composition parameters, boiler operating status parameters, and flue gas emission parameters; validity verification, noise filtering, and normalization preprocessing of the acquired raw data;

[0008] S2. Dynamic characteristic modeling: Based on preprocessed real-time data, a dynamic mathematical model is established to describe the co-firing process of coal and biomass gasification gas; the dynamic mathematical model characterizes the dynamic relationship between the co-firing ratio, boiler input conditions and combustion efficiency, pollutant emission concentration and key equipment status;

[0009] S3. Online update of model parameters: Based on real-time data, the dynamic mathematical model is run to generate predicted values ​​of operating parameters; the predicted values ​​are compared with the actual measured values ​​of boiler operating status parameters and flue gas emission parameters collected in real time, and the parameters of the dynamic mathematical model are dynamically adjusted based on the comparison results so that the output of the dynamic mathematical model continuously approximates the actual operating state.

[0010] S4. Optimization Objectives and Constraints: Set the objective function for co-firing optimization, which includes at least maximizing boiler thermal efficiency and minimizing the emission concentration of specific pollutants; at the same time, set system operation constraints, including the range of co-firing ratio, boiler load limit, steam parameter stability range, and equipment safety threshold.

[0011] S5. Multi-objective dynamic optimization solution: Based on the updated dynamic mathematical model, the objective function is solved online under the running constraints using an optimization algorithm to calculate the optimal blending ratio setting value and other suggested values ​​of operating variables for the next time period.

[0012] S6. Optimization command generation and issuance: Convert the optimal blending ratio setting value into specific control commands, and issue the control commands to the coal feeding system and the biomass gasification gas supply system;

[0013] S7. Real-time adjustment of the actuator: The coal feeding system and the biomass gasification gas supply system receive and execute the control command, and adjust the coal supply and biomass gasification gas supply in real time to achieve the set co-firing ratio.

[0014] S8. Closed-loop feedback monitoring: After the adjustment is executed, the boiler combustion status, efficiency and emission parameters are continuously monitored, and the actual operation effect data is fed back to the data acquisition link of step S1 to form a closed-loop control loop.

[0015] S9. Periodic verification of model and strategy: The accuracy of the dynamic mathematical model is periodically verified using historical operating data, and the optimization objectives, constraints and optimization algorithm parameters are adaptively adjusted according to the verification results and the phased changes in boiler operating conditions.

[0016] Preferably, in step S2, the dynamic mathematical model is a hybrid model, which is constructed by combining differential or algebraic equations based on combustion mechanism with a data-driven model trained based on real-time running data; the data-driven model is used to compensate for nonlinear dynamic characteristics and uncertainties not covered by the mechanism model.

[0017] Preferably, the data-driven model is one of a neural network model, a support vector machine regression model, or a Gaussian process regression model.

[0018] Preferably, in step S3, the online updating of the model parameters is achieved using recursive least squares, Kalman filtering, or gradient descent to ensure that the model parameters can quickly track the dynamic changes of the combustion system.

[0019] Preferably, step S5, the multi-objective dynamic optimization solution specifically includes the following sub-steps:

[0020] S51. Objective function normalization: Normalize optimization objectives with different dimensions and units, and convert them into dimensionless comprehensive evaluation indicators.

[0021] S52. Constraint handling: Transform system operation constraints into boundary conditions or penalty function forms that can be handled by optimization algorithms;

[0022] S53. Optimization Iterative Calculation: An optimization algorithm is used to search in the solution space that meets the constraints, and the optimal solution is continuously approximated through iterative calculation; the optimization algorithm calls the updated dynamic mathematical model in each iteration to predict the combustion efficiency and emission performance under different combinations of operating variables;

[0023] S54. Optimal Solution Selection and Verification: From the Pareto optimal solution set output by the optimization algorithm, select an optimal solution for final implementation according to the preset priority strategy or decision-maker preference, and verify the rationality of the solution.

[0024] Preferably, in step S5, the optimization algorithm is a particle swarm optimization algorithm, a genetic algorithm, or a model predictive control algorithm; when a model predictive control algorithm is used, its internal prediction model is a dynamic mathematical model.

[0025] Preferably, in step S6, the control command includes a set value for the volume percentage or heat percentage of biomass gasification gas co-firing, and a corresponding set value for the coal feeding rate; the control command is sent to the corresponding control module in the distributed control system through an industrial communication network.

[0026] Preferably, in step S9, the triggering conditions for the periodic verification include: a change in the source of biomass gasification gas, maintenance or modification of the boiler, seasonal changes causing the ambient temperature to change beyond a preset threshold, or continuous operation reaching a preset time period.

[0027] The present invention has the following advantages: by establishing and updating a dynamic mathematical model online, the present invention can accurately capture the dynamic response of the combustion system as fuel characteristics and load change, and realize the real-time and forward-looking optimization of the blending ratio;

[0028] This method effectively overcomes the shortcomings of poor adaptability of static models, significantly improves the overall thermal efficiency of the co-firing process, and ensures that pollutant emissions are stably controlled at a low level.

[0029] The closed-loop feedback and periodic verification mechanism further enhances the robustness and long-term applicability of the system, providing reliable technical support for the flexible, clean and efficient utilization of biomass gasification gas in coal-fired boilers. Attached Figure Description

[0030] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0031] Figure 1 The flowchart illustrates the optimized method for co-firing coal and biomass gasification gas based on dynamic modeling, as provided in this application embodiment. Detailed Implementation

[0032] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these embodiments are merely for further explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Technical engineers in the field can make some non-essential improvements and adjustments to the present invention based on the above-described content. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see Figure 1 The optimization method for co-firing coal and biomass gasification gas based on dynamic modeling includes the following steps:

[0034] S1. Data Acquisition and Preprocessing: Real-time acquisition of coal characteristic parameters, biomass gasification gas composition parameters, boiler operating status parameters, and flue gas emission parameters; validity verification, noise filtering, and normalization preprocessing of the acquired raw data;

[0035] S2. Dynamic Characteristic Modeling: Based on preprocessed real-time data, a dynamic mathematical model is established to describe the co-firing process of coal and biomass gasification gas. The model characterizes the dynamic relationship between the co-firing ratio, boiler input conditions and combustion efficiency, pollutant emission concentration and key equipment status.

[0036] S3. Online update of model parameters: Input the real-time collected boiler operating status parameters and flue gas emission parameters into the dynamic mathematical model, compare them with the model prediction values, and dynamically adjust the key parameters of the model based on the comparison results so that the model output continuously approximates the actual operating state.

[0037] S4. Optimization Objectives and Constraints: Set the objective function for co-firing optimization, which includes at least maximizing boiler thermal efficiency and minimizing the emission concentration of specific pollutants; at the same time, set system operation constraints, including the range of co-firing ratio, boiler load limit, steam parameter stability range, and equipment safety threshold.

[0038] S5. Multi-objective dynamic optimization solution: Based on the updated dynamic mathematical model, the objective function is solved online under the running constraints using an optimization algorithm to calculate the optimal blending ratio setting value and other key operation variable suggested values ​​for the next time period.

[0039] S6. Optimization command generation and issuance: Convert the optimal blending ratio setting value into specific control commands, and issue the control commands to the coal feeding system and the biomass gasification gas supply system;

[0040] S7. Real-time adjustment of the actuator: The coal feeding system and the biomass gasification gas supply system receive and execute the control command, and adjust the coal supply and biomass gasification gas supply in real time to achieve the set co-firing ratio.

[0041] S8. Closed-loop feedback monitoring: After the adjustment is executed, the boiler combustion status, efficiency and emission parameters are continuously monitored, and the actual operation effect data is fed back to the data acquisition link of step S1 to form a closed-loop control loop.

[0042] S9. Periodic verification of model and strategy: The long-term accuracy of the dynamic mathematical model is periodically verified using historical operating data, and the optimization objectives, constraints and optimization algorithm parameters are adaptively adjusted according to the verification results and the phased changes in boiler operating conditions.

[0043] In one embodiment, in step S1, the coal characteristic parameters include, but are not limited to, calorific value, volatile matter, moisture, and ash content; the biomass gasification gas composition parameters include, but are not limited to, the volume fraction and calorific value of hydrogen, carbon monoxide, and methane; the boiler operating status parameters include, but are not limited to, main steam flow rate, pressure, temperature, furnace temperature distribution, and air volume ratio; and the flue gas emission parameters include, but are not limited to, the concentrations of nitrogen oxides, sulfur dioxide, carbon monoxide, and oxygen content.

[0044] In another implementation, in step S2, the dynamic mathematical model is constructed using a hybrid modeling method that combines mechanism and data-driven approaches. First, a mechanistic model framework is established based on mass conservation, energy conservation, and chemical reaction kinetics. Then, data-driven models such as neural networks or support vector machines are trained using real-time running data to compensate for unmodeled dynamics and uncertainties, ultimately merging to form a high-precision hybrid dynamic model.

[0045] In step S5, the optimization algorithm can be selected from particle swarm optimization, genetic algorithm, or model predictive control algorithm. Based on the real-time requirements of online computation, the algorithm can be simplified to ensure that the solution is completed within the set control period.

[0046] In step S7, the coal feeding system controls the coal quantity by adjusting the speed of the coal feeder, and the biomass gasification gas supply system controls the gas quantity by adjusting the opening of the pneumatic valve or the speed of the compressor.

[0047] In step S2, the dynamic mathematical model is a hybrid model, which is constructed by combining differential or algebraic equations based on combustion mechanism with a data-driven model trained based on real-time running data; the data-driven model is used to compensate for nonlinear dynamic characteristics and uncertainties not covered by the mechanism model.

[0048] The data-driven model is one of the following: neural network model, support vector machine regression model, or Gaussian process regression model. It further enhances the ability of the hybrid model to represent the dynamics of complex combustion systems, especially the strong nonlinear interaction effect brought about by the incorporation of biomass gasification gas, and can achieve more refined simulation.

[0049] In step S3, the online updating of model parameters is achieved using recursive least squares, Kalman filtering, or gradient descent to ensure that the model parameters can quickly track the dynamic changes of the combustion system. These algorithms are specifically designed for online, recursive estimation and can efficiently and stably update model parameters using the latest collected limited data, thus giving the system powerful real-time self-learning and adaptive capabilities. When the biomass gas source changes, coal quality fluctuates, or equipment characteristics drift slowly, these algorithms can automatically and quickly adjust the model parameters, ensuring that the model always "follows" the dynamic changes of the actual object, avoiding gradual model mismatch due to long-term operation. This ensures the reliability of the optimization system throughout its entire life cycle and reduces the risk of optimization failure or control performance degradation due to model inaccuracy.

[0050] Step S5, the multi-objective dynamic optimization solution specifically includes the following sub-steps:

[0051] S51. Objective function normalization: Normalize optimization objectives with different dimensions and units, and convert them into dimensionless comprehensive evaluation indicators.

[0052] S52. Constraint handling: Transform system operation constraints into boundary conditions or penalty function forms that can be handled by optimization algorithms;

[0053] S53. Optimization Iterative Calculation: An optimization algorithm is used to search in the solution space that meets the constraints, and the optimal solution is continuously approximated through iterative calculation; the optimization algorithm calls the updated dynamic mathematical model in each iteration to predict the combustion efficiency and emission performance under different combinations of operating variables;

[0054] S54. Optimal Solution Selection and Verification: From the Pareto optimal solution set output by the optimization algorithm, select an optimal solution for final implementation according to the preset priority strategy or decision-maker preference, and verify the rationality of the solution to ensure that it meets the engineering practice and safety requirements.

[0055] By normalizing the data, thermal efficiency and various emission indicators with different dimensions are measured in a unified manner, facilitating comprehensive consideration. Constraint processing transforms rigid requirements such as safety and stability into boundaries identifiable by the algorithm. In iterative calculations, the algorithm simulates and evaluates different operational schemes based on a dynamic model, efficiently searching for the optimal solution set. Finally, the most engineering-feasible scheme is selected through verification. These steps transform complex engineering problems with multiple objectives and constraints into automated and intelligent decision-making, ensuring that the optimization result is not only theoretically optimal but also a comprehensive optimal solution that takes into account engineering practicality, safety constraints, and techno-economic efficiency, significantly improving the scientific rigor and practicality of the optimization decision.

[0056] In step S5, the optimization algorithm is a particle swarm optimization algorithm, a genetic algorithm, or a model predictive control algorithm. When a model predictive control algorithm is used, its internal prediction model is the dynamic mathematical model established in step S2. When these algorithms are implemented, they can reliably find high-quality optimal or suboptimal solutions from a large possible solution space within a limited control cycle, thereby meeting the stringent requirements of real-time control of industrial processes for computational speed.

[0057] In step S6, the control command includes a set value for the volume percentage or heat percentage of biomass gasification gas co-firing, and a corresponding set value for the coal feeding rate; the control command is sent to the corresponding control module in the distributed control system through the industrial communication network.

[0058] In step S9, the triggering conditions for the periodic verification include: significant changes in the biomass gasification gas source, major overhaul or modification of the boiler, significant changes in ambient temperature due to seasonal changes, or continuous operation reaching a preset time period. This institutionalizes and automates the long-term maintenance and calibration of the model, rather than relying on manual memory or subjective judgment. When these critical events occur, the system can automatically or remind operators to initiate the deep verification process, thereby ensuring that the dynamic mathematical model can adapt to significant changes in external conditions and internal equipment status in a timely manner, always maintaining its high fidelity as a "digital mirror."

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimization method for co-firing coal and biomass gasification gas based on dynamic modeling, characterized in that, Includes the following steps: S1. Data Acquisition and Preprocessing: Real-time acquisition of coal characteristic parameters, biomass gasification gas composition parameters, boiler operating status parameters, and flue gas emission parameters; The collected raw data undergoes validity verification, noise filtering, and normalization preprocessing. S2. Dynamic characteristic modeling: Based on preprocessed real-time data, a dynamic mathematical model is established to describe the co-firing process of coal and biomass gasification gas. The dynamic mathematical model represents the dynamic relationship between the blending ratio, boiler input conditions and combustion efficiency, pollutant emission concentration and key equipment status. S3. Online update of model parameters: Based on real-time data, the dynamic mathematical model is run to generate predicted values ​​of operating parameters; the predicted values ​​are compared with the actual measured values ​​of boiler operating status parameters and flue gas emission parameters collected in real time, and the parameters of the dynamic mathematical model are dynamically adjusted based on the comparison results so that the output of the dynamic mathematical model continuously approximates the actual operating state. S4. Optimization Objectives and Constraints: Set the objective function for co-firing optimization, which includes at least maximizing boiler thermal efficiency and minimizing the emission concentration of specific pollutants; at the same time, set system operation constraints, including the range of co-firing ratio, boiler load limit, steam parameter stability range, and equipment safety threshold. S5. Multi-objective dynamic optimization solution: Based on the updated dynamic mathematical model, the objective function is solved online under the running constraints using an optimization algorithm to calculate the optimal blending ratio setting value and other suggested values ​​of operating variables for the next time period. S6. Optimization command generation and issuance: Convert the optimal blending ratio setting value into specific control commands, and issue the control commands to the coal feeding system and the biomass gasification gas supply system; S7. Real-time adjustment of the actuator: The coal feeding system and the biomass gasification gas supply system receive and execute the control command, and adjust the coal supply and biomass gasification gas supply in real time to achieve the set co-firing ratio. S8. Closed-loop feedback monitoring: After the adjustment is executed, the boiler combustion status, efficiency and emission parameters are continuously monitored, and the actual operation effect data is fed back to the data acquisition link of step S1 to form a closed-loop control loop. S9. Periodic verification of model and strategy: The accuracy of the dynamic mathematical model is periodically verified using historical operating data, and the optimization objectives, constraints and optimization algorithm parameters are adaptively adjusted according to the verification results and the phased changes in boiler operating conditions.

2. The optimization method for co-firing coal and biomass gasification gas based on dynamic modeling according to claim 1, characterized in that, In step S2, the dynamic mathematical model is a hybrid model, which is constructed by combining differential or algebraic equations based on combustion mechanism with a data-driven model trained based on real-time running data; the data-driven model is used to compensate for nonlinear dynamic characteristics and uncertainties not covered by the mechanism model.

3. The optimization method for co-firing coal and biomass gasification gas based on dynamic modeling according to claim 2, characterized in that, The data-driven model is one of the following: neural network model, support vector machine regression model, or Gaussian process regression model.

4. The optimization method for co-firing coal and biomass gasification gas based on dynamic modeling according to claim 1, characterized in that, In step S3, the online updating of the model parameters is achieved using recursive least squares, Kalman filtering, or gradient descent to ensure that the model parameters can quickly track the dynamic changes of the combustion system.

5. The optimization method for co-firing coal and biomass gasification gas based on dynamic modeling according to claim 4, characterized in that, Step S5, the multi-objective dynamic optimization solution specifically includes the following sub-steps: S51. Objective function normalization: Normalize optimization objectives with different dimensions and units, and convert them into dimensionless comprehensive evaluation indicators. S52. Constraint handling: Transform system operation constraints into boundary conditions or penalty function forms that can be handled by optimization algorithms; S53. Optimization Iterative Calculation: An optimization algorithm is used to search in the solution space that meets the constraints, and the optimal solution is continuously approximated through iterative calculation; the optimization algorithm calls the updated dynamic mathematical model in each iteration to predict the combustion efficiency and emission performance under different combinations of operating variables; S54. Optimal Solution Selection and Verification: From the Pareto optimal solution set output by the optimization algorithm, select an optimal solution for final implementation according to the preset priority strategy or decision-maker preference, and verify the rationality of the solution.

6. The optimization method for co-firing coal and biomass gasification gas based on dynamic modeling according to claim 5, characterized in that, In step S5, the optimization algorithm is a particle swarm optimization algorithm, a genetic algorithm, or a model predictive control algorithm; when a model predictive control algorithm is used, its internal prediction model is a dynamic mathematical model.

7. The optimization method for co-firing coal and biomass gasification gas based on dynamic modeling according to claim 1, characterized in that, In step S6, the control command includes a set value for the volume percentage or heat percentage of biomass gasification gas co-firing, and a corresponding set value for the coal feeding rate; the control command is sent to the corresponding control module in the distributed control system through the industrial communication network.

8. The optimization method for co-firing coal and biomass gasification gas based on dynamic modeling according to claim 1, characterized in that, In step S9, the triggering conditions for the periodic verification include: changes in the source of biomass gasification gas, boiler maintenance or modification, seasonal changes causing ambient temperature changes to exceed a preset threshold, or continuous operation reaching a preset time period.

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