AI-based intelligent regulation and control method and equipment for low-carbon combustion of hydrogen fuel of cement kiln

Abstract

The invention discloses an AI-based intelligent regulation and control method and equipment for low-carbon combustion of hydrogen fuel of a cement kiln, and belongs to the technical field of cement industry. According to the method, through multi-source data acquisition and preprocessing, AI model construction and training, intelligent regulation and optimization and closed-loop verification and iteration, intelligent optimization regulation and control of the hydrogen fuel combustion process are realized, so that the thermal efficiency is improved, and the carbon emission intensity is reduced; the content of constructing the AI model and training comprises the following steps: constructing a multi-objective optimization model based on a specific algorithm, and training the model through supervised learning and reinforcement learning by taking kiln operation parameters as input and optimal combustion control parameters as output; the content of intelligent regulation and control and optimization comprises the steps that data are collected in real time and input into an AI model, multi-target optimization is achieved in combination with MPC and a self-adaptive optimization algorithm, and therefore the dynamic self-optimization capacity is achieved. The thermal efficiency of the cement kiln can be remarkably improved, and the carbon emission strength is greatly reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of cement industry, and particularly relates to an AI-based intelligent regulation and control method and equipment for low-carbon combustion of hydrogen fuel in a cement kiln. BACKGROUND

[0002] Traditional cement kilns mainly rely on fossil fuel combustion, which not only produces a large amount of carbon dioxide emissions, but also generates pollutants such as nitrogen oxides. In this context, the application of hydrogen fuel as a clean energy in cement kilns has become an important direction for industry transformation.

[0003] However, the combustion regulation of hydrogen fuel in cement kilns faces many technical challenges. The combustion characteristics of hydrogen gas are significantly different from traditional fossil fuels, with high flame temperature and fast combustion speed, which can easily lead to an imbalance in the thermal regime of the kiln. At the same time, the cement clinker burning process has strict requirements for temperature field distribution, and traditional control methods are difficult to achieve precise regulation and control of the hydrogen fuel combustion process.

[0004] Existing cement kiln control systems are mainly based on empirical models and conventional PID control, lacking self-adaptive ability to complex working conditions. In the face of uncertain factors such as changes in hydrogen fuel blending ratio and fluctuations in raw material composition, traditional control methods often have a lag in response, making it difficult to maintain stable operation of the kiln and stable quality of the clinker.

[0005] In recent years, artificial intelligence technology has shown great potential in the field of industrial process control. Through advanced algorithms such as deep learning and reinforcement learning, real-time analysis of kiln operation data can be performed, a multi-parameter coupled combustion model can be established, and the optimal control strategy under different working conditions can be predicted. The application of AI technology in the regulation and control of hydrogen fuel combustion in cement kilns can achieve intelligent optimization of the combustion process, while ensuring the quality of the clinker and minimizing carbon emissions, providing an innovative solution for the green transformation of the cement industry. SUMMARY

[0006] The present application aims to at least partially solve one of the above-mentioned technical problems in the related art.

[0007] To this end, the present application aims to provide an AI-based intelligent regulation and control method and equipment for low-carbon combustion of hydrogen fuel in a cement kiln, which can significantly improve the thermal efficiency of the cement kiln and greatly reduce the carbon emission intensity.

[0008] To solve the above technical problems, the present application is implemented as follows: The present application provides an AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln. The method realizes intelligent optimization and regulation of the hydrogen fuel combustion process through multi-source data acquisition and preprocessing, AI model construction and training, intelligent regulation and optimization, and closed-loop verification and iteration, thereby improving thermal efficiency and reducing carbon emission intensity.

[0009] In addition, the AI-based intelligent regulation method for low-carbon combustion of hydrogen fuel in a cement kiln according to the present application can also have the following additional technical features: In some embodiments thereof, the content of the multi-source data acquisition and preprocessing includes: Hydrogen fuel supply parameters, coal powder characteristic parameters, kiln operating condition parameters, emission control parameters, and clinker quality parameters are collected, processed through data cleaning, standardization, and feature extraction, and a combustion condition database is constructed. The reason for standardization before feature extraction is that the data dimensions differ greatly, standardization can eliminate the dimensional influence and avoid some features being dominated by large values, while improving the stability of feature extraction.

[0010] In some embodiments thereof, the hydrogen fuel supply parameters include flow rate, pressure, purity, and injection angle; The coal powder characteristic parameters include feeding amount, volatile content, and calorific value; The kiln operating condition parameters include peak temperature of the firing zone, tertiary air damper opening, and kiln tail oxygen concentration; The emission control parameters include nitrogen oxide concentration; The clinker quality parameters include f-CaO proportion.

[0011] In some embodiments thereof, the content of constructing an AI model and training includes: constructing a multi-objective optimization model based on a specific algorithm, taking kiln operating parameters as input and optimal combustion control parameters as output, and training the model through supervised learning and reinforcement learning.

[0012] In some embodiments thereof, the optimal combustion control parameters include hydrogen fuel blending ratio, secondary air volume, and kiln speed.

[0013] In some embodiments thereof, the specific algorithm is DNN and LSTM algorithm.

[0014] In some embodiments thereof, the content of intelligent regulation and optimization includes: real-time data acquisition inputting into the AI model, combining MPC and adaptive optimization algorithm, realizing multi-objective optimization, and thus having dynamic self-optimization capability.

[0015] In some embodiments thereof, the multi-objective optimization includes maximizing hydrogen fuel blending ratio, optimizing thermal efficiency, and minimizing pollutant emission.

[0016] In some embodiments thereof, the content of closed-loop verification and iteration includes: virtual verification of the control strategy using digital twin technology, continuous optimization of model parameters combined with actual production data, establishment of a dynamic feedback mechanism, and realization of iterative upgrading of the control strategy.

[0017] The embodiment of the present application also provides a computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to realize the content of the AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln as any one of the above.

[0018] Compared with the prior art, the present application has at least the following beneficial effects: In the embodiment of the present application, the AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln can realize intelligent optimization and regulation and control of the combustion process by constructing an advanced AI algorithm model to perform deep learning and real-time analysis on multi-dimensional data such as kiln working conditions, hydrogen fuel characteristics and combustion parameters; not only can it significantly improve the thermal efficiency of the cement kiln and greatly reduce the carbon emission intensity, but also can provide innovative technical support for the low-carbon transformation of the cement industry and promote the development of the industry towards green and intelligent manufacturing; In the embodiment of the present application, the AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln has the function of real-time dynamic optimization control: relying on the powerful real-time calculation capability of AI, it can analyze and process kiln operation data at a millisecond level, quickly generate the optimal control strategy and significantly improve the response speed of regulation and control. This real-time dynamic optimization capability perfectly meets the needs of continuous and stable operation of cement production and ensures that the kiln working conditions are always in the best state; In the embodiment of the present application, the AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln can realize multi-parameter collaborative precise regulation and control: through deep learning algorithms, the system can accurately master the complex mapping relationship between the combustion characteristics of hydrogen fuel and the thermal parameters of the kiln, and realize multi-parameter collaborative optimization of the combustion process; this precise control not only ensures stable clinker quality, but also maximizes the low-carbon advantages of hydrogen fuel; In the embodiment of the present application, the AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln can realize optimal energy efficiency intelligent management: under the premise of ensuring clinker quality, the AI intelligent optimization algorithm can automatically find the optimal hydrogen fuel blending ratio and process parameter combination, significantly reduce the energy consumption per unit product, improve energy utilization efficiency, and realize the dual improvement of economic benefits and environmental benefits; In the embodiment of the present application, the AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln realizes an innovative breakthrough in low-carbon combustion: the system deeply excavates the combustion potential of hydrogen fuel through innovative combustion control strategies, realizes ultra-low emission of pollutants such as NOx, and significantly reduces the CO2 emission intensity.

[0019] The computer device of the present application can implement the AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln, thus having at least all the features and advantages of the AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln, which will not be repeated here. Additional aspects and advantages of the present application will be partially given in the following description, partially become obvious from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of the AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln disclosed for an embodiment of the present application; Figure 2 An AI model structure schematic diagram based on a convolutional neural network disclosed for an embodiment of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0022] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0023] Please refer to Figure 1As shown, it demonstrates the whole process of data preprocessing of the hydrogen fuel combustion control system of the cement kiln. The system data collection is divided into five modules: raw material characteristic data collection (including solid waste physical parameters such as density, particle size, moisture content, and strength, and chemical composition such as element composition and molecular structure), fuel characteristic data collection (including hydrogen fuel core parameters such as purity, flow rate, and injection pressure, traditional fuel core parameters such as calorific value and volatile matter, and mixed fuel dynamic control parameters such as replacement rate), kiln working condition data collection (including kiln temperature field data such as firing zone / filter zone, pressure parameters such as kiln head negative pressure / preheater pressure difference, and emission monitoring such as Nox / Sox / CO related data), clinker quality data collection (including real-time online detection parameters such as vertical weight, free calcium f-CaO, and microstructure parameters such as C3S crystal morphology), and market dynamic data collection (including carbon emission policy requirements and cost constraints). After data collection is completed, data fusion stage is entered. The core of this stage is to integrate and collaboratively process multi-source heterogeneous data, which includes the following key operations: 1. Time alignment: unify the time series data of different sensors or sources to the same time reference, and ensure data synchronization; 2. Abnormality detection: identify and eliminate abnormal values or noise (such as sensor failure, transmission error, etc.) in the collected data; 3. Fuel compensation algorithm: real-time calibration of fuel calorific value, unified data unit, safety limiting and abnormality processing; and then data standardization as a subsequent step of fusion, which converts data to a unified format or dimension, facilitating subsequent analysis. Before data fusion, data cleaning (eliminating abnormal values, filling missing data, and eliminating duplicate records) is performed. Data standardization (using Z-score normalization method to convert multi-dimensional features to the same dimension), and after standardization, the final output structured data set is used for AI combustion optimization model training.

[0024] Please refer to Figure 2As shown, it intuitively presents a deep convolutional neural network model for hydrogen fuel combustion optimization in cement kiln, whose architecture is specially optimized for the complex characteristics of industrial combustion process. The model adopts a hierarchical and progressive feature extraction approach: the input layer receives standardized five-dimensional industrial data, including raw material composition parameters (such as CaO, SiO2 content), fuel characteristic data (hydrogen purity, coal calorific value and mixing ratio), real-time operating condition monitoring data (kiln temperature, pressure, etc.), clinker quality indicators (f-CaO, cubic weight), and carbon emission constraint parameters. These data are first subjected to preliminary feature extraction by a mixed convolutional layer with dilated convolution kernels, where an asymmetric convolution kernel (5x3) is specially designed to adapt to the shape characteristics of the flame in the kiln; then feature dimensionality reduction is performed through a dynamic spatial pooling layer, which automatically adjusts the pooling region according to the temperature field distribution, effectively preserving key combustion features. After further extracting high-order spatio-temporal features through a deep separable convolution layer, the model adopts a multi-task learning mechanism to output three sets of control parameters in parallel: combustion control commands (hydrogen fuel injection amount, secondary air quantity setting), quality warning signals (future 30-minute f-CaO trend), and carbon efficiency optimization suggestions (real-time emission reduction potential index). The entire network realizes feature fusion of fuel parameters and operating condition images through a cross-modal attention mechanism, and integrates an abnormal operating condition detection module that can quickly trigger safety interlocks when identifying risk features such as hydrogen pressure surges.

[0025] In some embodiments of the present application, an AI-based intelligent control method for low-carbon combustion of hydrogen fuel in a cement kiln is provided, comprising the following steps: Step 1, multi-source data acquisition and preprocessing: The system collects cement kiln operating data, including but not limited to hydrogen fuel supply parameters (flow rate / pressure / purity / injection angle), coal characteristics (feeding amount / volatile content / calorific value), kiln operating conditions (peak temperature of the burning zone / three-time air damper opening / kiln tail oxygen concentration), emission control (nitrogen oxide concentration), clinker quality (f-CaO (%)) and other key parameters. Through data cleaning algorithms, abnormal data and missing values are removed, key feature variables are extracted based on combustion mechanisms, and standardized processing is performed to build a high-precision kiln combustion operating condition database.

[0026] Step 2, AI model architecture and training: Based on deep neural network (DNN), long short-term memory network (LSTM) and other algorithms, a multi-objective optimization model for hydrogen fuel combustion in a cement kiln is constructed. Taking kiln operating parameters as input and optimal combustion control parameters (hydrogen fuel blending ratio, secondary air quantity, kiln speed, etc.) as output, the model is trained through a combination of supervised learning and reinforcement learning, enabling it to accurately grasp the complex coupling relationship between hydrogen fuel combustion characteristics and kiln operating conditions.

[0027] Step 3, intelligent control and optimization: The system collects kiln operation data in real time and inputs them into the AI model, and the model dynamically outputs the optimal control strategy. Combining model predictive control (MPC) and adaptive optimization algorithm, the multi-objective optimization of maximizing hydrogen fuel blending ratio, optimizing thermal efficiency and minimizing pollutant emissions is realized under the premise of ensuring clinker quality. The system has dynamic self-optimization capability and can adaptively adjust control parameters according to real-time working condition changes to ensure long-term operation stability.

[0028] Step 4, closed-loop verification and iteration: The control strategy is virtually verified through digital twin technology, and the model parameters are continuously optimized using actual production data. A dynamic feedback mechanism is established to real-time feedback the control effect evaluation (such as clinker f-CaO content, heat consumption index, etc.) to the AI model, realizing continuous iteration and upgrading of the control strategy, and ensuring the system to maintain optimal control performance in the long term.

[0029] Example 1

[0030] This example provides an AI-based intelligent regulation and control method for low-carbon combustion of hydrogen fuel in a cement kiln, which includes the following steps: Step 1, data collection and preprocessing: Taking a 5000t / d cement production line as the implementation object, the key parameter data of hydrogen fuel (purity ≥ 99.5%) are comprehensively collected, including flow rate (0-100Nm³ / h), pressure (0.5-3.0MPa), temperature (-253℃ to room temperature) and other physical property data, as well as basic data such as calorific value (4500-6500kcal / kg) and volatile matter (18-32%) of traditional fuels (such as coal powder). Through the DCS system, kiln working condition data are collected in real time, including sintering zone temperature (1200-1450℃), kiln tail pressure (-200 to -500Pa), secondary air temperature (800-1100℃), and NOx emission concentration (control target ≤500mg / Nm³) is monitored in real time by the kiln tail online gas analyzer, and a dynamic correlation model is established with the sintering zone temperature and secondary air temperature. Abnormal values are removed using industrial data cleaning algorithm, and Z-score standardization method is used to unify the dimension of multi-source heterogeneous data, ensuring that the data quality meets the model training requirements.

[0031] Step 2, AI model construction and training: A space-time convolutional neural network (ST-CNN) model for optimizing the combustion of a cement kiln was constructed. The model uses a dual-channel architecture: the time sequence channel processes fuel parameters and operating condition data, using a 1D causal convolution kernel (kernel_size=5) to extract features; the image channel processes the thermal image of the kiln head flame, using an asymmetric convolution kernel (5x3) to adapt to the flame shape. Feature fusion is achieved through a cross-modal attention mechanism, and the final output layer includes three predictions: hydrogen fuel flow setting (0-100%), secondary air damper opening (0-100%), and f-CaO prediction value (0-2%). The model was trained using historical production line data, using the Adam optimizer (lr=0.001) and a hybrid loss function, and after 200 epochs of training, the prediction accuracy of key parameters was over 90%.

[0032] Step 3, combustion control optimization: When the system detects that the hydrogen purity drops to 99.2%, the model automatically triggers a compensation mechanism: while maintaining the total heat value, the hydrogen flow is increased from 30 Nm³ / h to 32 Nm³ / h, and the coal feeding amount is reduced from 12 t / h to 11.5 t / h. By adjusting the tertiary air damper opening in real time (from 65% to 70%), the temperature field distribution in the kiln is ensured to be uniform. When the NOx concentration approaches 450 mg / Nm³, the model preferentially reduces the firing zone temperature (set value down by 20-30°C) and increases the hydrogen fuel ratio (by 2-3%), ensuring that the emissions meet the standards while maintaining f-CaO at 0.9±0.1%. The optimized combustion scheme stabilizes f-CaO at 0.9±0.1%, reducing the fluctuation range by 60% compared to manual control, and reducing CO2 emissions per ton of clinker by 12%.

[0033] Step 4, system verification and iteration: In actual operation, when the model detects abnormal fluctuations in the grate cooler pressure (more than ±5%), the digital twin simulation module is automatically started, predicting the future 15-minute operating condition trend. If the predicted f-CaO exceeds 1.2%, the hydrogen fuel ratio is adjusted in advance (reduced by 3-5%) and the kiln speed is increased (0.2-0.5 rpm). Actual production data (about 50 GB / day) is continuously collected for online learning of the model, and the model weights are updated once a month to ensure that the prediction accuracy is always within ±1.5%.

[0034] The collected multi-source data of the application can include: raw material characteristic data, fuel characteristic data, kiln working condition data, clinker quality data, market dynamic data and other data in several directions. There is a significant nonlinear coupling relationship between each input parameter and the system optimization target, and the influence weight presents obvious difference. Among them, the core driving parameters include: hydrogen fuel supply parameters (flow rate / pressure / purity / injection angle), coal powder characteristics (feeding amount / volatile content / calorific value), kiln working conditions (peak temperature of firing zone / three-stage air damper opening / kiln tail oxygen concentration), emission control (nitrogen oxide concentration), clinker quality (f-CaO (%)).

[0035] Example 2

[0036] This example is an optimal parameter combination scheme based on the empirical data of a 5000t / d cement production line.

[0037] Data collection and preprocessing: Taking a 5000t / d cement production line as the object, the hydrogen fuel system parameters (purity ≥ 99.5%, flow rate 30-36 Nm³ / h, pressure 2.5-3.0 Mpa, injection angle 14-16℃), traditional fuel system parameters (coal powder feeding amount 10.0-12.0 t / h, coal powder calorific value 5800-6000kcal / kg, volatile content 24-28%), kiln working condition parameters (firing zone temperature 1430-1450℃, three-stage air damper opening 60-75%, kiln tail oxygen concentration 2.6-2.9%, kiln speed 3.7-4.0 rpm, NOx emission concentration <500mg / Nm³) are comprehensively collected. Abnormal values are removed using an industrial data cleaning algorithm, and Z-score standardization method is used to unify the dimensions of multi-source heterogeneous data, ensuring that the data quality meets the model training requirements.

[0038] AI model construction and training: A space-time convolutional neural network (ST-CNN) model for cement kiln combustion optimization is constructed. The model adopts a double-path architecture: the time sequence path processes fuel parameters and working condition data, using a 1D causal convolution kernel (kernel_size=5) to extract features; the image path processes the kiln head flame thermograph, using an asymmetric convolution kernel (5x3) to adapt to the flame shape. Feature fusion is achieved through a cross-modal attention mechanism, and the final output layer includes three predictions: hydrogen fuel flow rate setting (30-36 Nm³ / h), three-stage air damper opening (60-75%) and f-CaO prediction value (0-2%). The model is trained using historical data from the production line, using the Adam optimizer (lr=0.001) and a hybrid loss function. After 200 epochs of training, the prediction accuracy of key parameters is above 90%.

[0039] Combustion control optimization: When the system detects that the hydrogen purity drops to 99.2%, the model automatically triggers the compensation mechanism: increase the hydrogen flow from 30 Nm³ / h to 32 Nm³ / h while maintaining the total heat value unchanged, and reduce the coal feeding amount from 12 t / h to 11.5 t / h. By adjusting the three-stage air door opening degree in real time (from 65% to 70%), the temperature field distribution in the kiln is ensured to be uniform. When the NOx concentration approaches 450 mg / Nm³, the model preferentially reduces the firing zone temperature (set value is lowered by 20-30°C) and increases the hydrogen fuel ratio (increased by 2-3%), ensuring that the emissions meet the standards while maintaining f-CaO at 0.9±0.1%. The optimized combustion scheme stabilizes f-CaO at 0.9±0.1%, reducing the fluctuation range by 60% compared to manual control, while reducing CO2 emissions per ton of clinker by 12%, NO x emissions by 45%, and thermal efficiency by 8.1%.

[0040] System verification and iteration: In actual operation, when the model detects abnormal fluctuations in the grate cooler pressure (more than ±5%), the digital twin simulation module is automatically started to predict the future 15-minute working condition trend. If the predicted f-CaO exceeds 1.2%, the hydrogen fuel ratio is adjusted in advance (reduced by 3-5%) and the kiln speed is increased (0.2-0.5 rpm). Actual production data (about 50 GB / day) is continuously collected for online learning of the model, and the model weight is updated once a month to ensure that the prediction accuracy is always within ±1.5%.

[0041] The AI-based intelligent regulation and control method for hydrogen fuel low-carbon combustion in a cement kiln provided by the present application realizes precise collaborative control of hydrogen fuel and traditional fuel through multi-source data fusion and intelligent algorithm optimization, effectively improves combustion efficiency and reduces carbon emissions, and has important significance for promoting green and low-carbon transformation of the cement industry.

[0042] The parts of the present application not described in detail can refer to the prior art or be known to those skilled in the art, and the present embodiment does not limit this, and will not be described in detail here.

[0043] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above specific embodiments, and the above specific embodiments are only illustrative and not limiting, and those skilled in the art can make many forms under the inspiration of the present application without departing from the scope of the present application and the scope protected by the claims.

Claims

1. An AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns, characterized in that: The method achieves intelligent optimization and control of the hydrogen fuel combustion process through multi-source data collection and preprocessing, AI model construction and training, intelligent control and optimization, and closed-loop verification and iteration, thereby improving thermal efficiency and reducing carbon emission intensity.

2. The AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns according to claim 1 is characterized in that: The multi-source data collection and preprocessing includes: The hydrogen fuel supply parameters, pulverized coal characteristic parameters, kiln operating parameters, emission control parameters, and clinker quality parameters are collected, and a combustion condition database is constructed through data cleaning, standardization, and feature extraction.

3. The AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns according to claim 2 is characterized in that: The hydrogen fuel supply parameters include flow rate, pressure, purity and injection angle; The coal powder characteristic parameters include feed rate, volatile matter content and calorific value; The kiln operating parameters include the peak temperature of the firing zone, the opening of the three air doors and the oxygen concentration at the kiln tail; The emission control parameters include nitrogen oxide concentration; The clinker quality parameters include the proportion of f-CaO.

4. The AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns according to claim 1 is characterized in that: The content of building and training the AI ​​model includes: building a multi-objective optimization model based on a specific algorithm, taking the kiln operating parameters as input and the optimal combustion control parameters as output, and training the model through supervised learning and reinforcement learning.

5. The AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns according to claim 4 is characterized in that: The optimal combustion control parameters include hydrogen fuel blending ratio, secondary air volume and kiln speed.

6. The AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns according to claim 4 is characterized in that: The specific algorithms are DNN and LSTM algorithms.

7. The AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns according to claim 1 is characterized in that: The intelligent control and optimization include: real-time data collection and input into the AI ​​model, combined with MPC and adaptive optimization algorithms to achieve multi-objective optimization, thereby having dynamic self-optimization capabilities.

8. The AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns according to claim 7 is characterized in that: The multi-objective optimization includes maximizing the hydrogen fuel blending ratio, optimizing thermal efficiency and minimizing pollutant emissions.

9. The AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns according to claim 1 is characterized in that: The closed-loop verification and iteration include: using digital twin technology to virtually verify control strategies, continuously optimizing model parameters based on actual production data, establishing a dynamic feedback mechanism, and realizing iterative upgrades of control strategies.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the content of the AI-based intelligent control method for low-carbon combustion of hydrogen fuel in cement kilns as described in any one of claims 1-9.

Images