Lime kiln and lime production system

Abstract

The invention relates to the field of lime production, and discloses a lime kiln and a lime production system.The lime kiln comprises a kiln body, a feeding device, a distributing device, a combustion device, an ash discharging device and a dust removal device, and the kiln body is of a double-chamber structure and is provided with a first chamber and a second chamber; the feeding device is used for conveying limestone raw materials to the first chamber; the material distributing device is used for uniformly distributing limestone raw materials in the first chamber; the combustion device comprises a coal mill and a combustor, the coal mill is used for preparing coal powder into fuel, and the combustor is used for spraying the coal powder into the first chamber and the second chamber for combustion; and the ash discharging device is used for discharging calcined quick lime from the first chamber and the second chamber. By introducing a scheme based on model predictive control, the energy consumption of the lime kiln production system is successfully reduced, and the MPC algorithm is combined with a system dynamic model and a predictive control strategy, so that the system can adjust operation parameters in real time to optimize energy utilization to the greatest extent.

Description

Technical Field

[0001] The invention relates to the technical field of lime production, in particular to a lime kiln and a lime production system. Background Art

[0002] Lime kiln and lime production system are equipment and systems used to produce lime; lime is an important building material, widely used in construction, metallurgy, chemical industry and other fields. Lime kiln is equipment used to calcine limestone (such as limestone) and produce lime; in the lime kiln, limestone is heated (calcined) at high temperature to decompose into calcium oxide (lime) and carbon dioxide; lime production system refers to the collection of equipment and processes in the entire lime production process, including lime kiln, limestone crushing, lime calcination, lime cooling, lime grinding and other links. Lime production system usually includes raw material handling system, lime kiln, lime cooler, limestone crushing equipment, limestone grinding equipment and other components; these equipment and systems convert limestone into usable lime products through a series of process steps. The design and operation of lime production system have an important impact on the quality, output and production efficiency of lime products; therefore, the design, optimization and operation management of lime kiln and lime production system are key aspects in the lime production process.

[0003] Traditional lime production systems face a series of challenges and limitations in limestone raw material particle size control and system overall performance optimization. These traditional systems are often based on empirical rules or simplified mathematical models. Although they may perform well in some cases, their limitations begin to emerge when dealing with complex nonlinear systems or multi-parameter optimization problems. This limitation may lead to low precision in particle size control and the inability of the overall system performance to reach the optimal state, thus affecting product quality and production efficiency. In the limestone production process, the accuracy of particle size control is crucial because it directly affects the quality and production efficiency of the final product. However, traditional production systems may be limited by model accuracy or difficulty in parameter adjustment, which makes particle size control difficult. Due to the lack of precise particle size control, product quality may fluctuate and production efficiency may not reach the optimal level. In addition, the optimization of the overall performance of the system is a complex and critical issue. Traditional optimization methods often find it difficult to effectively handle the complex relationship between multiple parameters, especially when facing a large-scale parameter space, which may lead to a series of limitations in the system's performance and stability, hindering the further optimization and improvement of the system. Therefore, understanding the limitations of traditional production systems in particle size control and overall performance optimization is crucial to promoting the intelligence and optimization of industrial production. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a lime kiln and a lime production system, which solves the problem of excessive energy consumption in the production process.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A lime kiln, including a kiln body, a feeding device, a material distribution device, a combustion device, an ash unloading device, and a dust removal device, wherein the kiln body is configured as a double-chamber structure, and is respectively provided with a first chamber and a second chamber;

[0006] The feeding device is used to transport the limestone raw material to the first chamber;

[0007] The material distribution device is used to evenly distribute the limestone raw materials in the first chamber;

[0008] The combustion device comprises a coal mill and a burner, wherein the coal mill is used to prepare coal powder into fuel, and the burner is used to spray the coal powder into the first chamber and the second chamber for combustion;

[0009] The ash discharge device is used to discharge the calcined quicklime from the first chamber and the second chamber;

[0010] The dust removal device is used to purify exhaust gas.

[0011] Preferably, the temperature of the first chamber and the second chamber of the kiln body is set to 1100°C to 1250°C.

[0012] Preferably, the coal powder injection rate function expression of the burner is:

[0013] Pulverized coal injection rate = k1*(T1-T2)+k2*(P1-P2);

[0014] Among them, T1 is the temperature in the first chamber of the lime kiln, T2 is the set target temperature, P1 is the atmosphere parameter in the lime kiln (oxygen content, CO / CO2 concentration), P2 is the set target atmosphere parameter, k1 and k2 are adjustment parameters used to control the response degree of the coal powder injection rate.

[0015] Preferably, the material distribution speed function expression of the material distribution device is:

[0016] Fabric speed = k3*(D1-D2)+k4*(C1-C2);

[0017] Among them, D1 is the particle size distribution of limestone raw materials, D2 is the set target particle size distribution, C1 is the fluidity and temperature distribution of the material in the lime kiln, C2 is the set target parameter, and k3 and k4 are adjustment parameters used to control the responsiveness of the material distribution speed.

[0018] A lime production system includes a sensor module, a dynamic model module, a controller design module, an optimization algorithm module, a control command generation module, and an interface display module;

[0019] The sensor module is used to monitor the temperature, atmosphere and key material flow parameters in the lime kiln in real time;

[0020] The dynamic model module is used to establish a dynamic model of the lime kiln system;

[0021] The controller design module is used to design and implement the MPC controller;

[0022] The optimization algorithm module is used to implement genetic algorithm to optimize data;

[0023] The control command generation module generates actual control commands according to the output of the controller and the optimization result;

[0024] The interface display module is used to display the status and optimization results of the lime kiln system.

[0025] Preferably, the sensor module includes a plurality of sensor nodes, each node is a sensor element and a data transmission module, and the sensor element includes a temperature sensor unit, a pressure sensor unit, and a flow sensor unit;

[0026] The temperature sensor unit is used to measure the temperature inside the lime kiln;

[0027] The pressure sensor unit is used to measure the air pressure or gas pressure inside the lime kiln;

[0028] The flow sensor unit is used to measure the speed or flow rate of material flow.

[0029] Preferably, the dynamic model module includes a mathematical model unit and a system identification unit;

[0030] The mathematical model unit is dynamically established based on the lime kiln temperature, and the lime kiln temperature dynamic function is:

[0031] dT / dt=(1 / τ)*(T s et-T)+α*Q;

[0032] Where T is the temperature of the lime kiln, T s et is the set target temperature, τ is the thermal time constant, Q is the heat input of the combustion device, α is the coefficient of influence of heat input on temperature, and dT / dt is the rate of change of temperature (T) with time (t);

[0033] The system identification unit establishes a dynamic model based on the historical data in the database, specifically:

[0034] y(t)=b0+b1*u(t-1)+b2*u(t-2)+...+bn*u(tn);

[0035] Among them, y(t) is the output variable of the lime kiln, u(t) is the input variable of the lime kiln, and b0, b1, b2, ..., bn are the parameters to be identified.

[0036] Preferably, the controller design module includes a system dynamic model unit, an optimization problem generation unit, a prediction model unit, and a control sequence generation unit;

[0037] The system dynamic model unit is used to establish a mathematical model that describes the dynamic behavior of the system. The model is established using discrete time state space equations. The specific formula is:

[0038] [x(k+1)=Ax(k)+Bu(k)][y(k)=Cx(k)];

[0039] Where x(k) is the system state vector, u(k) is the control input vector, y(k) is the system output vector, and A, B, and C are system parameter matrices;

[0040] The optimization problem generating unit is used to generate the optimization problem according to the system dynamic model and control requirements;

[0041] The prediction model unit is used to predict the state and output of the system in the future based on the system dynamic model, and its function expression is:

[0042]

[0043] in, is the optimal estimate of the system state at time |k, is the state estimate at the next moment predicted at time k, A is the system state transfer matrix, which describes how the system state evolves over time, B is the input matrix, which describes the impact of the control input on the system state, and C is the output matrix, which describes how the system state is mapped to the output;

[0044] The control sequence generation unit is used to convert the solution of the optimization problem into actual control actions to achieve control of the system. It uses the objective function for optimization. The specific formula is:

[0045]

[0046] Among them, J represents the cost function, N represents the number of time steps for prediction and optimization, y(k+i|k) represents the predicted value of the output at time k+i at time k, r(k+i) represents the reference output value at time k+i, Q represents the weight matrix of the output error, which is used to measure the impact of the output error, R represents the weight matrix of the control input, which is used to measure the impact of the control input, and u(k+i|k) represents the predicted value of the control input at time k+i at time k.

[0047] Preferably, the genetic algorithm adopts a fitness function, the specific expression of which is:

[0048] f(x)=w c ×e+w e ×p;

[0049] where w c represents the control error weight, w e represents the energy consumption weight, e represents the control error, and p represents the energy consumption;

[0050] The genetic algorithm parameter initialization information is: population size: 100, number of iterations: 500, mutation rate: 0.1.

[0051] Preferably, the control command generation module includes an input unit, a processing unit, and an output unit, and the interface display module includes a receiving unit, a conversion unit, and a display unit;

[0052] The input unit is used to receive the current system status and sensor data transmitted from the controller and obtain the results of the optimization algorithm or model;

[0053] The processing unit is used to perform necessary calculations and logic operations according to the received controller output and optimization results to generate actual control commands;

[0054] The actual control command generated by the output unit will be transmitted to the execution part of the system to adjust the temperature and ventilation speed of the lime kiln of the system;

[0055] The receiving unit is used to receive the temperature, humidity and production rate information of the lime kiln from the system;

[0056] The conversion unit is used to convert the received system status data and optimization result data into a format suitable for display on the interface;

[0057] The display unit is used to display the processed system status and optimization results, so that operators or managers can monitor the operating status and performance indicators of the system in real time through the interface.

[0058] The present invention provides a lime kiln and a lime production system. The present invention provides the following beneficial effects:

[0059] 1. The present invention successfully reduces the energy consumption of the lime kiln production system by introducing an intelligent energy consumption optimization scheme based on model predictive control. The MPC algorithm combines the system dynamic model and predictive control strategy, enabling the system to adjust operating parameters in real time to optimize energy utilization to the greatest extent. The implementation of this technology has led to a significant reduction in energy consumption, thereby reducing the production costs of the enterprise and helping to reduce the impact on the environment, which is in line with the concept of sustainable development.

[0060] 2. The present invention has successfully achieved a significant improvement in the control accuracy of the particle size of limestone raw materials by adopting an advanced neural network control algorithm. As a powerful learning algorithm, the neural network can accurately capture complex nonlinear relationships and optimize control parameters to achieve more precise particle size control. By improving product quality, the stability and controllability of the production process are improved, enabling enterprises to produce high-quality products that better meet customer needs and enhance market competitiveness.

[0061] 3. The present invention introduces evolutionary algorithms for comprehensive optimization of multiple parameters, and successfully achieves remarkable results in the overall performance and stability of the system. As a powerful optimization method, evolutionary algorithms can efficiently search complex parameter spaces and find optimal solutions, thereby improving the overall performance and stability of the system. By comprehensively optimizing multiple parameters, the system can better adapt to complex production environments and ensure the stability and reliability of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the system framework of the present invention;

[0063] Figure 2 is a flow chart of the sensor module of the present invention;

[0064] Figure 3 It is a flow chart of the dynamic model module of the present invention;

[0065] Figure 4 Design module flow chart for the controller of the present invention;

[0066] Figure 5 A flow chart of a control command generation module of the present invention;

[0067] Figure 6 It is a flow chart of the interface display module of the present invention. DETAILED DESCRIPTION

[0068] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Example:

[0070] Example 1: Intelligent control of lime kiln system

[0071] step:

[0072] S1. Install sensor modules to monitor key parameters in the lime kiln;

[0073] S2, establish dynamic model module, including mathematical model and system identification;

[0074] S3. Design and implement the MPC controller;

[0075] S4, using genetic algorithm to optimize control system;

[0076] S5. Design an interface display module to display the system status in real time.

[0077] Summary: Through the intelligent control system, accurate monitoring and optimized control of the lime kiln production process are achieved, improving production efficiency and product quality.

[0078] Example 2: Temperature control to optimize the lime kiln system

[0079] step:

[0080] S1. Set the temperature control target to 1100℃~1250℃;

[0081] S2, establishing mathematical model units according to temperature dynamics;

[0082] S3. Use the controller design module to establish the system dynamic model;

[0083] S4, optimizing temperature control through optimization algorithm module;

[0084] S5. Generate control commands and adjust the lime kiln temperature in real time.

[0085] Summary: Optimize the temperature control system to ensure that the lime kiln operates within the optimal temperature range and improve production efficiency.

[0086] Example 3: Particle size control to optimize the lime kiln system

[0087] step:

[0088] S1, set the target particle size distribution and control the material distribution speed;

[0089] S2, using the speed function expression of the material distribution device to control the particle size;

[0090] S3, real-time monitoring of the particle size distribution of limestone raw materials;

[0091] S4, optimizing the fabric speed according to the target parameters and the adjustment parameters;

[0092] S5. Improve product quality and production efficiency.

[0093] Summary: Through particle size control optimization, the particle size of lime products can be accurately controlled, and the product quality and market competitiveness can be improved.

[0094] Example 4: Energy consumption optimization lime kiln system

[0095] step:

[0096] S1, using genetic algorithm to optimize energy consumption;

[0097] S2, set the fitness function, considering the control error and energy consumption weight;

[0098] S3, initialize genetic algorithm parameters, including population size, number of iterations, and mutation rate;

[0099] S4. Optimize the control system and reduce energy consumption costs;

[0100] S5. Monitor energy consumption in real time and adjust system parameters.

[0101] Summary: Through energy consumption optimization, production costs are reduced and production efficiency and sustainability are improved.

[0102] Example 5: Multi-parameter comprehensive optimization of lime kiln system

[0103] step:

[0104] S1, comprehensively consider multiple parameters such as temperature, particle size, energy consumption, etc. for optimization;

[0105] S2. Establish a comprehensive optimization model, including controller design, optimization algorithm and dynamic model;

[0106] S3, real-time adjustment of control commands to achieve multi-parameter optimization goals;

[0107] S4. Implement real-time monitoring and feedback adjustment;

[0108] S5. Improve the efficiency and stability of the overall production system.

[0109] Summary: Through comprehensive optimization of multiple parameters, the overall performance of the lime kiln production system was improved, and production efficiency and product quality were improved.

[0110] Summary:

[0111] Through different implementation schemes, including intelligent control, temperature control optimization, particle size control optimization, energy consumption optimization and multi-parameter comprehensive optimization, the efficiency, quality and sustainability of the lime kiln production system can be effectively improved, bringing more innovation and development to the lime production industry.

[0112] Example 1: Intelligent control of lime kiln system

[0113] Test experiment:

[0114] Testing process:

[0115] a. Set the initial parameters of the lime kiln system: temperature, oxygen content, limestone feed rate, etc.;

[0116] b. Start the intelligent control system and run it for 24 hours;

[0117] c. Record key parameter data every hour;

[0118] d. Compare the production efficiency and product quality before and after the intelligent control system.

[0119] Test data:

[0120] Improved production efficiency: The initial production volume was 100 tons per hour, which was increased to 120 tons after intelligent control.

[0121] Improved product quality: Product qualification rate increased from 80% to 90%.

[0122] Test results:

[0123] The intelligent control system has significantly improved production efficiency and product quality, and the system operation is more stable.

[0124] Example 2: Temperature control to optimize the lime kiln system

[0125] Test experiment:

[0126] Testing process:

[0127] a. Set the target temperature range: 1100℃~1250℃;

[0128] b. Run the temperature control optimization system for 48 consecutive hours;

[0129] c. Record the lime kiln temperature data every 10 minutes;

[0130] d. Compare the temperature control effects before and after optimization.

[0131] Test data:

[0132] Improved temperature stability: Fluctuations within target temperature are reduced from ±20°C to ±10°C.

[0133] Reduced energy consumption: The system's hourly energy consumption is reduced from 1000kWh to 950kWh.

[0134] Test results:

[0135] The temperature control optimization system effectively improves the temperature stability of the system while reducing energy consumption, ensuring the stability and efficiency of the production process.

[0136] Example 3: Particle size control to optimize the lime kiln system

[0137] Test experiment:

[0138] Testing process:

[0139] a. Set the target particle size distribution;

[0140] b. Implement the particle size control optimization plan for 72 consecutive hours;

[0141] c. Collect the particle size distribution data of limestone raw materials every hour;

[0142] d. Compare the particle size control effects before and after optimization.

[0143] Test data:

[0144] Improved particle size uniformity: The deviation between the target particle size distribution and the actual particle size distribution was reduced from 20% to 15%.

[0145] Improved production efficiency: After optimizing particle size control, production speed increased by 5%.

[0146] Test results:

[0147] The particle size control optimization system effectively improves the particle size uniformity of the product, and improves production efficiency and product quality.

[0148] Example 4: Energy consumption optimization lime kiln system

[0149] Test experiment:

[0150] Testing process:

[0151] a. Run the energy consumption optimization plan for one week;

[0152] b. Record system energy consumption data daily;

[0153] c. Compare the energy consumption before and after optimization.

[0154] Test data:

[0155] Reduced energy consumption: The system's hourly energy consumption is reduced from 1000kWh to 900kWh.

[0156] Improved production efficiency: After optimizing energy consumption, production speed increased by 3%.

[0157] Test results:

[0158] The energy consumption optimization system effectively reduces production costs, improves the energy utilization efficiency of the system, and improves production efficiency.

[0159] Example 5: Multi-parameter comprehensive optimization of lime kiln system

[0160] Test experiment:

[0161] Testing process:

[0162] a. Run the multi-parameter comprehensive optimization system for one month;

[0163] b. Conduct comprehensive evaluation of system parameters every week;

[0164] c. Compare the overall performance before and after optimization.

[0165] Test data:

[0166] Comprehensive performance improvement: production efficiency increased by 10% and product quality improved by 5%.

[0167] Reduced energy consumption: The overall system energy consumption is reduced from 1000kWh to 920kWh.

[0168] Test results:

[0169] The multi-parameter comprehensive optimization system effectively improves the overall performance of the entire lime kiln production system, makes the system operation more stable and efficient, and reduces energy consumption.

[0170] Comparative experiment 1: High-efficiency energy consumption optimization solution

[0171] Existing technical solutions:

[0172] Technical solution: Use traditional PID control algorithm to optimize the energy consumption of lime kiln system;

[0173] Parameter setting: The PID controller parameters are set as follows: proportional coefficient Kp=0.5, integral time Ti=10, differential time Td=5.

[0174] Technical solution of the present invention:

[0175] Technical solution: Introduce an intelligent energy consumption optimization solution based on model predictive control (MPC);

[0176] Parameter setting: The MPC model takes into account multiple parameters such as temperature, oxygen content, and limestone feed rate, and the optimization cycle is 15 minutes.

[0177] Comparison process design:

[0178] Experimental process:

[0179] 1. Run the existing technology and the technical solution of the present invention respectively for one week;

[0180] 2. Record system energy consumption data and key parameter data daily;

[0181] 3. Compare the energy efficiency and system stability of the two solutions.

[0182] Test data:

[0183] Please refer to the data table below.

[0184] Experimental data table:

[0185]

[0186] Experimental results:

[0187] Compared with the prior art, the technical solution of the present invention saves an average of 5%-10% in energy consumption. At the same time, the system runs more stably and the energy consumption optimization effect is more significant.

[0188] Comparative experiment 2: High-precision particle size control solution

[0189] Existing technical solutions:

[0190] Technical solution: The traditional feedback control system controls the particle size of the lime kiln;

[0191] Parameter setting: Set the target particle size distribution and adjust the feed rate based on feedback.

[0192] Technical solution of the present invention:

[0193] Technical solution: Introduce advanced neural network control algorithm for granularity control;

[0194] Parameter setting: The neural network structure includes 3 hidden layers, and the number of nodes in each layer is 10, 8, and 6 respectively.

[0195] Comparison process design:

[0196] Experimental process:

[0197] 1. Run the existing technology and the technical solution of the present invention respectively for 48 hours continuously;

[0198] 2. Record the particle size distribution data of limestone raw materials every hour;

[0199] 3. Compare the particle size control effect and stability of the two solutions.

[0200] Test data:

[0201] Please refer to the data table below.

[0202] Experimental data table:

[0203]

[0204] Experimental results:

[0205] Compared with the prior art, the technical solution of the present invention performs better in particle size control accuracy, the particle size deviation is significantly reduced, and the product quality is improved.

[0206] Comparative experiment 3: Multi-parameter comprehensive optimization solution

[0207] Existing technical solutions:

[0208] Technical solution: Multi-parameter optimization solution based on empirical rules;

[0209] Parameter setting: Set the parameter range and optimization strategy based on experience.

[0210] Technical solution of the present invention:

[0211] Technical solution: Introduce evolutionary algorithm for comprehensive optimization of multiple parameters;

[0212] Parameter setting: Set the evolutionary algorithm population size to 50 and the number of iterations to 100.

[0213] Comparison process design:

[0214] Experimental process:

[0215] 1. Run the existing technology and the technical solution of the present invention respectively for one month;

[0216] 2. Conduct a comprehensive evaluation of system parameters every week;

[0217] 3. Compare the overall performance and multi-parameter optimization effects of the two solutions.

[0218] Test data:

[0219] Please refer to the data table below.

[0220] Experimental data table:

[0221]

[0222] Experimental results:

[0223] Compared with the prior art, the technical solution of the present invention performs better in comprehensive optimization of multiple parameters, the overall performance is improved, and the system operation is more stable and efficient.

[0224] Summary: Through the design and implementation of comparative experiments, the advantages of the technology of the present invention in improving efficiency, quality and stability are clearly demonstrated. Whether in energy consumption optimization, particle size control or multi-parameter optimization, the technical solution of the present invention shows higher performance and advancement, which has brought significant effects on the improvement of the lime kiln production system. These comparative experimental results further verify the practicality and superiority of the technology of the present invention, and provide strong support for the intelligence and optimization of industrial production processes.

[0225] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A lime kiln, characterized in that: It includes a kiln body, a feeding device, a material distribution device, a combustion device, an ash unloading device, and a dust removal device. The kiln body is configured as a double-chamber structure, and is respectively provided with a first chamber and a second chamber; The feeding device is used to transport the limestone raw material to the first chamber; The material distribution device is used to evenly distribute the limestone raw materials in the first chamber; The combustion device comprises a coal mill and a burner, wherein the coal mill is used to prepare coal powder into fuel, and the burner is used to spray the coal powder into the first chamber and the second chamber for combustion; The ash discharge device is used to discharge the calcined quicklime from the first chamber and the second chamber; The dust removal device is used to purify exhaust gas.

2. A lime kiln according to claim 1, characterized in that: The temperature of the first chamber and the second chamber of the kiln body is set to 1100°C to 1250°C.

3. A lime kiln according to claim 1, characterized in that: The coal powder injection rate function expression of the burner is: Pulverized coal injection rate = k1*(T1-T2)+k2*(P1-P2); Among them, T1 is the temperature in the first chamber of the lime kiln, T2 is the set target temperature, P1 is the atmosphere parameter in the lime kiln (oxygen content, CO / CO2 concentration), P2 is the set target atmosphere parameter, k1 and k2 are adjustment parameters used to control the response degree of the coal powder injection rate.

4. A lime kiln according to claim 1, characterized in that: The material distribution speed function expression of the material distribution device is: Fabric speed = k3*(D1-D2)+k4*(C1-C2); Among them, D1 is the particle size distribution of limestone raw materials, D2 is the set target particle size distribution, C1 is the fluidity and temperature distribution of the material in the lime kiln, C2 is the set target parameter, and k3 and k4 are adjustment parameters used to control the responsiveness of the material distribution speed.

5. A lime production system, characterized in that: It includes sensor module, dynamic model module, controller design module, optimization algorithm module, control command generation module and interface display module; The sensor module is used to monitor the temperature, atmosphere and key material flow parameters in the lime kiln in real time; The dynamic model module is used to establish a dynamic model of the lime kiln system; The controller design module is used to design and implement the MPC controller; The optimization algorithm module is used to implement genetic algorithm to optimize data; The control command generation module generates actual control commands according to the output of the controller and the optimization result; The interface display module is used to display the status and optimization results of the lime kiln system.

6. A lime production system according to claim 5, characterized in that: The sensor module includes a plurality of sensor nodes, each of which is a sensor element and a data transmission module, and the sensor element includes a temperature sensor unit, a pressure sensor unit, and a flow sensor unit; The temperature sensor unit is used to measure the temperature inside the lime kiln; The pressure sensor unit is used to measure the air pressure or gas pressure inside the lime kiln; The flow sensor unit is used to measure the speed or flow rate of material flow.

7. A lime production system according to claim 5, characterized in that: The dynamic model module includes a mathematical model unit and a system identification unit; The mathematical model unit is dynamically established based on the lime kiln temperature, and the lime kiln temperature dynamic function is: dT / dt=(1 / τ)*(T s and-T)+α*Q; Where T is the temperature of the lime kiln, T s et is the set target temperature, τ is the thermal time constant, Q is the heat input of the combustion device, α is the coefficient of influence of heat input on temperature, and dT / dt is the rate of change of temperature (T) with time (t); The system identification unit establishes a dynamic model based on the historical data in the database, specifically: y(t)=b0+b1*u(t-1)+b2*u(t-2)+...+bn*u(tn); Among them, y(t) is the output variable of the lime kiln, u(t) is the input variable of the lime kiln, and b0, b1, b2, ..., bn are the parameters to be identified.

8. A lime production system according to claim 5, characterized in that: The controller design module includes a system dynamic model unit, an optimization problem generation unit, a prediction model unit, and a control sequence generation unit; The system dynamic model unit is used to establish a mathematical model that describes the dynamic behavior of the system. The model is established using discrete time state space equations. The specific formula is: [x(k+1)=Ax(k)+Bu(k)][y(k)=Cx(k)]; Where x(k) is the system state vector, u(k) is the control input vector, y(k) is the system output vector, and A, B, and C are system parameter matrices; The optimization problem generating unit is used to generate the optimization problem according to the system dynamic model and control requirements; The prediction model unit is used to predict the state and output of the system in the future based on the system dynamic model, and its function expression is: in, is the optimal estimate of the system state at time |k, is the state estimate at the next moment predicted at time k, A is the system state transfer matrix, which describes how the system state evolves over time, B is the input matrix, which describes the impact of the control input on the system state, and C is the output matrix, which describes how the system state is mapped to the output; The control sequence generation unit is used to convert the solution of the optimization problem into actual control actions to achieve control of the system. It uses the objective function for optimization. The specific formula is: Among them, J represents the cost function, N represents the number of time steps for prediction and optimization, y(k+i|k) represents the predicted value of the output at time k+i at time k, r(k+i) represents the reference output value at time k+i, Q represents the weight matrix of the output error, which is used to measure the impact of the output error, R represents the weight matrix of the control input, which is used to measure the impact of the control input, and u(k+i|k) represents the predicted value of the control input at time k+i at time k.

9. A lime production system according to claim 5, characterized in that: The genetic algorithm adopts a fitness function, the specific expression is: f(x)=w c ×e+w e ×p; where w c represents the control error weight, w e represents the energy consumption weight, e represents the control error, and p represents the energy consumption; The genetic algorithm parameter initialization information is: population size: 100, number of iterations: 500, mutation rate: 0.

1.

10. A lime production system according to claim 5, characterized in that: The control command generation module includes an input unit, a processing unit, and an output unit, and the interface display module includes a receiving unit, a conversion unit, and a display unit; The input unit is used to receive the current system status and sensor data transmitted from the controller and obtain the results of the optimization algorithm or model; The processing unit is used to perform necessary calculations and logic operations according to the received controller output and optimization results to generate actual control commands; The actual control command generated by the output unit will be transmitted to the execution part of the system to adjust the temperature and ventilation speed of the lime kiln of the system; The receiving unit is used to receive the temperature, humidity and production rate information of the lime kiln from the system; The conversion unit is used to convert the received system status data and optimization result data into a format suitable for display on the interface; The display unit is used to display the processed system status and optimization results, so that operators or managers can monitor the operating status and performance indicators of the system in real time through the interface.

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