Metal smelting temperature control method and system

Abstract

The invention relates to the technical field of metal smelting, in particular to a metal smelting temperature control method and system. The system comprises a temperature monitoring module, a gas analysis module, a thermodynamic simulation module and a dynamic regulation and control module, the thermodynamic simulation module sets a fitness function according to the Gibbs free energy value, and obtains the optimal temperature and oxygen potential through a particle swarm optimization algorithm; the dynamic regulation and control module is used for converting the theoretical optimal temperature of the thermodynamic model into precise operation, and efficient and stable control over the smelting temperature is achieved. According to the method, the reaction process in the furnace and the current optimal temperature and oxygen potential are accurately controlled on the basis of the Gibbs free energy value and the particle swarm optimization algorithm, the theoretical optimal temperature of a thermodynamic model is converted into precise operation through multi-PID cooperative control, and efficient and stable control over the antimony smelting temperature is achieved; and therefore, the problem that the oxygen-enriched smelting temperature is not accurately controlled is fundamentally solved, the extraction rate of metal is increased, and energy consumption is reduced.

Description

Technical Field

[0001] This application relates to the field of metal smelting technology, and in particular to a method and system for controlling the temperature of metal smelting. Background Technology

[0002] Antimony, as a strategic rare metal, is irreplaceable in fields such as flame retardants and alloy manufacturing. Currently, about 95% of my country's antimony ore resources are smelted using pyrometallurgical processes, which generally suffer from complex processes and high energy consumption. To address these issues in pyrometallurgical antimony ore resources, some domestic antimony smelting enterprises have adopted oxygen-enriched smelting methods to reduce energy consumption and waste gas emissions.

[0003] However, due to systemic defects in the core temperature control process, the extraction rate of Sb remains unsatisfactory. Current oxygen-enriched smelting processes for antimony ore mainly rely on manual experience thresholds or fixed-parameter PID controllers for temperature regulation, lacking real-time perception of the dynamic thermodynamic state of the smelting process and the ability to optimize multiple objectives collaboratively. This leads to over-burning or under-burning of antimony ore, increasing energy costs, resulting in low Sb₂O₃ volatilization rates and high formation of byproducts Sb₂O₄ and Sb₂O₅. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, this invention provides a method and system for controlling the temperature of metal smelting. Based on Gibbs free energy and particle swarm optimization algorithm, it accurately controls the reaction process in the furnace and the current optimal temperature and oxygen potential. Through multi-PID collaborative control, it transforms the theoretical optimal temperature of the thermodynamic model into precise operation, achieving efficient and stable control of the smelting temperature. This fundamentally solves the problem of inaccurate temperature control in oxygen-enriched smelting, improves the metal extraction rate, and reduces energy consumption.

[0005] In a first aspect, the present invention provides a metal smelting temperature control system, including a temperature monitoring module, a gas analysis module, a thermodynamic simulation module, and a dynamic control module; The temperature monitoring module is used to collect temperature gradient data of the slag phase layer and the gas phase layer inside the smelting furnace. The gas analysis module is used to detect the mixed gas in the smelting furnace and obtain the partial pressures of Sb / S / O. The thermodynamic simulation module analyzes the chemical reaction process based on temperature gradient data and Sb / S / O partial pressures, obtains the Gibbs free energy value, sets the fitness function based on the Gibbs free energy value, and outputs the optimal temperature and oxygen potential through a particle swarm optimization algorithm. The dynamic control module calculates the error based on the obtained optimal temperature and oxygen potential, and uses an incremental PID algorithm to calculate the increment. It then combines the error and the increment to control the oxygen-enriched air flow rate, fuel supply rate, and cooling water flow rate.

[0006] As a preferred embodiment of the present invention, the gas analysis module is used to detect the mixed gas in the smelting furnace and obtain the Sb / S / O partial pressure, including: The mixed gas includes SO2, O2, and Sb2O3; High-frequency laser modulation and signal excitation: The high-frequency modulation frequency is set according to the characteristic absorption peak of Sb2O3. A three-dimensional resonant cavity is constructed using a Mach-Zehnder interferometer to excite the vibrational resonance of Sb2O3 molecules. The second harmonic 2f signal is focused by a lock-in amplifier (LIA) to output the amplitude of the 2f detection signal. The amplification of the 2f detection signal is highly linearly correlated with the concentration of Sb2O3. Environmental interference can be eliminated by reference channel technology to obtain the true amplification of the 2f signal. Standard gas fitting and concentration inversion: High-purity N2 and Sb2O3 saturated vapor are mixed in a certain proportion to generate standard gases with several gradient concentrations. The molar absorptivity of each concentration standard gas is measured. The 2f real signal amplification is substituted into the absorbance regression equation to obtain the Sb concentration. The Sb concentration is then substituted into the ideal gas equation for inversion to obtain the Sb partial pressure. The S content is quantified by the intensity of characteristic spectral lines, the S partial pressure is inverted by combining the ideal gas law, and the O partial pressure is obtained by combining the Sb partial pressure and the S partial pressure.

[0007] As a preferred embodiment of the present invention, the reference channel technology includes: A parallel optical path reference channel is used to monitor environmental scattering and light source fluctuations in real time, and dynamic compensation is established by combining the optical path of Sb2O3 gas. ,in The scattering coefficient is... To amplify the output 2f detection signal, The signal is used as a reference channel to monitor environmental interference. To eliminate interference and increase the actual signal strength by 2f.

[0008] As a preferred embodiment of the present invention, the absorbance regression equation formula is as follows: ; in, For the 2f real signal amplification, To experimentally determine the molar absorptivity, This refers to the concentration of Sb₂O₃. For optical path length.

[0009] As a preferred embodiment of the present invention, the thermodynamic simulation module inputs temperature gradient data and Sb / S / O partial pressures to analyze the chemical reaction process in real time, and uses a particle swarm optimization algorithm to calculate the optimal temperature and oxygen potential, including: Data preprocessing unit: Obtains the mass of Sb / S / O in the smelting raw materials using an XRF analyzer, standardizes the Sb / S / O mass, temperature gradient data, and Sb / S / O partial pressure, and maps the parameters to... Numerical range of thermodynamic software; The standardization process includes: The mass values ​​of Sb, S, and O in the raw material measured by XRF analyzer were converted into molar contents; the temperature gradient data were linearly scaled to 1000-2500 K; and the units of Sb / S / O partial pressures were uniformly converted to bar. Limit the partial pressure to less than or equal to 1 bar; Dynamic optimization objective function: A mathematical model is constructed with the total Gibbs free energy of the system as the objective. The standardized molar content of Sb / S / O, temperature gradient data, and partial pressures of Sb / S / O are imported into the Gibbs free energy calculation formula to obtain the Gibbs free energy value under the current reaction process. If the calculated Gibbs free energy value is less than 0, the reaction is determined to be spontaneous, and the oxygen supply needs to be reduced or the temperature lowered; if the calculated Gibbs free energy value approaches 0, the reaction is determined to be approaching equilibrium, and no adjustment is needed; if the calculated Gibbs free energy value is greater than 0, the reaction is determined to be a non-spontaneous process, and the oxygen supply needs to be increased or the temperature raised. Nonlinear programming optimal solution: The particle swarm optimization algorithm is used to quantify the adjustment of temperature and oxygen potential, and output the optimal temperature and oxygen potential.

[0010] As a preferred embodiment of the present invention, the Gibbs free energy calculation formula includes: ; in, This is the Gibbs free energy value. For grouping, For the first The total Gibbs free energy of the phase, , and The first The molar content of the phase, temperature, and pressure.

[0011] As a preferred embodiment of the present invention, the particle swarm optimization algorithm includes: Parameter settings: Set the number of particles, number of iterations, and initialize the inertia weight. acceleration constant and ; Particle swarm initialization: A feasible region for optimal temperature and oxygen potential is defined. The initial positions of particles are randomly generated within this feasible region, and the initial velocities are 0 or a small range of random values. Individual velocities are optimized. The initial position of the particle is the global optimum. The position of the particle with the highest fitness; Fitness function and iteration function design: The fitness function, particle velocity iteration function, and position iteration function are defined based on the Gibbs free energy. If the fitness of the current particle position is better than the historical value, then the fitness function is updated. If the current particle position is better than all other particle positions, then update. ; Termination condition definition: Reaching the maximum number of iterations or the globally optimal continuous iteration. The number of times less than the threshold; Output the optimal temperature and oxygen potential for the current reaction.

[0012] As a preferred embodiment of the present invention, the fitness function and the iteration function include: Fitness function calculation formula: ; Formula for calculating particle velocity iteration function: ; Formula for calculating particle position iteration function: ; in, and These are the weighting coefficients, and =1, To achieve the optimal temperature and oxygen potential. for The volatilization rate, where C is the percentage of antimony in the slag phase. For the target Gibbs free energy, and for Random numbers between these ranges increase the randomness of the search. and The first The particle in the first Second and third The speed of the next iteration and The first The particle in the first Second and third The position of the next iteration.

[0013] As a preferred embodiment of the present invention, the dynamic control module includes: Difference Calculation and Error Handling: The optimal temperature obtained from the particle swarm optimization algorithm is set as SP. The error e is calculated by combining the PV values ​​collected by the infrared thermocouple / FBG sensor. ; PID output calculation: An incremental PID algorithm is used, outputting only the increment of the control quantity. rather than absolute control Each adjustment is based only on the current and the two most recent errors, without accumulating historical error integrals. ; in, This is the proportionality coefficient. For integral coefficients, The differential coefficients are... and These are the current error value and the previous error value, respectively. Multivariable collaborative control logic: For the three key variables—oxygen-enriched airflow, fuel supply rate, and cooling water flow—independent PID controllers are deployed, and the control logic is based on the independent PID controllers'... To achieve multivariable coupled control, the regulation logic includes: Cooling water flow rate PID Increase immediately, without waiting for the integral term to accumulate slowly, while simultaneously reducing the fuel supply rate PID. ; Only fine-tuning the PID of oxygen-enriched airflow Approaching zero to balance the exothermic reaction of oxidation; Fuel supply rate PID Immediately increase the fuel flow rate and simultaneously increase the oxygen-enriched air flow rate (PID). ; Adaptive parameter optimization: Using LSTM networks to analyze historical operation data and predict optimal parameters for different processes. Response curve, real-time online fine-tuning The weighting coefficients, the The weighting coefficients include proportional coefficients, integral coefficients, and differential coefficients.

[0014] Secondly, the present invention also provides a method for controlling the temperature of metal smelting, applied to the metal smelting temperature control system described above, comprising the following steps: S1. High-precision infrared thermocouples and FBG sensors are used to collect temperature gradient data inside the furnace, and a three-dimensional temperature field cloud map with a resolution of 1 cm³ is generated by combining the finite element method; wavelength modulation technology is used to obtain the Sb / S / O voltage divider. S2, Input Sb / S / O molar content, temperature gradient data, partial pressure to Thermodynamic model, to obtain Gibbs free energy value, and through the Gibbs free energy value determination process; S3. Construct a fitness function using the Gibbs free energy value, and use a particle swarm optimization algorithm to calculate the optimal temperature and oxygen potential for the current reaction. S4. Calculate the error based on the obtained optimal temperature and oxygen potential. And use an incremental PID algorithm to calculate the increment. The PID control of oxygen-enriched air flow, fuel supply rate, and cooling water flow is combined with error and increment. S5. Use an LSTM network to analyze historical operating data and predict the optimal operating conditions under different operating conditions. Response curve, real-time online fine-tuning The weighting coefficients.

[0015] The beneficial effects of the present invention include: This invention employs high-frequency laser modulation and reference channel technology to eliminate interference from environmental factors such as flue gas on the amplification of the 2f true signal. It accurately obtains the Sb / S / O partial pressures through standard gas fitting, concentration inversion, and characteristic spectral line intensity. Then, based on temperature gradient data and the Sb / S / O partial pressures, a thermodynamic model is analyzed to monitor the reaction process in real time. The optimal temperature and oxygen potential under the current reaction conditions are calculated using a particle swarm optimization algorithm. Finally, multi-PID collaborative control transforms the theoretical optimal temperature from the thermodynamic model into precise operation, achieving efficient and stable control of the smelting temperature. This fundamentally solves the problem of inaccurate temperature control in oxygen-enriched smelting, effectively improving metal extraction rate and reducing energy consumption. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a metal smelting temperature control system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a temperature control process in a metal smelting temperature control system provided in an embodiment of the present invention. Figure 3 This is a schematic flowchart of a metal smelting temperature control method provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are also within the scope of protection of this application.

[0019] The preferred embodiments of the present invention will be further described below with reference to the accompanying drawings; See also Figure 1 and Figure 2 This embodiment provides a metal smelting temperature control system, including a temperature monitoring module, a gas analysis module, a thermodynamic simulation module, and a dynamic control module; The temperature monitoring module is used to deploy high-precision infrared thermocouples and FBG temperature sensors in key areas of the smelting furnace to collect temperature gradient data in real time, including: The furnace interior space is divided into a slag phase layer and a gas phase layer; High-precision infrared thermocouple deployment: one thermocouple is placed every 20cm in the slag phase layer to capture temperature changes in the slag phase layer; one thermocouple is placed every 30cm in the gas phase layer to monitor the temperature gradient in the high-temperature zone; two thermocouples are placed at the flue gas outlet to monitor the flue gas temperature and provide feedback on the Sb2O3 volatilization efficiency. FBG temperature sensor deployment: Three fiber optic grating arrays are arranged along the height of the furnace body, each containing 20 measuring points, covering the slag phase layer and the gas phase layer; four optical fibers are evenly arranged on the circumference of the furnace body cross-section, each containing 15 measuring points, to monitor the radial temperature distribution.

[0020] Temperature field visualization: Based on the finite element method, discrete measurement point data are interpolated into a three-dimensional temperature field model, and a temperature gradient distribution cloud map inside the furnace is generated with a resolution of 1 cm. 3 .

[0021] The gas analysis module is used to detect the mixed gas in the smelting furnace and obtain the Sb / S / O partial pressure, including: The mixed gas includes SO2, O2, and Sb2O3; High-frequency laser modulation and signal excitation: The high-frequency modulation frequency is set according to the characteristic absorption peak of Sb2O3. A three-dimensional resonant cavity is constructed using a Mach-Zehnder interferometer to excite the vibrational resonance of Sb2O3 molecules. The second harmonic 2f signal is focused by a lock-in amplifier (LIA) to output the amplitude of the 2f detection signal. Environmental interference is eliminated by reference channel technology to obtain the true 2f signal amplification. The 2f real signal amplification is a key parameter used to enhance the detection sensitivity and anti-interference capability of Sb2O3 characteristic signals by combining high-frequency laser modulation technology with the reference channel. It can be used as an indicator parameter for detecting the concentration of Sb2O3. The aforementioned reference channel technology employs a parallel optical path reference channel to monitor environmental scattering and light source fluctuations in real time, and establishes dynamic compensation by combining the optical path of Sb₂O₃ gas. Where α is the scattering coefficient. To amplify the output 2f detection signal, The signal is used as a reference channel to monitor environmental interference. To amplify the actual 2f signal after eliminating interference; Standard gas fitting and concentration inversion: High-purity N2 and Sb2O3 saturated vapor were mixed in a certain proportion to generate standard gases with concentrations of 0.5%, 1%, 2%, 3% and 5%. The molar absorptivity of each concentration of standard gas was measured. The amplification of the 2f true signal was substituted into the absorbance regression equation to obtain the Sb concentration. The Sb concentration was then substituted into the ideal gas equation for inversion to obtain the Sb partial pressure. The S content is quantified by the intensity of characteristic spectral lines, the S partial pressure is inverted by the ideal gas law, and the O partial pressure is obtained by combining the Sb partial pressure and the S partial pressure. The absorbance regression equation formula is as follows: ; in, For the 2f real signal amplification, To experimentally determine the molar absorptivity, This refers to the concentration of Sb₂O₃. For optical path length.

[0022] The thermodynamic simulation module inputs temperature gradient data and Sb / S / O partial pressures to analyze the chemical reaction process in real time, and uses a particle swarm optimization algorithm to calculate the optimal temperature and oxygen potential, including: Data preprocessing unit: Obtains the mass of Sb / S / O in the smelting raw materials using an XRF analyzer, standardizes the Sb / S / O mass, temperature gradient data, and Sb / S / O partial pressure, and maps the parameters to... Numerical range of thermodynamic software; The standardization process includes: The mass values ​​of Sb, S, and O in the raw material measured by XRF analyzer were converted into molar contents; the temperature gradient data were linearly scaled to 1000-2500 K; and the units of Sb / S / O partial pressures were uniformly converted to bar. Limit the partial pressure to less than or equal to 1 bar; Dynamic optimization objective function: A mathematical model is constructed with the total Gibbs free energy of the system as the objective. The initial molar content of Sb / S / O after standardization, temperature gradient data, and partial pressure of Sb / S / O are imported into the Gibbs free energy calculation formula to obtain the Gibbs free energy value under the current reaction process. If the calculated Gibbs free energy value is less than 0, the reaction is determined to be spontaneous, and the oxygen supply needs to be reduced or the temperature lowered; if the calculated Gibbs free energy value approaches 0, the reaction is determined to be approaching the equilibrium state, and no adjustment is needed; if the calculated Gibbs free energy value is greater than 0, the reaction is determined to be a non-spontaneous process, and the oxygen supply needs to be increased or the temperature raised. The Gibbs free energy calculation formula includes: ; in, This is the Gibbs free energy value. For grouping, For the total Gibbs free energy of phase i, , , These are the molar content, temperature, and pressure of the i-th phase, respectively; Nonlinear programming optimal solution: The particle swarm optimization algorithm is used to quantify the adjustment of temperature and oxygen potential, and output the optimal temperature and oxygen potential; The particle swarm optimization algorithm includes: Parameter settings: Set the number of particles, number of iterations, and initialize the inertia weight. acceleration constants c1 and c2; Particle swarm initialization: A feasible region for optimal temperature and oxygen potential is defined. The initial positions of particles are randomly generated within this feasible region, and the initial velocities are 0 or a small range of random values. Individual velocities are optimized. The initial position of the particle is the global optimum. The position of the particle with the highest fitness; Fitness function and iteration function design: Combine the objective function to define the fitness function, particle velocity iteration function, and position iteration function. If the fitness of the current particle position is better than the historical value, then update... If the current particle position is better than all other particle positions, then update. ; The fitness function and iteration function include: Fitness function calculation formula: ; Formula for calculating particle velocity iteration function: ; Formula for calculating particle position iteration function: ; in, and These are the weighting coefficients, and =1, To achieve the optimal temperature and oxygen potential. for The volatilization rate, where C is the percentage of antimony in the slag phase. For the target Gibbs free energy, and for Random numbers between these ranges increase the randomness of the search. and The first The particle in the first Second and third The speed of the next iteration and The first The particle in the first Second and third The position of the next iteration; Termination condition definition: The maximum number of iterations is reached or the global optimum is less than the threshold for N consecutive iterations; Output the optimal temperature and oxygen potential for the current reaction.

[0023] The dynamic control module calculates the error based on the obtained optimal temperature and oxygen potential, and uses an incremental PID algorithm to calculate the increment. Combining the error and the increment, it regulates the oxygen-enriched air flow rate, fuel supply rate, and cooling water flow rate. Difference Calculation and Error Handling: The optimal temperature obtained from the particle swarm optimization algorithm is set as SP. The error e is calculated by combining the PV values ​​collected by the infrared thermocouple / FBG sensor. ; PID output calculation: An incremental PID algorithm is used, outputting only the increment of the control quantity. rather than absolute control Each adjustment is based only on the current and the two most recent errors, without accumulating historical error integrals. ; in, This is the proportionality coefficient. For integral coefficients, These are the differential coefficients. and These are the current error value and the previous error value, respectively. Multivariable collaborative control logic: For the three key variables—oxygen-enriched airflow, fuel supply rate, and cooling water flow—independent PID controllers are deployed, and the control logic is based on the independent PID controllers'... To achieve multivariable coupled control, the regulation logic includes: Cooling water flow rate PID Increase immediately, without waiting for the integral term to accumulate slowly, while simultaneously reducing the fuel supply rate PID. ; Only fine-tuning the PID of oxygen-enriched airflow Approaching zero to balance the exothermic reaction of oxidation; Fuel supply rate PID Immediately increase the fuel flow rate and simultaneously increase the oxygen-enriched air flow rate (PID). ; Adaptive parameter optimization: Using LSTM networks to analyze historical operation data and predict optimal parameters for different processes. Response curve, real-time online fine-tuning The weighting coefficients, the The weighting coefficients include proportional coefficients, integral coefficients, and differential coefficients.

[0024] See also Figure 3 This embodiment provides a metal smelting temperature control method, applied to the aforementioned metal smelting temperature control system, comprising: S1. High-precision infrared thermocouples and FBG sensors are used to collect temperature gradient data inside the furnace, and a three-dimensional temperature field cloud map with a resolution of 1 cm³ is generated by combining the finite element method; wavelength modulation technology is used to obtain the Sb / S / O voltage divider. S2, Input Sb / S / O molar content, temperature gradient data, partial pressure to Thermodynamic model, to obtain Gibbs free energy value, and through the Gibbs free energy value determination process; S3. Construct a fitness function using the Gibbs free energy value, and use a particle swarm optimization algorithm to calculate the optimal temperature and oxygen potential for the current reaction. S4. Calculate the error based on the obtained optimal temperature and oxygen potential. And use an incremental PID algorithm to calculate the increment. The PID control of oxygen-enriched air flow, fuel supply rate, and cooling water flow is combined with error and increment. S5. Use an LSTM network to analyze historical operating data and predict the optimal operating conditions under different operating conditions. Response curve, real-time online fine-tuning The weighting coefficients.

[0025] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A metal smelting temperature control system, characterized in that, It includes a temperature monitoring module, a gas analysis module, a thermodynamic simulation module, and a dynamic control module; The temperature monitoring module is used to collect temperature gradient data of the slag phase layer and the gas phase layer inside the smelting furnace. The gas analysis module is used to detect the mixed gas in the smelting furnace and obtain the partial pressures of Sb / S / O. The thermodynamic simulation module analyzes the chemical reaction process based on temperature gradient data and Sb / S / O partial pressures to obtain the Gibbs free energy value. Then, it sets the fitness function based on the Gibbs free energy value and obtains the optimal temperature and oxygen potential through a particle swarm optimization algorithm. The dynamic control module calculates the error based on the obtained optimal temperature and oxygen potential, and uses an incremental PID algorithm to calculate the increment. It then combines the error and the increment to control the oxygen-enriched air flow rate, fuel supply rate, and cooling water flow rate.

2. The metal smelting temperature control system according to claim 1, characterized in that, The mixed gas includes SO2, O2, and Sb2O3; obtaining the Sb / S / O partial pressure includes: High-frequency laser modulation and signal excitation: The high-frequency modulation frequency is set according to the characteristic absorption peak of Sb2O3. A three-dimensional resonant cavity is constructed using a Mach-Zehnder interferometer to excite the vibrational resonance of Sb2O3 molecules. The second harmonic 2f signal is focused by a lock-in amplifier (LIA) to output the amplitude of the 2f detection signal. The amplification of the 2f detection signal is highly linearly correlated with the concentration of Sb2O3. Environmental interference is eliminated by reference channel technology to obtain the true amplification of the 2f signal. Standard gas fitting and concentration inversion: High-purity N2 and Sb2O3 saturated vapor are mixed in a certain proportion to generate standard gases with several gradient concentrations. The molar absorptivity of each concentration standard gas is measured. The 2f real signal amplification is substituted into the absorbance regression equation to obtain the Sb concentration. The Sb concentration is then substituted into the ideal gas equation for inversion to obtain the Sb partial pressure. The S content is quantified by the intensity of characteristic spectral lines, the S partial pressure is inverted by combining the ideal gas law, and the O partial pressure is obtained by combining the Sb partial pressure and the S partial pressure.

3. The metal smelting temperature control system according to claim 2, characterized in that, The reference channel technology includes: A parallel optical path reference channel is used to monitor environmental scattering and light source fluctuations in real time, and dynamic compensation is established by combining the optical path of Sb2O3 gas. ,in The scattering coefficient is... To amplify the output 2f detection signal, The signal is used as a reference channel to monitor environmental interference. To eliminate interference and increase the actual signal strength by 2f.

4. The metal smelting temperature control system according to claim 2, characterized in that, The absorbance regression equation is expressed as: ； in, For the 2f real signal amplification, To experimentally determine the molar absorptivity, This refers to the concentration of Sb₂O₃. For optical path length.

5. The metal smelting temperature control system according to claim 1, characterized in that, Obtaining the optimal temperature and oxygen potential includes: Data preprocessing unit: Obtains the mass of Sb / S / O in the smelting raw materials using an XRF analyzer, standardizes the Sb / S / O mass, temperature gradient data, and Sb / S / O partial pressure, and maps the parameters to... Numerical range of thermodynamic software; The standardization process includes: The mass values ​​of Sb, S, and O in the raw material measured by XRF analyzer were converted into molar contents; the temperature gradient data were linearly scaled to 1000-2500 K; and the units of Sb / S / O partial pressures were uniformly converted to bar. Limit the partial pressure to less than or equal to 1 bar; Dynamic optimization objective function: Construct a mathematical model with the total Gibbs free energy of the system as the objective, and import the standardized molar content of Sb / S / O, temperature gradient data and Sb / S / O partial pressure into the Gibbs free energy calculation formula to obtain the Gibbs free energy value under the current reaction process; If the calculated Gibbs free energy is less than 0, the reaction is considered to be spontaneous, and the oxygen supply should be reduced or the temperature lowered; if the calculated Gibbs free energy is close to 0, the reaction is considered to be approaching equilibrium, and no adjustment is needed; if the calculated Gibbs free energy is greater than 0, the reaction is considered to be a non-spontaneous process, and the oxygen supply should be increased or the temperature raised. Nonlinear programming optimal solution: The particle swarm optimization algorithm is used to quantify the adjustment of temperature and oxygen potential, and output the optimal temperature and oxygen potential.

6. The metal smelting temperature control system according to claim 5, characterized in that, The formula for calculating the Gibbs free energy is expressed as follows: ； in, This is the Gibbs free energy value. For grouping, For the first The total Gibbs free energy of the phase, , and The first The molar content of the phase, temperature, and pressure.

7. The metal smelting temperature control system according to claim 6, characterized in that, The particle swarm optimization algorithm includes: Parameter settings: Set the number of particles, number of iterations, and initialize the inertia weight. acceleration constant and ; Particle swarm initialization: A feasible region for optimal temperature and oxygen potential is defined. The initial positions of particles are randomly generated within this feasible region, and the initial velocities are 0 or a small range of random values. Individual velocities are optimized. The initial position of the particle is the global optimum. The position of the particle with the highest fitness; Fitness function and iteration function design: The fitness function, particle velocity iteration function, and position iteration function are defined based on the Gibbs free energy. If the fitness of the current particle position is better than the historical value, then the fitness function is updated. If the current particle position is better than all other particle positions, then update. ; Termination condition definition: Reaching the maximum number of iterations or the globally optimal continuous iteration. The number of times less than the threshold; Output the optimal temperature and oxygen potential for the current reaction.

8. The metal smelting temperature control system according to claim 7, characterized in that, The design of the fitness function and iteration function includes: Fitness function calculation formula: ； Formula for calculating particle velocity iteration function: ； Formula for calculating particle position iteration function: ； in, and These are the weighting coefficients, and =1, To achieve the optimal temperature and oxygen potential. for The volatilization rate, where C is the percentage of antimony in the slag phase. For the target Gibbs free energy, and for Random numbers between these ranges increase the randomness of the search. and The first The particle in the first Second and third The speed of each iteration and The first The particle in the first Second and third The position of the next iteration.

9. The metal smelting temperature control system according to claim 1, characterized in that, The regulation of oxygen-enriched air flow rate, fuel supply rate, and cooling water flow rate by combining error and increment includes: Difference Calculation and Error Handling: The optimal temperature obtained from the particle swarm optimization algorithm is set as SP. The error e is calculated by combining the PV values ​​collected by the infrared thermocouple / FBG sensor. ; PID output calculation: An incremental PID algorithm is used, outputting only the increment of the control quantity. rather than absolute control Each adjustment is based only on the current and the two most recent errors, without accumulating historical error integrals. ； in, This is the proportionality coefficient. For integral coefficients, These are the differential coefficients. and These are the current error value and the previous error value, respectively. Multivariable collaborative control logic: For the three key variables—oxygen-enriched airflow, fuel supply rate, and cooling water flow—independent PID controllers are deployed, and the control logic is based on the independent PID controllers'... To achieve multivariable coupled control, the regulation logic includes: Cooling water flow rate PID Increase immediately, without waiting for the integral term to accumulate slowly, while simultaneously reducing the fuel supply rate PID. ; Only fine-tuning the PID of oxygen-enriched airflow Approaching zero to balance the exothermic reaction of oxidation; Fuel supply rate PID Immediately increase the fuel flow rate and simultaneously increase the oxygen-enriched air flow rate (PID). ; Adaptive parameter optimization: Using LSTM networks to analyze historical operation data and predict optimal parameters for different processes. Response curve, real-time online fine-tuning The weighting coefficients, The weighting coefficients include proportional coefficients, integral coefficients, and differential coefficients.

10. A method for controlling the temperature of metal smelting, applied to the metal smelting temperature control system as described in any one of claims 1 to 9, characterized in that: Includes the following steps: S1. High-precision infrared thermocouples and FBG sensors are used to collect temperature gradient data inside the furnace, and a three-dimensional temperature field cloud map with a resolution of 1 cm³ is generated by combining the finite element method; wavelength modulation technology is used to obtain the Sb / S / O voltage divider. S2, Input Sb / S / O molar content, temperature gradient data, partial pressure to Thermodynamic model, to obtain Gibbs free energy value, and through the Gibbs free energy value determination process; S3. Construct a fitness function using the Gibbs free energy value, and use a particle swarm optimization algorithm to calculate the optimal temperature and oxygen potential for the current reaction. S4. Calculate the error based on the obtained optimal temperature and oxygen potential. And use an incremental PID algorithm to calculate the increment. The PID control of oxygen-enriched air flow, fuel supply rate, and cooling water flow is combined with error and increment. S5. Use an LSTM network to analyze historical operating data and predict the optimal operating conditions under different operating conditions. Response curve, real-time online fine-tuning The weighting coefficients.

Images