Method for detecting temperature of melt in heating furnace

Abstract

The invention relates to a method for detecting the temperature of a melt in a heating furnace, belongs to the technical field of temperature measurement at high temperature, and solves the problems that a thermocouple is easy to burn out and the temperature detection is inaccurate in a high-temperature section during contact type temperature measurement in the heating furnace in the prior art. The method comprises the following steps: acquiring a plurality of wavelengths and corresponding gray values of a melt in a heating furnace; obtaining the temperature of the melt in the heating furnace by using a free energy minimization temperature emissivity inversion method based on each wavelength and the corresponding gray value; and adjusting the temperature of the melt in the heating furnace based on the temperature of the melt in the heating furnace. The temperature of the melt in the heating furnace is measured by a non-contact method, the temperature of the melt with different emissivity can be accurately measured in real time, the production cost is reduced, and the temperature measurement precision is high.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature measurement at high temperatures, and particularly to a method for detecting the temperature of a melt in a heating furnace. Background Art

[0002] The metallurgical industry is a basic industry of the national economy and an important symbol of the country's industrial development level. The development of various fields such as machinery, chemical industry, construction, transportation, energy, aerospace, and national defense is inseparable from the progress of the metallurgical industry. In the existing metallurgical industry, especially when measuring the temperature of a heating furnace, manual measurement is carried out using a platinum-rhodium thermocouple. Due to the excessively high temperature during smelting, the tip of the thermocouple will be burned out instantly when it contacts the surface of the molten steel during the temperature measurement process. As a result, multiple thermocouples are required to measure the temperature at the same temperature measurement point. Therefore, this measurement method not only has high costs, poor safety, few temperature measurement points, and poor representativeness of temperature measurement, but also has a low end point hit rate, large consumption of raw materials, poor product quality, and is prone to waste of energy and raw materials.

[0003] In order to reduce the consumption of energy and raw materials during the smelting process of the heating furnace and improve the product quality, it is necessary to better control the entire smelting process, shorten the smelting time, and increase the end point hit rate. The prerequisite for achieving these goals is to timely and effectively monitor the temperature and molten steel composition during the smelting process. As the basis of metallurgical production, it is of great significance to quickly and accurately obtain the temperature information of the measured target. However, the harsh environment of high temperature, dust, and severe interference at the production site of the smelting furnace has caused great difficulties for on-line temperature measurement, and there is currently no particularly effective detection method. Summary of the Invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide a method for detecting the temperature of a melt in a heating furnace to solve the problem that the contact temperature measurement in the existing heating furnace is prone to burning out the thermocouple in the high-temperature section and the temperature detection is inaccurate.

[0005] The object of the present invention is mainly achieved through the following technical solutions:

[0006] The present invention provides a method for detecting the temperature of a melt in a heating furnace, including the following steps:

[0007] Obtain multiple wavelengths and corresponding gray values of the melt in the heating furnace;

[0008] Based on each wavelength and the corresponding gray value, use the temperature emissivity inversion method of free energy minimization to obtain the temperature of the melt in the heating furnace;

[0009] Adjust the temperature of the melt in the heating furnace based on the temperature of the melt in the heating furnace.

[0010] Further, when the heating furnace starts to work, a pressurization operation is performed on the heating furnace; after the pressure in the heating furnace reaches a preset value, the melt in the heating furnace is heated and a spectrometer is turned on to measure multiple wavelengths and corresponding gray values of the melt in the heating furnace.

[0011] Further, the temperature adjustment of the melt in the heating furnace based on the temperature of the melt in the heating furnace includes:

[0012] Compare the temperature of the melt in the heating furnace with a preset temperature to obtain a temperature deviation;

[0013] Take the temperature deviation as an input parameter of a fuzzy PID controller, and use the temperature deviation and the differential of the temperature deviation to correct the initial control parameters of the fuzzy PID controller to obtain corrected control parameters;

[0014] Based on the control parameters, the fuzzy PID controller outputs a temperature adjustment power.

[0015] Further, the use of the temperature deviation and the differential of the temperature deviation to correct the initial control parameters of the fuzzy PID controller to obtain corrected control parameters includes:

[0016] According to the deviation levels of the temperature deviation and the differential of the temperature deviation, determine the dynamic values of the control parameters according to a preset self-tuning fuzzy rule control table;

[0017] The corrected control parameters are the sum of the initial control parameters and the dynamic values of the control parameters.

[0018] Further, based on each wavelength and the corresponding gray value, using the temperature emissivity inversion method with minimized free energy to obtain the temperature of the melt in the heating furnace includes:

[0019] Based on each wavelength and the corresponding gray value, obtain the spectral radiance corresponding to each wavelength according to pre-calibrated data;

[0020] Based on the spectral radiance corresponding to each wavelength, obtain the blackbody radiation temperature of each wavelength;

[0021] Based on the maximum temperature T max among the blackbody radiation temperatures of each wavelength, obtain the equivalent spectral emissivity of each wavelength;

[0022] Based on the maximum temperature T max and the equivalent spectral emissivity of each wavelength, use the EM optimization strategy to obtain the temperature of the melt in the heating furnace after inversion.

[0023] Further, obtaining the temperature of the melt in the heating furnace after inversion using the EM optimization strategy based on the maximum temperature and the equivalent spectral emissivities at each wavelength includes:

[0024] Taking the maximum temperature and the equivalent spectral emissivities at each wavelength as the initial temperature and the initial spectral emissivities at each wavelength for the first iteration of the EM optimization strategy;

[0025] In the maximization step of the EM optimization strategy: calculating the optimal temperature as the hidden parameter of the EM optimization strategy using the free energy minimization method based on the initial spectral emissivities at each wavelength as the maximum likelihood function of the EM optimization strategy;

[0026] In the expectation step of the EM optimization strategy: obtaining the spectral emissivities at each wavelength at this temperature as the expected values of the spectral emissivities corresponding to each wavelength based on the optimal temperature;

[0027] Taking the optimal temperature and the expected values of the spectral emissivities corresponding to each wavelength as the initial temperature and the initial spectral emissivities at each wavelength for the next iteration;

[0028] When the expected values of the spectral emissivities corresponding to each wavelength and the initial spectral emissivities at each wavelength satisfy the convergence condition, the obtained optimal temperature is used as the temperature of the melt in the heating furnace after inversion, and the iteration ends.

[0029] Further, calculating the optimal temperature T using the free energy minimization method opt , and its formula is as follows:

[0030]

[0031]

[0032]

[0033] where T opt is the optimal temperature; ΔA is the change in internal energy; C V is the heat capacity of the material; is the initial spectral emissivity at wavelength λ i ; I obs (λ i ) is the spectral radiance corresponding to the wavelength λ i to be separated; I B (λ i ,T opt ) is the spectral radiance of the ideal blackbody at wavelength λ i at temperature T opt .

[0034] Further, based on the optimal temperature, the spectral emissivity at each of the wavelengths at this temperature is obtained using the following formula

[0035]

[0036] Further, based on the spectral radiance corresponding to each of the wavelengths, the blackbody radiation temperature at each of the wavelengths is obtained using Planck's blackbody radiation formula, and the maximum temperature T among them is obtained max 。

[0037] Further, based on the maximum temperature T among the blackbody radiation temperatures at each of the wavelengths max , the equivalent spectral emissivity at each of the wavelengths is obtained using Planck's blackbody radiation formula.

[0038] Compared with the prior art, the present invention can at least achieve one of the following beneficial effects:

[0039] 1. The present invention uses a spectrometer to measure the wavelengths and gray values of the melt in the heating furnace, and uses an emissivity-temperature separation inversion algorithm to achieve non-contact measurement of the melt temperature in the heating furnace. It can measure the temperature of the melt with different emissivities in real time and accurately, reduce production costs, and has high temperature measurement accuracy;

[0040] 2. The emissivity-temperature separation inversion algorithm used in the present invention can separately calculate and obtain the spectral emissivity and temperature of the melt in the heating furnace only by relying on the heat capacity function of the melt in the heating furnace, and this function is known for most materials, with simple calculation and high reliability;

[0041] 3. The present invention uses a fuzzy PID control method during temperature regulation, making the temperature control of the melt in the heating furnace have high precision and strong stability. Under the action of strong non-linear disturbances, an ideal control curve can still appear through parameter optimization.

[0042] In the present invention, the above technical solutions can also be combined with each other to achieve more preferred combination schemes. Other features and advantages of the present invention will be described in the subsequent specification, and some advantages can be made obvious from the specification, or understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the content specifically pointed out in the specification and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention. Throughout the drawings, the same reference numerals represent the same components.

[0044] Figure 1 It is a schematic flow chart of a method for detecting the temperature of a melt in a heating furnace in an embodiment of the present invention;

[0045] Figure 2 Schematic diagram of the EM optimization strategy process in an embodiment of the present invention;

[0046] Figure 3 Schematic diagram of the process of the spectral emissivity fitting smoothing method in an embodiment of the present invention;

[0047] Figure 4 Schematic diagram of the self - controlled fuzzy PID control principle in an embodiment of the present invention. Detailed implementation manners

[0048] Next, in combination with the accompanying drawings, the preferred embodiments of the present invention will be specifically described. Among them, the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to explain the principle of the present invention, rather than to limit the scope of the present invention.

[0049] A specific embodiment of the present invention discloses a method for detecting the temperature of the melt in a heating furnace. As Figure 1 shown, it includes the following steps:

[0050] Step S1: Obtain multiple wavelengths and corresponding gray - scale values of the melt in the heating furnace;

[0051] Specifically, when the heating furnace starts to work, a pressurization operation is performed on the heating furnace; after the pressure in the heating furnace reaches a preset value, the melt in the heating furnace is heated and a spectrometer is turned on to measure multiple wavelengths and corresponding gray - scale values of the melt in the heating furnace.

[0052] Step S2: Based on each wavelength and the corresponding gray - scale value, use the temperature - emissivity inversion method with minimized free energy to obtain the temperature of the melt in the heating furnace;

[0053] Furthermore, based on each wavelength and the corresponding gray - scale value, obtain the spectral radiance corresponding to each wavelength according to the pre - calibrated data;

[0054] Specifically, for the spectrometer used to obtain the wavelengths and gray - scale values of the melt in the heating furnace, the signal - to - noise ratio is relatively low at both ends of the obtained wavelength region, and the gray - scale value will be negative after subtracting the background. Therefore, it needs to be removed.

[0055] Furthermore, the pre - calibrated data is the corresponding data of the gray - scale value - spectral radiance value.

[0056] Based on the spectral radiance corresponding to each of the wavelengths, obtain the black - body radiation temperature of each of the wavelengths;

[0057] Furthermore, according to Planck's quantum hypothesis, use the following formula to obtain the black - body temperature of each of the wavelengths when the material is an ideal black - body:

[0058]

[0059] wherein, is the spectral radiance of an ideal black body at wavelength λ i at temperature ; C1 is the first radiation constant with a value of 3.74×10 -16 (W×m 2 ); C2 is the second radiation constant with a value of 1.4398×10 -2 (m×K).

[0060] Based on the maximum temperature T max among the blackbody radiation temperatures at each of the wavelengths, the equivalent spectral emissivity at each of the wavelengths is obtained, and its formula is:

[0061]

[0062]

[0063] wherein, is the equivalent spectral emissivity at wavelength λ i ; T max is the maximum temperature; I obs (λ i ) is the spectral radiance corresponding to the wavelength λ i to be separated; I B (λ i , T max ) is the spectral radiance of an ideal black body at wavelength λ i at temperature T max ; e is the natural constant.

[0064] Based on the maximum temperature T max and the equivalent spectral emissivity at each of the wavelengths, the temperature of the melt in the heating furnace after inversion is obtained using the EM optimization strategy.

[0065] Specifically, the EM optimization strategy is an iterative optimization strategy, and each iteration in its calculation method is divided into two steps, one is the expectation step, i.e., the E step, and the other is the maximization step, i.e., the M step, which is to solve the parameter estimation problem in the case of missing data (including latent variables).

[0066] Furthermore, the basic idea of the EM optimization strategy is as follows: First, based on the given observed data, estimate the values of the model parameters (initialization); then, based on the parameter values estimated in the previous step, estimate the values of the missing data, and then re - estimate the parameter values according to the estimated missing data plus the previously observed data, and then iterate repeatedly until convergence, at which point the iteration ends.

[0067] In this embodiment, the maximum temperature T maxand the equivalent spectral emissivity at each of the wavelengths The initial temperature and the initial spectral emissivity at each of the wavelengths as the first iteration of the EM optimization strategy;

[0068] As Figure 2 shown, the EM optimization strategy includes the following steps in one iteration process:

[0069] Step S201, in the maximization step of the EM optimization strategy: Using the free energy minimization method as the maximum likelihood function of the EM optimization strategy based on the initial spectral emissivity at each of the wavelengths to calculate the optimal temperature as the hidden parameter of the EM optimization strategy;

[0070] Specifically, when calculating the emissivity at a certain wavelength point according to the measured data, it often implies that the data is under a certain temperature condition, and it also implies that the emissivities at other wavelength points can be calculated based on this temperature, that is, the obtained emissivity information is related to the temperature.

[0071] In this embodiment, the emissivity-temperature separation algorithm based on free energy minimization refers to calculating the entropy using the spectral emissivity and calculating the internal energy using the material heat capacity, and obtaining the optimal temperature through free energy minimization.

[0072] In statistics, the KL divergence is generally used to measure the "distance" between two probability distribution functions, and can describe the relative distance between P i and Q i to some extent:

[0073]

[0074] Similarly, in thermodynamics, the KL divergence corresponds to the sum of the entropy changes of the system and the environment in these two different states:

[0075]

[0076] where ΔS is the change in entropy; ΔU env is the change in the internal energy of the environment; ΔS sys is the entropy of the system; ΔS env is the entropy of the environment; T0 is the temperature.

[0077] Specifically, according to the definition of Helmholtz free energy:

[0078] A = U - TS

[0079] Assuming that the volume and energy of the system can be ignored relative to the environment, and the temperature change of the environment before and after the energy exchange with the system can be ignored, the total free energy change of the system and the environment before and after the state change can be defined as:

[0080] ΔA = ΔU - TΔS = ΔU - TD KL (ε2||ε1)

[0081] Among them, ΔU is the change in internal energy of the system and can be calculated through heat capacity. The formula is:

[0082] ΔU = C v (T - T0)

[0083] Among them, C v is the heat capacity of the material and is generally a function of temperature: C v (T) = f(T); Many scientists have accurately measured the heat capacity values of various substances at various temperatures by experimental methods and obtained empirical expressions representing the relationship between heat capacity and temperature.

[0084] Furthermore, the optimal temperature T is calculated using the free energy minimization method as the maximum likelihood function of the maximization step of the EM optimization strategy opt ;

[0085] Specifically, the formula of the free energy minimization method is:

[0086]

[0087]

[0088]

[0089] Among them, T opt is the optimal temperature and is a hidden parameter of the EM optimization strategy; ΔA is the change in internal energy; C V is the heat capacity of the material; is the initial spectral emissivity at wavelength λ i ; I obs (λ i ) is the spectral radiance corresponding to the wavelength λ i to be separated; I B (λ i , T opt ) is the spectral radiance of the ideal blackbody at wavelength λ i at temperature T opt .

[0090] Step S202, in the expectation step of the EM optimization strategy: Based on the optimal temperature, obtain the spectral emissivity of each wavelength at this temperature as the expected value of the spectral emissivity corresponding to each wavelength;

[0091] Specifically, use the following formula to calculate the spectral emissivity of each wavelength at the optimal temperature

[0092]

[0093] Further, the spectral emissivity fitting smoothing method is used to fit and smooth the spectral emissivities at each of the wavelengths;

[0094] Specifically, during the measurement process, due to reasons such as detector noise, wavelength calibration, and stray radiation interference, the measured data is always accompanied by a large amount of fluctuations. Therefore, the calculated spectral emissivity of the material is also accompanied by a large amount of fluctuations. Effective fitting and smoothing of the spectral emissivity of the material is also very crucial in this embodiment.

[0095] Further, as Figure 3 shown, the spectral emissivity fitting smoothing method includes:

[0096] Step S2021: Obtain the single-wavelength radiation entropy at each of the wavelengths based on the spectral emissivities corresponding to each of the wavelengths;

[0097] Specifically, according to the probability characteristics of the emissivity and the Lebesgue measure, the entropy of the system at this wavelength can be measured by the following formula:

[0098]

[0099] where g(m) is the degeneracy of the state when exactly m particles radiate and de-excite, and its numerical value is indicating that there are g(m) microscopic states with the same energy.

[0100] Further, from the above formula, it can be deduced that:

[0101] H λ = -N[ε λ lnε λ +(1 - ε λ )ln(1 - ε λ )]

[0102] Therefore, the single-wavelength radiation entropy of a single particle at each wavelength is:

[0103]

[0104] where, is the single-wavelength radiation entropy at wavelength λ i ; is the spectral emissivity corresponding to wavelength λ i ; in order to avoid the situation where the spectral emissivity is greater than or equal to 1 or less than or equal to 0 (in these cases the calculation result is a complex number), the modulus of the calculation result is taken to obtain

[0105] It can be seen that the entropy of the system is equal to the entropy of a single particle multiplied by the total number of particles N, and its magnitude is related to the emissivity of the material in different wavelength bands.

[0106] Step S2022: Use the high-order polynomial fitting method for the single-wavelength radiation entropy of each wavelength to obtain the smoothed wavelength radiation entropy;

[0107] Specifically, the high-order polynomial fitting method includes: for each wavelength, use the kernel function as the weight to perform local polynomial kernel regression to obtain a high-order polynomial as the regression function, and use the regression function to calculate the single-wavelength radiation entropy of each wavelength as the smoothed wavelength radiation entropy; among them, local polynomial kernel regression includes: select a preset number of wavelengths adjacent to this wavelength, and perform polynomial fitting based on the kernel function within this range.

[0108] It should be noted that the preset number is the number of data points participating in the calculation on both sides of the smoothing point, which controls how many adjacent data points are used in the calculation; the larger the preset number range, the smoother the result. Preferably, the preset number N is selected to be 10 - 50.

[0109] Furthermore, according to solid-state physics theory, the spectral emissivity reflects the microscopic photoacoustic coupling characteristics of the material, and it should exhibit certain short-range continuity, smoothness, and certain long-range correlation in hyperspectral data. The smoothness of the material spectral emissivity means that the derivative of the spectral emissivity is continuous when the wavelength changes. The short-range correlation and long-range correlation are the external manifestations of the energy band structure of the material. When the material is a mixture, the long-range correlation and short-range correlation weaken, but still maintain relatively high spectral continuity and smoothness.

[0110] Furthermore, the continuity and smoothness of the material spectral emissivity are due to the fact that the radiation entropy H at different wavelengths at the microscopic level λ has smooth and continuous characteristics.

[0111] Specifically, high-order polynomial fitting is a commonly used method in data analysis and machine learning. It can fit a set of data into a high-order polynomial model. This method can improve the fitting accuracy of the data to a certain extent.

[0112] The basic idea of high-order polynomial fitting is to find an optimal polynomial function to fit the given data set, so as to minimize the error between the fitting function and the original data set. To achieve this goal, we need to select a suitable polynomial function and solve the coefficients of the polynomial function by the least squares method.

[0113] Specifically, we can select a polynomial about the independent variable λ iA polynomial function of degree n is used to fit the data. In this embodiment, this polynomial function can be expressed as the regression function:

[0114] H(λ i ) = a0 + a1λ i + a2λ i 2 + a3λ i 3 + … + a n λ i n

[0115] where λ i is the wavelength value of the i-th wavelength; a0, a1…a n are the coefficients of the polynomial function, and n is the degree of the polynomial function. By the least squares method, we can solve for the optimal values of these coefficients, thus obtaining an optimal polynomial function to fit the data.

[0116] It should be noted that high-order polynomial fitting may cause overfitting problems in some cases. Overfitting means that when fitting the data, in order to achieve a better fitting effect, the original data set is overfitted, resulting in a poor prediction effect for new data. Therefore, when performing high-order polynomial fitting, it is necessary to select the degree of the polynomial according to the specific situation and make appropriate adjustments and optimizations.

[0117] Specifically, the degree of the polynomial is from 2 to 11, preferably an odd number.

[0118] In this embodiment, the degree is preferably 5.

[0119] As can be seen from the above, as long as the polynomial coefficients of the radiation entropy at different wavelengths are obtained, the material emissivity can be fitted and smoothed. However, if a fixed polynomial is used, this fitting method has strong long-range correlation, which does not conform to the actual material characteristics.

[0120] Therefore, in this embodiment, kernel regression is used to fit the entropy, so that while meeting the requirements of smoothness and continuity, the hard constraint of its long-range correlation is reduced.

[0121] Specifically, traditional linear regression can only fit a straight line. Kernel regression is a regression method based on nonlinear mapping, which is a method that only uses multiple data points near the data point for regression. In essence, it uses the kernel function as the weight function to establish a nonlinear regression model.

[0122] Furthermore, according to the least squares method, local polynomial kernel regression is to solve the coefficients θ of the polynomial function to minimize the following objective function J(θ):

[0123] J(θ) = (Xθ - Y) T K(Xθ - Y)

[0124] Where θ is the coefficient vector of the polynomial function; X is the Vandermonde matrix with respect to the wavelength; K is the diagonalized kernel function matrix; and Y is the single-wavelength radiation entropy vector of each of the wavelengths.

[0125] Therefore, according to the coefficient calculation formula of the polynomial function can be obtained:

[0126]

[0127] Furthermore, the Vandermonde matrix X with respect to the wavelength is:

[0128]

[0129] Where λ is the smoothed wavelength; i is the sequence number of the i-th wavelength currently selected; and N is the preset quantity.

[0130] It should be noted that in the matrix, if i - N is less than 1, the calculation starts from 1; if i + N is greater than the total number of wavelengths within the wavelength range to be fitted, the calculation is only performed up to the last wavelength.

[0131] Furthermore, the diagonalized kernel function matrix K is:

[0132] K = diag(k(λ, λ i-N ), … k(λ, λ i ), … k(λ, λ i+N ))

[0133] Where k(λ, λ i ) is the kernel function; λ is the smoothed wavelength; i is the sequence number of the i-th wavelength currently selected; and N is the preset quantity.

[0134] Specifically, the kernel function defines the similarity measurement method of the input data in the feature space. Commonly used kernel functions include Gaussian kernel function, polynomial kernel function, sigmoid kernel function, etc.

[0135] Preferably, the Gaussian kernel function regression model is selected in the present invention:

[0136]

[0137] Where σ is the standard deviation.

[0138] Specifically, the Gaussian kernel function can be regarded as a weight negatively correlated with the distance from the center; during smoothing, adjusting the standard deviation is to adjust the influence degree of the surrounding wavelengths on the current wavelength. Increasing σ increases the influence degree of the distant wavelengths on the central wavelength, and the filtering result is smoother.

[0139] Further, the single-wavelength radiation entropy vector of the wavelength is:

[0140]

[0141] where is the single-wavelength radiation entropy of wavelength λ i ; i is the i-th wavelength number selected currently; N is the preset quantity.

[0142] Up to this point, after substituting the wavelength λ i into the regression function H(λ), the calculated H(λ i ) is the entropy after smoothing for this wavelength

[0143] Step S2023: Obtain the fitted and smoothed spectral emissivity based on the smoothed wavelength radiation entropy.

[0144] Specifically, the fitted and smoothed spectral emissivity of the wavelength range to be fitted is obtained by using the following formula based on the smoothed wavelength radiation entropy:

[0145]

[0146] where is the spectral emissivity of the smoothed wavelength λ i , which is the expected value of the spectral emissivity of the wavelength λ i for this iteration; is the single-wavelength radiation entropy of the smoothed wavelength λ i .

[0147] Step S203: Use the optimal temperature and the expected values of the spectral emissivities corresponding to each wavelength as the initial temperature and the initial spectral emissivities of each wavelength for the next iteration;

[0148] Step S204: When the expected values of the spectral emissivities corresponding to each wavelength and the initial spectral emissivities of each wavelength satisfy the convergence condition, use the obtained optimal temperature as the temperature of the melt in the heating furnace after inversion, and end the iteration.

[0149] Specifically, the method for judging the convergence condition is to calculate . When its value is less than a specific value, it is judged to satisfy the convergence condition.

[0150] It should be noted that the specific value can be adjusted according to the actual situation and accuracy requirements; in this embodiment, the specific value is taken as 1E - 8.

[0151] Step S3: Adjust the temperature of the melt in the heating furnace based on the temperature of the melt in the heating furnace.

[0152] Specifically, compare the temperature of the melt in the heating furnace with a preset temperature to obtain a temperature deviation.

[0153] Use the temperature deviation as an input parameter of the fuzzy PID controller, and use the temperature deviation and the differential of the temperature deviation to correct the initial control parameters of the fuzzy PID controller to obtain corrected control parameters.

[0154] Based on the control parameters, the fuzzy PID controller outputs a temperature regulation power.

[0155] Specifically, the temperature control of the melt in the heating furnace is another key technology of the present invention. For a linear time-invariant system or a system with a determined mathematical model in the industrial production process, PID control has the advantages of simple structure, strong reliability, easy implementation, and can eliminate steady-state errors. However, the temperature control system in industrial applications has characteristics such as large inertia and long delay time. When using traditional PID control, the dynamic and static characteristic indexes of the controlled object cannot meet the requirements in the application. By combining fuzzy control and PID, using the experience of experts to write fuzzy control rules to control the temperature control system, it can not only meet the requirements of the system dynamic performance indexes, but also control the steady-state error of the system within the allowable range.

[0156] As Figure 4 shown, this embodiment adopts adaptive fuzzy PID control, and the structure of a two-dimensional fuzzy controller is used. The temperature deviation e obtained by comparing the temperature of the melt in the heating furnace with a preset temperature and the differential of the temperature deviation are used as the error change rate ec as input variables. According to the changes of the two parameter variables, using the fuzzy control rule table, the three parameters K p 、K i and K d of PID are modified and adjusted. After the fuzzification link, the approximate reasoning link, and the final defuzzification link, the obtained output quantities are respectively added to the PID controller to adjust the three parameters in real time online.

[0157] Specifically, the control parameter K p represents the proportional adjustment coefficient, which is used to accelerate the response speed of the system and improve the adjustment accuracy of the system; the control parameter K i represents the integral adjustment coefficient, which is used to eliminate the residual error; the control parameter K d represents the differential adjustment coefficient, which is used to improve the dynamic performance of the system.

[0158] Furthermore, in order to ensure that the control system under the fuzzy self-tuning PID control law is globally stable, then the fuzzy adjustment of K p 、K i and K dThe deviation strategy is as follows. Before control, the initial PID parameters K p0 , K i0 and K d0 are obtained.

[0159] During control, according to the deviation value e and the deviation change rate ec, the dynamic values ΔK p , ΔK i and ΔK d of the three parameters of the PID controller are continuously calculated; finally, the control parameters of the PID are calculated according to the following formula:

[0160] K p = K p0 + ΔK p

[0161] K i = K i0 + ΔK i

[0162] K d = K d0 + ΔK d

[0163] Specifically, when determining the dynamic values of the control parameters K p , K i and K d according to the fuzzy rules, the deviation levels of the deviation value e and the deviation change rate ec include: large positive deviation, medium positive deviation, small positive deviation, zero deviation, small negative deviation, medium negative deviation and large negative deviation.

[0164] In summary, a method for detecting the melt temperature in a heating furnace according to an embodiment of the present invention has the following beneficial effects:

[0165] 1. The present invention uses a spectrometer to measure the wavelength and gray value of the melt in the heating furnace, and uses an emissivity-temperature separation inversion algorithm to realize non-contact measurement of the melt temperature in the heating furnace, which can measure the temperature of the melt with different emissivities in real time and accurately, reduce production costs, and has high temperature measurement accuracy;

[0166] 2. The emissivity-temperature separation inversion algorithm used in the present invention can separately calculate and obtain the spectral emissivity and temperature of the melt in the heating furnace only by relying on the heat capacity function of the melt in the heating furnace, and this function is known for most materials, with simple calculation and high reliability;

[0167] 3. The present invention uses a fuzzy PID adjustment method during temperature adjustment, so that the melt temperature control in the heating furnace has high precision and strong stability, and an ideal control curve can still appear through parameter optimization under the action of strong non-linear disturbances.

[0168] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for detecting the temperature of a melt in a heating furnace, characterized in that, It includes the following steps: Obtain multiple wavelengths and corresponding gray values of the melt in the heating furnace; Based on each wavelength and the corresponding gray value, use the temperature emissivity inversion method with minimized free energy to obtain the temperature of the melt in the heating furnace; Adjust the temperature of the melt in the heating furnace based on the temperature of the melt in the heating furnace.

2. The method according to claim 1, wherein When the heating furnace starts to work, perform a pressurization operation on the heating furnace; after the pressure in the heating furnace reaches a preset value, heat the melt in the heating furnace and turn on the spectrometer to measure multiple wavelengths and corresponding gray values of the melt in the heating furnace.

3. The method according to claim 2, wherein The adjusting the temperature of the melt in the heating furnace based on the temperature of the melt in the heating furnace includes: Compare the temperature of the melt in the heating furnace with a preset temperature to obtain a temperature deviation; Use the temperature deviation as an input parameter of the fuzzy PID controller, and use the temperature deviation and the differential of the temperature deviation to correct the initial control parameters of the fuzzy PID controller to obtain corrected control parameters; Based on the control parameters, the fuzzy PID controller outputs a temperature adjustment power.

4. The method according to claim 3, wherein The using the temperature deviation and the differential of the temperature deviation to correct the initial control parameters of the fuzzy PID controller to obtain corrected control parameters includes: According to the deviation levels of the temperature deviation and the differential of the temperature deviation, determine the dynamic value of the control parameter according to a preset self-tuning fuzzy rule control table; The corrected control parameter is the sum of the initial control parameter and the dynamic value of the control parameter.

5. The method according to claim 2, wherein The obtaining the temperature of the melt in the heating furnace by using the temperature emissivity inversion method with minimized free energy based on each wavelength and the corresponding gray value includes: Based on each wavelength and the corresponding gray value, obtain the spectral radiance corresponding to each wavelength according to pre-calibrated data; Based on the spectral radiance corresponding to each wavelength, obtain the blackbody radiation temperature of each wavelength; The maximum temperature T among the blackbody radiation temperatures at each of the wavelengths max , and obtain the equivalent spectral emissivity at each of the wavelengths; Based on the maximum temperature T max and the equivalent spectral emissivity at each of the wavelengths, the temperature of the melt in the heating furnace after inversion is obtained using the EM optimization strategy.

6. The method according to claim 5, wherein The obtaining the temperature of the melt in the inverted heating furnace by using the EM optimization strategy based on the maximum temperature and the equivalent spectral emissivity of each wavelength includes: Use the maximum temperature and the equivalent spectral emissivity of each wavelength as the initial temperature and the initial spectral emissivity of each wavelength for the first iteration of the EM optimization strategy; In the maximization step of the EM optimization strategy: use the free energy minimization method based on the initial spectral emissivity of each wavelength as the maximum likelihood function of the EM optimization strategy to calculate the optimal temperature as the hidden parameter of the EM optimization strategy; In the expectation step of the EM optimization strategy: obtain the spectral emissivity of each wavelength at this temperature as the spectral emissivity expectation value corresponding to each wavelength based on the optimal temperature; Use the optimal temperature and the spectral emissivity expectation values corresponding to each wavelength as the initial temperature and the initial spectral emissivity of each wavelength for the next iteration; When the spectral emissivity expectation values corresponding to each wavelength and the initial spectral emissivity of each wavelength meet the convergence condition, the obtained optimal temperature is used as the temperature of the melt in the inverted heating furnace, and the iteration ends.

7. The method according to claim 6, characterized in that, The optimal temperature T calculated using the free energy minimization method opt , and its formula is as follows: Among them, T opt is the optimal temperature; ΔA is the change in internal energy; C V is the heat capacity of the material; is the initial spectral emissivity at wavelength λ i ; I obs (λ i ) is the spectral radiance corresponding to the wavelength λ i to be separated; I B (λ i , T opt ) is the spectral radiance of a perfect blackbody at wavelength λ i at temperature T opt .

8. The method according to any one of claims 6-7, characterized in that, Based on the optimal temperature, the spectral emissivity at each of the wavelengths at this temperature is obtained using the following formula 9. The method according to claim 5, wherein Based on the spectral radiance corresponding to each of the wavelengths, the blackbody radiation temperature of each of the wavelengths is obtained using the Planck blackbody radiation formula, and the maximum temperature T among them is obtained max .

10. The method according to claim 9, wherein The maximum temperature T among the blackbody radiation temperatures at each of the wavelengths max , and the equivalent spectral emissivity at each of the wavelengths is obtained using the Planck blackbody radiation formula.

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