Soft measurement method, device, terminal and medium for key monitoring variables of zinc rotary kiln

Abstract

The present invention discloses a soft measurement method, device, terminal and medium for key monitoring variables of a zinc rotary kiln, including: analyzing the temperature field conservation theory and chemical reaction process in the zinc rotary kiln, and constructing a mechanism model for the coupling of the temperature and chemical reaction of the zinc rotary kiln; identifying key parameters of the model through analysis and mining of preset observation data, and obtaining digital twin parameters of the rotary kiln consistent with the equipment working conditions; using variable step-size bidirectional finite difference to solve the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln; dynamically updating the digital twin parameters and timely simulating the temperature and chemical composition in the rotary kiln according to changes in equipment control parameters and external disturbance data, to achieve real-time feedback on the control operation. The present invention provides a method for constructing a digital twin model of a zinc rotary kiln and virtual-real interaction, and realizes soft measurement of key monitoring variables of the zinc rotary kiln through digital twin.

Description

Technical Field

[0001] The present invention relates to the field of metallurgical equipment modeling and application technology, and in particular to a soft measurement method, device, terminal and medium for key monitoring variables of a zinc rotary kiln. Background Art

[0002] The nonferrous metallurgy industry is characterized by high energy consumption and carbon emissions. Zinc rotary kilns, the core equipment in the recovery phase of the zinc smelting process, separate and recover zinc from the leaching residue through high-temperature combustion and redox reactions. These high energy consumption and high carbon emissions are particularly pronounced. However, fluctuations in raw materials, coke grade, environmental factors, and the ratio of slag to coke lead to random variations in reaction conditions within the rotary kiln. Insufficient reaction conditions within the kiln result in low zinc recovery rates and wasteful resources. Excessive reaction conditions lead to excessive coke consumption, resulting in wasteful coke use and increased carbon emissions, while also causing burn-throughs and safety hazards. Workers control the kiln speed, feed rate, and kiln head air flow to achieve optimal reaction conditions within the kiln. This minimizes coke usage while maintaining a zinc recovery rate of ≥95%. To achieve optimal energy-saving and carbon-reduction operations in zinc rotary kilns, real-time monitoring of key reaction variables is essential to ensure the kiln maintains an optimal and stable reaction state.

[0003] Due to the unique characteristics of zinc rotary kilns, such as their long axial dimensions, high temperature, closed enclosure, and continuous rotation, key monitoring variables reflecting the reaction conditions within the kiln are difficult to directly obtain. This results in workers relying solely on visual observation of the flame morphology at the kiln head, the temperature at the kiln tail, and the composition of the slag to control equipment, a process that is both blind and random. To simulate the reaction conditions within the kiln, traditional zinc rotary kiln modeling and simulation methods are often performed offline based on theoretical and empirical assumptions. The incomplete fusion of the established model with equipment data results in significant errors in the simulation results. Furthermore, because the model fails to dynamically track changes in the actual equipment operating conditions, the simulation results are inconsistent with the actual conditions, limiting their application value.

[0004] For example, existing methods primarily simulate the internal components of rotary kilns using one-dimensional finite differences based on temperature field analysis and reaction analysis based on mass and energy conservation. However, because they fail to consider the impact of time-varying operating data, they are difficult to apply to field environments with variable operating conditions. Another example is the use of numerical simulation techniques based on thermal balance analysis to establish numerical simulation models and sintering temperature mechanism models, and the use of BP neural networks to compensate for errors in the established models. However, this fails to consider the complex multi-field coupling and mutual influence within the rotary kiln, resulting in a model that can only reflect a single physical characteristic within the kiln.

[0005] Digital twins are based on physical models, historical data, and real-time data updates, integrating multidisciplinary, multi-physics, and multi-scale simulation processes. They synchronize virtual and real equipment in digital space, reflecting the equipment's actual operating conditions. The effective fusion of digital twin mechanisms and data, and the real-time mapping of virtual models and real equipment, are key differences from traditional offline modeling and simulation. Highly accurate and dynamically consistent digital twin models are essential for effectively monitoring the internal reaction conditions of rotary kilns. By constructing a digital twin model that reflects the reaction conditions of a zinc rotary kiln and synchronizes it with the on-site rotary kiln in real time, this model overcomes the difficulties of traditional modeling and simulation in integrating mechanism data and synchronizing virtual and real data. Real-time digital twin simulations provide monitoring results for key variables within the kiln, including high-temperature reaction zone temperature, reaction rate, zinc recovery progress, and carbon consumption progress. This overcomes the problem of relying solely on indirect reliance on limited observational data to infer reaction conditions in the high-temperature zone, providing timely feedback on the effectiveness of rotary kiln control.

[0006] However, after searching, it was found that there has been no report on model construction and verification based on the digital twin concept on zinc rotary kiln, and then on the realization of soft measurement method of key variables of rotary kiln.

[0007] Therefore, the existing technology needs to be improved. Summary of the Invention

[0008] The technical problem to be solved by the present invention is that, in response to the defects of the existing technology, the present invention provides a soft measurement method, device, terminal and medium for key monitoring variables of a zinc rotary kiln, so as to realize soft measurement of key monitoring variables of a zinc rotary kiln through digital twin.

[0009] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0010] In a first aspect, the present invention provides a soft measurement method for key monitoring variables of a zinc rotary kiln, comprising:

[0011] Analyze the temperature field conservation theory and chemical reaction process in the zinc rotary kiln, and construct a mechanism model of the coupling between the temperature and chemical reaction of the zinc rotary kiln;

[0012] By analyzing and mining preset observation data, key model parameters are identified and the digital twin parameters of the rotary kiln consistent with the equipment operating conditions are obtained;

[0013] To solve the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln, a digital twin simulation was performed using a variable-step-size bidirectional finite difference method.

[0014] According to the changes in equipment control parameters and external disturbance data, the digital twin parameters are dynamically updated and the temperature and chemical composition inside the rotary kiln are simulated in a timely manner to achieve real-time feedback on the control operations.

[0015] In one implementation, the analysis of the temperature field conservation theory and chemical reaction process in the zinc rotary kiln includes:

[0016] The rotary kiln is divided into three parts: the kiln head area, the high-temperature reaction area and the kiln tail area. Complex heat transfer processes occur between materials, gases and kiln walls in the segmented intervals, and chemical reactions between materials and gases occur in the high-temperature reaction area.

[0017] In one implementation, analyzing the temperature field conservation theory and chemical reaction process in the zinc rotary kiln and constructing a mechanism model of the coupling between the temperature and chemical reaction of the zinc rotary kiln includes:

[0018] Analyze the convective heat transfer and radiation heat transfer between the flue gas, the kiln wall and the material, and construct the heat conservation equation of the flue gas, the material and the kiln wall;

[0019] The main processes in the volatilization kiln are high-temperature decomposition in the preheating section, coke combustion in the high-temperature reaction zone, reduction of zinc oxide, and reaction of iron compounds;

[0020] The mechanism analysis of the chemical reaction is carried out based on the conservation of mass and energy, and a mechanism model of the coupling between the zinc rotary kiln temperature and the chemical reaction is constructed.

[0021] In one implementation, the constructing of a mechanism model for coupling the zinc rotary kiln temperature and the chemical reaction includes:

[0022] Conduct temperature field analysis and modeling separately, and use preliminary temperature field results as the main basis for calculating reaction rates in chemical reactions;

[0023] The temperature field is corrected and coupled modeling is performed through analytical calculation of enthalpy changes during chemical reactions.

[0024] In one implementation, the analysis and mining of preset observation data to identify key model parameters and obtain digital twin parameters of the rotary kiln consistent with the equipment operating conditions include:

[0025] The temperature, concentration and pressure data during the operation of the rotary kiln are collected and stored in the form of discrete time series data through the DCS system;

[0026] Based on the experience of on-site fire monitoring and control of rotary kiln operation mode, provide guidance on rotary kiln operating conditions, and analyze and process DCS data and flame image observation data;

[0027] Through time series data analysis and image processing, the complete data corresponding to the rotary kiln status is extracted.

[0028] In one implementation, the extraction of the complementary data corresponding to the rotary kiln state through time series data analysis and image processing includes:

[0029] Through the time series data analysis and the image processing, the time domain features, frequency domain features, static features of the flame image, and dynamic features of the flame image corresponding to the rotary kiln state are extracted.

[0030] In one implementation, the method of extracting the complementary data corresponding to the rotary kiln state through time series data analysis and image processing includes:

[0031] The parameter estimates are recursively calculated each time the data is observed, and the digital twin model is updated and verified based on the prediction results.

[0032] In one implementation, the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln are solved by using a variable-step-size bidirectional finite difference method for digital twin simulation, including:

[0033] Divide the rotary kiln into N units along the axial direction, and determine the mass conservation formula existing in each unit;

[0034] According to the mass conservation formula in each unit, the difference form is used to derive and form a recursive formula;

[0035] The variable step-size bidirectional finite difference method is used to perform digital twin simulation solution.

[0036] In one implementation, the digital twin simulation solution is performed using the variable-step-size bidirectional finite difference method, including:

[0037] Aiming at the problem of countercurrent of material and gas phase and non-co-location of boundary conditions in zinc rotary kiln, the material composition matrix MH1 is defined at the kiln head of rotary kiln, and the material composition matrix MT1 is defined at the kiln tail of rotary kiln.

[0038] The initial conditions of the gas phase in the kiln head material composition matrix MH1 are calculated based on the compressed air and oxygen-enriched air injection rate at the kiln head, and the initial conditions of the material phase in the kiln tail material composition matrix MT1 are obtained based on the raw material composition analysis of the kiln tail.

[0039] The kiln head material composition matrix MH1 and the kiln tail material composition matrix MT1 are solved respectively, and after one solution is completed, the material phase in the kiln head material composition matrix MHi is updated by the material phase result in the kiln tail material composition matrix MTi, and the gas phase in the kiln tail material composition matrix MTi is updated by the gas phase result in the kiln head material composition matrix MHi.

[0040] In one implementation, dynamically updating digital twin parameters and timely simulating the temperature and chemical composition inside the rotary kiln based on changes in equipment control parameters and external disturbance data includes:

[0041] According to the changes in the equipment control parameters and the external disturbance data, the temperature, reaction rate, zinc production progress and carbon consumption progress variables in the rotary kiln are obtained through dynamically updated digital twin simulation, thereby achieving real-time feedback on the control operation.

[0042] In a second aspect, the present invention provides a soft measurement device for key monitoring variables of a zinc rotary kiln, comprising:

[0043] Model building module, used to analyze the temperature field conservation theory and chemical reaction process in the zinc rotary kiln, and to build a mechanism model for the coupling of zinc rotary kiln temperature and chemical reaction;

[0044] The digital twin parameter module is used to analyze and mine preset observation data, identify key model parameters, and obtain the digital twin parameters of the rotary kiln consistent with the equipment operating conditions;

[0045] The simulation solution module is used to solve the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln by using variable-step-size bidirectional finite difference to perform digital twin simulation solutions;

[0046] The dynamic update module is used to dynamically update the digital twin parameters and timely simulate the temperature and chemical composition inside the rotary kiln according to changes in equipment control parameters and external disturbance data, thereby achieving real-time feedback on the control operations.

[0047] In a third aspect, the present invention provides a terminal comprising: a processor and a memory, wherein the memory stores a soft measurement program for key monitoring variables of a zinc rotary kiln, and when the soft measurement program for key monitoring variables of a zinc rotary kiln is executed by the processor, it is used to implement the operation of the soft measurement method for key monitoring variables of a zinc rotary kiln as described in the first aspect.

[0048] In a fourth aspect, the present invention further provides a medium, which is a computer-readable storage medium, and which stores a soft measurement program for key monitoring variables of a zinc rotary kiln. When the soft measurement program for key monitoring variables of a zinc rotary kiln is executed by a processor, it is used to implement the operation of the soft measurement method for key monitoring variables of a zinc rotary kiln as described in the first aspect.

[0049] The present invention adopts the above technical solution to achieve the following effects:

[0050] The present invention constructs a digital twin model that reflects the reaction status of the zinc rotary kiln, and maintains synchronous mapping with the on-site rotary kiln in real time, thereby making up for the defects of difficult mechanism data fusion and virtual-reality synchronization in traditional modeling and simulation; and obtains monitoring results of key variables in the kiln through real-time simulation of the digital twin, including the temperature of the high-temperature reaction zone, reaction rate, zinc recovery progress and carbon consumption progress, thereby solving the problem of only being able to indirectly rely on limited observation data to infer the reaction status of the high-temperature zone, and providing timely feedback on the control effect of the rotary kiln, serving the green and intelligent operation of the zinc rotary kiln; the present invention provides a digital twin model construction and virtual-reality interaction method for the zinc rotary kiln, and realizes soft measurement of key monitoring variables of the zinc rotary kiln through the digital twin. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0052] Figure 1 The present invention is a flowchart of a soft measurement method for key monitoring variables of a zinc rotary kiln in one implementation mode of the present invention.

[0053] Figure 2 It is a schematic diagram of coupled modeling of temperature and chemical reaction in one implementation of the present invention.

[0054] Figure 3 It is a schematic diagram of the dynamic update of digital twin parameters in one implementation of the present invention.

[0055] Figure 4 It is a schematic diagram of the prediction of key parameters of digital twins in one implementation of the present invention.

[0056] Figure 5 It is a schematic diagram of solving the gas-solid two-phase countercurrent process by bidirectional finite difference in one implementation of the present invention.

[0057] Figure 6 It is a schematic diagram of the digital twin platform effect of a zinc rotary kiln in one implementation of the present invention.

[0058] Figure 7 It is a schematic diagram of the digital twin simulation results in one implementation of the present invention.

[0059] Figure 8 It is a functional principle diagram of a terminal in one implementation of the present invention.

[0060] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of the present invention more clear and distinct, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] Exemplary Methods

[0063] Digital twins are based on physical models, historical data, and real-time data updates, integrating multidisciplinary, multi-physics, and multi-scale simulation processes. They synchronize virtual and real equipment in digital space, reflecting the equipment's actual operating conditions. The effective fusion of digital twin mechanisms and data, and the real-time mapping of virtual models and real equipment, are key differences from traditional offline modeling and simulation. Highly accurate and dynamically consistent digital twin models are essential for effectively monitoring the internal reaction conditions of rotary kilns. By constructing a digital twin model that reflects the reaction conditions of a zinc rotary kiln and synchronizes it with the on-site rotary kiln in real time, this model overcomes the difficulties of traditional modeling and simulation in integrating mechanism data and synchronizing virtual and real data. Real-time digital twin simulations provide monitoring results for key variables within the kiln, including high-temperature reaction zone temperature, reaction rate, zinc recovery progress, and carbon consumption progress. This overcomes the problem of relying solely on indirect reliance on limited observational data to infer reaction conditions in the high-temperature zone, providing timely feedback on the effectiveness of rotary kiln control.

[0064] However, a search revealed that there is no method to build and verify a model based on the digital twin concept on a zinc rotary kiln, and thus to achieve soft measurement of key variables of the rotary kiln.

[0065] In response to the above technical problems, this embodiment provides a soft measurement method for key monitoring variables of a zinc rotary kiln. By constructing a digital twin model that reflects the reaction status of the zinc rotary kiln, it maintains synchronous mapping with the on-site rotary kiln in real time, thereby making up for the defects of difficult mechanism data fusion and virtual-real synchronization in traditional modeling and simulation; and through real-time simulation of the digital twin, the monitoring results of key variables in the kiln are obtained, including the temperature of the high-temperature reaction zone, the reaction rate, the zinc recovery progress and the carbon consumption progress, thereby realizing soft measurement of key monitoring variables of the zinc rotary kiln.

[0066] like Figure 1 As shown, an embodiment of the present invention provides a soft sensing method for key monitoring variables of a zinc rotary kiln, comprising the following steps:

[0067] Step S100: analyzing the temperature field conservation theory and chemical reaction process in the zinc rotary kiln, and constructing a mechanism model of the coupling between the temperature and chemical reaction of the zinc rotary kiln.

[0068] In this embodiment, the soft measurement method for key monitoring variables of a zinc rotary kiln is applied to a terminal, which includes but is not limited to: a computer and other equipment.

[0069] Zinc rotary kilns are critical energy consumers and carbon emitters in zinc smelting. Due to the lack of key monitoring variables within the kiln, control based on worker experience is blind and suboptimal. Digital twins, which integrate virtual and real-world data and synchronize virtual and real-world data, are essential for achieving real-time and accurate monitoring of key variables in zinc rotary kilns. To overcome the shortcomings of existing technologies, the present invention provides a method for constructing a digital twin model and implementing virtual-real interaction for zinc rotary kilns. This digital twin allows for soft measurement of key monitoring variables in zinc rotary kilns.

[0070] First, by analyzing the temperature field conservation theory and chemical reaction processes within a zinc rotary kiln, a mechanism model for the coupled temperature and chemical reactions within the kiln was constructed. Through limited observation data analysis and mining, key model parameters were identified, resulting in a digital twin of the kiln consistent with the equipment's operating conditions. Furthermore, a digital twin simulation solution based on the variable-step-size bidirectional Euler method was proposed to address the gas-solid two-phase countercurrent flow and gas-solid boundary conditions within the kiln. This approach improved the accuracy of the simulation solution for key variables within the kiln. Comparative analysis with data collected from actual industrial sites demonstrated that the proposed method effectively simulated the gas-solid two-phase countercurrent flow process within the zinc rotary kiln digital twin.

[0071] Thus, based on changes in equipment control parameters (feeding rate, kiln speed, blast status) and external disturbance data (ambient temperature, coke grade, coke ratio), key variables such as the temperature, reaction rate, zinc generation progress, and carbon consumption progress within the rotary kiln can be simulated in real time through a dynamically updated digital twin. This provides real-time feedback on control operations for workers to use as a reference, effectively resolving the blind and random control issues caused by the lack of key variables. Ultimately, this achieves optimal operation of the zinc rotary kiln for energy conservation and carbon reduction, contributing to the green and intelligent development of non-ferrous metallurgical equipment.

[0072] Specifically, in one implementation of this embodiment, the following steps are included before step S100:

[0073] Step S001, the rotary kiln is divided into three parts: the kiln head area, the high-temperature reaction area and the kiln tail area. A complex heat transfer process occurs between the material, gas and the kiln wall in the segmented intervals, and a chemical reaction process occurs between the material and gas in the segmented high-temperature reaction area.

[0074] In this embodiment, there are complex heat and mass transfer processes such as material transfer, heat transfer and chemical reaction in the zinc rotary kiln, among which the temperature field is an important influencing factor of multiple reactions and fields, and the chemical reaction field is a direct reflection of the final process indicators of zinc recovery in the zinc rotary kiln. Therefore, digital twin modeling of the temperature field and the chemical reaction field is mainly performed. The rotary kiln is divided into three parts: the kiln head area, the high-temperature reaction area and the kiln tail area. Complex heat transfer processes occur between the material, gas and kiln wall in the segmented intervals, and chemical reaction processes occur between the material and the gas in the high-temperature reaction area. The complex chemical reactions in the kiln are accompanied by heat transfer, which mainly includes convection heat transfer and radiation heat transfer between the flue gas, the kiln wall and the material. The flue gas exchanges heat with the kiln wall and the material through heat convection, the material exchanges heat with the kiln wall through heat radiation, and the outer wall of the kiln exchanges heat with the external environment through radiation and convection.

[0075] Specifically, in one implementation of this embodiment, step S100 includes the following steps:

[0076] Step S101, analyzing the convection heat transfer and radiation heat transfer between the flue gas, the kiln wall and the material, and constructing the heat conservation equation of the flue gas, the material and the kiln wall;

[0077] In step S102, the volatilization kiln mainly undergoes high-temperature decomposition in the preheating section, coke combustion in the high-temperature reaction zone, reduction of zinc oxide, and reaction of iron compounds;

[0078] Step S103 , performing a mechanism analysis of the chemical reaction based on the conservation of mass and the conservation of energy, and constructing a mechanism model of the coupling between the zinc rotary kiln temperature and the chemical reaction.

[0079] In this example, heat conservation equations for the flue gas, materials, and kiln walls were constructed based on an analysis of the heat transfer of the complex chemical reactions occurring within the kiln. Furthermore, the chemical reaction mechanisms within the volatilization kiln were analyzed based on the conservation of mass and energy, as the primary reactions occurring within the kiln include pyrolysis in the preheating section, coke combustion in the high-temperature reaction zone, zinc oxide reduction, and reactions of iron compounds.

[0080] Under steady-state conditions, assuming that the heat transfer process at any radial section of the rotary kiln does not consider the time-varying effect, each unit satisfies the energy conservation equation:

[0081]

[0082] Where M represents mass flow, Cp represents specific heat, Q represents heat change, T represents temperature, x represents the axial length of the kiln, subscripts s and g represent material and gas, subscripts ew and es represent exposed kiln wall and exposed material, subscripts cw and cs represent covered kiln wall and covered material, R and CV represent thermal radiation and heat conduction, ΔH represents the enthalpy change of chemical reaction, Q c= represents the heat released by coke combustion. Since there is no energy accumulation in the kiln wall, the energy conservation equation of the kiln wall is:

[0083] Q sh =Q g-ew +Q es-ew +Q cs-cw (2)

[0084] where Q sh represents the heat transfer between the kiln wall and the environment. Since the heat released by carbon combustion is the main source of heat in the rotary kiln, ignoring the chemical reaction enthalpy change ΔH and substituting the heat conduction and heat convection coefficients into formulas (1) and (2), we can further obtain the formula:

[0085]

[0086] Where A represents the heat transfer area, h represents the heat transfer coefficient, and i and j represent the components of the material and gas. Based on the analysis and modeling of the rotary kiln temperature field mechanism, the mechanism modeling of the chemical reaction process of the materials in the kiln is further carried out. The main components of zinc leaching slag are ZnFe2O4, ZnSO4 and ZnO. The thermal decomposition reactions that first occur in the kiln head area mainly include:

[0087] 6ZnFe2O4+C=6ZnO+4Fe3O4+CO2

[0088] Fe3O4+CO=3FeO+CO2

[0089] 2ZnSO4=2ZnO+2SO2+O2 (4)

[0090] The main chemical reactions in the high-temperature reaction zone inside the kiln are divided into two parts: inside the material layer and above the material layer. Inside the material layer, coke combustion, zinc oxide reduction, and iron compound reactions occur. The specific reactions are as follows:

[0091] C+O2=CO2(1) CO2+C=2CO(2)

[0092] ZnO+CO=Zn↑+CO2(3) ZnO+C=Zn↑+CO(4)

[0093] FeO+CO=Fe+CO2(5) ZnO+Fe=Zn↑+FeO(6) (5)

[0094] Above the material layer, CO combustion and Zn oxidation mainly occur. The specific reaction formula is:

[0095] 2CO+O2=2CO2(7) 2Zn↑+O2=2ZnO(8) (6)

[0096] The rates of the eight chemical reactions mentioned above are important factors affecting the zinc oxide production efficiency in the high-temperature reaction zone of the kiln and are also key parameters for digital twin modeling. The multi-step finite reaction rate Arrhenius equation is used to model the rates of the main reaction processes in the kiln:

[0097]

[0098] Where x and y represent reactants or products, respectively, c is the concentration of the substance, a and b are the reaction orders, A is the pre-exponential factor, Ea is the activation energy, R is the molar gas constant, and T is the absolute temperature. Based on the Arrhenius equations for the above chemical reactions and multi-step reactions, the rate formulas for the nine reactions are constructed:

[0099]

[0100] According to the material generation and consumption of the reaction in the kiln, the mass conservation formula of the solid and gas components in the kiln is constructed:

[0101]

[0102] The main factors affecting the recovery efficiency and carbon consumption of zinc rotary kiln are the internal temperature field and chemical reaction process.

[0103] Specifically, in one implementation of this embodiment, step S103 includes the following steps:

[0104] Step S103a, performing temperature field analysis and modeling separately, and using preliminary temperature field results as the main basis for calculating the reaction rate in the chemical reaction;

[0105] Step S103b: Correcting the temperature field and performing coupled modeling through analytical calculation of enthalpy changes during the chemical reaction process.

[0106] In this embodiment, in order to realize the coupled analysis of the temperature field and chemical reaction field in the kiln, based on the premise that the heat generated by the combustion of coke in the kiln accounts for more than 90% of the total heat, the temperature field is first analyzed and modeled separately, and the preliminary results of the temperature field are used as the main basis for calculating the reaction rate in the chemical reaction. Then, the temperature field is corrected and coupled modeling is performed through the analysis and calculation of the enthalpy change ΔH during the chemical reaction. The specific process is as follows. Figure 2 As shown:

[0107] Use chemical reaction enthalpy to correct equations (1) and (2), where:

[0108] ΔH s =r3ΔH3+r4ΔH4+r5ΔH5+r6ΔH6 (10)

[0109] ΔH g =r8ΔH8 (11)

[0110] This embodiment constructs a mechanism model of the coupling of zinc rotary kiln temperature and chemical reaction by analyzing the temperature field conservation theory and chemical reaction process in the zinc rotary kiln, and identifies the key parameters of the model through limited observation data analysis and mining, thereby realizing a digital twin of the rotary kiln that is consistent with the equipment operating conditions.

[0111] like Figure 1 As shown, in one implementation of the embodiment of the present invention, the soft measurement method for key monitoring variables of a zinc rotary kiln further includes the following steps:

[0112] In step S200, the key parameters of the model are identified through analysis and mining of preset observation data to obtain the digital twin parameters of the rotary kiln that are consistent with the equipment operating conditions.

[0113] In this embodiment, since the control parameters, observation data and disturbance data change in real time during the operation of the zinc rotary kiln, the digital twin model established by starting from the analysis of the multi-field coupling mechanism is only consistent with the physical characteristics of the actual rotary kiln in terms of model structure, and the key parameters in the model also need to be synchronized with the constantly changing working conditions of the actual equipment. The unknown parameters of the digital twin model are obtained through analysis and processing based on limited observation data, and the control parameters and disturbance data are used as input parameters for the twin model simulation. There are few observation data available on site, and the main observation data (i.e., preset observation data) include worker experience, kiln head flame / material morphology, kiln body / kiln tail temperature and kiln slag composition. It is necessary to effectively analyze and calculate the unknown parameters of the zinc rotary kiln digital twin based on the above limited observation data, and realize the dynamic tracking of the model to the real rotary kiln, so as to ensure the dynamic consistency of the twin model with the actual equipment.

[0114] Specifically, in one implementation of this embodiment, step S200 includes the following steps:

[0115] Step S201, collecting and storing temperature, concentration and pressure data during the operation of the rotary kiln in the form of discrete time series data through the DCS system;

[0116] Step S202: Based on the experience of on-site fire monitoring and control of the rotary kiln operation mode, the rotary kiln operating condition analysis and guidance are carried out, and the DCS data and flame image observation data are analyzed and processed;

[0117] Step S203: extracting the complementary data corresponding to the rotary kiln state through time series data analysis and image processing.

[0118] In this embodiment, the temperature, concentration and pressure data during the operation of the rotary kiln are collected and stored in the form of discrete time series data through the DCS system. Figure 3As shown, combined with workers' on-site experience in monitoring and controlling the kiln's operating mode, this paper provides guidance for analyzing the kiln's operating conditions. This analysis processes limited observation data, such as DCS data and flame images. Through time series data analysis and image processing, complete data related to the kiln's status is extracted.

[0119] Specifically, in one implementation of this embodiment, step S203 includes the following steps:

[0120] Step S203a: extracting the time domain features, frequency domain features, static features of the flame image, and dynamic features of the flame image corresponding to the rotary kiln state through the time series data analysis and the image processing.

[0121] In this embodiment, the extracted supplementary data related to the rotary kiln state mainly includes time domain features, frequency domain features, static features of the flame image, and dynamic features of the flame image; in the process of extracting data, these data need to be preprocessed.

[0122] Data preprocessing includes numerical transformation and missing value supplementation, trend item elimination, smoothing and noise reduction, and encoding and transformation of data attributes. Trend item elimination is used to eliminate the zero drift of sensor signals caused by temperature changes. It is achieved by using a method based on polynomial least squares curve fitting. Suppose the data collected by the rotary kiln sensor is Y=(y1,y2,...,y m ), and assuming that the fitting curve for the sampled data is:

[0123]

[0124] In order to fit the sample data as closely as possible, the sum of squared errors must be satisfied. Minimum, that is, the solution that satisfies the least square method. n ,a n-1 ,...a0] T The fitting equation of the curve can be obtained. A satisfies:

[0125]

[0126] The fitting equation of time series data can be obtained by calculating the above formula. The smoothing and noise reduction of sensor data are achieved by averaging method, and the formula is:

[0127]

[0128] The feature extraction of zinc rotary kiln sensor data is mainly carried out in the time domain and frequency domain. In the time domain, the time domain amplitude waveform is analyzed and the extracted features are the effective value Xrms, the peak value X max , skewness α and kurtosis β, which are calculated as follows:

[0129]

[0130] The frequency domain features are extracted by Fourier transform to convert the time domain signal into the frequency domain signal for analysis. Assume that the discrete signal collected by the rotary kiln is (x0, x1, x2, ..., x N-1 ), the Fourier transform of its spectrum calculation is:

[0131]

[0132] To reduce the amount of computation, a series of iterative operations, namely the Fast Fourier Transform algorithm, are used. The calculated and analyzed effective features of the rotary kiln DCS data are used for online identification and calibration of the digital twin model parameters.

[0133] Specifically, in one implementation of this embodiment, the following steps are included after step S203:

[0134] In step S204, the parameter estimation value is recursively calculated each time the data is observed, and the digital twin model is updated and verified based on the prediction results.

[0135] In this example, to address the nonlinear characteristics of the rotary kiln and achieve virtual-real synchronization of the digital twin, support vector regression is used to predict model parameters online. This means that parameter estimates are recursively calculated for each data observation, and the digital twin model is updated and verified based on the prediction results. If the verification results meet the requirements, the digital twin model with virtual-real synchronization is complete. If not, the model parameters are re-identified, thus recursively implementing the digital twin model of the zinc rotary kiln.

[0136] In one implementation of this embodiment, through analysis of the rotary kiln operating conditions and reaction mechanisms, it is found that the temperature inside the kiln and the mass ratio of related substances in the kiln have the greatest impact on the process indicators of the rotary kiln. Therefore, by installing sensor equipment at the zinc smelting site for data collection, and based on existing literature, the reaction rate and mass ratio distribution results of existing kilns of other sizes are obtained, and an SVR model is constructed and predicted for different reaction rates in the rotary kiln. Based on the field data and related data, 85 groups of related results on the content of substances in the kiln and the reaction rate can be obtained. After data analysis and normalization, the SVR model training is carried out, as shown in the following example. Figure 4 shown.

[0137] The input x is composed of the mass and temperature of the kiln components = [m1 i ,m2 i ,...,mk i ,Ts i ,Tg i ,Tsh i] to train the different chemical reaction rates in the kiln y = [r1, r2, ..., r9]. Where i = 1, 2, ..., N is the number of axial segments of the kiln, mk i is the mass of the i-th segment of substance k, Ts i is the material temperature, Tg i is the gas temperature, Tsh i is the kiln wall temperature. Then, through training, an SVR model is developed to predict reaction rates. The input data for reaction rate prediction comes from real-world data collected from a volatile kiln, including equipment parameters, initial material mass, and gas and solid temperatures. This allows for recursive prediction of the reaction rate within the kiln, thereby continuously updating the key parameters of the rotary kiln digital twin model and enabling dynamic tracking of the real equipment. The pseudocode for the dynamic update of the rotary kiln digital twin based on the SVR prediction model is shown below:

[0138]

[0139] By solving the above equation and taking the extreme value, this embodiment can obtain the maximum likelihood parameter estimation of the online recursive digital twin model of the zinc rotary kiln, which is used to update the twin model coefficients, coupling coefficients and boundary conditions, realize dynamic tracking of the on-site zinc rotary kiln, and ensure the virtual and real synchronization of the digital twin.

[0140] like Figure 1 As shown, in one implementation of the embodiment of the present invention, the soft measurement method for key monitoring variables of a zinc rotary kiln further includes the following steps:

[0141] In step S300, a digital twin simulation is performed to solve the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln using a variable step-size bidirectional finite difference method.

[0142] In this example, building on the aforementioned digital twin model simulation, a digital twin simulation solution based on the variable-step-size bidirectional Euler method was employed to address the gas-solid counterflow and gas-solid boundary conditions within the kiln. This improved the accuracy of the simulation solution for key variables within the kiln, resulting in a continuously decreasing error in the simulation solution for materials within the zinc rotary kiln, consistent with laboratory and actual conditions. Comparative analysis with data collected from actual industrial sites demonstrated that the proposed method effectively simulated the gas-solid counterflow process in the zinc rotary kiln digital twin.

[0143] Specifically, in one implementation of this embodiment, step S300 includes the following steps:

[0144] Step S301: Divide the rotary kiln into N units along the axial direction, and determine the mass conservation formula existing in each unit;

[0145] Step S302: deriving the mass conservation formula in each unit in a differential form to form a recursive formula;

[0146] Step S303: Use the variable-step-size bidirectional finite difference method to perform digital twin simulation solution.

[0147] Currently, simulations of key variables within rotary kilns primarily utilize finite-difference methods. However, due to the unique bidirectional countercurrent flow of material and gas within a rotary kiln, existing methods struggle to precisely define the boundary conditions between the material and gas phases, leading to divergent solutions. The modeling process needs to account for the discrepancies between initial conditions caused by the countercurrent flow of gas and solid phases within the zinc rotary kiln, thereby enabling mechanistic modeling and simulation of the material composition field during the convection reaction within the kiln and coupling the temperature field with the chemical reaction field. This approach improves the accuracy of simulation results within the zinc rotary kiln.

[0148] In this embodiment, if Figure 5 As shown, in order to construct and simulate the reaction field model in the rotary kiln, the rotary kiln is divided into N units along the axial direction. In each unit, there is a mass conservation formula:

[0149]

[0150] in is the mass of a substance j at node i, is the reaction rate of substance j at node i, M j is the amount of substance j, v is the velocity of the material or gas, and the axial length of the zinc rotary kiln is 68m and the inner diameter is 4.5m. The difference form is used for derivation to form a recursive formula:

[0151]

[0152] The meaning of this formula is that the mass of substance j at the node i+1 is equal to the sum of the mass of substance j at the previous node i and the mass of the substance generated by the reaction. Since the material is loaded into the zinc rotary kiln from the kiln tail, the initial condition of the material exists at the kiln head; the gas is blown into the rotary kiln from the kiln tail, and the initial condition of the gas exists at the kiln tail. The digital twin model of the rotary kiln established based on the above-mentioned chemical reaction and mass conservation mechanism analysis, if the finite difference method is used to solve it, the initial conditions defined at the same boundary will lead to large errors in the simulation results, and the simulation of some reaction processes will diverge, resulting in the solution not being in line with the actual situation. At the same time, due to the different speeds of temperature changes in different sections of the kiln, the use of a fixed-step finite difference will lead to large errors in certain nodes such as the transition section between the preheating zone and the high-temperature reaction zone. Therefore, a variable-step bidirectional finite difference method is proposed to simulate the digital twin solution of the zinc rotary kiln, as shown below. Figure 5 shown.

[0153] Specifically, in one implementation of this embodiment, step S303 includes the following steps:

[0154] Step S303a, to address the problem of countercurrent flow of material and gas phases in the zinc rotary kiln and the problem of boundary conditions not being in the same position, define a kiln head material composition matrix MH1 at the kiln head of the rotary kiln and a kiln tail material composition matrix MT1 at the kiln tail of the rotary kiln;

[0155] Step S303b, calculating the initial conditions of the gas phase in the kiln head material composition matrix MH1 based on the compressed air and oxygen-enriched air injection rate at the kiln head, and obtaining the initial conditions of the material phase in the kiln tail material composition matrix MT1 based on the raw material composition analysis of the kiln tail feed;

[0156] Step S303c, respectively solve the kiln head material composition matrix MH1 and the kiln tail material composition matrix MT1, and after one solution is completed, update the material phase in the kiln head material composition matrix MHi by the material phase result in the kiln tail material composition matrix MTi, and update the gas phase in the kiln tail material composition matrix MTi by the gas phase result in the kiln head material composition matrix MHi.

[0157] In this embodiment, the grid cells are first divided along the length of the kiln. The step size dx of each cell is defined as follows based on the temperature change slope within the cell from the preliminary analysis. This allows for densification of the cell division in areas with drastic temperature changes, eliminates solution errors caused by rapid temperature changes, and improves the accuracy of the twin simulation.

[0158]

[0159] In order to solve the problem of countercurrent of material and gas phases and the boundary conditions in not being in the same position in the zinc rotary kiln, a material composition matrix MH1 (kiln head) and MT1 (kiln head) of the same size are defined at the kiln head and kiln tail respectively. The number of rows in the matrix represents the number of nodes N, and the number of columns represents the type of material.

[0160] In a zinc rotary kiln, the primary considerations are the changes in the solid phases of ZnFe2O4, ZnSO4, Fe3O4, FeO, Fe, ZnO, and C in the kiln head zone, and the gaseous phases of SO2, CO, CO2, and O2. Furthermore, the reactions of nine substances in the high-temperature reaction zone—C, Fe, FeO, and ZnO (solid) in the material phase, and CO, CO2, O2, Zn (gaseous), and ZnO (gaseous) in the gas phase—are considered. The initial conditions for the material phase in the kiln head composition matrix MT1 are derived from the raw material composition analysis of the kiln head, while the initial conditions for the gas phase in the kiln head composition matrix MH1 are calculated from the compressed air and oxygen injection rates at the kiln head. During the digital twin model solution process, the two matrices are solved separately in each iteration. After a single solution is completed, the material phase results in the kiln head composition matrix MHi are used to update the material phase in the kiln head composition matrix MTi, and the gas phase results in the kiln head composition matrix MHi are used to update the gas phase in the kiln head composition matrix MTi.

[0161] After a single solution, the algorithmic difference between the two matrices MTi and MHi is calculated. If the error does not meet the requirements, it indicates that the simulation calculation has not reached a stable state within the kiln. The iterative solution is then continued until the error between the two matrices MHK and MTK meets the requirements. The solution process ends at this point, at which point the two component matrices within the kiln are considered to be approximate, and the simulation results have also reached a stable state within the kiln. The calculated content of each material component within the kiln is considered to represent the operating state of the rotary kiln after regulation and stable operation. When new operating conditions such as the rotary kiln speed, material discharge rate, and air volume change, simulation solutions are performed under these new operating conditions to obtain key monitoring variables within the rotary kiln, which are then used to guide workers in optimizing the rotary kiln.

[0162] like Figure 1 As shown, in one implementation of the embodiment of the present invention, the soft measurement method for key monitoring variables of a zinc rotary kiln further includes the following steps:

[0163] In step S400, according to the changes in equipment control parameters and external disturbance data, the digital twin parameters are dynamically updated and the temperature and chemical composition inside the rotary kiln are simulated in a timely manner to achieve real-time feedback on the control operation.

[0164] In this embodiment, based on the above-mentioned digital twin simulation solution method, dynamic updates are performed according to changes in on-site equipment control parameters and external disturbance data, thereby guiding workers to optimize the control of the rotary kiln.

[0165] Specifically, in one implementation of this embodiment, step S400 includes the following steps:

[0166] In step S401, based on the changes in the equipment control parameters and the external disturbance data, the temperature, reaction rate, zinc production progress, and carbon consumption progress variables in the rotary kiln are obtained through dynamically updated digital twin simulation to achieve real-time feedback on the control operation.

[0167] In this embodiment, the equipment control parameters include parameters such as feeding speed, kiln rotation speed, and blast status, and the external disturbance data include ambient temperature, coke grade, and coke ratio; according to the changes in these parameters, the key variables such as the temperature in the rotary kiln, reaction rate, zinc generation progress, and carbon consumption progress can be obtained through timely simulation through dynamically updated digital twins, thereby achieving real-time feedback on the control operation for workers' reference.

[0168] In an actual application scenario of this embodiment, by analyzing the coupling mechanism of the temperature field and chemical reaction field of the zinc rotary kiln, a digital twin simulation kernel of the kiln was constructed. On this basis, based on the working condition data analysis and twin parameter update, a digital twin software platform for key state monitoring of the zinc rotary kiln was realized. Figure 6 The software platform utilizes the MVVM (Model-View-ViewModel) architecture, achieving front-end and back-end separation between rich visual information and the core twin mechanism. The platform's functions primarily include rotary kiln operating status monitoring, operating condition identification, data analysis and parameter updates, and key variable simulation.

[0169] The temperature field and chemical reaction process of the core part of the rotary kiln digital twin are solved and simulated based on the mechanism analysis modeling and dynamic parameter update. The variable step-size bidirectional finite difference method is used to improve the simulation accuracy of the zinc rotary kiln digital twin model. Finally, the key monitoring variables reflecting the working conditions in the kiln are obtained by simulation, including the axial temperature field model and the axial component reaction progress model. The results are as follows: Figure 7 shown.

[0170] When the traditional method is used to solve the digital twin simulation of the rotary kiln, the gas phase calculation process diverges, and the solution results deviate far from the actual conditions. For example, the calculation of the gas phase O2 composition shows that the mass ratio of the kiln head is greater than 1, and the CO2 composition is less than 0. The digital twin model of the zinc rotary kiln is solved by bidirectional finite difference with a variable step size. The error of the simulation solution process of the material in the zinc rotary kiln continues to decrease, and is consistent with the test and actual conditions. Limited by the current data detection conditions, the digital twin simulation results are currently compared and verified by the CO concentration composition at the kiln tail and the Zn content in the kiln slag. After multiple comparisons and verifications, the zinc rotary kiln simulation results of the Zn content in the kiln slag and the CO content at the kiln tail are more consistent with the actual DCS monitoring results. The simulation method proposed in this embodiment improves the accuracy of the digital twin simulation solution of the rotary kiln and provides feedback guidance for the parameter control of the rotary kiln.

[0171] In another implementation of this embodiment, corresponding variations include one or a combination of the following:

[0172] (1) This embodiment proposes a coupled modeling approach for temperature field and chemical reaction, which is not limited to the coupling of these two reaction processes, but can also be extended to coupled modeling of temperature field and flow field, and temperature field and material field.

[0173] (2) This embodiment proposes a zinc rotary kiln digital twin simulation solution method with a variable step-size bidirectional finite difference, which can also be extended to further methods belonging to finite difference, such as the Euler-Euler method, the bidirectional Runge-Kutta method, and the bidirectional improved Euler method.

[0174] (3) This embodiment proposes a method and idea for virtual-real synchronization of zinc rotary kiln digital twin, which mainly includes operation mode analysis, time series data analysis, flame feature extraction and twin parameter generation. The actual implementation may include all or several of these methods.

[0175] (4) This embodiment proposes a method and approach for virtual-real synchronization of a zinc rotary kiln digital twin, wherein data preprocessing includes numerical transformation and missing value supplementation, trend item elimination, smoothing and noise reduction, and encoding and transformation of data attributes. Specific implementation may include all or some of these methods.

[0176] (5) This example addresses the gas-solid two-phase countercurrent and gas-solid boundary condition issues in zinc rotary kilns. A digital twin simulation solution based on variable-step-size bidirectional finite differences is proposed, which improves the simulation accuracy of key variables within the kiln. The proposed method is also applicable to other rotary kilns with gas-solid two-phase countercurrent, such as cement rotary kilns, waste incineration rotary kilns, and lime rotary kilns.

[0177] (6) This embodiment proposes a soft measurement method for key variables in a zinc rotary kiln based on digital twins. The method is not limited to the soft measurement of temperature and chemical reaction components. The method of this embodiment can also be applied to the soft measurement of variables such as material movement and gas flow in the rotary kiln.

[0178] This embodiment achieves the following technical effects through the above technical solution:

[0179] This embodiment constructs a digital twin model that reflects the reaction status of the zinc rotary kiln, and maintains synchronous mapping with the on-site rotary kiln in real time, thereby compensating for the defects of difficult mechanism data fusion and virtual-reality synchronization in traditional modeling and simulation; and obtains monitoring results of key variables in the kiln through real-time simulation of the digital twin, including the temperature of the high-temperature reaction zone, reaction rate, zinc recovery progress and carbon consumption progress, thereby solving the problem of only being able to indirectly rely on limited observation data to infer the reaction status of the high-temperature zone, and providing timely feedback on the control effect of the rotary kiln, serving the green and intelligent operation of the zinc rotary kiln; this embodiment provides a method for constructing a digital twin model of a zinc rotary kiln and interacting with the virtual and real, and realizes soft measurement of key monitoring variables of the zinc rotary kiln through the digital twin.

[0180] Exemplary devices

[0181] Based on the above embodiment, the present invention further provides a soft measurement device for key monitoring variables of a zinc rotary kiln, comprising:

[0182] Model building module, used to analyze the temperature field conservation theory and chemical reaction process in the zinc rotary kiln, and to build a mechanism model for the coupling of zinc rotary kiln temperature and chemical reaction;

[0183] The digital twin parameter module is used to analyze and mine preset observation data, identify key model parameters, and obtain the digital twin parameters of the rotary kiln consistent with the equipment operating conditions;

[0184] The simulation solution module is used to solve the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln by using variable-step-size bidirectional finite difference to perform digital twin simulation solutions;

[0185] The dynamic update module is used to dynamically update the digital twin parameters and timely simulate the temperature and chemical composition inside the rotary kiln according to changes in equipment control parameters and external disturbance data, thereby achieving real-time feedback on the control operations.

[0186] Based on the above embodiment, the present invention further provides a terminal, whose principle block diagram can be shown as follows: Figure 8 shown.

[0187] The terminal includes: a processor, memory, interface, display screen and communication module connected via a system bus; wherein the processor of the terminal is used to provide computing and control capabilities; the memory of the terminal includes a storage medium and an internal memory; the storage medium stores an operating system and computer programs; the internal memory provides an environment for the operation of the operating system and computer programs in the storage medium; the interface is used to connect to external devices, such as mobile terminals and computers; the display screen is used to display corresponding information; and the communication module is used to communicate with a cloud server or a mobile terminal.

[0188] When the computer program is executed by a processor, it is used to implement the operation of the soft measurement method of key monitoring variables of the zinc rotary kiln.

[0189] It will be understood by those skilled in the art that Figure 8 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0190] In one embodiment, a terminal is provided, which includes: a processor and a memory, wherein the memory stores a soft measurement program for key monitoring variables of a zinc rotary kiln, and when the soft measurement program for key monitoring variables of a zinc rotary kiln is executed by the processor, it is used to implement the operation of the soft measurement method for key monitoring variables of a zinc rotary kiln as described above.

[0191] In one embodiment, a storage medium is provided, wherein the storage medium stores a zinc rotary kiln key monitoring variable soft measurement program, which is used to implement the above zinc rotary kiln key monitoring variable soft measurement method when executed by a processor.

[0192] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The computer program can be stored in a non-volatile storage medium. When executed, the computer program can include the processes in the above-described method embodiments. Any reference to memory, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory.

[0193] In summary, the present invention provides a soft measurement method, device, terminal and medium for key monitoring variables of a zinc rotary kiln. The method includes: analyzing the temperature field conservation theory and chemical reaction process in the zinc rotary kiln, and constructing a mechanism model for the coupling of the temperature and chemical reaction of the zinc rotary kiln; identifying key parameters of the model through analysis and mining of preset observation data, and obtaining digital twin parameters of the rotary kiln consistent with the equipment working conditions; using variable step-size bidirectional finite difference to solve the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln; dynamically updating digital twin parameters and timely simulating the temperature and chemical composition in the rotary kiln according to changes in equipment control parameters and external disturbance data, to achieve real-time feedback on the control operation. The present invention provides a method for constructing a digital twin model and virtual-real interaction of a zinc rotary kiln, and realizes soft measurement of key monitoring variables of the zinc rotary kiln through digital twins.

[0194] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A soft measurement method for key monitoring variables of a zinc rotary kiln, characterized in that: include: Analyze the temperature field conservation theory and chemical reaction process in the zinc rotary kiln, and construct a mechanism model of the coupling between the temperature and chemical reaction of the zinc rotary kiln; By analyzing and mining preset observation data, key model parameters are identified and the digital twin parameters of the rotary kiln consistent with the equipment operating conditions are obtained; To solve the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln, a digital twin simulation was performed using a variable-step-size bidirectional finite difference method. Based on changes in equipment control parameters and external disturbance data, the digital twin parameters are dynamically updated and the temperature and chemical composition inside the rotary kiln are simulated in a timely manner, achieving real-time feedback on the control operation. The above-mentioned digital twin simulation solution for the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln is solved by using a variable step-size bidirectional finite difference method, including: Divide the rotary kiln into N units along the axial direction, and determine the mass conservation formula existing in each unit; According to the mass conservation formula in each unit, the difference form is used to derive and form a recursive formula; The variable step-size bidirectional finite difference method is used to perform digital twin simulation solution; The digital twin simulation solution using the variable step-size bidirectional finite difference method includes: In order to solve the problem of countercurrent of material and gas phase and boundary conditions in zinc rotary kiln and the problem of not being in the same position, the material composition matrix of the kiln head is defined at the kiln head of the rotary kiln. , and define the material composition matrix at the kiln tail of the rotary kiln ; The material composition matrix of the kiln head is calculated based on the compressed air and oxygen-enriched air injection amount of the kiln head. The initial conditions of the gas phase are determined, and the composition matrix of the kiln tail material is obtained based on the composition of the raw materials fed to the kiln tail. Initial conditions of the material phase; The kiln head material composition matrix and kiln tail material composition matrix Solve the problem and after one solution is completed, the kiln tail material composition matrix Update the kiln head material composition matrix with the material phase results in The material phase in the kiln is determined by the kiln head material composition matrix Update the kiln tail material composition matrix based on the gas phase results in in the gas phase.

2. The soft measurement method for key monitoring variables of a zinc rotary kiln according to claim 1, characterized in that: The analysis of the temperature field conservation theory and chemical reaction process in the zinc rotary kiln previously included: The rotary kiln is divided into three parts: the kiln head area, the high-temperature reaction area and the kiln tail area. Complex heat transfer processes occur between materials, gases and kiln walls in the segmented intervals, and chemical reactions between materials and gases occur in the high-temperature reaction area.

3. The soft measurement method for key monitoring variables of a zinc rotary kiln according to claim 1, characterized in that: The analysis of the temperature field conservation theory and chemical reaction process in the zinc rotary kiln and the construction of a mechanism model for the coupling of zinc rotary kiln temperature and chemical reaction include: Analyze the convective heat transfer and radiation heat transfer between the flue gas, the kiln wall and the material, and construct the heat conservation equation of the flue gas, the material and the kiln wall; The main processes in the volatilization kiln are high-temperature decomposition in the preheating section, coke combustion in the high-temperature reaction zone, reduction of zinc oxide, and reaction of iron compounds; The mechanism analysis of the chemical reaction is carried out based on the conservation of mass and energy, and a mechanism model of the coupling between the zinc rotary kiln temperature and the chemical reaction is constructed.

4. The soft measurement method for key monitoring variables of a zinc rotary kiln according to claim 3, characterized in that: The construction of the mechanism model of the coupling between the zinc rotary kiln temperature and the chemical reaction includes: Conduct temperature field analysis and modeling separately, and use preliminary temperature field results as the main basis for calculating reaction rates in chemical reactions; The temperature field is corrected and coupled modeling is performed through analytical calculation of enthalpy changes during chemical reactions.

5. The soft measurement method for key monitoring variables of a zinc rotary kiln according to claim 1, characterized in that: The preset observation data is analyzed and mined to identify key model parameters, and the digital twin parameters of the rotary kiln consistent with the equipment operating conditions are obtained, including: The temperature, concentration and pressure data during the operation of the rotary kiln are collected and stored in the form of discrete time series data through the DCS system; Based on the experience of on-site fire monitoring and control of rotary kiln operation mode, provide guidance on rotary kiln operating conditions, and analyze and process DCS data and flame image observation data; Through time series data analysis and image processing, the complete data corresponding to the rotary kiln status is extracted.

6. The soft sensing method for key monitoring variables of a zinc rotary kiln according to claim 5, characterized in that: The method of extracting the complementary data corresponding to the rotary kiln state through time series data analysis and image processing includes: Through the time series data analysis and the image processing, the time domain features, frequency domain features, static features of the flame image, and dynamic features of the flame image corresponding to the rotary kiln state are extracted.

7. The soft measurement method for key monitoring variables of a zinc rotary kiln according to claim 5, characterized in that: The method of extracting the complementary data corresponding to the rotary kiln state through time series data analysis and image processing includes: The parameter estimates are recursively calculated each time the data is observed, and the digital twin model is updated and verified based on the prediction results.

8. The soft sensing method for key monitoring variables of a zinc rotary kiln according to claim 1, characterized in that: According to the changes in equipment control parameters and external disturbance data, the digital twin parameters are dynamically updated and the temperature and chemical composition inside the rotary kiln are simulated in a timely manner, including: According to the changes in the equipment control parameters and the external disturbance data, the temperature, reaction rate, zinc production progress and carbon consumption progress variables in the rotary kiln are obtained through dynamically updated digital twin simulation, thereby achieving real-time feedback on the control operation.

9. A soft measurement device for key monitoring variables of a zinc rotary kiln, used to implement the soft measurement method for key monitoring variables of a zinc rotary kiln according to any one of claims 1 to 8, characterized in that: include: Model building module, used to analyze the temperature field conservation theory and chemical reaction process in the zinc rotary kiln, and to build a mechanism model for the coupling of zinc rotary kiln temperature and chemical reaction; The digital twin parameter module is used to analyze and mine preset observation data, identify key model parameters, and obtain the digital twin parameters of the rotary kiln consistent with the equipment operating conditions; The simulation solution module is used to solve the gas-solid two-phase countercurrent and gas-solid boundary condition problems in the kiln by using variable-step-size bidirectional finite difference to perform digital twin simulation solutions; The dynamic update module is used to dynamically update the digital twin parameters and timely simulate the temperature and chemical composition inside the rotary kiln according to changes in equipment control parameters and external disturbance data, thereby achieving real-time feedback on the control operations.

10. A terminal, characterized in that: include: A processor and a memory, wherein the memory stores a soft measurement program for key monitoring variables of a zinc rotary kiln, and when the soft measurement program for key monitoring variables of a zinc rotary kiln is executed by the processor, it is used to implement the operation of the soft measurement method for key monitoring variables of a zinc rotary kiln as described in any one of claims 1 to 8.

11. A medium, characterized in that The medium is a computer-readable storage medium, which stores a soft measurement program for key monitoring variables of a zinc rotary kiln. When the soft measurement program for key monitoring variables of a zinc rotary kiln is executed by a processor, it is used to implement the operation of the soft measurement method for key monitoring variables of a zinc rotary kiln as described in any one of claims 1 to 8.

Images