Method and system for automatically controlling temperature stability in zinc smelting and roasting process
By constructing adaptive adjustment terms for thermal inertia index and reaction efficiency index, the problem of unstable temperature control during zinc smelting roasting was solved, achieving more precise temperature regulation and improved system safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-03-27
AI Technical Summary
During the zinc smelting roasting process, traditional PID controllers cannot effectively cope with temperature fluctuations and changes in raw material properties in a large-delay thermodynamic system, resulting in unstable furnace temperature control and potential safety hazards.
By constructing thermal inertia index and reaction efficiency index, an adaptive adjustment term is established to dynamically adjust the feeding frequency and air volume, thereby achieving stable temperature control of the zinc smelting roasting process.
It improves the accuracy of temperature control in the zinc smelting roasting process, reduces furnace temperature fluctuations and overshoot, and enhances the safety and adaptability of the system.
Smart Images
Figure CN121739748A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automated control technology in non-ferrous metal smelting. More specifically, this invention relates to an automatic control method and system for stabilizing temperature during the zinc smelting roasting process. Background Technology
[0002] In the zinc smelting industrial production chain, fluidized bed roasting is a crucial preliminary step in converting zinc sulfide concentrate into zinc oxide. Its conversion efficiency and quality directly determine the subsequent leaching rate and the quality of electrolytic zinc. To ensure a complete and stable chemical reaction, the temperature of the fluidized bed within the roasting furnace must be strictly maintained within a specific process range, typically 900℃ to 950℃. Existing industrial control schemes mainly rely on traditional PID controllers or manual adjustments by experienced operators, observing the furnace temperature rise and fall to adjust the feed rate in an attempt to maintain thermal balance.
[0003] However, fluidized bed roasting furnaces have extremely high heat capacity and are typical thermodynamic systems with large time lags, resulting in a significant time delay in the temperature response to changes in feed. Traditional PID control strategies often rely on feedback adjustment based on the current temperature deviation. By the time a temperature deviation is detected, the thermodynamic state inside the furnace has already changed, making intervention at this point often too late. Furthermore, excessive adjustment can lead to reverse temperature overshoot, causing large fluctuations in furnace temperature around the setpoint and creating unconverged sawtooth oscillations that negatively impact roasting quality.
[0004] Furthermore, the characteristics of raw materials in actual production, such as moisture content, sulfur content, and furnace operating conditions, are dynamically changing, directly altering the system's process gain. Simultaneously, the blast volume, as a crucial variable participating in the reaction, also affects furnace temperature due to fluctuations. For example, when the raw material moisture content is high, increasing the feed rate by one unit will result in a much smaller temperature rise compared to dry raw materials, due to the heat absorption from moisture evaporation. Current technologies lack the real-time sensitivity to these comprehensive operating conditions, including the influence of blast volume. Control models using fixed parameters cannot adapt to changing conditions, easily leading to uncontrolled temperature drops under humid conditions or over-adjustment under dry conditions, resulting in slagging and posing significant safety hazards. Summary of the Invention
[0005] To address the aforementioned technical problem of poor temperature control during the zinc smelting roasting process, the present invention provides solutions in the following aspects.
[0006] In a first aspect, the present invention provides an automatic temperature stabilization control method for a zinc smelting roasting process, comprising: A sampling frequency is set; real-time operating data of the zinc smelting roasting furnace is collected, including at least the furnace temperature, the feeding frequency of the feeder, and the air volume of the blower. The real-time operating data is stored in time sequence to construct a historical data sequence. Based on the deviation between the furnace temperature and the set temperature within the historical time window, and combined with the rate of change of the temperature difference between adjacent time moments, the thermal inertia index, which characterizes the cumulative degree of thermodynamic state of the current system, is calculated. The preceding average furnace temperature, the preceding average feeding frequency, and the preceding average air volume within a preset long-period window before each time moment are obtained. Based on the ratio of the preceding average furnace temperature to the sum of the preceding average feeding frequency and the preceding average air volume after being weighted by the feeding weight coefficient and the blower weight coefficient, respectively, the reaction efficiency, which characterizes the reaction sensitivity of the raw materials under the current operating conditions, is calculated. An adaptive adjustment term is constructed using the thermal inertia index and the reaction efficiency at each time moment, and the preset benchmark feeding frequency is corrected based on the adaptive adjustment term to obtain the target feeding frequency at each time moment to control the operation of the feeder.
[0007] This invention introduces a thermal inertia index reflecting the accumulation of the system's thermodynamic state and a reaction efficiency index characterizing the sensitivity of the raw material reaction. By constructing an adaptive adjustment term using these two indices, the system can sense the heat accumulation caused by large hysteresis and intervene in advance. Simultaneously, it dynamically adjusts the control gain based on changes in reaction efficiency, thereby solving the model mismatch problem caused by fluctuations in raw material properties, reducing large fluctuations and sawtooth wave phenomena in furnace temperature, and improving the accuracy of temperature control under complex operating conditions.
[0008] Preferably, obtaining the furnace temperature includes: The values of multiple K-type thermocouples installed in the middle of the boiling layer are read at the sampling frequency. The maximum and minimum values are removed and the arithmetic mean is taken as the original furnace temperature at each moment. A sliding window is established to perform a sliding average filter on the original furnace temperature to obtain the furnace temperature at each moment.
[0009] Preferably, the acquisition of the feeding frequency of the feeder and the blowing volume of the blower includes: The feedback frequency of the feed disc or feed belt frequency converter at each moment is read using the sampling frequency and recorded as the feed frequency of the feeder at the corresponding moment; the value of the orifice plate flow meter or vortex flow meter on the main air duct of the furnace at each moment is read and recorded as the blower volume at each moment.
[0010] Preferably, the thermal inertia index satisfies the expression: ; In the formula, The thermal inertia index is represented at time t. This represents the total number of sampling points in the historical data sequence; , This represents the historical data sequence at time t, where the first element is the first element. The furnace temperature at the k+1th sampling point; Indicates the set temperature required by the process; Indicates the weighting coefficient for the rate of change; Indicates the sampling time interval.
[0011] This invention introduces a rate of change weighting coefficient, which can nonlinearly amplify the deviation signal when the temperature deviates rapidly from the set value, thereby providing early warning and multiplier response to the trend of temperature deterioration. This mechanism overcomes the inertia of thermodynamic systems and generates a sufficiently strong adjustment signal before the temperature overshoots significantly, reducing the overshoot of the system.
[0012] Preferably, the acquisition of the preceding average furnace temperature, preceding average feeding frequency, and preceding average blast volume within a preset long-term window before each time point includes: For any given time, acquire data from a long period window in the past; calculate the arithmetic mean of the furnace temperature, the arithmetic mean of the feeding frequency, and the arithmetic mean of the blast volume within the long period window, and record them as the preceding average furnace temperature, the preceding average feeding frequency, and the preceding average blast volume at the given time, respectively.
[0013] Preferably, the reaction efficiency satisfies the expression: ; In the formula, This represents the reaction efficiency at time t; This represents the preceding average furnace temperature at time t; This represents the preceding average feed frequency at time t; This indicates the feed weighting coefficient; This represents the preceding average blower volume at time t; This represents the blower weighting coefficient; It represents a tiny positive value.
[0014] This invention uses weighted feeding and blasting as the overall input and furnace temperature as the output to obtain the reaction efficiency under the current operating conditions in real time. This indicator can keenly detect the energy efficiency reduction caused by the increase in raw material moisture content or the decrease in air permeability, thus providing the control system with a real-time parameter that can reflect the changes in process gain, solving the problem of traditional control failing to detect changes in environmental parameters and causing adjustment failure.
[0015] Preferably, the target feeding frequency satisfies the expression: ; In the formula, This represents the target feeding frequency at time t; Indicates the maximum frequency allowed by the frequency converter; Indicates the minimum frequency allowed by the frequency converter; This represents the theoretical feed frequency at time t; Describes the minimum value function; This represents the maximum value function.
[0016] This invention introduces a maximum and minimum value clamping process based on the physical limitations of the frequency converter, ensuring that the instructions output by the control algorithm are always within the safe allowable range of the actuator (feeder), preventing the frequency converter from tripping or the equipment from being damaged due to extreme values calculated by the algorithm, and improving the safety of continuous operation of the automated control system.
[0017] Preferably, the theoretical feeding frequency at time t satisfies the expression: ; In the formula, This represents the theoretical feed frequency at time t; Indicates the reference feeding frequency; Indicates the adjustment gain coefficient; The thermal inertia index is represented at time t. This represents the reaction efficiency at time t; This represents the second smallest positive value.
[0018] This invention combines thermal inertia index with reaction efficiency. When the raw material becomes wet, causing a decrease in reaction efficiency, the adjustment term amplitude automatically increases, providing stronger adjustment to overcome environmental resistance; conversely, when the raw material is dry and the reaction is sensitive, the adjustment force is automatically reduced.
[0019] Preferably, the adaptive adjustment term is the ratio of the thermal inertia index at each moment to the reaction efficiency at the corresponding moment.
[0020] Secondly, the present invention provides an automatic control system for stabilizing the temperature in a zinc smelting roasting process, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned automatic control method for stabilizing the temperature in a zinc smelting roasting process is implemented.
[0021] By adopting the above technical solution, a computer program is generated for the above-mentioned automatic temperature control method for zinc smelting roasting process, and stored in a memory for loading and execution by a processor. Terminal equipment is then made based on the memory and processor for convenient use.
[0022] The beneficial effects of this invention are as follows: (1) In view of the large hysteresis characteristics of zinc smelting roasting, the present invention uses historical deviation sequence and change rate to construct thermal inertia index to predict the heat accumulation state of the system. (2) In view of the nonlinear interference such as the fluctuation of raw material moisture, the present invention establishes an input-output energy efficiency model to calculate the reaction efficiency, and dynamically adjusts the control gain accordingly; (3) The present invention combines thermal inertia index and reaction efficiency to achieve precise adjustment of variable parameters with stronger adjustment as the working conditions worsen, thereby reducing the furnace temperature oscillation problem caused by lag and model mismatch in traditional PID. Attached Figure Description
[0023] Figure 1 This is a flowchart illustrating an automatic temperature stabilization control method for a zinc smelting roasting process according to the present invention. Figure 2 This is a schematic diagram showing the temperature stability comparison during the zinc smelting roasting process; Figure 3 This is a schematic diagram illustrating the comparison of dynamic response adjustments to the feed rate. Detailed Implementation
[0024] This invention discloses an automatic temperature stabilization control method for the zinc smelting roasting process, referring to... Figure 1 This includes steps S1-S4: S1: Collect real-time operating data of the zinc smelting roasting furnace. The real-time operating data includes at least the furnace temperature, the feeding frequency of the feeder, and the air volume of the blower. The real-time operating data is then stored in time sequence to construct a historical data sequence.
[0025] It should be noted that the zinc smelting fluidized bed roasting process has large time lags and nonlinear characteristics, and isolated data at a single moment cannot reflect the dynamic trend of the system. Therefore, it is necessary to construct a high-frequency, continuous time-series data stream and eliminate field interference through preprocessing to provide high-quality data input for subsequent thermal inertia analysis and other tasks.
[0026] Specifically, real-time operating data of the zinc smelting roasting furnace is collected, and the real-time operating data is stored in time series to construct a historical data sequence, including: It should be noted that a high data sampling frequency needs to be set in order to ensure that the data can capture transient changes.
[0027] The system establishes communication with the field DCS (Distributed Control System) via industrial Ethernet and sets the sampling frequency. Real-time reading of the roasting furnace's operating data. For example, setting... .
[0028] It should be noted that the working conditions of the boiling layer inside the furnace are harsh, and the intense gas-solid mixing leads to extremely large fluctuations in single-point measurements. Direct use of such measurements would cause control oscillations. Therefore, this invention employs a dual preprocessing strategy for furnace temperature data, which involves extreme value removal through averaging and sliding filtering.
[0029] Acquire and process furnace temperature data: Read the values from multiple K-type thermocouples installed in the middle of the boiling layer at a sampling frequency. Remove the maximum and minimum values and take the arithmetic mean as the raw furnace temperature at each moment. Establish a sliding window and apply a moving average filter to the raw furnace temperature to obtain the furnace temperature at each moment. For example, there are 6 K-type thermocouples, and the size of the sliding window is... .
[0030] Read the feedback frequency of the frequency converter of the feeding disc or feeding belt at each moment, and record it as the feeding frequency of the feeder at the corresponding moment.
[0031] Read the values of the orifice plate flow meter or vortex flow meter on the main air duct of the furnace at each moment, and record them as the blower volume at each moment.
[0032] It should be noted that, in order to support subsequent backtracking analysis of historical trends, the system needs to allocate a circular buffer in memory.
[0033] Set the total number of sampling points for the historical data sequence The system acquires the furnace temperature, feeder frequency, and blower volume of the previous N-1 time points for each given moment, and combines these data with the current furnace temperature, feeder frequency, and blower volume at the corresponding moment in chronological order to form the historical data sequence for each moment. For example, This represents data from the past 59 seconds.
[0034] At this point, we have obtained the real-time running data and the historical data sequence of the real-time running data.
[0035] S2: Based on the deviation between the furnace temperature and the set temperature within the historical time window, and combined with the rate of change of the temperature difference between adjacent times, calculate the thermal inertia index, which characterizes the cumulative degree of the current system's thermodynamic state.
[0036] It should be noted that in a hysteresis system, simply looking at the current temperature error is insufficient. If the temperature deviates from the set value and the rate of deviation continues to accelerate, it indicates that the system has accumulated significant thermal inertia energy, and subsequent temperature overshoot will continue. Therefore, this invention constructs a thermal inertia index to evaluate the temperature overshoot performance and duration at each moment, for use in subsequent feedforward compensation.
[0037] Specifically, based on the deviation between the furnace temperature and the set temperature within the historical time window, and combined with the rate of change of the temperature difference between adjacent time points, the thermal inertia index, which characterizes the cumulative degree of the current system's thermodynamic state, is calculated, including: It should be noted that, considering that static error represents the cumulative deficit or surplus of heat, while the rate of temperature change represents the subsequent trend of this deficit or surplus, this invention introduces the rate of change as a dynamic weight. When the temperature deviates rapidly, the dynamic weight increases non-linearly, thereby amplifying the deviation signal and achieving early warning of deteriorating trends.
[0038] The thermal inertia index at any given time satisfies the following expression: ; In the formula, The thermal inertia index is represented at time t. This represents the total number of sampling points in the historical data sequence; , This represents the historical data sequence at time t, where the first element is the first element. The furnace temperature at the k+1th sampling point; Indicates the set temperature required by the process; Indicates the weighting coefficient for the rate of change; This indicates the sampling time interval. It should be noted that... Used to adjust the system's sensitivity to the rate of temperature change. The larger the value, the more violently the system responds to rapid temperature fluctuations. For example, for , , .
[0039] In the formula, This represents the historical data sequence at time t, where the first element is the first element. The difference between the sampling point and the set temperature required by the process; This represents the historical data sequence at time t, where the first element is the first element. The instantaneous rate of temperature change at each sampling point; The dynamic weighting factor is represented by the rate of change approaching 0 when the temperature is stable, and the dynamic weighting factor approaches 1, so the formula becomes an integral over the furnace temperature; when the temperature rises or falls rapidly, the dynamic weighting factor is significantly greater than 1, thus amplifying the deviation. This indicates that the temperature change affects the value of the first element in the historical data sequence at time t. The differences between each sampling point and the set temperature required by the process are weighted. This represents a weighted summation of the differences between the historical data sequence at time t and the set temperature required by the process. The larger this value is, the greater the difference between the historical data sequence and the set temperature required by the process, thus indicating a larger thermal inertia index.
[0040] Thus, the thermal inertia index, which characterizes the energy state of the system, was obtained.
[0041] S3: Obtain the preceding average furnace temperature, preceding average feeding frequency, and preceding average blast volume within the preset long-period window at each time point; calculate the reaction efficiency, which characterizes the reaction sensitivity of the raw materials under the current operating conditions, based on the ratio of the preceding average furnace temperature to the sum of the preceding average feeding frequency and preceding average blast volume after being weighted by the feeding weight coefficient and the blast weight coefficient, respectively.
[0042] It should be noted that the thermal inertia index can characterize the cumulative amount of deviation that needs to be adjusted. To determine the amount of feed needed, it is also necessary to determine the feed efficiency at each moment. In zinc smelting, the raw material moisture content, bed permeability, and other operating conditions are dynamically changing, and the temperature rise effect produced by the same feed amount is not the same under different operating conditions. Therefore, this invention establishes an input-output energy efficiency model to calculate the reaction efficiency in real time.
[0043] Specifically, the preceding average furnace temperature, preceding average feed frequency, and preceding average blast volume within a preset long-term window are obtained at each time point. Based on the ratio of the preceding average furnace temperature to the sum of the preceding average feed frequency and preceding average blast volume (weighted by feed weighting coefficients and blast volume weighting coefficients respectively), the reaction efficiency, characterizing the raw material reaction sensitivity under the current operating conditions, is calculated, including: It should be noted that, in order to eliminate the impact of short-term fluctuations, the assessment of response efficiency needs to be based on statistical data over a longer time scale.
[0044] Data from a long-term window is acquired at any given time. The arithmetic mean of the furnace temperature, the arithmetic mean of the feeding frequency, and the arithmetic mean of the blast volume within the long-term window are calculated and denoted as the preceding average furnace temperature, the preceding average feeding frequency, and the preceding average blast volume at that time, respectively. For example, the long-term window is 300 seconds.
[0045] It should be noted that this invention employs a black-box model, treating the furnace as a black box, the feeding and blasting as the overall input, and the furnace temperature as the output. The reaction efficiency is then defined as the temperature level maintained per unit of overall input. Therefore, the reaction efficiency at any given time can be calculated.
[0046] The reaction efficiency at any given time satisfies the expression: ; In the formula, This represents the reaction efficiency at time t; This represents the preceding average furnace temperature at time t; This represents the preceding average feed frequency at time t; This indicates the feed weighting coefficient; This represents the preceding average blower volume at time t; This represents the blower weighting coefficient; This represents a small positive value, used to avoid a denominator of 0. For example, .
[0047] It should be noted that the feed weighting coefficient is used to characterize the thermal conversion contribution per unit feed amount, and is calibrated based on the average sulfur content and calorific value of the zinc concentrate. For example, The blower weighting coefficient is used to characterize the heat balance impact per unit blower volume, and is obtained through heat balance calculations. For example... .
[0048] In the formula, A systematic weighted comprehensive input index was constructed. The lower the value, the more effort is required to maintain the same temperature, indicating worse operating conditions, such as wet raw materials or sluggish reactions; the higher the value, the better the operating conditions.
[0049] Thus, the reaction efficiency, which characterizes the quality of the operating conditions at each moment, was obtained.
[0050] S4: Construct an adaptive adjustment term using the thermal inertia index and reaction efficiency at each moment, and correct the preset benchmark feeding frequency based on the adaptive adjustment term to obtain the target feeding frequency at each moment to control the operation of the feeder.
[0051] It should be noted that by combining thermal inertia index and reaction efficiency, adaptive control can be achieved, where the adjustment gain increases as the operating conditions worsen. This can solve the problem of poor adaptability caused by fixed parameters in traditional PID control when raw material properties fluctuate.
[0052] Specifically, an adaptive adjustment term is constructed using the thermal inertia index and reaction efficiency at each moment, and the preset benchmark feeding frequency is corrected based on the adaptive adjustment term to obtain the target feeding frequency at each moment to control the operation of the feeder, including: It should be noted that, in order to ensure the stability of control, this invention uses a fixed reference frequency as the center and superimposes dynamic adjustment amounts.
[0053] Obtain a preset reference feed frequency, which is a standard reference value for maintaining thermal balance in the process. For example, the reference feed frequency is 35Hz.
[0054] It should be noted that the feed adjustment amount is equal to the product of error and gain. In this invention, the error is characterized by the thermal inertia index, while the gain is not a fixed constant but dynamically determined inversely proportional to the reaction efficiency. A decrease in reaction efficiency means a deterioration in operating conditions and a sluggish system response; the adjustment gain will automatically increase to provide stronger control. This leads to the construction of an expression for calculating the feed frequency.
[0055] The theoretical feed frequency at any given time satisfies the expression: ; In the formula, This represents the theoretical feed frequency at time t; Indicates the reference feeding frequency; Indicates the adjustment gain coefficient; The thermal inertia index is represented at time t. This represents the reaction efficiency at time t; This represents the second smallest positive value, used to avoid a denominator of 0. For example, .
[0056] It should be noted that, The strength of the feedback control is adjusted by obtaining the adjustment range through on-site commissioning, ensuring it covers 50% of the inverter's range. For example, .
[0057] In the formula, The ratio of the thermal inertia index at time t to the reaction efficiency at time t reflects the reverse operating condition regulation mechanism. For example, when the raw material becomes wet, leading to... When halved, in order to correct the same thermal inertia deviation The amplitude of the adjustment item will automatically double, thereby overcoming environmental resistance and achieving rapid temperature control.
[0058] It should be noted that the calculated theoretical frequency may exceed the physical limitations of the frequency converter, therefore boundary clamping processing is required to control the numerical range. Thus, boundary clamping processing is applied to the theoretical feeding frequency to obtain the target feeding frequency.
[0059] The target feed frequency at any given time satisfies the expression: ; In the formula, This represents the target feeding frequency at time t; Indicates the maximum frequency allowed by the frequency converter; Indicates the minimum frequency allowed by the frequency converter; This represents the theoretical feed frequency at time t; Describes the minimum value function; This represents the function that maximizes the value. For example, , .
[0060] The target feeding frequency is sent to the feeder through the frequency converter interface, and the feeding amount is controlled in a closed loop to achieve stable temperature control.
[0061] It should be noted that, as Figure 2This is a comparison chart of temperature stability during the zinc smelting roasting process. The horizontal axis represents roasting time (seconds), and the vertical axis represents the fluidized bed temperature (°C). The dashed line represents the target set temperature of 930°C. It illustrates the difference in control performance between this invention and existing technologies when dealing with operational disturbances. Existing technologies, as shown in the temperature curve under traditional PID control, exhibit significant temperature fluctuations after a sudden change in raw material moisture content at the 100-second mark, with a peak-to-trough difference exceeding 20°C, and a long recovery time, exhibiting typical underdamped oscillation characteristics. This invention, however, shows the temperature curve after employing the proposed thermal inertia and efficiency compensation algorithm. Under the same disturbance, the temperature only experiences a slight shift before quickly and smoothly returning to the set value without repeated oscillations.
[0062] It should be noted that, as Figure 3 This is a comparison chart of the dynamic response adjustment of the feeding rate. The horizontal axis represents time, and the vertical axis represents the feeding rate (tons / hour). It shows the difference in the feeding rate output of the control system of the present invention and the prior art when dealing with operating disturbances. The prior art shows that the adjustment action is gradual but lagging, and only begins to adjust significantly after the temperature has deviated significantly. In contrast, the present invention shows that at the moment the disturbance occurs, 100 seconds later, the control output shows a steep peak, indicating that the algorithm successfully predicted the risk of thermal inertia and, combined with the current response efficiency, performed a significant feedforward compensation operation in advance.
[0063] This completes the temperature control of the zinc smelting roasting process.
[0064] This invention also discloses an automatic control system for stabilizing the temperature in a zinc smelting roasting process, comprising a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an automatic control method for stabilizing the temperature in a zinc smelting roasting process according to the present invention.
[0065] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.
[0066] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.
Claims
1. An automatic temperature control method for zinc smelting roasting process, characterized in that, include: Set the sampling frequency; collect real-time operating data of the zinc smelting roasting furnace, the real-time operating data including at least the furnace temperature, the feeding frequency of the feeder and the air volume of the blower, and store the real-time operating data in time sequence to construct a historical data sequence; Based on the deviation between the furnace temperature and the set temperature within the historical time window, and combined with the rate of change of the temperature difference between adjacent times, the thermal inertia index, which characterizes the cumulative degree of the current system's thermodynamic state, is calculated. The preceding average furnace temperature, preceding average feeding frequency, and preceding average blast volume within a preset long-term window are obtained at each time point. Based on the ratio of the preceding average furnace temperature to the sum of the preceding average feeding frequency and preceding average blast volume after being weighted by the feeding weight coefficient and the blast weight coefficient, the reaction efficiency, which characterizes the reaction sensitivity of the raw materials under the current operating conditions, is calculated. An adaptive adjustment term is constructed using the thermal inertia index and reaction efficiency at each moment, and the preset benchmark feeding frequency is corrected based on the adaptive adjustment term to obtain the target feeding frequency at each moment to control the operation of the feeder.
2. The automatic temperature stabilization control method for zinc smelting roasting process according to claim 1, characterized in that, The acquisition of the furnace temperature includes: The values of multiple K-type thermocouples installed in the middle of the boiling layer are read at the sampling frequency. The maximum and minimum values are removed and the arithmetic mean is taken as the original furnace temperature at each moment. A sliding window is established to perform a sliding average filter on the original furnace temperature to obtain the furnace temperature at each moment.
3. The automatic temperature stabilization control method for zinc smelting roasting process according to claim 1, characterized in that, The acquisition of the feeding frequency of the feeder and the blowing volume of the blower includes: The feedback frequency of the feed disc or feed belt frequency converter at each moment is read using the sampling frequency and recorded as the feed frequency of the feeder at the corresponding moment; the value of the orifice plate flow meter or vortex flow meter on the main air duct of the furnace at each moment is read and recorded as the blower volume at each moment.
4. The automatic temperature stabilization control method for zinc smelting roasting process according to claim 1, characterized in that, The thermal inertia index satisfies the following expression: ； In the formula, The thermal inertia index is represented at time t. This represents the total number of sampling points in the historical data sequence; , This represents the historical data sequence at time t, where the first element is the first element. The furnace temperature at the k+1th sampling point; Indicates the set temperature required by the process; Indicates the weighting coefficient for the rate of change; Indicates the sampling time interval.
5. The automatic temperature stabilization control method for zinc smelting roasting process according to claim 1, characterized in that, The acquisition of the preceding average furnace temperature, preceding average feeding frequency, and preceding average air volume within a preset long-period window before each time point includes: For any given time, acquire data from a long period window in the past; calculate the arithmetic mean of the furnace temperature, the arithmetic mean of the feeding frequency, and the arithmetic mean of the blast volume within the long period window, and record them as the preceding average furnace temperature, the preceding average feeding frequency, and the preceding average blast volume at the given time, respectively.
6. The automatic temperature stabilization control method for zinc smelting roasting process according to claim 1, characterized in that, The reaction efficiency satisfies the expression: ； In the formula, This represents the reaction efficiency at time t; This represents the preceding average furnace temperature at time t; This represents the preceding average feed frequency at time t; This indicates the feed weighting coefficient; This represents the preceding average blower volume at time t; This represents the blower weighting coefficient; This represents the first tiny positive value.
7. The automatic temperature stabilization control method for zinc smelting roasting process according to claim 1, characterized in that, The target feeding frequency satisfies the expression: ； In the formula, This represents the target feeding frequency at time t; Indicates the maximum frequency allowed by the frequency converter; Indicates the minimum frequency allowed by the frequency converter; This represents the theoretical feed frequency at time t; Describes the minimum value function; This represents the maximum value function.
8. The automatic temperature stabilization control method for zinc smelting roasting process according to claim 7, characterized in that, The theoretical feed frequency at time t satisfies the expression: ； In the formula, This represents the theoretical feed frequency at time t; Indicates the reference feeding frequency; Indicates the adjustment gain coefficient; The thermal inertia index is represented at time t. This represents the reaction efficiency at time t; This represents the second smallest positive value.
9. The automatic temperature stabilization control method for zinc smelting roasting process according to claim 1, characterized in that, The adaptive adjustment term is the ratio of the thermal inertia index at each moment to the reaction efficiency at the corresponding moment.
10. An automatic control system for stabilizing temperature during zinc smelting roasting process, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement an automatic temperature stabilization control method for a zinc smelting roasting process according to any one of claims 1-9.