Ash frying machine with high-strength slag frying pan

Abstract

The invention relates to the technical field of secondary aluminum processing equipment, and discloses an ash frying machine with a high-strength slag frying pan, and the ash frying machine comprises the high-strength slag frying pan, a gas collection sealing plate, a smoke pipe, a stirring module and a stirring speed self-adaptive regulation and control system. The stirring speed self-adaptive regulation and control system is used for regulating the stirring speed of the stirring main shaft in real time based on the instantaneous power and the torque change rate of the stirring main shaft, the temperature gradient, the standard deviation and the temperature rise rate in the pot, the flue gas oxygen and dust concentration, the accumulated specific energy consumption, the molten aluminum outflow rate and the flue gas carbon monoxide concentration; respectively calculating to obtain a force-resistance interaction coefficient, a thermal dynamic coefficient, a comprehensive working condition quality factor and a progress coefficient, and calculating a target stirring speed according to the parameters for adjustment. According to the invention, intelligent self-adaptive adjustment of the stirring speed is realized, and the treatment efficiency and the operation stability are obviously improved.

Description

Technical Field

[0001] This invention belongs to the technical field of recycled aluminum processing equipment, and particularly relates to a slag frying machine with a high-strength slag frying pot. Background Technology

[0002] The aluminum ash roasting machine is a core piece of equipment in the recycled aluminum processing industry, specifically designed to process the high-temperature hot aluminum ash generated during aluminum smelting. This equipment uses a stirring device to mechanically agitate and crush the aluminum ash, while a heating system provides heat energy to melt, aggregate, and ultimately separate and recycle the residual molten aluminum. In actual operation, the ash roasting machine typically consists of a slag roasting pot, a stirring module (including a motor, reducer, stirring shaft, and stirring blades), and a heating system. During operation, the stirring blades continuously agitate the material to achieve crushing and heat exchange. However, the existing stirring speed control mechanism of ash roasting machines has significant defects. Traditional equipment generally adopts a constant stirring speed or a preset multi-speed regulation strategy. This static control method cannot respond to the dynamic evolution of the material's state. Throughout the entire ash roasting process, the physical state of the material undergoes complex transformations: in the initial stage, the aluminum ash is in a blocky solid state, requiring a high stirring speed to generate sufficient mechanical force to crush large pieces; in the middle stage, the material enters a semi-molten state, at which point the stirring speed should be moderate to optimize heat conduction and aluminum molten separation efficiency; in the later stage, the material is close to a completely liquid state, and the stirring speed needs to be reduced to decrease dust generation and avoid incomplete combustion. A fixed-speed stirring strategy is difficult to balance the process requirements at each stage, resulting in low crushing efficiency in the initial stage, insufficient heat exchange in the middle stage, and increased dust emissions in the later stage, which in turn leads to a decrease in overall processing efficiency and an increase in energy consumption.

[0003] Furthermore, existing equipment is overly simplistic in terms of operational condition monitoring and control. The ash-burning process is essentially a strongly coupled process involving multiple physical fields, such as mechanical stirring, temperature, and flue gas flow. However, existing control systems typically monitor only a single parameter (e.g., pot temperature or motor current), neglecting comprehensive analysis of multi-dimensional information such as mechanical load characteristics (e.g., instantaneous power and torque change rate), temperature distribution uniformity (e.g., temperature gradient, temperature standard deviation, and temperature rise rate), and flue gas composition (e.g., oxygen concentration, dust concentration, and carbon monoxide concentration). This one-sided monitoring method makes it impossible for the equipment to accurately assess whether the current operating conditions are ideal, easily leading to risks such as localized overheating, incomplete combustion, and equipment overload, affecting process stability and equipment lifespan. More significantly, the adjustment of stirring speed heavily relies on the operator's subjective experience. Operators must manually adjust the stirring speed by visually observing the material's state (e.g., color, flowability). This method is not only labor-intensive but also prone to fluctuations in process parameters due to individual experience differences, making it difficult to maintain production consistency and repeatability. Meanwhile, in the face of sudden operating conditions (such as sudden agglomeration of materials or a sudden rise in temperature), manual response is often delayed, making it impossible to optimize operating parameters in a timely manner, thus increasing the possibility of process runaway. Therefore, existing technologies lack an intelligent control system that can dynamically adjust the stirring speed based on real-time multi-source data to adapt to the complexity of material state changes and multi-physics field interactions.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this invention is to provide a slag roasting machine with a high-strength slag roasting pot, in order to solve the above-mentioned problems.

[0006] This invention is implemented as follows: a slag roasting machine with a high-strength slag roasting pot, comprising a high-strength slag roasting pot, a gas collecting and sealing plate installed on the top of the high-strength slag roasting pot, a flue pipe and a stirring module installed on the top of the gas collecting and sealing plate, and further comprising: a stirring speed adaptive control system, which is used to adjust the stirring speed of the stirring spindle in real time, the stirring speed adaptive control system comprising: a mechanical load sensing module, which calculates and obtains the force resistance interaction coefficient based on the instantaneous power and torque change rate of the stirring spindle; a thermal field uniformity evaluation module, which calculates and obtains the thermal dynamic coefficient based on the temperature gradient, temperature standard deviation and temperature rise rate inside the pot; a multi-source information fusion evaluation module, which calculates and obtains the comprehensive working condition quality factor based on the oxygen concentration and dust concentration of the flue gas under the force resistance interaction coefficient and the thermal dynamic coefficient; a process progress estimation module, which calculates and obtains the progress coefficient based on the cumulative specific energy consumption and aluminum liquid outflow rate; and a base speed decision and execution module, which calculates and obtains the target stirring speed based on the comprehensive working condition quality factor, the progress coefficient and the carbon monoxide concentration of the flue gas, and adjusts the current stirring speed to the target stirring speed.

[0007] A further technical solution involves the following process for calculating the target stirring speed: obtaining the comprehensive operating condition quality factor, the process coefficient, and the flue gas carbon monoxide concentration; performing maximum-minimum normalization on the flue gas carbon monoxide concentration to obtain the flue gas carbon monoxide concentration index; determining the speed regulation coefficient based on the comprehensive operating condition quality factor, the process coefficient, and the flue gas carbon monoxide concentration index; the speed regulation coefficient is positively correlated with the comprehensive operating condition quality factor and negatively correlated with the process coefficient and the flue gas carbon monoxide concentration index; and calculating the target stirring speed based on the speed regulation coefficient and preset minimum and maximum allowable stirring speeds.

[0008] A further technical solution involves the following process for calculating the process coefficient: obtaining the cumulative specific energy consumption and the aluminum liquid outflow rate; comparing the cumulative specific energy consumption and the aluminum liquid outflow rate with the cumulative specific energy consumption and the maximum aluminum liquid outflow rate at the completion of the process, respectively, to obtain the cumulative specific energy consumption index and the aluminum liquid outflow rate index; calculating the process coefficient using a nonlinear combination method based on the cumulative specific energy consumption index and the aluminum liquid outflow rate index; the process coefficient increases with the increase of the cumulative specific energy consumption index and increases with the decrease of the aluminum liquid outflow rate index, and the process coefficient ranges from 0 to 1.

[0009] A further technical solution involves calculating the comprehensive operating condition quality factor as follows: obtaining the force resistance interaction coefficient and thermal dynamic coefficient, as well as the oxygen concentration and dust concentration of the flue gas; performing maximum-minimum normalization on the oxygen concentration and dust concentration of the flue gas to obtain the oxygen concentration index and dust concentration index; and substituting the force resistance interaction coefficient, thermal dynamic coefficient, oxygen concentration index, and dust concentration index into the formula. Obtain the comprehensive working condition quality factor , When all variables reach their ideal values, the exponent is 0. The greater the deviation of any variable from its ideal value, the larger the negative value of the exponential part. The closer to 0, the more... The global sensitivity coefficient is greater than 0. , For force resistance interaction coefficient, , The thermal dynamic coefficient, , This is the oxygen concentration index. , The dust concentration index. For ideal value, As weight, , .

[0010] A further technical solution involves calculating the force-resistance interaction coefficient as follows: obtaining the instantaneous power and torque change rate of the stirring spindle; performing dynamic impact correction on the instantaneous power of the stirring spindle based on the torque change rate to obtain the equivalent impact power; and performing maximum-minimum normalization on the equivalent impact power to obtain the force-resistance interaction coefficient. , , The closer it is to 1, the closer the load is to the device's limit; The closer it is to 0, the closer it is to no load.

[0011] A further technical solution involves the following process for calculating and obtaining the thermal dynamic coefficient: obtaining the temperature gradient, temperature standard deviation, and temperature rise rate within the pot; comparing the temperature gradient and temperature standard deviation with the maximum allowable temperature gradient and maximum allowable temperature standard deviation within the pot to obtain the temperature gradient index and temperature standard deviation index; comparing the absolute value of the temperature rise rate with the optimal temperature rise rate with the allowable fluctuation range of the temperature rise rate to obtain the temperature rise rate index; merging the temperature gradient index, temperature standard deviation index, and temperature rise rate index into a temperature field distortion metric; and converting the temperature field distortion metric into a thermal dynamic coefficient with a value between 0 and 1 through a nonlinear transformation.

[0012] In a further technical solution, the thermal dynamic coefficient increases as the temperature field distortion metric decreases, and takes a value of 1 when all deviations are zero.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention achieves precise matching between stirring speed and material state evolution process through multi-parameter fusion and dynamic adaptive control, significantly improving aluminum ash treatment efficiency, aluminum liquid recovery rate and energy utilization efficiency, and overcoming the defects of poor matching of traditional constant speed or gear speed regulation processes.

[0014] 2. This invention comprehensively considers multiple dimensions of operating conditions, such as mechanical load, thermal uniformity, flue gas emissions, and process progress. While improving efficiency, it effectively suppresses dust generation and carbon monoxide emissions, avoids equipment overload and local overheating, extends equipment service life, and achieves multi-objective synergistic optimization of efficiency, safety, and environmental protection. Attached Figure Description

[0015] Figure 1 This invention provides a structural schematic diagram of a slag-frying machine with a high-strength slag-frying pot; Figure 2 The present invention provides an adaptive stirring speed control system.

[0016] In the attached diagram: 1. High-strength slag frying pot; 2. Gas collection sealing plate; 3. Stirring module; 4. Smoke pipe. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In traditional ash-making machines, the stirring speed is limited to constant speed or simple multi-speed modes, failing to respond to the dynamic evolution of the material's state. Specifically, the phase transition from a blocky solid to a semi-molten liquid state is not monitored and analyzed in real time, leading to difficulties in matching process requirements at each stage: high torque crushing is needed in the initial stage, optimized heat exchange efficiency in the middle stage, and dust suppression in the later stage. Furthermore, the multi-physics coupling effects between the mechanical stirring field, temperature field, and flue gas flow field lack a comprehensive evaluation mechanism, relying solely on single parameters such as temperature or motor current for control. This results in systematic biases in operating condition judgment, easily leading to technical problems such as abnormal local temperature increases, incomplete combustion reactions, or equipment overload. Consequently, processing efficiency is suppressed, energy consumption increases, and the stability of process parameters cannot be guaranteed.

[0019] For example, in the actual operation of a recycled aluminum processing production line, when the material in the slag-cooking pot is in its initial lumpy stage, the stirring blades need to crush the material with high torque. However, a fixed stirring speed causes the motor load to exceed the safe range, resulting in poor crushing effect. In the mid-stage semi-molten state, the temperature gradient distribution inside the pot is uneven, and the stirring speed fails to adaptively adjust to promote heat exchange, leading to reduced heat transfer efficiency. In the later liquid stage, during the outflow of molten aluminum, high-speed stirring causes an abnormal increase in dust concentration in the flue gas, affecting the performance of the environmental control system. In this scenario, operators rely on visual inspection to manually adjust the speed, resulting in a delayed response, significant fluctuations in process parameters, and difficulty in maintaining stable operation.

[0020] If the above problems are not addressed, the ash-frying machine will operate under suboptimal conditions for an extended period, leading to longer processing cycles, increased energy consumption, and potential structural damage or safety hazards due to abnormally high localized temperatures. Simultaneously, fluctuations in process parameters will reduce the consistency of aluminum molten metal recovery quality, affecting the stability of subsequent processing stages, and abnormal flue gas composition will fail to meet environmental control requirements, increasing system maintenance complexity. Furthermore, the unreliability of manual operation hinders the standardization and automation of the production process.

[0021] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0022] like Figure 1 and Figure 2As shown, this invention provides an ash-frying machine with a high-strength slag-frying pot, an industrial device for processing hot aluminum ash. Its core function is to separate and recover residual molten aluminum from the aluminum ash through mechanical stirring and heating. This equipment is commonly used in the recycled aluminum industry to improve the efficiency of aluminum resource recovery. It includes a high-strength slag-frying pot 1, which is the main working chamber of the ash-frying machine and is used to hold the aluminum ash material to be processed. Its high-strength characteristics are designed to withstand high temperatures, corrosion, and mechanical impacts generated during stirring, ensuring long-term stable operation of the equipment under harsh conditions. A gas-collecting sealing plate 2 is installed on the top of the high-strength slag-frying pot 1. The gas-collecting sealing plate 2 is installed on the top of the high-strength slag-frying pot 1, and its function is to seal the pot body, preventing the leakage of flue gas and dust, and providing an installation interface for the flue pipe 4 and the stirring module 3. This sealing plate helps maintain the stability of the environment inside the pot and guides the flue gas into the processing system. The top of the gas collecting and sealing plate 2 is equipped with a smoke pipe 4 and a stirring module 3. The smoke pipe 4 is used to discharge the flue gas generated during the ash roasting process. This flue gas may contain harmful substances such as dust and carbon monoxide, and needs to be guided through the smoke pipe 4 to a subsequent flue gas treatment device for purification to meet environmental emission requirements. The stirring module 3 is the core actuator of the ash roasting machine, responsible for mechanically stirring the materials in the pot. This module typically consists of a motor, a reducer, a stirring shaft, and stirring blades. The motor provides power, the reducer adjusts the speed and torque, the stirring shaft transmits power, and the stirring blades directly contact the materials to achieve tumbling, crushing, and mixing.

[0023] It also includes: an adaptive stirring speed control system, which is a key innovation of this embodiment. Its function is to automatically adjust the rotational speed of the stirring spindle based on real-time operating parameters during the ash-frying process. This system aims to optimize the stirring effect, improve processing efficiency, reduce energy consumption, and adapt to dynamic changes in the material's state. The adaptive stirring speed control system includes: The mechanical load sensing module calculates the force-resistance interaction coefficient based on the instantaneous power and torque change rate of the stirring shaft. This coefficient measures the mechanical resistance experienced by the stirring shaft within the material and its degree of change. Calculated by analyzing the instantaneous power and torque change rate of the stirring shaft, this coefficient reflects the viscosity, density, and interaction strength between the stirring blades and the material.

[0024] The thermal field uniformity assessment module calculates the thermal dynamic coefficient based on the temperature gradient, temperature standard deviation, and temperature rise rate within the pot. The thermal dynamic coefficient is an indicator for evaluating the uniformity and stability of the temperature field within the slag-frying pot. This coefficient is calculated by analyzing the temperature gradient, temperature standard deviation, and temperature rise rate within the pot, reflecting the heating effect, heat transfer efficiency, and the presence of localized overheating or uneven cooling.

[0025] The multi-source information fusion evaluation module calculates the comprehensive operating condition quality factor based on the oxygen and dust concentrations of flue gas under the force resistance interaction coefficient and thermal dynamic coefficient. This comprehensive operating condition quality factor is a comprehensive indicator used to evaluate the overall quality of the current ash-burning operation. This factor combines the force resistance interaction coefficient, thermal dynamic coefficient, and the oxygen and dust concentrations of the flue gas, aiming to comprehensively reflect mechanical load, thermal state, combustion, and dust emissions.

[0026] The process progress estimation module calculates a progress coefficient based on cumulative specific energy consumption and aluminum molten flow rate. The progress coefficient is an indicator of the completion status of the ash-frying process. This coefficient is calculated by analyzing cumulative specific energy consumption and aluminum molten flow rate, reflecting the progress of the process, such as the degree of aluminum molten separation in the material and total energy consumption.

[0027] The base speed decision and execution module calculates the target stirring speed based on the comprehensive operating condition quality factor, process coefficient, and flue gas carbon monoxide concentration, and adjusts the current stirring speed to the target stirring speed. The target stirring speed is the most suitable stirring speed under the current operating conditions, calculated by the adaptive stirring speed control system. The system will adjust the actual operating speed of the stirring spindle according to this target speed to optimize the process effect.

[0028] This embodiment introduces an adaptive stirring speed control system, achieving real-time and intelligent adjustment of the stirring speed, overcoming the limitations of traditional fixed-speed stirring. Specifically, existing technologies typically focus on only a single parameter, such as motor current or pot temperature, which is insufficient to comprehensively reflect the complex ash-frying conditions. This embodiment, however, calculates the force-resistance interaction coefficient, thermal dynamic coefficient, comprehensive operating condition quality factor, and process coefficient by comprehensively considering data from multiple dimensions, including the instantaneous power and torque change rate of the stirring shaft, the pot temperature gradient, temperature standard deviation, temperature rise rate, oxygen concentration in the flue gas, dust concentration, cumulative specific energy consumption, aluminum liquid outflow rate, and carbon monoxide concentration in the flue gas. This fusion analysis of multi-physics field coupled information enables the system to more accurately determine the true state of the current operating conditions, such as the viscosity of the material, the uniformity of the temperature field, the degree of combustion, and the progress stage of the process.

[0029] For example, in the above example, when the material changes from a blocky state to a semi-molten state, conventional equipment may still maintain a high stirring speed, resulting in wasted energy. However, the system in this embodiment can sense the decrease in the force resistance interaction coefficient and the increase in the thermal dynamic coefficient, thereby determining the change in the material state and correspondingly reducing the stirring speed to adapt to the new operating conditions. This adaptive capability significantly improves processing efficiency, reduces energy consumption, and reduces dust generation.

[0030] Furthermore, the operation of existing carbon monoxide roasting machines relies heavily on manual experience, requiring operators to manually adjust the stirring speed based on visual inspection. This is not only labor-intensive but also makes it difficult to ensure the consistency and stability of the process. The adaptive control system in this embodiment achieves automated and intelligent stirring speed management, reducing reliance on manual experience and improving operational accuracy and response speed. For example, when the carbon monoxide concentration in the flue gas suddenly increases, the system can quickly identify and adjust the stirring speed to optimize combustion conditions and avoid incomplete combustion.

[0031] In summary, the technical solution of this embodiment effectively solves the technical problems of existing ash roasting machines in terms of stirring speed control, multi-physical field coupling considerations, and intelligent operation by constructing a multi-parameter, multi-dimensional, and adaptive stirring speed control mechanism, thereby achieving optimized operation of the ash roasting process and demonstrating significant progress and practical value.

[0032] This application further proposes the following process for calculating and obtaining the target stirring speed: The system acquires the comprehensive operating condition quality factor, process coefficient, and flue gas carbon monoxide concentration. The comprehensive operating condition quality factor, ranging from 0 to 1, measures the degree of matching between the current operating conditions and ideal operating conditions. It comprehensively considers multiple factors such as force-resistance interaction, thermal dynamics, and oxygen and dust concentrations in the flue gas. It can be calculated from real-time data collected by sensors using a preset algorithm. The process coefficient, also ranging from 0 to 1, reflects the degree of process completion. It comprehensively considers cumulative specific energy consumption and aluminum liquid outflow rate, and can also be calculated from real-time data collected by sensors using a preset algorithm. Flue gas carbon monoxide concentration is a key indicator for measuring combustion efficiency and safety, and can be obtained in real-time through a flue gas analyzer.

[0033] The carbon monoxide concentration in flue gas is subjected to maximum-minimum normalization to obtain the carbon monoxide concentration index. The purpose of normalization is to convert physical quantities with different dimensions or ranges to a uniform scale, such as between 0 and 1, to facilitate weighting or comparison in subsequent formulas. Maximum-minimum normalization is a commonly used method, and its formula is typically the deviation between the current value and the minimum value divided by the deviation between the maximum value and the minimum value. The maximum and minimum values ​​are the allowable minimum and maximum values ​​for carbon monoxide concentration in the flue gas, respectively, and can be preset according to safety standards or process requirements. For example, a safe upper limit can be set as the maximum value, while the minimum value can be set to 0 or an extremely low background value.

[0034] The speed regulation coefficient is determined based on the comprehensive operating condition quality factor, the process coefficient, and the flue gas carbon monoxide concentration index. This speed regulation coefficient is positively correlated with the comprehensive operating condition quality factor and negatively correlated with the process coefficient and the flue gas carbon monoxide concentration index. Based on the speed regulation coefficient and preset minimum and maximum allowable stirring speeds, the target stirring speed is calculated. Specifically, the calculation method involves substituting the comprehensive operating condition quality factor, the process coefficient, and the flue gas carbon monoxide concentration index into the formula. Obtain the target stirring speed This formula is the core model for determining the stirring speed. It dynamically calculates the most suitable stirring speed for the current operating conditions by weightedly combining multiple key process parameters. To allow for the minimum stirring speed, To allow the maximum stirring speed, the minimum stirring speed is allowed. and maximum permissible stirring speed These are the speed boundaries of the stirring spindle within a safe and effective operating range. They can be preset according to equipment design, material characteristics, or process requirements and stored in the parameter database of the control system, for example, through experimental testing or based on the mechanical strength of the stirring spindle and the power limitations of the motor. For comprehensive working condition quality factors, For process coefficients, This represents the carbon monoxide concentration index in the flue gas. Each factor in the formula represents a different process consideration: The range of adjustment for the stirring speed is defined; The higher the (Comprehensive Working Condition Quality Factor), the closer the working conditions are to the ideal, and the faster the mixing speed can be, thus improving efficiency. The higher the complement of the process coefficient, the earlier the process is in its initial stage, requiring stronger stirring to promote reaction or mixing; as the process nears completion... (Approaching point 1), the stirring intensity can be appropriately reduced; The higher the complement of the carbon monoxide concentration index (CNOOC index), the lower the carbon monoxide concentration, the more complete the combustion, and the higher the stirring speed can be. Conversely, if the carbon monoxide concentration is too high, the stirring speed may need to be adjusted to improve combustion conditions or reduce risk. After obtaining the target stirring speed, the control system will issue a command to adjust the current stirring speed of the stirring spindle to the target stirring speed through the motor and reducer in stirring module 3. This is usually achieved through a frequency converter or other speed controller.

[0035] This application's solution provides a specific and mathematical target stirring speed calculation model, enabling the ash roaster to accurately calculate and adjust the stirring speed of the stirring shaft based on real-time changing operating parameters. This model cleverly integrates the comprehensive operating condition quality factor, process coefficient, and flue gas carbon monoxide concentration index, employing a specific weighted combination method to achieve refined and intelligent control of the stirring speed. When operating conditions are favorable, the process is in its early stages, and the carbon monoxide concentration is low, the system can increase the stirring speed to accelerate the reaction and improve processing efficiency. Conversely, when the carbon monoxide concentration rises or the process is nearing completion, the stirring speed can be appropriately reduced to ensure operational safety and avoid over-stirring, thereby optimizing energy consumption. This mechanism overcomes the potential lag, inaccuracy, and insufficient consideration of the coupled effects of multiple factors inherent in traditional stirring speed adjustment methods, making the adjustment of the stirring speed more scientific and rational.

[0036] The following is a concrete example. Suppose that the ash-frying machine is processing aluminum ash. First, the control system obtains the minimum allowable stirring speed from preset parameters. The maximum permissible stirring speed is 10 RPM. The speed is 100 RPM. Simultaneously, the system monitors and calculates the current overall operating condition quality factor in real time. The process coefficient is 0.85 (indicating good operating conditions). The value is 0.2 (indicating the process is in its early stages), and the concentration of carbon monoxide in the flue gas. The value is 50 ppm. Next, the carbon monoxide concentration in the flue gas is normalized. Assuming the preset maximum value of carbon monoxide concentration in the flue gas is 200 ppm and the minimum value is 0 ppm, the carbon monoxide concentration index is then... The calculated value is 0.25. Substitute these values ​​into the formula: The target stirring speed is calculated. The speed is approximately 55.9 RPM. At this point, stirring module 3 will receive a command to adjust the stirring speed of the stirring spindle to approximately 55.9 RPM. If, during subsequent operations, the carbon monoxide concentration in the flue gas suddenly rises to 150 ppm, the carbon monoxide concentration index will be... The calculated value is 0.75. Assuming other parameters remain unchanged, the target stirring speed is recalculated. The stirring speed will be approximately 25.3 RPM. At this point, the stirring speed will automatically decrease to approximately 25.3 RPM to address the safety risks associated with increased carbon monoxide concentration and may improve combustion conditions by reducing the stirring speed.

[0037] Through the above technical solution, the ash roasting machine can accurately calculate and adjust the stirring speed of the stirring shaft based on real-time changing operating parameters. This solution achieves refined and intelligent control of the stirring speed by introducing a comprehensive operating condition quality factor, process coefficient, and flue gas carbon monoxide concentration index, and using a specific mathematical model for weighted combination. This overcomes the problems of lag, inaccuracy, and insufficient consideration of the coupled effects of multiple factors that may exist in traditional stirring speed adjustment methods. Especially during the ash roasting process, it can effectively balance process efficiency, product quality, and operational safety. For example, when operating conditions are good, the process is in its early stages, and the carbon monoxide concentration is low, the system can increase the stirring speed to accelerate the reaction; while when the carbon monoxide concentration increases or the process is nearing completion, the stirring speed can be appropriately reduced to ensure safety and avoid over-stirring. This adaptive stirring speed adjustment mechanism significantly improves the operational stability, energy utilization efficiency, and environmental friendliness of the ash roasting machine, while reducing operational risks.

[0038] This application further proposes the following process for calculating and obtaining the process coefficient: The cumulative specific energy consumption and aluminum liquid outflow rate are obtained. "Cumulative specific energy consumption" refers to the energy consumption per unit mass of material accumulated in real time during the current operating phase of the slag roasting machine. This parameter can be obtained through real-time monitoring and cumulative calculation using an energy metering module or power sensor integrated into the slag roasting machine control system. "Aluminum liquid outflow rate" refers to the instantaneous speed at which aluminum liquid flows out of the slag roasting pot 1, as monitored in real time during the current operating phase of the slag roasting machine. This parameter can be obtained in real time using a flow sensor, level sensor combined with time change rate, or weighing sensor installed at the aluminum liquid outlet.

[0039] The cumulative specific energy consumption and aluminum liquid outflow rate are compared with the cumulative specific energy consumption and maximum aluminum liquid outflow rate at the completion of the process, respectively, to obtain the cumulative specific energy consumption index and the aluminum liquid outflow rate index. "Cumulative specific energy consumption at the completion of the process" refers to the total energy consumed per unit mass of material by the system when the ash-frying process reaches its expected completion state; it can serve as a reference value for the process target energy consumption. This value can be obtained through historical data statistics, experimental calibration, or expert experience setting. "Maximum aluminum liquid outflow rate" refers to the maximum instantaneous velocity of aluminum liquid flowing out of the slag-frying pot 1 during the ash-frying process; it can serve as an upper limit reference for the aluminum liquid outflow capacity. This value can be determined through equipment design parameters, actual operation tests, or empirical values. Ratio processing converts data of different dimensions or magnitudes into dimensionless standardized indices to facilitate calculation and comparison within a unified mathematical model. The "cumulative specific energy consumption index" is obtained by dividing the currently monitored cumulative specific energy consumption by the cumulative specific energy consumption at the completion of the process. This index reflects the relative progress of the current process in terms of energy consumption towards achieving the target. The "aluminum molten metal outflow rate index" is obtained by dividing the current real-time monitored aluminum molten metal outflow rate by the maximum aluminum molten metal outflow rate. This index reflects the activity level of the current aluminum molten metal outflow activity; a smaller value usually indicates that aluminum molten metal separation is nearing completion.

[0040] Based on the cumulative specific energy consumption index and the aluminum liquid outflow rate index, a process coefficient is calculated using a nonlinear combination method. This process coefficient increases with the increase of the cumulative specific energy consumption index and increases with the decrease of the aluminum liquid outflow rate index, with the process coefficient ranging from 0 to 1. Specifically, the calculation method involves substituting the cumulative specific energy consumption index and the aluminum liquid outflow rate index into the formula. Obtain process coefficient , ,when The closer the value is to 1, the closer the process is to completion in terms of both energy input and aluminum liquid separation; when... The closer the value is to 0, the more it indicates that the process has just started or is far from completion. This calculation method can comprehensively and intuitively quantify the progress of the process. The cumulative energy consumption index, It reflects the degree of completion of the process in terms of energy input; the larger the value, the closer the energy input is to the target. This is the aluminum molten metal outflow rate index. This reflects the degree of completion of aluminum liquid separation. When the aluminum liquid outflow rate is very low, this term is close to 1, indicating that the aluminum liquid separation is basically complete. By multiplying these two indices and taking the square root, a "process coefficient" between 0 and 1 can be obtained. ).

[0041] This application's solution acquires multiple data points directly related to the progress of the ash-frying process in real time, including cumulative specific energy consumption and aluminum molten flow rate. These real-time data are then compared with preset cumulative specific energy consumption and maximum aluminum molten flow rate at process completion, thereby transforming the original physical quantities into dimensionless cumulative specific energy consumption and aluminum molten flow rate indices. This standardization process allows parameters of different properties to be compared and calculated within a unified framework. These two indices are then substituted into specific mathematical formulas. In this process, a progress coefficient that comprehensively reflects the degree of technological advancement can be obtained through calculation using this formula. The process coefficient The value ranges from 0 to 1. A value closer to 1 indicates that the ash-frying process is nearing completion in terms of energy consumption and aluminum liquid separation; conversely, a value closer to 0 indicates that the process is in its early stages or not yet complete. In this way, the ash-frying machine can dynamically and quantitatively grasp the current stage of the process, providing an accurate basis for the intelligent adjustment of the subsequent stirring speed. For example, when the process coefficient... At higher levels, the adaptive stirring speed control system, taking into account both the flue gas carbon monoxide concentration and the overall operating condition quality factor, can adjust the stirring speed according to a preset formula. This allows for the calculation of a target stirring speed that is more suitable for the current process stage, thereby enabling precise control of the stirring spindle.

[0042] The following is a concrete example illustrating how the ash-frying machine can be configured with appropriate sensors and control units to calculate the aforementioned process coefficients. For instance, an energy meter or power sensor can be installed at the power input terminal of the ash-frying pot 1 to measure and accumulate the energy consumption during the ash-frying process in real time, thereby calculating the cumulative specific energy consumption. Simultaneously, an ultrasonic level sensor or weighing sensor can be installed at the aluminum liquid discharge port of the ash-frying pot 1, combined with the time change rate, to monitor the aluminum liquid outflow rate in real time. The cumulative specific energy consumption and maximum aluminum liquid outflow rate at the completion of the process can be pre-calibrated through multiple experiments or based on historical production data and stored in the memory of the control unit. When the ash-frying process starts, the control unit (e.g., an industrial programmable logic controller (PLC) or embedded microprocessor) periodically reads the current cumulative specific energy consumption and aluminum liquid outflow rate data. Subsequently, the control unit executes a preset program, dividing the real-time acquired cumulative specific energy consumption by the cumulative specific energy consumption at the completion of the process to obtain the cumulative specific energy consumption index; and dividing the real-time acquired aluminum liquid outflow rate by the maximum aluminum liquid outflow rate to obtain the aluminum liquid outflow rate index. Finally, the control unit substitutes these two exponents into the formula. The calculations are performed to update the process coefficients in real time. The value of . For example, in the early stages of the process, the cumulative specific energy consumption index is low, and the aluminum liquid outflow rate index is high, resulting in a calculated value. The value will approach 0. As the process progresses, the cumulative specific energy consumption index gradually increases, while the aluminum melt outflow rate index gradually decreases. The value will gradually increase until the process is nearing completion. The value is close to 1.

[0043] Through the above technical solution, this application provides a precise and dynamic method for evaluating the progress of aluminum ash frying. By comprehensively considering two key indicators—cumulative specific energy consumption and aluminum liquid outflow rate—and converting them into dimensionless exponents, a progress coefficient is calculated using a specific mathematical model. This allows the ash frying machine to accurately quantify the current stage of the process. This precise progress coefficient serves as an important input for the adaptive stirring speed control system, enabling the system to more fully consider the actual progress of the process when calculating the target stirring speed. For example, in the early stages of the process, the system may identify a low progress coefficient, potentially allowing a higher stirring speed to promote material mixing and reaction; while in the later stages, when the progress coefficient is high, the system may identify that the process is nearing completion, potentially reducing the stirring speed to avoid over-stirring or energy waste. This not only improves the intelligence and precision of stirring speed adjustment, avoiding insufficient or excessive stirring due to inaccurate process judgment, but also optimizes the efficiency of the ash frying process, reduces energy consumption, and helps improve the quality and recovery rate of aluminum ash treatment.

[0044] This application further proposes the following process for calculating and obtaining the comprehensive operating condition quality factor: The force resistance interaction coefficient and thermal dynamic coefficient, as well as the oxygen concentration and dust concentration of the flue gas, can be obtained. Oxygen sensors and dust sensors can be installed at point 4 of the flue to monitor the oxygen concentration and dust concentration of the flue gas in real time.

[0045] The oxygen and dust concentrations in the flue gas are subjected to maximum-min normalization to obtain the oxygen concentration index and dust concentration index. This maximum-min normalization aims to transform data with different dimensions and numerical ranges to a uniform scale (e.g., between 0 and 1) to eliminate the influence of dimensions and ensure their fair participation in the calculation of the comprehensive operating condition quality factor. This process is typically achieved by subtracting the minimum value from the original data and then dividing by the difference between the maximum and minimum values.

[0046] Substituting the force resistance interaction coefficient, thermal dynamic coefficient, oxygen concentration index, and dust concentration index into the formula Obtain the comprehensive working condition quality factor , This formula is the core of calculating the comprehensive working condition quality factor. It uses an exponential decay method, weighting the sum of squares of the deviations of each parameter from its ideal value, and adjusting it through a global sensitivity coefficient. The final value is mapped to a value between 0 and 1. When all variables reach their ideal values, the exponent is 0. This indicates perfect operating conditions; the greater any variable deviates from the ideal value, the larger the negative value of the exponent. The closer to 0, the worse the quality of the working condition. The global sensitivity coefficient is greater than 0. This coefficient is used to adjust the sensitivity of the comprehensive working condition quality factor to deviations of various parameters from the ideal value. It can be preset, for example, by setting it according to the characteristics and empirical values ​​of the ash-making process, or by dynamically adjusting it through historical data analysis and optimization algorithms. , For force resistance interaction coefficient, , The thermal dynamic coefficient, , This is the oxygen concentration index. , The dust concentration index. For ideal value, To resist the ideal value of interaction, For ideal thermal dynamics, This represents the ideal oxygen concentration. This represents the ideal dust concentration. , force resistance to interaction ideal value Ideal values ​​of thermal dynamics Ideal oxygen concentration Ideal dust concentration This represents the optimal or target state of various parameters in the ash-making process under specific operating conditions. For example, the ideal value of force resistance interaction can correspond to the optimal stirring resistance, the ideal value of thermal dynamics can correspond to a uniform and stable temperature field, the ideal value of oxygen concentration can correspond to the optimal redox atmosphere, and the ideal value of dust concentration can be set to 0, indicating no dust emission or the lowest dust concentration. These ideal values ​​can be determined through detailed process studies, experimental optimization, or based on historical best operating data. As weight, , , To prevent interactive weights, For thermal dynamic weights, Weighted by oxygen concentration, Dust concentration weights, force resistance interaction weights Thermal dynamic weight Oxygen concentration weight Dust concentration weight These parameters are used to characterize the relative importance of the force-resistance interaction coefficient, thermal dynamics coefficient, oxygen concentration index, and dust concentration index in assessing overall operating conditions. For example, in some process stages, thermal dynamics may be more critical, and a higher weight can be assigned to it; while in other stages, stirring load may be more important, and the force-resistance interaction weight can be increased. These weights can be determined and adjusted through expert experience, historical data analysis, machine learning algorithms, or optimization models.

[0047] This application further proposes a process for calculating the comprehensive operating condition quality factor, aiming to achieve precise quantification of the operating conditions of the ash roaster through comprehensive evaluation of multi-dimensional parameters. The process first obtains the force resistance interaction coefficient calculated from the instantaneous power and torque change rate of the stirring shaft, the thermal dynamic coefficient calculated from the temperature gradient inside the pot, the temperature standard deviation, and the temperature rise rate, as well as the oxygen and dust concentrations of the flue gas. Subsequently, to eliminate dimensional differences and unify the data range, the oxygen and dust concentrations of the flue gas are subjected to maximum-minimum normalization processing, resulting in oxygen concentration indices and dust concentration indices. After obtaining all necessary input parameters, the force resistance interaction coefficient, thermal dynamic coefficient, oxygen concentration index, and dust concentration index are substituted into a specific exponential decay formula for calculation. The core idea of ​​this formula is to quantify the influence of each parameter on the overall operating condition quality by calculating the square of the deviation between each parameter and its corresponding ideal value and multiplying it by a preset weight. The sum of the squares of all weighted deviations is then multiplied by a global sensitivity coefficient to obtain the negative power of the exponential function. This exponentially decaying mathematical model ensures that when all parameters are in an ideal state, the overall operating condition quality factor reaches its maximum value of 1, indicating perfect operating conditions. However, the greater any parameter deviates from its ideal value, the greater its impact on the negative power of the exponential function, causing the overall operating condition quality factor to rapidly decrease and approach 0, thus accurately reflecting the degree of deterioration in operating condition quality. In this way, the proposed solution can comprehensively and dynamically evaluate the overall operating quality of the ash-burning process, considering not only the stirring load and thermodynamic state but also the environmental indicators of flue gas emissions. This comprehensive quality factor can more accurately guide the adaptive control of the stirring speed, ensuring that the stirring module 3 always operates in the optimal state under complex and changing conditions, thereby effectively solving the problem that it is difficult to accurately evaluate operating condition quality based on a single or simple combination of parameters.

[0048] For example, during the operation of the ash roasting machine, the instantaneous power and torque change rate of the stirring shaft can be acquired in real time through torque and power sensors installed on the stirring shaft, and the controller calculates the force-resistance interaction coefficient according to the above process. Simultaneously, through a temperature sensor array arranged at different locations within the high-strength ash roasting pot 1, the temperature gradient, temperature standard deviation, and temperature rise rate within the pot can be acquired, thereby calculating the thermal dynamic coefficient. Furthermore, oxygen and dust sensors are installed at the flue pipe 4 to monitor the oxygen and dust concentrations of the flue gas in real time. The controller or industrial computer pre-stores or dynamically loads global sensitivity coefficients, force-resistance interaction weights, thermal dynamic weights, oxygen concentration weights, dust concentration weights, and ideal values ​​for each parameter. For example, the ideal dust concentration value... This can be set to 0, indicating a desired zero dust emission. After sensor data is transmitted to the controller, the controller first performs maximum-min normalization on the oxygen and dust concentrations of the flue gas, converting them into an exponent within the range of 0 to 1. Subsequently, the controller substitutes the force resistance interaction coefficient, thermal dynamic coefficient, normalized oxygen concentration exponent, and dust concentration exponent, along with preset weights, ideal values, and global sensitivity coefficient, into the aforementioned exponent decay formula to calculate the current comprehensive operating condition quality factor in real time. For example, if the stirring load is too large, causing the force resistance interaction coefficient to decrease... Significantly deviates from the ideal value Or uneven temperature distribution inside the pot leads to a decrease in the thermal dynamic coefficient. Deviation from ideal value Alternatively, if the oxygen concentration in the flue gas is too high or too low, deviating from the ideal value, these deviations will increase the deviation term in the formula, thereby affecting the calculated comprehensive operating condition quality factor. reduce.

[0049] Through the above technical solution, this application overcomes the potential bias or inaccuracy of traditional ash roasting machines in evaluating operating conditions. By introducing a global sensitivity coefficient, various weights, and ideal values, and employing an exponentially decaying mathematical model, this application organically integrates multiple key parameters such as force-resistance interaction, thermal dynamics, flue gas oxygen concentration, and dust concentration, and accurately quantifies their deviation from the ideal state. This method of calculating comprehensive operating condition quality factors enables the system to perform highly sensitive and accurate evaluation of the overall operating status of the ash roasting process, thus providing a comprehensive and reliable decision-making basis for the adaptive control system of stirring speed. This not only helps to promptly detect and correct potential process anomalies and avoid misjudgments caused by fluctuations in a single parameter, but also optimizes energy consumption and effectively controls flue gas emissions while ensuring ash roasting efficiency and product quality, significantly improving the intelligence and automation level of the ash roasting machine.

[0050] This application further proposes the following process for calculating and obtaining the force-resistance interaction coefficient: The instantaneous power and torque change rate of the mixing shaft are obtained. The instantaneous power of the mixing shaft refers to the electrical or mechanical power consumed by the shaft at a given moment. It reflects the immediate energy requirement of the mixing system to overcome material resistance. Instantaneous power can be measured in real time by a power sensor installed on the motor or transmission system, or calculated by measuring the motor's voltage and current and combining this with the power factor. The torque change rate of the mixing shaft refers to the rate at which the shaft torque changes over time. It reflects the dynamic characteristics of the load during mixing, such as material agglomeration, sudden viscosity changes, or impacts on the mixing blades. The torque change rate can be obtained by measuring the torque in real time using a torque sensor installed on the mixing shaft, and then performing a time-differential operation on the measured torque signal. Alternatively, the torque change rate can be indirectly inferred by monitoring rapid fluctuations in the motor current.

[0051] The instantaneous power of the stirring spindle is dynamically impact-corrected based on the torque change rate of the stirring spindle to obtain the equivalent impact power. The specific calculation method is as follows: substitute the instantaneous power and torque change rate into the formula... Obtain equivalent impact power Equivalent impact power It is a comprehensive indicator that combines the instantaneous power of the stirring spindle with the impact effect caused by the torque change rate, by multiplying the torque change rate by the impact energy conversion factor. This is converted into an equivalent power component, and then compared with the instantaneous power. The summation provides a more comprehensive reflection of the actual load and potential impact borne by the stirring spindle. Among these, This refers to the instantaneous power of the stirring spindle. The impact energy conversion factor has a value range of 0-1. It is a proportional factor that converts the impact effect caused by the rate of change of torque into equivalent power, i.e. The proportionality factor used to quantify the impact effect of torque change rate on equipment typically ranges from 0 to 1. It represents the proportion by which the impact effect caused by torque change rate is converted into equivalent power. This coefficient can be preset or dynamically adjusted based on the characteristics of the material being stirred, the structural strength of the stirring equipment, and the desired protection level. For example, a higher value can be set for materials prone to severe impact or for scenarios requiring high equipment protection. For stable operating conditions, a lower value can be set; conversely, for stable operating conditions, a lower value can be set. value; The torque change rate of the stirring spindle; The equivalent impact power is subjected to maximum-minimum normalization to obtain the force-resistance interaction coefficient. Maximum-minimum normalization is a data processing method designed to linearly transform raw data to a specific range, such as [0,1]. Its purpose is to eliminate the influence of different physical dimensions and numerical ranges on subsequent calculations, making the data comparable. A specific implementation method is to subtract the historical minimum equivalent impact power from the current equivalent impact power, and then divide by the difference between the historical maximum and minimum equivalent impact power. Another method is to normalize the data by setting a theoretical maximum and minimum value based on the equipment's rated power and maximum impact withstand capability. , The closer it is to 1, the closer the load is to the device's limit; The closer it is to 0, the closer it is to no load.

[0052] This application's solution incorporates a comprehensive consideration of the instantaneous power and torque change rate of the stirring shaft to more accurately assess the actual load conditions during the stirring process. First, the system acquires the instantaneous power and torque change rate of the stirring shaft in real time. Instantaneous power reflects the continuous energy consumption of stirring, while the torque change rate captures any instantaneous impacts or severe load fluctuations that may occur during stirring. To quantify the dynamic impact effect caused by the torque change rate and incorporate it into the load assessment, this solution introduces an impact energy conversion factor. The torque change rate is converted into an equivalent power component. Then, this equivalent power component is compared with the instantaneous power... Add them together to calculate the equivalent impact power. Equivalent impact power This not only includes continuous energy consumption but also incorporates the impact of dynamic impacts, thus providing a more comprehensive and accurate reflection of the actual load borne by the stirring spindle. To ensure this load indicator can be standardized for subsequent comprehensive operating condition quality factor calculations, this scheme includes an equivalent impact power... Max-min normalization is performed to map it to the range [0,1], thus obtaining the force-resistance interaction coefficient. The force-resistance interaction coefficient This approach intuitively represents the current load relative to the equipment's limits, providing crucial, dynamic, and comprehensive load assessment input for subsequent calculations of the overall operating condition quality factor. In this way, the solution can more sensitively perceive changes in mixing conditions, especially transient shocks that may potentially damage the equipment, thus providing a more reliable basis for adaptive control of the mixing speed.

[0053] The following is a concrete example. During the operation of the ash-cooking machine, torque and power sensors can be installed on the drive chain of the stirring shaft to monitor the instantaneous torque and power of the stirring shaft in real time. The torque sensor can be a strain gauge type; its output signal, after amplification and filtering, is sent to the controller for processing. The instantaneous power can be directly measured by the power sensor, or calculated by measuring the motor input voltage and current and combining this with the power factor. After receiving the instantaneous torque signal, the controller can use a digital differential algorithm, such as differential calculation of continuous sampling points, to obtain the torque change rate. Impact energy conversion factor. It can be determined based on empirical values ​​or through experimental calibration. For example, when processing materials that are prone to caking and are viscous, such as aluminum ash slag, The value can be set between 0.5 and 0.8 to fully account for the impact effect. When obtaining instantaneous power... Torque change rate Impact energy conversion factor Then, the controller substitutes these values ​​into the formula. Calculate equivalent impact power Subsequently, in order to Convert to force-resistance interaction coefficient The controller will adjust the power output based on the device's historical operating data or preset power limits. Perform maximum-minimum normalization. For example, you can set a historical maximum equivalent impact power and a historical minimum equivalent impact power, and then calculate the minimum equivalent impact power by using the ratio of the difference between the current value and the minimum value to the difference between the maximum and minimum values. Thus, the result is The value is a dimensionless quantity between 0 and 1, which can intuitively reflect the current load status of the stirring spindle.

[0054] The above technical solution, when calculating the force-resistance interaction coefficient, not only considers the instantaneous power of the stirring shaft, but also creatively introduces the torque change rate, which is converted into equivalent power through an impact energy conversion factor, thus forming the equivalent impact power. This comprehensive load assessment method can more comprehensively and sensitively capture dynamic load changes such as instantaneous impacts, material agglomeration, or sudden viscosity changes that may occur during stirring, avoiding the lag or inaccuracy that may result from assessment based solely on instantaneous power. The force-resistance interaction coefficient is obtained by performing maximum-minimum normalization on the equivalent impact power. It can accurately reflect the actual load on the mixing spindle and the risk of it approaching its limits in a standardized form. This makes the subsequent calculation of the comprehensive operating condition quality factor more accurate, thus providing a more reliable and precise input for the adaptive mixing speed control system. This helps the system adjust the mixing speed more promptly and accurately, effectively avoiding equipment overload or idling, extending equipment life, and optimizing mixing efficiency.

[0055] This application further proposes the following procedure for calculating and obtaining the thermal dynamic coefficient: The temperature gradient, temperature standard deviation, and temperature rise rate within the frying pot are obtained. The "temperature gradient" refers to the degree of temperature difference between different locations within the frying pot 1, reflecting the uniformity of heat distribution within the pot. The "temperature standard deviation" quantifies the dispersion of the temperature within the pot relative to the average value, and is another important indicator for measuring temperature uniformity. The "temperature rise rate" indicates how quickly the temperature within the frying pot 1 changes over time, and is crucial for controlling the thermal reaction rate during the ash-frying process. Accurate acquisition of these parameters is fundamental to precisely assessing the thermal dynamics within the pot, and can be obtained through real-time monitoring and calculation using multiple temperature sensors arranged inside the frying pot 1.

[0056] The temperature gradient and temperature standard deviation inside the pot are compared with the maximum allowable temperature gradient and the maximum allowable temperature standard deviation inside the pot to obtain the temperature gradient index and temperature standard deviation index. The "maximum allowable temperature gradient" and "maximum allowable temperature standard deviation" are preset and acceptable upper limits of the non-uniform temperature distribution inside the slag frying pot 1. They define the boundary conditions of the ideal thermal state and can usually be determined by experiments or experience according to specific process requirements and equipment characteristics.

[0057] The absolute value of the temperature rise rate inside the pot compared to the optimal temperature rise rate is compared with the allowable fluctuation range of the temperature rise rate to obtain the temperature rise rate index. This ratio unifies physical quantities of different dimensions or ranges into an exponential form between 0 and 1, facilitating subsequent comprehensive calculations and comparisons, thus objectively reflecting the degree of deviation between the current thermal state and the ideal thermal state. Similarly, the absolute value of the temperature rise rate inside the pot compared to the optimal temperature rise rate is compared with the allowable fluctuation range of the temperature rise rate. This process compares the actual temperature rise rate with the preset optimal temperature rise rate and considers its allowable fluctuation range. In this way, the degree to which the current temperature rise rate deviates from the ideal state can be quantified and transformed into a dimensionless index. This index reflects the stability and suitability of the temperature rise rate, ensuring that the ash-frying process is carried out under optimal thermal reaction conditions.

[0058] The temperature gradient index, temperature standard deviation index, and temperature rise rate index are integrated into a temperature field distortion metric. This metric is then converted into a thermal dynamic coefficient ranging from 0 to 1 through a nonlinear transformation. The thermal dynamic coefficient increases as the temperature field distortion metric decreases, and reaches a value of 1 when all deviations are zero. Specifically, the calculation method involves substituting the temperature gradient index, temperature standard deviation index, and temperature rise rate index into the formula. Obtain temperature field distortion measurement This formula integrates multiple indicators reflecting the deviation of the thermal state inside the pot into a single measure of temperature field distortion by calculating the square root of the sum of the squares of the temperature gradient exponent, the temperature standard deviation exponent, and the rate of temperature rise exponent. Temperature field distortion measurement It is a comprehensive indicator that can fully quantify the overall non-uniformity and instability of the internal thermal state of the slag-frying pot 1. The larger the value, the more serious the deviation of the thermal state from the ideal condition. Among them, It is the temperature gradient exponent. The standard deviation index of temperature. The rate of temperature rise is an exponent. Measurement of temperature field distortion Substitute into the formula Obtain the thermal dynamic coefficient , When all deviations are zero , This indicates a perfect thermal state; as the deviation increases, Decrease, approaching 0, where, The attenuation coefficient is greater than 0 and is used to adjust the sensitivity of the temperature field distortion to the thermal dynamic coefficient. Its value determines the rate at which the thermal dynamic coefficient decreases as the temperature field distortion increases. This step measures the temperature field distortion. Transformed into a "thermal dynamic coefficient" between 0 and 1 This formula uses an exponential decay form, so that when the temperature field distortion is 0 (i.e., the thermal state is perfect), The value is 1; as the distortion increases, It decreases exponentially and approaches 0. This nonlinear mapping relationship can sensitively reflect small changes in thermal state and quantify them into an intuitive quality factor, which facilitates decision-making in subsequent adaptive stirring speed control systems.

[0059] The proposed solution provides accurate input to the adaptive stirring speed control system by finely evaluating the internal thermal dynamics of the slag-frying pot 1. First, the system acquires key thermal parameters such as the "temperature gradient inside the pot," "temperature standard deviation," and "temperature rise rate" in real time. To eliminate the influence of different parameter dimensions and ranges and transform them into a unified standard, these raw thermal parameters are compared with preset values ​​for the "maximum allowable temperature gradient," "maximum allowable temperature standard deviation," "optimal temperature rise rate," and "allowable fluctuation range of temperature rise rate," resulting in dimensionless "temperature gradient index," "temperature standard deviation index," and "temperature rise rate index." These indices intuitively reflect the degree of deviation between the current thermal state and the ideal state. Subsequently, to comprehensively quantify the overall non-uniformity and instability of the internal thermal state of the slag-frying pot 1, the above three indices are substituted into the formula... The temperature field distortion metric was calculated. Temperature field distortion measurement This value combines the uniformity of temperature distribution and the stability of temperature changes; a larger value indicates a more unstable or uneven thermal state inside the pot. Finally, the calculated temperature field distortion measure is... Substitute into the exponential decay formula And combined with the preset "attenuation coefficient" ( ), ultimately obtaining the "thermal dynamic coefficient" ( The thermal dynamic coefficient is a quality factor between 0 and 1, which is relevant when the internal thermal state of the pot is perfect. The value is 1. As the thermal state deviates from the ideal condition, the distortion degree increases. The temperature decreases exponentially. In this way, the scheme can transform complex internal thermal dynamics information into a simple and accurate numerical value, serving as an important component of the overall operating condition quality factor. This allows the adaptive stirring speed control system to more accurately determine the current thermal state and adjust the stirring speed accordingly. This refined thermal dynamics assessment mechanism effectively compensates for the shortcomings of relying solely on a single temperature parameter, ensuring the thermal efficiency and stability of the ash-frying process.

[0060] As a specific implementation, the high-strength slag-frying pot 1 of the ash-frying machine can be equipped with multiple thermocouples or infrared temperature sensors arranged radially and axially inside to monitor the temperature of different areas within the pot in real time. For example, sensor arrays can be set at different heights on the pot bottom, pot walls, and the central area of ​​the pot. The temperature data collected by these sensors allows for the calculation of the average, maximum, and minimum temperatures within the pot at any given time. The "temperature gradient within the pot" can be obtained by calculating the ratio of the temperature difference between adjacent sensors to their distance, or by fitting the entire temperature field and calculating the gradient. The "temperature standard deviation" can be obtained by calculating the standard deviation of all sensor temperature readings using statistical methods. The "temperature rise rate" can be calculated by measuring the rate of change of the average temperature within the pot over time. During system initialization or according to process requirements, the "maximum allowable temperature gradient" can be set to, for example, 5 degrees Celsius per meter, and the "maximum allowable temperature standard deviation" to be 2 degrees Celsius. Simultaneously, the "optimal temperature rise rate" can be set to 3 degrees Celsius per minute, allowing fluctuations within a range of ±0.5 degrees Celsius. Once the real-time temperature gradient, temperature standard deviation, and temperature rise rate are obtained, for example, if the current temperature gradient is 8 degrees Celsius per meter, then the "temperature gradient index" is 8 / 5 = 1.6. If the current temperature standard deviation is 3 degrees Celsius, then the "temperature standard deviation index" is 3 / 2 = 1.5. If the current temperature rise rate is 2.8 degrees Celsius per minute, then the absolute difference between it and the optimal value of 3 degrees Celsius is 0.2 degrees Celsius, which is less than the allowable fluctuation range of 0.5 degrees Celsius. In this case, the "temperature rise rate index" can be set to 0 (indicating it is within the allowable range), or it can be mapped using a more refined function. Subsequently, these indices are substituted into the formula to calculate the temperature field distortion measure. For example, if the calculation yields... The value is 0.8. Finally, Value and preset "attenuation coefficient" ( For example, set it to 2, and substitute it into the formula. The current thermal dynamic coefficient can then be calculated. .For example, The calculated value is 0.2019. This calculated value... This value will serve as an important input for calculating the overall working condition quality factor, guiding the adjustment of the stirring speed.

[0061] Through the above technical solution, this application provides a more refined and accurate method for evaluating the internal thermal dynamic state of the slag-frying pot 1. Traditional temperature monitoring may only focus on the average temperature or the temperature at a few points, making it difficult to comprehensively reflect the complex temperature distribution and changing trends within the pot. This solution comprehensively considers three key thermodynamic indicators: "temperature gradient within the pot," "temperature standard deviation," and "temperature rise rate," transforming them into dimensionless exponents, and further integrating them into a temperature field distortion measure. Finally, the "thermal dynamic coefficient" is obtained through the exponential decay function. This multi-dimensional, quantitative, and non-linear evaluation mechanism enables the thermal dynamic coefficient to more sensitively and accurately reflect the actual thermal state inside the slag-frying pot 1, including temperature uniformity, stability, and trends. When this precise thermal dynamic coefficient is integrated into the calculation of the "comprehensive operating condition quality factor" of the adaptive stirring speed control system, the system can more accurately determine the thermodynamic quality of the current operating condition, thereby achieving more precise and timely adaptive control of the stirring speed of the stirring spindle. This not only helps to avoid local overheating or insufficient heat, improving the thermal efficiency and stability of the ash-frying process, but also effectively reduces energy consumption, extends equipment lifespan, and ultimately improves the efficiency of aluminum ash treatment and aluminum recovery rate.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A slag roasting machine with a high-strength slag roasting pot, comprising a high-strength slag roasting pot, a gas collecting and sealing plate installed on the top of the high-strength slag roasting pot, and a flue and a stirring module installed on the top of the gas collecting and sealing plate, characterized in that, Also includes: An adaptive stirring speed control system is used to adjust the stirring speed of the stirring spindle in real time. The adaptive stirring speed control system includes: The mechanical load sensing module calculates and obtains the force-resistance interaction coefficient based on the instantaneous power and torque change rate of the stirring spindle. The thermal field uniformity assessment module calculates and obtains the thermal dynamic coefficient based on the temperature gradient inside the pot, the temperature standard deviation, and the temperature rise rate. The multi-source information fusion evaluation module calculates and obtains the comprehensive working condition quality factor based on the oxygen concentration and dust concentration of flue gas under the force resistance interaction coefficient and thermal dynamic coefficient. The process progress estimation module calculates and obtains the process coefficient based on the cumulative specific energy consumption and aluminum liquid outflow rate; The base speed decision and execution module calculates the target stirring speed based on the comprehensive operating condition quality factor, process coefficient, and flue gas carbon monoxide concentration, and adjusts the current stirring speed to the target stirring speed.

2. The ash roasting machine with a high-strength slag roasting pot according to claim 1, characterized in that, The process for calculating and obtaining the target stirring speed is as follows: Obtain the overall operating condition quality factor, process coefficient, and flue gas carbon monoxide concentration; The carbon monoxide concentration in flue gas was subjected to maximum-minimum normalization to obtain the carbon monoxide concentration index in flue gas. The speed regulation coefficient is determined based on the comprehensive operating condition quality factor, process coefficient, and flue gas carbon monoxide concentration index. The speed regulation coefficient is positively correlated with the comprehensive operating condition quality factor and negatively correlated with the process coefficient and the flue gas carbon monoxide concentration index. The target stirring speed is calculated based on the speed regulation coefficient and the preset minimum and maximum allowable stirring speeds.

3. The ash roasting machine with a high-strength slag roasting pot according to claim 2, characterized in that, The process for calculating and obtaining the process coefficient is as follows: Obtain the cumulative specific energy consumption and aluminum liquid outflow rate; The cumulative specific energy consumption and aluminum liquid outflow rate are respectively compared with the cumulative specific energy consumption and the maximum aluminum liquid outflow rate at the end of the process to obtain the cumulative specific energy consumption index and the aluminum liquid outflow rate index. The process coefficient is calculated using a nonlinear combination method based on the cumulative specific energy consumption index and the aluminum liquid outflow rate index. The process coefficient increases as the cumulative specific energy consumption index increases and decreases as the aluminum liquid outflow rate index decreases, and the process coefficient takes a value between 0 and 1.

4. The ash roasting machine with a high-strength slag roasting pot according to claim 1, characterized in that, The process for calculating and obtaining the comprehensive working condition quality factor is as follows: Obtain the force resistance interaction coefficient and thermal dynamic coefficient, as well as the oxygen concentration and dust concentration of the flue gas; The oxygen concentration and dust concentration of the flue gas were subjected to maximum-minimum normalization to obtain the oxygen concentration index and dust concentration index. Substituting the force resistance interaction coefficient, thermal dynamic coefficient, oxygen concentration index, and dust concentration index into the formula Obtain the comprehensive working condition quality factor , When all variables reach their ideal values, the exponent is 0. The greater the deviation of any variable from its ideal value, the larger the negative value of the exponential part. The closer to 0, the more... The global sensitivity coefficient is greater than 0. , For force resistance interaction coefficient, , The thermal dynamic coefficient, , This is the oxygen concentration index. , The dust concentration index. For ideal value, As weight, , .

5. The ash roasting machine with a high-strength slag roasting pot according to claim 4, characterized in that, The process for calculating and obtaining the force-resistance interaction coefficient is as follows: Obtain the instantaneous power and torque change rate of the stirring spindle; The instantaneous power of the stirring spindle is dynamically impact-corrected based on the torque change rate of the stirring spindle to obtain the equivalent impact power. The equivalent impact power is subjected to maximum-minimum normalization to obtain the force-resistance interaction coefficient. , , The closer it is to 1, the closer the load is to the device's limit; The closer it is to 0, the closer it is to no load.

6. The ash roasting machine with a high-strength slag roasting pot according to claim 4, characterized in that, The process for calculating and obtaining the thermal dynamic coefficient is as follows: Obtain the temperature gradient, temperature standard deviation, and temperature rise rate inside the pot; The temperature gradient and temperature standard deviation inside the pot are compared with the maximum allowable temperature gradient and the maximum allowable temperature standard deviation inside the pot to obtain the temperature gradient index and temperature standard deviation index, respectively. The absolute value of the temperature rise rate inside the pot and the optimal temperature rise rate is compared with the allowable fluctuation range of the temperature rise rate to obtain the temperature rise rate index. The temperature gradient index, temperature standard deviation index, and temperature rise rate index are combined into a temperature field distortion measure. The temperature field distortion metric is converted into a thermal dynamic coefficient with a value between 0 and 1 through a nonlinear transformation.

7. The ash roasting machine with a high-strength slag roasting pot according to claim 6, characterized in that, The thermal dynamic coefficient increases as the temperature field distortion metric decreases, and takes a value of 1 when all deviations are zero.

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