System for monitoring and optimizing uniformity of temperature field in lithium manganate sintering kiln

Abstract

The invention belongs to the technical field of lithium manganate sintering, and provides a system for monitoring and optimizing the uniformity of a temperature field in a lithium manganate sintering kiln, which comprises a uniformity abnormity judgment module, a temperature field uniformity optimization module, a temperature field uniformity optimization module and a temperature field uniformity optimization module, and the uniformity of the temperature field is analyzed based on the monitored real-time process parameter data, and whether the uniformity is abnormal or not is judged. The uniformity and stability of a temperature field and a reaction environment in a kiln are improved, better batch consistency of the lithium manganate positive electrode material, higher crystal structure integrity and more stable electrochemical performance are directly converted, and meanwhile, by avoiding unnecessary global process fluctuation and accurately handling local problems, the lithium manganate positive electrode material can be obtained. The energy consumption can be effectively reduced, the rejection rate is reduced, the superior product rate and the overall productivity are improved, and reliable technical guarantee is provided for high-quality, high-efficiency and low-cost manufacturing of the lithium ion battery positive electrode material.

Description

Technical Field

[0001] This invention belongs to the field of lithium manganese oxide sintering technology, specifically a monitoring and optimization system for the temperature field uniformity inside a lithium manganese oxide sintering kiln. Background Technology

[0002] As one of the important cathode materials for lithium-ion batteries, the crystallinity, purity and uniformity of the crystal structure of lithium manganese oxide directly determine the capacity, cycle life and safety performance of the battery. Sintering is a key heat treatment process in the preparation of lithium manganese oxide cathode materials. It is usually carried out in continuous kilns such as tunnel kilns and roller kilns. The uniformity of the temperature field inside the kiln is a decisive factor in ensuring the batch consistency of sintered materials and the achievement of performance indicators, which is directly related to the yield and quality of the final product.

[0003] Currently, the temperature field control of lithium manganese oxide sintering kilns commonly employs a PID (proportional-integral-derivative) control strategy based on fixed-point thermocouple feedback. The core objective is to make the temperature at the monitoring point as close as possible to the preset process curve. However, this conventional method primarily responds to and corrects temperature deviations caused by physical factors such as uneven heating element power, aging insulation materials, or unreasonable airflow organization. It fails to effectively identify and address another, more insidious and harmful source of disturbance: unplanned trace impurities (such as Fe, Na, K, etc.) or non-uniform doping introduced from precursor raw materials, leading to chemical anomalies. These impurities alter the material's reaction kinetics, locally triggering abnormal emissions. The heat or endothermic effect interferes with oxygen diffusion and consumption, thus forming "hot spots," "cold spots," or "oxygen-deficient zones" in the kiln that are difficult to trace using traditional sensors and cannot be properly handled by conventional control logic. Since it is impossible to distinguish whether the root cause of uneven temperature is physical factors or chemical impurities, existing technologies often adopt a one-size-fits-all approach, such as redistributing power globally or adjusting transmission speed when an increase in overall temperature difference is detected. This crude strategy not only fails to solve the problem when faced with local impurities, but may also lead to overburning of materials in clean areas, increased energy consumption, and unexplained decrease in production capacity. Moreover, the deterioration effect of contaminated areas will continue, ultimately resulting in severe performance differentiation and reduced yield of the entire batch of products.

[0004] To this end, the present invention provides a monitoring and optimization system for the temperature field uniformity inside a lithium manganese oxide sintering kiln. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a monitoring and optimization system for the temperature field uniformity inside a lithium manganese oxide sintering kiln, comprising:

[0007] Uniformity anomaly determination module: Real-time process parameter data of multiple spatial points set in the lithium manganese oxide sintering kiln, and analysis of temperature field uniformity based on the monitored real-time process parameter data to determine whether there is an anomaly in uniformity;

[0008] Impurity type identification module: If there is an anomaly in uniformity, the module identifies whether there is unplanned impurity doping based on the spatiotemporal distribution characteristics of the uniformity anomaly data and in combination with historical cases.

[0009] Pollution impact range analysis module: If there is unplanned impurity doping, the impact range of unplanned impurity doping is further determined by the spatiotemporal distribution characteristics, whether it is local pollution or global pollution;

[0010] Dynamic optimization execution module: Executes dynamically selected optimization strategies based on the impact range judgment results;

[0011] If it is a global contamination, the required reaction time offset is calculated based on the concentration and type of unplanned impurities, and the pusher speed value is adjusted according to the reaction time offset.

[0012] If the pollution is localized, dynamic isolation and compensation optimization will be implemented. Dynamic isolation and compensation optimization is a control strategy for pollution carriers that is different from the global pollution control method. It includes active environmental isolation and dynamic thermal field correction.

[0013] The beneficial effects of this invention are as follows:

[0014] This invention achieves automatic identification and differentiation of non-uniformity caused by physical factors and non-uniformity caused by chemical impurities in continuous sintering production through high-density three-dimensional synchronous monitoring of temperature / oxygen concentration, calculation of temperature field uniformity index, and intelligent matching based on spatiotemporal distribution feature vectors and historical case database. It fundamentally solves the problem of having to adopt a one-size-fits-all approach to adjustment due to the inability to identify the source of disturbance, and lays a scientific decision-making foundation for subsequent precise optimization.

[0015] This invention forms a complete closed loop of perception-cognition-decision-execution, from detecting uniformity anomalies to identifying impurity types, then determining the scope of pollution impact (local / global), and finally executing dynamically selected optimization strategies (global speed adjustment or local isolation correction), realizing intelligent management and proactive intervention of the entire process and chain of complex chemical pollution disturbances;

[0016] This invention proposes differentiated and quantitative optimization strategies for different pollution scenarios to improve control precision. For global pollution: based on the principles of chemical reaction kinetics, a quantitative model is constructed using real-time monitoring data to directly calculate the reaction time offset (Δt) and precisely adjust the pusher speed accordingly. This method systematically ensures the uniformity of the reaction degree of the entire batch of materials by scientifically compensating for the changes in reaction kinetics caused by impurities, avoiding the blindness of traditional experience-based adjustments. For localized pollution: a dynamic isolation and compensation optimization strategy is proposed and implemented. By actively isolating the environment to suppress the spread of pollution and combining dynamic correction of the thermal field to eliminate local hot spots / cold spots, the optimization impact is strictly limited to a very small area around the pollution carrier. Thus, while effectively handling anomalies, the normal sintering process of most clean materials is protected to the greatest extent, avoiding production capacity loss, energy consumption increase, and performance risks caused by global adjustments, and achieving precise optimization actions.

[0017] The implementation of this invention improves the uniformity and stability of the temperature field and reaction environment within the kiln, directly resulting in better batch consistency, higher crystal structure integrity, and more stable electrochemical performance of lithium manganese oxide cathode materials. At the same time, by avoiding unnecessary global process fluctuations and precisely addressing local issues, it can effectively reduce energy consumption, reduce scrap rate, increase yield and overall production capacity, providing a reliable technical guarantee for the high-quality, high-efficiency, and low-cost manufacturing of lithium-ion battery cathode materials. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a framework diagram of the monitoring and optimization system for temperature field uniformity inside the lithium manganese oxide sintering kiln of the present invention.

[0020] Figure 2 This is a flowchart of the steps of the method for monitoring and optimizing the temperature field uniformity inside the lithium manganese oxide sintering kiln of the present invention.

[0021] Figure 3 This is a flowchart of some steps in the method for monitoring and optimizing the temperature field uniformity inside the lithium manganese oxide sintering kiln of the present invention. Detailed Implementation

[0022] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0023] Example 1

[0024] Please see Figure 1 As shown in the embodiment of the present invention, the monitoring and optimization system for temperature field uniformity inside the lithium manganese oxide sintering kiln specifically includes:

[0025] Uniformity anomaly determination module: Real-time process parameter data of multiple spatial points set in the lithium manganese oxide sintering kiln, and analysis of temperature field uniformity based on the monitored real-time process parameter data to determine whether there is an anomaly in uniformity;

[0026] Specifically, inside the lithium manganese oxide sintering kiln, the area that has the most critical influence on the sintering process is selected as the judgment benchmark area.

[0027] Among them, the judgment benchmark area is a three-dimensional spatial monitoring array in the middle of the constant temperature zone of the kiln;

[0028] The three-dimensional spatial monitoring array consists of n (n≥9) temperature sensors fixedly installed in a spatial matrix distribution according to the upper, middle and lower layers, and the left, middle and right positions of each layer. The temperature sensors synchronously collect the real-time temperature value of their respective positions at the same sampling frequency.

[0029] At each sampling moment, a comprehensive temperature field uniformity index is calculated based on all real-time temperature values ​​collected from monitoring points in the three-dimensional spatial monitoring array.

[0030] The calculation process for the temperature field uniformity index is as follows:

[0031] Calculate the arithmetic mean of the temperature values ​​at all monitoring points to obtain the average temperature in three-dimensional space at the sampling time;

[0032] Calculate the absolute value of the difference between the temperature value at each monitoring point and the average temperature to obtain the absolute temperature deviation value at each monitoring point;

[0033] The maximum value of the absolute temperature deviation from all monitoring points is extracted as the maximum absolute temperature deviation.

[0034] The maximum absolute temperature deviation is defined as the temperature field uniformity index at the current sampling time;

[0035] The temperature field uniformity index is compared with the anomaly detection threshold, and a continuous anomaly time limit is introduced.

[0036] If the temperature field uniformity index continuously exceeds the anomaly judgment threshold and reaches the continuous anomaly time limit, it is determined that the temperature field uniformity is abnormal; otherwise, it is determined that the temperature field uniformity is not abnormal.

[0037] It should be noted that the anomaly judgment threshold is preset by those skilled in the art based on the requirements of temperature field uniformity in the lithium manganese oxide sintering process; the continuous anomaly time limit is set by those skilled in the art based on the actual need to avoid the impact of instantaneous disturbances (such as vehicle entry and exit, short-term airflow fluctuations) and in combination with historical normal production data statistics.

[0038] The significance of the uniformity anomaly detection module lies in the fact that by conducting high-density, synchronized real-time monitoring of the temperature field in key areas and using the temperature field uniformity index for quantitative evaluation, this embodiment will only begin subsequent more complex diagnosis and decision-making when the uniformity is determined to be persistently abnormal. This transforms the system from passively maintaining temperature to actively detecting anomalies caused by chemical contamination, which is a necessary prerequisite for the start-up and operation of the entire closed-loop intelligent optimization.

[0039] Impurity type identification module: If there is an anomaly in uniformity, the module identifies whether there is unplanned impurity doping based on the spatiotemporal distribution characteristics of the uniformity anomaly data and in combination with historical cases.

[0040] Specifically, if there is an anomaly in uniformity, the associated data output by the aforementioned uniformity anomaly determination module will be received;

[0041] Among them, the associated data includes at least the original temperature time series data of all temperature sensors in the three-dimensional spatial monitoring array of the judgment reference area during the period of anomaly, as well as the time series data of oxygen concentration sensors at the corresponding spatiotemporal locations.

[0042] Based on the associated data, spatiotemporal distribution features used to characterize the nature of the anomaly are extracted, and a spatiotemporal distribution feature vector is constructed.

[0043] Specifically, the process of constructing spatiotemporal distribution feature vectors is as follows:

[0044] Based on the temperature data of the three-dimensional spatial monitoring array, the temperature deviation between the average temperature of each monitoring point and the preset benchmark temperature during the period of anomaly is calculated, and the coordinates (layer number, horizontal position number) of the monitoring point with the largest deviation value are determined as the core coordinates of the anomaly.

[0045] Using the coordinates of the anomaly core as the center, calculate the average temperature deviation from the surrounding adjacent monitoring points, which is used as the local diffusion intensity;

[0046] The ratio of the number of monitoring points in the entire three-dimensional array whose temperature deviation exceeds a preset number to the total number of monitoring points is used as the global diffusion ratio.

[0047] Based on the two-dimensional spatial distribution map of temperature deviation, the geometric morphology of the abnormal area is analyzed, and optionally, equivalent diameter analysis is used.

[0048] The calculation process for the equivalent diameter is as follows: Let N be the number of monitoring points in the abnormal region where the temperature deviation exceeds the morphological threshold, and S be the actual physical area represented by each monitoring point. Then the total area of ​​the abnormal region is N*S, and the equivalent diameter D is calculated using the formula... Calculate and characterize the spatial coverage of the anomalous region;

[0049] From the temperature time series curve of the anomaly core coordinates, extract the following feature points: the anomaly start time (the time when the temperature first exceeds the threshold), the peak time (the time when the temperature reaches the maximum value), and the end time (or the current time), and calculate the heating duration (from the start time to the peak time) and the total duration.

[0050] Calculate the average heating rate from the initial moment to the peak moment, and the average cooling rate of the temperature change curve after the peak moment;

[0051] Calculate the standard deviation of the temperature series during the duration of the anomaly to characterize the severity of temperature fluctuations;

[0052] Extract the oxygen concentration time series curves of the oxygen concentration sensors that are at the same or closest coordinate spatial location to the anomaly core during the same period, and analyze the time lag relationship between temperature rise / fall and oxygen concentration change.

[0053] For example, whether the temperature rise precedes the oxygen concentration decrease, and the lag time;

[0054] Calculate the temperature rise when the temperature reaches its peak and the oxygen concentration decrease at the corresponding moment, and calculate the ratio of the magnitude of the changes;

[0055] The extracted feature values ​​are combined in order and dimension to construct a spatiotemporal distribution feature vector;

[0056] For example, the spatiotemporal distribution feature vector F can be represented as: F = [core coordinates (upper layer, left), local diffusion intensity (8°C), global diffusion ratio (0.1), equivalent diameter (0.15m), heating duration (15min), average heating rate (2°C / min), peak temperature rise (12°C), oxygen concentration lag time (2min), change amplitude ratio (-1.5°C / %), ...];

[0057] The spatiotemporal distribution feature vectors are matched with a pre-stored historical impurity feature case library for similarity matching;

[0058] Among them, the historical impurity feature case library is a database built through the accumulation of experimental or historical production data. It stores a variety of known unplanned impurities (such as iron (Fe), sodium (Na), potassium (K), carbon (C) etc.) that have been verified to be typical abnormal feature vector templates during the sintering process of lithium manganese oxide.

[0059] The cosine similarity between the spatiotemporal distribution feature vector and each anomaly feature vector template is calculated as the similarity value.

[0060] Extract candidate impurity types whose similarity values ​​exceed a matching threshold (e.g., 0.75);

[0061] If no candidate impurity type exists, it is determined that no known impurity was matched.

[0062] If there is only one candidate impurity type whose similarity exceeds the matching threshold, it is directly determined that there is unplanned impurity doping corresponding to the template, and the impurity type is output as the identification result.

[0063] If the similarity of multiple candidate impurity types exceeds the matching threshold, the following decision logic is executed:

[0064] The candidate impurity type with the highest similarity was selected as the primary candidate impurity type.

[0065] Perform a key feature consistency check: compare the key features in the spatiotemporal distribution feature vector (such as "oxygen concentration lag time" and "change amplitude ratio") with the corresponding feature allowable range in the primary candidate impurity type. If all highly discriminative features fall within the allowable range, the primary candidate is adopted.

[0066] If the key features of the primary candidate impurity type are inconsistent, the candidate impurity types with the second highest similarity are checked in turn until a candidate impurity type with consistent key features is found. If none of them are consistent, it is determined to be a multiple match but the features do not match, and is regarded as an unknown interference.

[0067] The significance of the impurity type identification module lies in: by analyzing abnormal data, it can determine whether the uneven temperature field is caused by unplanned impurity doping. Specifically, by extracting and analyzing the coupled spatiotemporal characteristics of temperature and oxygen concentration, it can identify characteristic impurities, which is the decisive basis for determining the subsequent pollution range analysis and optimization strategy selection.

[0068] Pollution impact range analysis module: If there is unplanned impurity doping, the impact range of unplanned impurity doping is further determined by the spatiotemporal distribution characteristics, whether it is local pollution or global pollution;

[0069] Specifically, it receives the impurity type identification result output by the aforementioned impurity type identification module, and synchronously calls the complete spatiotemporal distribution raw data and the constructed spatiotemporal distribution feature vector corresponding to the abnormal period.

[0070] The original spatiotemporal distribution data should include at least the time-series readings of all temperature and oxygen concentration sensors of the three-dimensional spatial monitoring array in the determination reference area during the period of anomaly.

[0071] Based on the original spatiotemporal distribution data, two indicators are calculated to quantify the spatial distribution of pollution, including:

[0072] Cross-sectional anomaly coverage rate: Select the monitoring section where the core coordinates of the anomaly are located as the target section, count the number of monitoring points on the target section whose temperature deviation continuously exceeds a low threshold (e.g., ±3°C of the preset reference temperature), and calculate the ratio of the number of monitoring points to the total number of monitoring points on the target section as the cross-sectional anomaly coverage rate.

[0073] Longitudinal synchronous anomaly ratio: During the period of anomaly, the upstream and downstream monitoring sections adjacent to the target section are examined as the first adjacent section and the second adjacent section. The proportion of monitoring points in the same time period of the first adjacent section and the second adjacent section that exhibit the same type of anomaly pattern as the target section (such as positive temperature deviation accompanied by negative oxygen concentration deviation) is calculated as the first monitoring point proportion and the second monitoring point proportion. The average of the first monitoring point proportion and the second monitoring point proportion is calculated as the longitudinal synchronous anomaly ratio.

[0074] Analyze whether the spatial occurrence of abnormal patterns is strongly correlated with the movement trajectory of the material carrier, and determine whether contamination adheres to a specific carrier, including:

[0075] It should be noted that anomaly patterns refer to identifiable combinations of features exhibited by the coupling of temperature deviations and oxygen concentration changes in a specific manner (such as timing and amplitude).

[0076] Vehicle correlation check: If the kiln system is equipped with a vehicle identification and tracking system (such as RFID), then retrieve the vehicle identification information that passed through the space at the starting moment when the temperature deviation first appeared at the core coordinate position of the anomaly, track the movement of the corresponding vehicle in the kiln, and analyze whether similar abnormal temperature and oxygen concentration patterns are reproduced in other spatial positions it subsequently reaches. If they are reproduced, it indicates that the pollution moves with the vehicle, which is a strong indication of local pollution.

[0077] Signal mobility analysis (without vehicle tracking): By analyzing the movement trajectory of the maximum temperature deviation in the three-dimensional spatial monitoring array over a continuous time period, if the trajectory shows a continuous and orderly spatial displacement along the kiln transmission direction, it indicates that the abnormal source moves with the vehicle, pointing to local contamination.

[0078] Based on the above analysis results, and based on the preset judgment rules, the following judgments are made:

[0079] If the cross-sectional anomaly coverage rate is lower than the local pollution space threshold (e.g., set to 40%), it indicates that the anomalies are highly concentrated in the cross-sectional space.

[0080] By checking the correlation between the vehicles, it is confirmed that the abnormal pattern is associated with the movement trajectory of at least one specific vehicle; or, by analyzing the signal mobility, it is confirmed that the movement trajectory of the maximum temperature deviation shows continuity along the kiln transport direction.

[0081] When the above conditions are met, the pollution is determined to be localized pollution;

[0082] If the cross-sectional anomaly coverage rate is higher than the global contamination space threshold (e.g., set to 70%), and the longitudinal synchronous anomaly ratio is higher than the preset global synchronization threshold (e.g., set to 60%), it indicates that the anomalies are widespread and occur synchronously in multiple cross-sections of the kiln.

[0083] The abnormal patterns are static and diffuse in space, meaning that neither vehicle correlation checks nor signal mobility analysis found any clear correlation between the anomalies and any specific vehicle movement or ordered spatial displacement.

[0084] If the above conditions are met, the pollution is determined to be global pollution.

[0085] The significance of the pollution impact range analysis module lies in its ability to accurately determine whether pollution is isolated in a single carrier or has spread to the entire kiln by analyzing quantitative indicators such as cross-sectional abnormal coverage rate and longitudinal synchronous abnormality ratio, combined with carrier correlation checks. This provides factual basis for implementing drastically different optimization strategies. Without this module, the system would be unable to distinguish between local and global pollution. If a global adjustment (such as overall speed reduction) is mistakenly initiated when faced with a local hotspot caused by a single polluting carrier, most clean materials would undergo unnecessary long sintering times, resulting in capacity loss and performance degradation. This ensures the accuracy of optimization actions.

[0086] Dynamic optimization execution module: Executes dynamically selected optimization strategies based on the impact range judgment results;

[0087] If it is a global contamination, the required reaction time offset is calculated based on the concentration and type of unplanned impurities, and the pusher speed value is adjusted according to the reaction time offset.

[0088] Specifically, during the period of anomaly, the average temperature time series data, overall oxygen concentration change time series data, and related spatiotemporal distribution feature vectors of the kiln judgment benchmark area are obtained.

[0089] Based on the principles of chemical reaction kinetics, a quantitative calculation model is constructed using real-time monitoring data to determine the reaction time offset required to compensate for the influence of impurities. ;

[0090] The calculation process is based on the following logical relationship formula: ;

[0091] in, and These are the target reaction degree and the initial reaction degree, respectively. The target reaction degree is set to a fixed value according to process requirements; the initial reaction degree is estimated from the material state at the moment of abnormal initiation and can be correlated with the initial oxygen concentration reading. Under clean process conditions free from impurities, the standard theoretical time required to reach the target reaction level is set by the process baseline. The effective reaction rate constant under the current impurity influence is the core variable in this calculation, and it is derived from real-time data using the following formula: ;

[0092] Where A is the pre-factor, which is the intrinsic parameter of the lithium manganese oxide host reaction; This represents the intrinsic activation energy of the main reaction of lithium manganese oxide. The change in activation energy introduced by impurities is a function of the impurity type, obtained through learning from a historical case library or theoretical calculations; R is the ideal gas constant. The measured average temperature of the baseline area during the period of anomaly is the real-time monitoring value.

[0093] Based on the calculated reaction time offset, the speed of the pusher plate in the kiln is adjusted proportionally to change the residence time of the material in the critical sintering zone.

[0094] The calculation process is as follows: sum the standard theoretical time and the reaction time offset, then calculate the ratio of the standard theoretical time to this sum to obtain the proportionality coefficient, and multiply the proportionality coefficient with the current reference push plate speed to obtain the new push plate speed.

[0095] If the pollution is localized, dynamic isolation and compensation optimization will be implemented. Dynamic isolation and compensation optimization is a control strategy for pollution carriers that is different from the global pollution control method. It includes: active environmental isolation and dynamic thermal field correction.

[0096] Specifically, proactive environmental isolation includes:

[0097] If the kiln is equipped with an RFID or visual recognition system, the precise location and movement trajectory of the contaminant can be obtained in real time;

[0098] In critical sections (such as constant temperature zones) through which the contaminant vehicle passes, the local atmosphere conditioning subsystem is activated to inject inert gas (such as N2) or adjust the oxygen partial pressure around the vehicle to form a local micro-atmosphere barrier and inhibit the diffusion of impurity reaction products.

[0099] Adjust the exhaust flow rate in the vicinity of the area to enhance local suction and prevent the spread of polluted air masses;

[0100] The specific process of dynamic adjustment of local exhaust flow is as follows: When the contaminated vehicle enters the target control zone, the control system, based on its real-time position, instructs the regulating valve on the independent exhaust branch corresponding to that zone to increase its opening or to increase the speed of the variable frequency fan on that branch. This creates a stronger negative pressure suction effect in the local area around the contaminated vehicle. This enhanced local exhaust effect, combined with the simultaneous local atmosphere injection, forms a dynamic physical barrier that can promptly and directionally extract the abnormal gas products released by the contaminated vehicle, effectively preventing them from spreading to the adjacent clean area, thereby strictly limiting the impact of pollution to a very small range.

[0101] Dynamic correction of the thermal field includes:

[0102] The main purpose is to compensate for the temperature in the local area where the contaminant is located, so as to reduce the impact of hot or cold spots on the surrounding materials and the overall thermal balance of the kiln.

[0103] If the kiln is equipped with a carrier tracking system (such as RFID or visual recognition), the real-time coordinates of the contamination carrier (longitudinal position L, lateral position W, and layer height H) can be directly read.

[0104] If there is no direct tracking, the vehicle's current position is estimated based on the signal mobility trajectory using a time-velocity model;

[0105] Based on the actual layout of the kiln heating system (usually multiple independent temperature control zones along the kiln length, with each zone containing multiple heating elements in the upper, lower, left, and right directions), the carrier coordinates (L, W, H) are mapped to the corresponding smallest controllable heating unit (i.e., "control zone").

[0106] Among them, the smallest controllable heating unit refers to the smallest physical heating area or heating element group in the kiln heating system that can independently receive instructions and accurately, quickly and independently adjust its output power.

[0107] For example, it is determined that the vehicle is located within the influence range of the heating element in the "mth temperature zone, left side, upper layer";

[0108] From the three-dimensional monitoring array, select the temperature sensor that is closest to the spatial location of the mapped zone of the contaminated vehicle, read its current temperature value, and obtain the set target temperature of the zone under the clean process.

[0109] If the current temperature is greater than the set target temperature, it indicates the presence of local hot spots, and negative power compensation needs to be applied.

[0110] If the current temperature is lower than the set target temperature, it indicates that there are local cold spots and positive power compensation needs to be applied.

[0111] Since the triggering condition is the existence of localized contamination, the current temperature value will definitely not be equal to the set target temperature;

[0112] The difference between the current temperature value and the set target temperature is calculated to obtain the local temperature deviation value;

[0113] The power compensation amount is obtained by multiplying the local temperature deviation value with the effective heat transfer coefficient and the equivalent heat transfer area corresponding to the control zone, and then calculating the ratio of the product with the expected correction time constant.

[0114] The effective heat transfer coefficient is a parameter calibrated using historical data or experiments, which comprehensively reflects the heat exchange efficiency of gas convection, radiation, and material carrier heat conduction within the kiln; the desired correction time constant is set according to the adjustment speed allowed by the process (for example, if the main correction is required to be completed within 5 minutes, then 300s can be used).

[0115] The significance of the dynamic optimization module lies in its ability to adaptively execute distinctly different and highly quantified optimization strategies based on the accurate judgment of the nature and scope of contamination by the preceding modules. When global contamination is determined, it directly calculates the required process compensation time through a quantitative model based on the principles of chemical reaction kinetics and adjusts the global push plate speed to systematically adapt to the reaction kinetic changes introduced by impurities, ensuring the uniformity of the reaction degree of the entire batch of materials. When local contamination is determined, it initiates refined control (active isolation and thermal field correction) for specific contamination carriers, strictly limiting the optimization impact to a very small area around the contamination source. Thus, while eliminating local anomalies, it maximizes the protection of the normal sintering process of most clean materials, avoiding capacity loss and performance risks caused by global adjustments.

[0116] This embodiment first collects process parameters in real time in the kiln and calculates the temperature field uniformity index to determine whether the uniformity is abnormal, realizing a panoramic perception of the thermochemical state inside the kiln. Then, when an abnormality is determined, it enters the diagnostic stage to distinguish whether the abnormality is caused by unplanned chemical impurities (such as Fe, Na, etc.), realizing the identification of impurity types. After identifying the impurities, it further analyzes the spatial distribution pattern of the abnormality and determines the scope of the abnormality's impact. It realizes scientific decision-making based on the nature of pollution and optimized strategy based on the impact range, forming a complete decision chain of "monitoring → diagnosis → classification → execution". This achieves an efficient, accurate, and intelligent solution to the complex non-uniformity problem introduced by chemical impurities in the lithium manganese oxide sintering process.

[0117] Example 2

[0118] Based on the same inventive concept as the monitoring and optimization system for temperature field uniformity in the lithium manganese oxide sintering kiln described in the foregoing embodiments, such as... Figure 2 and Figure 3As shown, this application provides a method for monitoring and optimizing the temperature field uniformity inside a lithium manganese oxide sintering kiln, wherein the method specifically includes the following steps:

[0119] Step S100: Real-time process parameter data of multiple spatial points set in the lithium manganese oxide sintering kiln, and analyze the temperature field uniformity based on the monitored real-time process parameter data to determine whether there is any abnormality in the uniformity.

[0120] Step S200: If there is an anomaly in uniformity, then based on the spatiotemporal distribution characteristics of the anomaly data and in combination with historical cases, identify whether there is unplanned impurity doping.

[0121] Step S300: If unplanned impurity doping exists, the impact range of unplanned impurity doping is further determined by the spatiotemporal distribution characteristics to determine whether it is localized or global contamination.

[0122] Step S400: Execute the dynamic selection optimization strategy based on the impact range judgment result;

[0123] S401: If it is a global contamination, calculate the required reaction time offset based on the concentration and type of unplanned impurities, and adjust the pusher speed value according to the reaction time offset.

[0124] S402: If the pollution is localized, dynamic isolation and compensation optimization will be carried out. Dynamic isolation and compensation optimization is a control strategy for pollution carriers that is different from the global pollution control method. It includes: active environmental isolation and dynamic thermal field correction.

[0125] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A monitoring and optimization system for temperature field uniformity within a lithium manganese oxide sintering kiln, characterized in that: include: Uniformity anomaly determination module: Real-time process parameter data of multiple spatial points set in the lithium manganese oxide sintering kiln, and analysis of temperature field uniformity based on the monitored real-time process parameter data to determine whether there is an anomaly in uniformity. Impurity type identification module: If there is an anomaly in uniformity, the module identifies whether there is unplanned impurity doping based on the spatiotemporal distribution characteristics of the uniformity anomaly data and in combination with historical cases. Pollution impact range analysis module: If there is unplanned impurity doping, the impact range of unplanned impurity doping is further determined by the spatiotemporal distribution characteristics, whether it is local pollution or global pollution; Dynamic optimization execution module: Executes dynamically selected optimization strategies based on the impact range judgment results; If it is a global contamination, the required reaction time offset is calculated based on the concentration and type of unplanned impurities, and the pusher speed value is adjusted according to the reaction time offset. If the pollution is localized, dynamic isolation and compensation optimization will be implemented. Dynamic isolation and compensation optimization is a control strategy for pollution carriers that is different from the global pollution control method. It includes active environmental isolation and dynamic thermal field correction.

2. The monitoring and optimization system for temperature field uniformity in lithium manganese oxide sintering kiln according to claim 1, characterized in that: The process for determining whether there are any abnormalities in uniformity is as follows: A three-dimensional spatial monitoring array is set up in the constant temperature zone of the kiln, which includes at least three layers, with at least three temperature sensors distributed in a spatial matrix in each layer; The temperature values ​​of all temperature sensors are collected in real time, and the temperature field uniformity index at the current moment is calculated. The temperature field uniformity index is the maximum value among the absolute values ​​of the differences between the temperature values ​​at all monitoring points and the average temperature. The temperature field uniformity index is compared with a preset anomaly detection threshold. If the index continues to exceed the anomaly detection threshold for a continuous anomaly time limit, then the uniformity is determined to be abnormal.

3. The monitoring and optimization system for temperature field uniformity in lithium manganese oxide sintering kiln according to claim 1, characterized in that: The process for identifying the presence of unplanned impurity doping is as follows: During the period of anomaly, acquire the raw temperature time series data of all temperature sensors in the three-dimensional spatial monitoring array of the kiln judgment reference area, as well as the time series data of oxygen concentration sensors at the corresponding spatiotemporal locations. Extract spatiotemporal distribution features to characterize the nature of the anomaly, and construct a spatiotemporal distribution feature vector; Calculate the cosine similarity between the spatiotemporal distribution feature vector and each abnormal feature vector template in the historical impurity feature case library; Extract candidate impurity types whose similarity values ​​exceed a preset matching threshold; If there is one and only one candidate impurity type whose similarity exceeds the matching threshold, it is determined that there is unplanned impurity doping corresponding to the template. If the similarity of multiple candidate impurity types exceeds the matching threshold, the one with the highest similarity is selected as the primary candidate, and its key features are checked for consistency with the corresponding features in the spatiotemporal distribution feature vector. If the check passes, it is adopted as the primary candidate impurity type.

4. The monitoring and optimization system for temperature field uniformity in lithium manganese oxide sintering kiln according to claim 3, characterized in that: Spatiotemporal distribution characteristics, including: Anomaly core coordinates and local diffusion intensity determined based on temperature data; Global diffusion ratio and equivalent diameter characterizing the spatial coverage of anomalies; The duration of temperature rise, average temperature rise rate, peak temperature rise, and standard deviation of temperature fluctuation were extracted from the time series curve of the abnormal core temperature. The ratio of the lag time and the magnitude of the change with respect to temperature change, extracted from the corresponding oxygen concentration time series curve.

5. The monitoring and optimization system for temperature field uniformity in a lithium manganese oxide sintering kiln according to claim 1, characterized in that: Determining whether the impact of unplanned impurity doping is localized or global contamination includes: Calculate the cross-sectional anomaly coverage rate and longitudinal synchronous anomaly ratio used to quantify the spatial distribution of pollution; Analyze whether the spatial distribution of abnormal patterns is related to the movement trajectory of the material carrier to determine whether contamination adheres to a specific carrier; Based on the analysis results of cross-sectional anomaly coverage rate, longitudinal synchronous anomaly ratio, and the correlation between anomaly patterns and vehicle motion, and according to the preset judgment rules, the scope of the impact of unplanned impurity doping is determined to be either localized pollution or global pollution.

6. The monitoring and optimization system for temperature field uniformity in a lithium manganese oxide sintering kiln according to claim 5, characterized in that: The calculation process for cross-sectional anomaly coverage rate and longitudinal synchronous anomaly ratio: Select the monitoring section where the core coordinates of the anomaly are located as the target section. Count the number of monitoring points on the target section where the temperature deviation continuously exceeds the lower threshold. Calculate the ratio of this number to the total number of monitoring points on the target section as the anomaly coverage rate of the section. During the period of anomaly, the upstream and downstream monitoring sections adjacent to the target section are examined as the first adjacent section and the second adjacent section. The proportion of monitoring points in the first adjacent section and the second adjacent section that exhibit the same type of anomaly pattern as the target section in the same time period is calculated as the first monitoring point proportion and the second monitoring point proportion. The average of the first monitoring point proportion and the second monitoring point proportion is calculated as the longitudinal synchronous anomaly ratio.

7. The monitoring and optimization system for temperature field uniformity in lithium manganese oxide sintering kiln according to claim 5, characterized in that: The process for determining whether contamination has adhered to a specific vehicle is as follows: Vehicle correlation check: If the kiln system is equipped with a vehicle identification and tracking system, the vehicle identification information that passed through the space at the starting moment when the temperature deviation first appeared at the core coordinate position of the anomaly is retrieved, the movement of the corresponding vehicle in the kiln is tracked, and the similar temperature and oxygen concentration anomaly patterns are analyzed in other spatial positions that it subsequently reaches. If they are reproduced, it indicates that the pollution moves with the vehicle. Signal mobility analysis: By analyzing the movement trajectory of the maximum temperature deviation in the three-dimensional spatial monitoring array over a continuous time period, if the trajectory shows a continuous and orderly spatial displacement along the kiln transmission direction, it indicates that the abnormal source moves with the carrier.

8. The monitoring and optimization system for temperature field uniformity in lithium manganese oxide sintering kiln according to claim 1, characterized in that: If it is a global contamination, based on the identified unplanned impurity types and their impact, the reaction time offset required to compensate for the impact of the impurities is calculated according to the chemical reaction kinetic model; The calculation process is as follows: an effective reaction rate constant model is established, which includes the change in activation energy determined by the type of impurities. The effective reaction rate constant under the influence of the current impurities is inverted by using the real-time average temperature data of the kiln's judgment benchmark zone during the abnormal period. Then, the theoretical time deviation from the standard process conditions is calculated by combining the target reaction degree and the initial reaction degree. The standard theoretical time is summed with the reaction time offset, and then the ratio of the standard theoretical time to this sum is calculated to obtain the proportionality coefficient. The proportionality coefficient is then multiplied by the current reference push plate speed to obtain the new push plate speed.

9. The monitoring and optimization system for temperature field uniformity in a lithium manganese oxide sintering kiln according to claim 1, characterized in that: Active environmental isolation includes: Real-time acquisition of the location of the contamination vehicle inside the kiln; In critical sections traversed by contaminant vehicles, local atmosphere conditioning is initiated to create micro-atmosphere barriers. Atmosphere conditioning includes injecting inert gases or adjusting local oxygen partial pressure. Adjust the exhaust flow rate of this key section simultaneously.

10. The monitoring and optimization system for temperature field uniformity in a lithium manganese oxide sintering kiln according to claim 1, characterized in that: Dynamic correction of the thermal field includes: Determine the real-time coordinates of the contaminant carrier within the kiln and map them to the corresponding minimum controllable heating unit; Obtain the current temperature value of the temperature monitoring point closest to the spatial location of the heating unit, and compare it with the set target temperature to obtain the local temperature deviation value; The power compensation amount is obtained by multiplying the local temperature deviation value with the effective heat transfer coefficient and the equivalent heat transfer area corresponding to the control zone, and then calculating the ratio of the product to the expected correction time constant.

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