Lime kiln partition intelligent temperature control system and storage medium

Abstract

The invention relates to the technical field of lime kiln control, and discloses a lime kiln partition intelligent temperature control system and a storage medium, and the system comprises a data collection unit which is used for collecting state data of each region of a lime kiln; the working condition recognition unit recognizes working conditions based on the state data; the temperature control unit is provided with a PID (Proportion Integration Differentiation) control strategy and a prediction control strategy, and the opening degrees of the fuel regulating valves and the air regulating valves in different areas in the lime kiln are calculated according to the corresponding control strategies selected on the basis of working conditions; the discharging control unit is provided with a balance control strategy, the balance control strategy obtains the current discharging speed through the current productivity of the lime kiln and the material level height in the lime kiln, and the execution unit correspondingly controls the lime kiln according to the control quantity. The method effectively solves the problems of difficulty in multi-area coordination, poor working condition adaptability, high energy consumption and the like in a traditional control mode, and has the remarkable advantages of improving the temperature control precision, ensuring the production stability and reducing the energy consumption.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lime kiln control, and particularly relates to a lime kiln partition intelligent temperature control system and a storage medium. BACKGROUND

[0002] In the lime production process, the temperature control effect of the lime kiln directly determines the product quality, production efficiency and energy consumption level. The current traditional temperature control mode has significant shortcomings. The traditional lime kiln temperature control mode often relies on manual experience, has low control precision and poor stability, and causes the problem of unbalanced kiln temperature field. Specifically, manual adjustment cannot achieve coordinated control of multiple regions in the kiln, and the temperature gradient control between the preheating zone, the calcining zone and the cooling zone is not accurate; the conventional PID control cannot adapt to the complex working condition changes of the lime kiln, and is prone to overshoot when the raw material composition fluctuates or the production load changes; there is a lack of linkage control of the kiln material level and the temperature field, resulting in a mismatch between the combustion efficiency and the discharge speed. These problems not only cause unstable lime activity, but also cause serious fuel waste and high unit product energy consumption. In addition, the existing control system lacks a compensation mechanism for the temperature coupling effect of adjacent control regions, and the temperature deviation between the edge region and the center region exists for a long time, affecting the service life of the kiln. In view of the above problems, the existing technology needs to be improved. SUMMARY

[0003] In view of the deficiencies in the prior art, the present application aims to provide a lime kiln partition intelligent temperature control system and a storage medium, which has the advantages of improving the kiln temperature control precision, enhancing the working condition adaptability and optimizing the energy utilization efficiency.

[0004] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0005] A lime kiln partition intelligent temperature control system, comprising:

[0006] A data acquisition unit, which is arranged in the lime kiln and is used to acquire state data of each region of the lime kiln;

[0007] A working condition identification unit, which identifies the working condition based on the state data;

[0008] A temperature control unit, which is provided with a PID control strategy and a predictive control strategy, and calculates the opening degree of the fuel adjusting valve and the air adjusting valve in different regions of the lime kiln based on the corresponding control strategy selected according to the working condition;

[0009] A discharge control unit, which is provided with a balance control strategy, and obtains the current discharge speed through the current production capacity of the lime kiln and the material level height in the lime kiln;

[0010] The execution unit controls the lime kiln accordingly based on the opening degrees of the fuel regulating valve and the air regulating valve and the discharging speed in the lime kiln output by the temperature control unit and the discharging control unit.

[0011] In the present invention, preferably, the state data includes the temperature data of each area in the lime kiln, the fuel flow rate, the air flow rate, the current production capacity data, and the material level height data in the lime kiln.

[0012] In the present invention, preferably, a fuzzy control algorithm is used to dynamically adjust the parameters of the PID control strategy. The input variables of the fuzzy control algorithm are the temperature deviation e and the deviation change rate Δe, and the output variable is the parameter correction coefficient ΔK of the PID control strategy. p 、ΔK i 、ΔK d， Where ΔK p is the proportional correction coefficient of the PID control strategy, ΔK i is the integral correction coefficient, and ΔK d is the differential correction coefficient.

[0013] In the present invention, preferably, the fuzzy control algorithm specifically includes:

[0014] Coarse adjustment stage, when the temperature deviation ∣e>T1,

[0015] When the temperature deviation e>T1 and the deviation change rate Δe>0, then

[0016] ΔK p =-0.3K p ,

[0017] ΔK i =-0.5K i ,

[0018] ΔK d =+0.2K d ;

[0019] When the temperature deviation e<-T1 and the deviation change rate Δe<0, then

[0020] ΔK p =+0.3K p ,

[0021] ΔK i =+0.5K i ,

[0022] ΔK d =+0.2K d ;

[0023] Transition stage, when the temperature deviation T2<e∣<T1,

[0024] When the temperature deviation e > 0 and Δe > 0, then

[0025]

[0026] ΔK d = +0.1K d ,

[0027] When the temperature deviation e < 0 and Δe < 0, then

[0028]

[0029] ΔK d = +0.2K d ,

[0030] In the fine-tuning stage, when the temperature deviation |e| < T2 and the deviation change rate |Δe| < A, then

[0031] ΔK p = -0.02K p ,

[0032]

[0033] ΔK d = +0.05K d ;

[0034] When e·Δe < 0 and |e| < B, then

[0035]

[0036] ΔK i = +0.05K i ,

[0037]

[0038] where T1, T2, A, and B are preset thresholds.

[0039] In the present invention, preferably, the lime kiln is divided into 9 independent control regions along the axial preheating zone, calcination zone, and cooling zone, and radially at the center and the edge, and each region is configured with an independent PID parameter set.

[0040] In the present invention, preferably, the coupling coefficient matrix C of the temperature change between adjacent regions ij , dynamically adjusts the control quantity:

[0041]

[0042] In the formula, is the original control quantity of region i, ΔT j(t) represents the temperature deviation between adjacent regions j.

[0043] In this invention, preferably, the predictive control strategy includes:

[0044] Predicting temperature variations in different zones of the lime kiln based on historical temperature, fuel flow rate, and air flow rate:

[0045]

[0046] In the formula, Q is the fuel flow rate, A is the air flow rate, and n = 10 (historical window length);

[0047] Simultaneously, temperature data from the indoor model is integrated to weight and fuse the predicted temperature changes:

[0048]

[0049] In the formula, α is the weight.

[0050] Target fuel flow rate and target air flow rate are calculated based on the predicted temperature.

[0051] The opening degrees of the fuel regulating valve and the air regulating valve are calculated based on the target fuel flow rate and the target air flow rate.

[0052] In this invention, preferably, the unloading speed calculated by the balance control strategy is:

[0053]

[0054] In the formula, Q production To set the production capacity (t / h), ρ is the bulk density of lime (t / m³). 3 A is the cross-sectional area of ​​the discharge port (m²) 2 ), where η is the unloading efficiency coefficient (0.7-0.9, determined by the wear condition of the unloading port).

[0055] In this invention, preferably, it also includes capacity adjustment:

[0056] When the kiln temperature deviates from the set value, the capacity correction formula is as follows:

[0057] δ Q =1+k Q ·(T actual -T set ),

[0058] In the formula, k Q = -0.01 / ℃, the corrected unloading speed is:

[0059] υ′ theory =v theory ·δQ .

[0060] Compared with the prior art, the beneficial effects of the present invention are:

[0061] The system of this invention acquires kiln status data in real time through a data acquisition unit, dynamically selects temperature control strategies in conjunction with a working condition identification unit, and adopts a balance control strategy to achieve coordinated adjustment of unloading speed and temperature field. This effectively solves the problems of multi-area coordination difficulties, poor working condition adaptability and high energy consumption in traditional control methods, and has significant advantages in improving temperature control accuracy, ensuring production stability and reducing energy consumption. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the structure of a zoned intelligent temperature control system for a lime kiln according to the present invention.

[0063] Figure 2 This is a schematic diagram of the process for a zoned intelligent temperature control system for a lime kiln as described in this invention. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0066] In existing technologies, kiln temperature control during lime production relies on manual experience to adjust fuel supply and unloading speed. Traditional methods have monitoring blind spots; operators rely solely on local temperature instrument data and cannot perceive real-time changes in the conditions of multiple areas within the kiln. Equipment operating parameter adjustments lag behind changes in operating conditions, often resulting in uneven temperature distribution. Furthermore, the lack of a linkage mechanism between unloading speed and material balance within the kiln easily leads to localized overheating or under-burning.

[0067] To address these issues, engineers discovered that incomplete data acquisition led to a weak foundation for control decisions, making it difficult for a single control strategy to adapt to complex operating conditions. Multiple experiments revealed that insufficient coordination between the fuel valve and air valve exacerbated temperature fluctuations. Material balance and temperature field regulation were found to be coupled, and independent control could not maintain production stability. Based on this, a multi-dimensional collaborative control framework was proposed. This framework establishes global awareness through real-time data acquisition, develops an adaptive control strategy for operating conditions, and designs a dynamic balancing algorithm for unloading speed.

[0068] Therefore, please see Figure 1 and Figure 2 A preferred embodiment of the present invention provides a zoned intelligent temperature control system for a lime kiln, comprising a data acquisition unit installed inside the kiln for collecting status data of each zone; a working condition identification unit for identifying the current working condition based on the status data; a temperature control unit configured with a PID control strategy and a predictive control strategy, which selects the corresponding strategy to calculate the opening degree of the fuel regulating valve and the air regulating valve according to the working condition; a discharge control unit configured with a balance control strategy, which calculates the discharge speed based on the current production capacity and material level; and an execution unit for controlling the lime kiln accordingly based on the opening degree of the fuel regulating valve and the air regulating valve and the discharge speed output by the temperature control unit and the discharge control unit.

[0069] The data acquisition unit refers to the sensor array arranged in the axial preheating zone, calcination zone, cooling zone, and radial center and edges of the kiln body. Specifically, it can be implemented using thermocouples, flow meters, and level gauges to acquire data on temperature, fuel flow rate, air flow rate, and material height. The operating condition identification unit refers to the pattern classification algorithm running on the controller. Specifically, it can be implemented using a state machine model based on threshold judgment to determine whether the kiln is in stable operation, transitional heating, or abnormal operating conditions. The temperature control unit is a computational module containing dual control strategies. Specifically, it can be implemented using a programmable logic controller (PLC) to switch control algorithms according to operating conditions to optimize and adjust response speed. The unloading control unit is a calculation module based on the material balance equation. Specifically, it can be implemented using a proportional-integral (PI) algorithm to dynamically correct the operating frequency of the unloading mechanism through material level feedback.

[0070] Specifically, the sensor array continuously collects temperature and flow data from nine independent zones within the kiln, transmitting this data to the controller to form a real-time operating condition map. When the operating condition identification unit detects a temperature deviation exceeding a threshold, it triggers a predictive control strategy to calculate future heat demand, simultaneously adjusting the system in real-time using a PID algorithm. The opening degrees of the fuel valve and air valve are synchronously adjusted via a coupling equation to ensure optimal combustion efficiency. The unloading speed is dynamically calculated based on the ratio of real-time production capacity data to the material level. When the kiln temperature deviates from the set value, the unloading speed is adjusted using a correction coefficient, forming a temperature-production capacity closed-loop control.

[0071] Compared with existing technologies, which rely on single-point temperature monitoring, this solution achieves full-area temperature field monitoring through a multi-region sensor network; traditional PID control with fixed parameters is not suitable for dynamic operating conditions, while this solution achieves adaptive switching of control strategies through operating condition identification; conventional unloading control only considers material level factors, while this solution introduces a capacity correlation algorithm to achieve coordinated optimization of production cycle and thermal state.

[0072] Through the above technical solutions, this application achieves precise control of the temperature field inside the kiln, eliminating temperature fluctuations caused by the lag of manual operation; the synergistic effect of the dual control strategies improves the stability of regulation under complex working conditions; and the dynamic balance mechanism of unloading speed effectively prevents material accumulation or empty kiln phenomena, maintaining the temperature stability of continuous production.

[0073] In this embodiment, the status data includes temperature data, fuel flow rate, air flow rate, current production capacity data, and material level height data in various areas of the lime kiln.

[0074] Temperature data refers to the real-time material temperature distribution data in the axial preheating zone, calcination zone, cooling zone, and radial center and edge of the lime kiln, monitored by temperature sensors. This can be achieved using thermocouple arrays or infrared temperature measurement modules, and is used to characterize the spatial differences in the thermodynamic state within the kiln. Fuel flow rate refers to the input rate data of gas or solid fuel collected by a mass flow meter, specifically using a Coriolis flow meter, used to quantify the energy input intensity of the combustion process. Air flow rate refers to the combustion air supply data measured by a differential pressure flow meter, specifically using a Venturi tube in conjunction with a pressure transmitter, used to calculate the oxygen content balance of the combustion reaction. Current production capacity data refers to the finished lime production rate data obtained through a combination of weighing sensors and encoders, specifically using a belt scale combined with a speed detection device, used to reflect the real-time progress of the calcination reaction of materials within the kiln. Material level height data within the lime kiln refers to the material accumulation height data obtained by radar level gauges or laser rangefinders, specifically using a non-contact level detection device, used to estimate the residence time distribution of materials within the kiln.

[0075] Specifically, temperature data is used to construct a three-dimensional temperature field model through a multi-point sensor network. Fuel flow rate and air flow rate data are analyzed in real time to calculate combustion efficiency. Current production capacity data and material level data are correlated over time to establish a material balance equation. The operating condition identification unit uses temperature field gradient, combustion efficiency deviation, and material balance status as joint inputs. When the temperature fluctuation in the calcination zone exceeds a threshold, it simultaneously retrieves historical fuel flow rate data for the corresponding area to determine if incomplete combustion is the cause. Combined with the current production capacity decline trend and abnormal material level rise, it identifies a heat transfer obstruction condition caused by material accumulation. The temperature control unit selects a predictive control strategy based on this condition, predicts the temperature change trend based on the coupling relationship between fuel flow rate and air flow rate, and adjusts the control parameters of adjacent areas to eliminate heat conduction hysteresis.

[0076] In this embodiment, a dynamic operating condition identification strategy is used to identify real-time operating conditions, which are divided based on the rate of temperature change and combustion efficiency. When the rate of temperature change is greater than 5°C / min and the combustion efficiency is less than 80%, it is considered the start-up phase; when the rate of temperature change is less than 5°C / min and the combustion efficiency is greater than 80%, it is considered the steady-state phase; all other phases are considered disturbance phases. During the start-up phase, the openings of the fuel regulating valve and air regulating valve are adjusted based on the initial settings. When the steady-state phase is reached, a PID control strategy is used to calculate the control quantity and adjust the openings of the fuel regulating valve and air regulating valve. During the disturbance phase, a predictive control strategy is used to calculate the control quantity and adjust the openings of the fuel regulating valve and air regulating valve.

[0077] In this embodiment, a fuzzy control algorithm is used to dynamically adjust the parameters of the PID control strategy. The input variables of the fuzzy control algorithm are the temperature deviation e and the rate of change of deviation Δe, and the output variable is the parameter correction coefficient ΔK of the PID control strategy. p ΔK i ΔK d , where ΔK p ΔK is the proportional correction coefficient for the PID control strategy. i ΔK is the integral correction factor. d This is the differential correction coefficient.

[0078] Among them, the fuzzy control algorithm refers to a nonlinear control method based on fuzzy logic rules. Specifically, it can be implemented using a Mamdani-type fuzzy inference system. A parameter adjustment rule base is established through fuzzification processing of temperature deviation and its rate of change. Temperature deviation e refers to the difference between the current temperature measurement value and the set value, which can be collected by thermocouples or infrared temperature measuring devices. Deviation rate of change Δe refers to the change in temperature deviation per unit time, which can be obtained through differential calculation or sliding window averaging. Parameter correction coefficients ΔKp, ΔKi, and ΔKd correspond to the dynamic adjustment of parameters in the proportional, integral, and derivative links, respectively, thereby achieving real-time matching of PID parameters with operating conditions.

[0079] Compared with existing technologies, traditional PID control relies on fixed parameter combinations and cannot cope with nonlinear disturbances caused by fluctuations in fuel calorific value and uneven material distribution during lime kiln production. This solution, however, uses fuzzy logic to transform operational experience into parameter adjustment rules, enabling the control system to have adaptive parameter tuning capabilities, improving response speed during rapid temperature changes and maintaining control accuracy during steady-state conditions.

[0080] Through the above technical solution, this application solves the problems of lag and large overshoot of fixed parameter PID under lime kiln variable operating conditions. By dynamically adjusting the proportional coefficient to enhance the system's anti-interference ability, optimizing the integral coefficient to eliminate steady-state error, and adjusting the derivative coefficient to suppress temperature fluctuations, the rapid convergence and stable maintenance of the temperature field inside the kiln are achieved.

[0081] Compared to existing technologies, traditional lime kiln control relies solely on a single temperature parameter for feedback adjustment, failing to distinguish whether temperature fluctuations are fundamentally caused by abnormal combustion or abnormal material distribution, easily leading to over- or under-adjustment. This solution, by introducing dynamic monitoring of fuel and air flow rates, can directly determine the operating status of the combustion system. Combined with correlation analysis of material level and production capacity data, it can accurately identify the problem of decreased heat transfer efficiency caused by material retention, achieving precise control through multi-factor synergy.

[0082] Through the above technical solution, this application can simultaneously monitor combustion process parameters and material distribution parameters during the operation of a lime kiln. When the temperature of the calcination zone fluctuates, it can quickly distinguish the fault type and select the optimal control strategy. For example, when it detects that the fuel flow rate is normal but the material level continues to rise, it automatically triggers the adjustment of the unloading speed instead of the fuel supply adjustment, avoiding violent temperature fluctuations caused by misoperation and maintaining the thermodynamic balance in the kiln.

[0083] In this embodiment, the fuzzy control algorithm specifically includes a coarse adjustment stage, a transition stage, and a fine adjustment stage. In the coarse adjustment stage, when the absolute value of the temperature deviation is greater than the first preset threshold, proportional correction coefficient adjustment rules are set for positive and negative deviations respectively; in the transition stage, when the absolute value of the temperature deviation is between the first preset threshold and the second preset threshold, the integral correction coefficient and the differential correction coefficient are adjusted according to the deviation direction; in the fine adjustment stage, when the absolute value of the temperature deviation is less than the second preset threshold, different parameter correction strategies are executed according to whether the absolute value of the deviation change rate is less than the third preset threshold or whether the product of the deviation and the change rate is less than zero.

[0084] Among them, the coarse adjustment stage refers to the working stage when the system is in a large deviation state. In the coarse adjustment stage, when the temperature deviation |e| > T1,

[0085] when the temperature deviation e > T1 and the deviation change rate Δe > 0, then

[0086] ΔK p =-0.3K p ,

[0087] ΔK i =-0.5K i ,

[0088] ΔK d =+0.2K d ;

[0089] when the temperature deviation e < -T1 and the deviation change rate Δe < 0, then

[0090] ΔK p =+0.3K p ,

[0091] ΔK i =+0.05K i ,

[0092] ΔK d =+0.2K d ;

[0093] In the transition stage, the transition stage refers to the working stage when the system is in a medium deviation state. When the temperature deviation T2 < |e| < T1,

[0094] when the temperature deviation e > 0 and Δe > 0, then

[0095]

[0096] ΔK d =+0.1K d ,

[0097] When the temperature deviation e < 0 and Δe < 0, then

[0098]

[0099] ΔK d = +0.2K d ,

[0100] In the fine-tuning stage, the fine-tuning stage refers to the working stage when the system is approaching the steady state. Specifically, it can be achieved by using the deviation change rate threshold as the judgment condition. When the temperature deviation |e| < T2 and the deviation change rate |Δe| < A, then

[0101] ΔK p = -0.02K p ,

[0102]

[0103] ΔK d = +0.05K d ;

[0104] When e·Δe < 0 and |e| < B, then

[0105]

[0106] ΔK i = +0.05K i ,

[0107]

[0108] Among them, T1, T2, A, and B are preset thresholds. The preset thresholds can be specifically implemented by statistically analyzing historical operation data to determine the numerical range, and the stage switching of the control strategy is achieved through multi-level threshold division.

[0109] Compared with the prior art, the traditional PID control method uses a fixed parameter combination, and it is difficult to balance the contradictory requirements of quickly eliminating large deviations and stably maintaining small deviations in the lime kiln temperature control. In the prior art, the parameter tuning method is mostly offline setting, which cannot adapt to the dynamic changes of the kiln conditions. This solution ensures both the fast response ability under high-temperature conditions and the control accuracy in the steady state by setting multi-level adjustment stages and automatically switching the control strategy at different development stages of the deviation.

[0110] Through the above technical solution, this application effectively solves the technical problem of balancing dynamic characteristics and steady-state accuracy in lime kiln temperature control. When large-scale temperature fluctuations occur in the kiln, the temperature field balance can be quickly restored through the strong proportional action in the coarse adjustment stage; when the temperature approaches the target value, the differential correction in the fine adjustment stage effectively suppresses overshoot and oscillation; and in the transition stage, the directional adjustment through integral action avoids the regulation lag problem caused by integral saturation in traditional control, thus achieving stable control across the entire operating range.

[0111] In this embodiment, the lime kiln is divided into three functional sections along the axial direction: a preheating zone, a calcination zone, and a cooling zone. At the same time, each functional section is further divided into a central area and an edge area in the radial direction, forming nine independent control areas. An independent PID parameter group is configured for each area.

[0112] The axial division consists of three functional segments corresponding to the three thermal stages of material in the lime kiln: preheating, high-temperature calcination, and cooling. The temperature change patterns in these three stages differ significantly. The radial division consists of a central region and an edge region, corresponding to spatial locations within the kiln with different heat transfer efficiencies. The central region experiences high heat conduction resistance due to the high material density, while the edge region experiences rapid heat loss due to its proximity to the kiln wall. Each independent control region is configured with a dedicated PID parameter set. By differentiating the proportional, integral, and derivative coefficients, the control parameters can be matched to the differences in thermal inertia and heat transfer characteristics of each region.

[0113] Specifically, the axial preheating zone, where the material is in the initial heating stage, requires a larger integral coefficient in its PID parameter set to suppress temperature fluctuations; the calcination zone, as the core reaction zone, uses a higher proportional coefficient for rapid adjustment; and the cooling zone, with its gentle temperature gradient, uses a smaller derivative coefficient to prevent over-adjustment. The radial central region uses a lower proportional coefficient and a longer integral time to accommodate its slow thermal response; while the edge regions use a higher proportional coefficient and a shorter integral time to compensate for their heat dissipation effect. Nine independent parameter sets are used to perform closed-loop control of the fuel and air valves in the nine zones, avoiding the shortcomings of traditional single parameter sets in adapting to spatially heterogeneous operating conditions. Simultaneously, dynamic compensation of the control quantity is achieved through the inter-zone coupling coefficient matrix.

[0114] Compared with existing technologies, traditional lime kiln temperature control methods typically employ integrated PID control or simple axial partitioning control modes, which cannot address the problem of uneven radial heat field distribution. This solution, through a dual-dimensional division of axial functional segments and radial positions, combined with fine-tuning of independent parameter groups, solves the problem of insufficient adaptability of traditional methods to differences in heat transfer efficiency and spatial heterogeneity of thermal inertia, effectively suppressing the imbalance of the temperature field within the kiln.

[0115] Through the above technical solution, this application can implement precise control over the thermal field characteristics at different thermal stages and spatial locations, thereby reducing the temperature fluctuation amplitude in various areas of the kiln, improving the temperature stability of the calcination zone, and making the temperature gradient distribution between the preheating zone and the cooling zone more reasonable. This solution also reduces the number of frequent fuel valve adjustments caused by mismatched control parameters and reduces temperature control lag.

[0116] In this embodiment, the coupling coefficient matrix C of temperature changes in adjacent regions ij Dynamically adjust the control quantity:

[0117]

[0118] In the formula, Let ΔT be the initial control variable for region i. j (t) represents the temperature deviation of adjacent region j. The coupling coefficient matrix Cij is a set of coefficients reflecting the degree of temperature interaction between different control zones of the lime kiln. Specifically, it can be obtained by experimental measurement or thermodynamic simulation to obtain the heat transfer parameters between each zone. The establishment of this matrix quantifies the intensity of thermal field interaction between zones. The dynamic adjustment control quantity refers to the compensation correction quantity of the fuel and air regulation quantity of the current zone based on the temperature deviation of adjacent zones. Specifically, it can be achieved by linearly superimposing the coupling coefficient and the temperature deviation using matrix multiplication. This correction process can offset the interference of temperature fluctuations in adjacent zones on the current zone.

[0119] Specifically, when a temperature deviation occurs in a certain area, the system calculates the temperature impact of adjacent areas on that area using a coupling coefficient matrix. For example, when the temperature of area j is higher than the set value, its positive deviation, after being weighted by the coupling coefficient Cij, generates a compensation command for the control quantity of area i. This command reduces the fuel supply to area i to prevent overheating. Temperature data from all adjacent areas are fused in real time through matrix operations to form a closed-loop control loop, ensuring that the adjustment action of each area includes temperature compensation for surrounding areas. Thus, the thermal field coupling effect between areas is transformed into a calculable compensation term, achieving dynamic coordinated control across areas.

[0120] Compared to existing technologies, traditional control methods only adjust individual regions independently, failing to consider the spatial correlation characteristics of heat conduction within the lime kiln. This scheme establishes a dynamic correlation model of the temperature field across multiple regions by constructing a coupling coefficient matrix, transforming the previously decentralized independent control into collaborative control. The temperature fluctuation diffusion problem caused by local adjustments in existing technologies is effectively suppressed in this scheme through a matrix operation compensation mechanism.

[0121] Through the above technical solution, this application can eliminate the mutual interference of temperature regulation between regions and prevent cascading temperature fluctuations caused by local temperature adjustments. When temperature anomalies occur at the boundary between the preheating zone and the calcination zone in the kiln, the system can quickly calculate the combined impact of the three adjacent zones on this area and maintain thermal field equilibrium through coordinated adjustment of the fuel valve opening. This control method ensures that the axial and radial temperature gradients in the kiln are always kept within the process requirements, solving the temperature field imbalance problem caused by traditional control methods.

[0122] In this embodiment, the predictive control strategy employs a bidirectional LSTM network with an attention mechanism, and the predictive control strategy includes:

[0123] Predicting temperature variations in different zones of the lime kiln based on historical temperature, fuel flow rate, and air flow rate:

[0124]

[0125] In the formula, Q is the fuel flow rate, A is the air flow rate, and n = 10 (historical window length);

[0126] Simultaneously, temperature data from the indoor model is integrated to weight and fuse the predicted temperature changes:

[0127]

[0128] In the formula, α is the weight.

[0129] The target fuel flow rate and target air flow rate are calculated based on the predicted temperature, where the target fuel flow rate is:

[0130]

[0131] In the formula, c is the specific heat capacity of lime, m is the mass of the material; η is the combustion efficiency (LSTM synchronous prediction value), and Q is the mass of the material. net Net calorific value of fuel;

[0132] The target airflow is:

[0133] V air (t+Δt)=λ opt ·Q fuel (t+Δt)·ρ air In the formula, λ opt This is the theoretical air-fuel ratio (dynamically adjusted according to the type of fuel).

[0134] The opening degrees of the fuel regulating valve and the air regulating valve are calculated based on the target fuel flow rate and the target air flow rate. The opening degree of the fuel regulating valve is:

[0135]

[0136] In the formula, Cv is the valve flow coefficient (determined by the valve model), ΔP is the pressure difference across the valve, and ρfuel is the fuel density.

[0137] Air regulating valve opening:

[0138]

[0139] In the formula T std / P std For standard state parameters, T air This refers to the air temperature.

[0140] Historical temperature refers to the temperature data of various areas of the lime kiln over a past time series. Specifically, it can be achieved using continuous time series data collected by sensors. By setting a time window length, the historical data variation patterns can be extracted to capture the dynamic trends of temperature changes. Fuel flow rate and air flow rate refer to the volume of fuel and air entering the combustion zone per unit time. This can be monitored in real time using flow meters, and a correlation between input quantities and temperature changes can be established by combining historical data. The physical model refers to a mathematical model based on the heat transfer and combustion reaction mechanisms of the lime kiln. Specifically, it can use thermodynamic equations and mass conservation equations to simulate the temperature field and provide theoretical prediction results. Target fuel flow rate and air flow rate refer to the amount of fuel and air required to achieve the predicted temperature. This can be calculated by working backward from the combustion efficiency formula to establish a quantitative relationship between temperature and flow rate.

[0141] Specifically, the bidirectional LSTM network with an integrated attention mechanism adds an attention mechanism layer to the original bidirectional LSTM network to enhance the influence of time steps. The input layer of the bidirectional LSTM network receives temperature features at 24 time steps, captures forward and reverse time steps through the bidirectional LSTM layer, and simultaneously strengthens the influence of key time steps through the attention mechanism layer, ultimately outputting temperature prediction values ​​for multiple regions for the next minute. It was initially trained using a large amount of lime kiln temperature change data, and validation datasets have verified its small prediction error and fast response speed. It can achieve long-term temperature prediction based on the thermal inertia of lime kilns, significantly improving temperature control accuracy and production stability, and providing core data support for intelligent kiln control.

[0142] Specifically, when predicting temperature changes, historical temperature, fuel flow rate, and air flow rate data for 10 consecutive time points are first acquired. Their trends and correlations are analyzed to establish a time series prediction model. Current fuel and air flow rates are then input into the model to predict future temperature change trends. Simultaneously, based on the lime kiln's structural parameters and combustion reaction equations, a physical model is constructed, and theoretical temperature change values ​​are calculated. The two prediction results are then weighted and fused. The weights of the data model and the physical model are dynamically adjusted based on historical prediction errors; for example, when the data model error increases, the weight of the physical model is increased to strengthen theoretical constraints. The final temperature prediction is then calculated based on the fused values. The required fuel and air flow rates are calculated in reverse using the combustion efficiency formula. Based on the calibration relationship between flow rate and valve opening, control commands for the regulating valve are generated.

[0143] Compared to existing technologies, traditional control methods rely solely on operator experience or a single model for temperature prediction, resulting in response lag and accumulated biases. For example, human experience struggles to handle multivariate coupling relationships in real time, while a single data model is prone to prediction inaccuracies during sudden changes in operating conditions. This solution employs a dual prediction mechanism combining historical data and a physical model, preserving the adaptability of data-driven approaches to complex conditions while introducing the constraints of theoretical models for extreme situations. Furthermore, a dynamic weight allocation strategy is used to adjust the fusion ratio based on real-time errors, preventing the continuous impact of single-model biases on control accuracy.

[0144] Through the above technical solution, this application solves the prediction lag problem caused by the reliance on human experience in traditional control. By capturing temperature change patterns through historical data windows, it achieves the forward-looking generation of control commands. Simultaneously, it reduces the risk of accumulated control deviations and maintains prediction reliability during operating condition fluctuations through the dynamic fusion of physical and data models. Ultimately, it improves the dynamic response capability of temperature control, enabling more precise matching of fuel and air flow regulation to actual needs and ensuring a stable temperature field within the kiln.

[0145] In this embodiment, the unloading speed calculated by the balance control strategy is:

[0146]

[0147] In the formula, Q production To set the production capacity (t / h), ρ is the bulk density of lime (t / m³). 3 A is the cross-sectional area of ​​the discharge port (m²) 2 ), where η is the unloading efficiency coefficient (0.7-0.9, determined by the wear condition of the unloading port).

[0148] The set capacity refers to the target output of lime per unit time. Real-time values ​​are input through the capacity setting module to reflect the production plan's baseline requirement for unloading speed. Lime bulk density refers to the mass per unit volume of material within the kiln, obtained through periodic measurement by a density sensor, used to convert capacity requirements into volumetric flow rate. The cross-sectional area of ​​the unloading port refers to the geometric dimensions of the material drop channel, preset according to equipment design parameters, used to calculate the theoretical maximum unloading speed. The unloading efficiency coefficient is the attenuation factor due to equipment wear during material flow. It is dynamically corrected by detecting changes in the roughness of the unloading port's inner wall using a wear sensor, used to compensate for the decrease in unloading capacity caused by mechanical wear.

[0149] Specifically, in the calculation of the unloading speed, the production capacity is set as the target driving value. Combined with the lime bulk density and the unloading port area, an initial benchmark value for the theoretical unloading speed can be derived. The unloading efficiency coefficient is further dynamically adjusted based on the real-time wear status of the unloading port. For example, when the wear of the unloading port intensifies and the efficiency coefficient drops to the 0.7 range, the system automatically increases the unloading speed to compensate for the increased material flow resistance. By integrating the production capacity target, material characteristic parameters, and equipment status parameters into the mathematical model, the unloading speed can be automatically adjusted according to fluctuations in the kiln material level and changes in production capacity demand. This ensures that the material output rate remains synchronized with the thermal balance requirements of the kiln temperature field, avoiding a sudden drop in cooling zone temperature due to excessively fast unloading or heat accumulation in the calcination zone due to excessively slow unloading.

[0150] Compared to existing technologies, traditional methods rely on operators' experience to manually adjust the unloading speed, using only a single parameter—material level—for coarse control. This fails to correlate production capacity targets with equipment status, leading to frequent temperature fluctuations within the kiln due to unloading. This solution establishes a multi-parameter coupled mathematical model, incorporating key factors such as production capacity demand, material density, and equipment wear into the calculation logic. This achieves dynamic closed-loop control of the unloading speed, overcoming the limitations of isolated parameter adjustments in manual control.

[0151] Through the above technical solution, this application can accurately match the unloading speed with the thermal state inside the kiln, effectively suppressing temperature field disturbances caused by sudden changes in the unloading rate and maintaining stable temperature gradients in areas such as the calcination zone and cooling zone. Simultaneously, by compensating in real-time for the impact of equipment wear on unloading efficiency, it avoids the accumulation of control deviations caused by mechanical performance degradation, significantly improving the robustness of unloading control.

[0152] This implementation also includes capacity adjustment:

[0153] When the kiln temperature deviates from the set value, the capacity correction formula is as follows:

[0154] δ Q =1+k Q ·(T actual-T set ),

[0155] In the formula, k Q = -0.01 / ℃, the corrected unloading speed is:

[0156] v′ theory =v theory ·δ Q .

[0157] The capacity correction formula is a mathematical model that generates a correction coefficient based on the ratio of the absolute value of the temperature deviation to the set temperature. Specifically, it can be implemented using a piecewise function combined with an exponential decay function, used to dynamically adjust the baseline capacity value according to the temperature fluctuation range. The corrected unloading speed calculation formula involves substituting the corrected set capacity into the material balance equation. This can be achieved through the material conservation equation linked with a hydraulic actuator, establishing an inverse correlation mechanism between temperature change and material residence time.

[0158] Specifically, when the temperature sensor detects that the actual temperature deviates from the set value, the ratio of the absolute value of the temperature deviation to the set temperature is first calculated. A correction coefficient is then generated based on a preset threshold, selecting either a linear or nonlinear correction mode. This coefficient smooths out the interference caused by sudden temperature changes using an exponential function. The corrected set capacity is then input into the unloading speed calculation model, adjusting the output frequency of the unloading machine's frequency converter in real time. The dynamic adjustment of material residence time alters the duration of fuel combustion and heat exchange within the kiln, creating a two-way coupling effect between temperature control and material flow, thereby suppressing the continued expansion of the temperature deviation.

[0159] Compared to existing technologies, traditional methods adjust the unloading speed based solely on a fixed capacity setting, failing to address material accumulation or kiln emptying caused by temperature fluctuations. This solution introduces a temperature feedback signal to perform secondary correction of the unloading speed, constructing a closed-loop control circuit of temperature-capacity-unloading speed, effectively solving the kiln ring formation or over-burning defects caused by thermal imbalance.

[0160] Through the above technical solution, this application achieves dynamic matching between the material residence time and temperature change in the kiln, avoiding overall thermal instability caused by local temperature anomalies. At the same time, the response speed of the temperature control system is improved by the lag compensation function of the unloading speed, ensuring the continuous stability of combustion efficiency and product quality in the kiln.

[0161] In some other preferred embodiments of the present invention, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps of the system as described in the above embodiments.

[0162] Storage media refers to the physical carrier used to store computer program code. Specifically, it can be implemented using solid-state drives (SSDs) or flash memory chips. Its non-volatility ensures that the control program remains intact after power loss, and its erasable / rewritable nature supports remote updates to the control algorithm. The computer program refers to a set of executable instructions, specifically generated through compiling the control strategy logic. It includes operational flows such as data acquisition, operating condition identification, multi-zone temperature collaborative control, and unloading speed calculation, used to transform the control system's operational logic into an automated execution sequence.

[0163] Specifically, after the computer program is loaded by the processor, it automatically collects real-time data on temperature, fuel flow, and material level in each area of ​​the lime kiln. Based on a preset algorithm, it identifies the current operating condition and selects the corresponding control strategy to calculate the adjustment amount of the fuel valve and air valve openings. The program dynamically adjusts the PID parameters through a fuzzy control algorithm and compensates for temperature change trends using a predictive model, thus resolving the coupling relationship between multiple control areas. In the unloading control stage, the program calculates the unloading speed based on real-time production capacity and material level, and dynamically corrects the production capacity target value through temperature deviation, forming a fully closed-loop control.

[0164] Compared to existing technologies, traditional lime kilns rely on operators' experience to manually adjust valve openings, which cannot handle multi-zone temperature coupling effects in real time, and parameter adjustments are subject to lag. This solution, however, uses a programmed control strategy to simultaneously process data from dozens of sensors, completing multi-variable coupling calculations within milliseconds, achieving rapid response and accurate output of control commands.

[0165] Through the above technical solution, this application solves the problem of kiln temperature field imbalance caused by low precision in manual control. By programmatically executing a fusion algorithm of fuzzy PID and predictive control, it ensures that the temperature fluctuation range of the calcination zone is controlled within a set threshold. Simultaneously, based on real-time material level data, the unloading speed is dynamically adjusted to avoid sudden pressure changes within the kiln caused by uneven unloading. The programmable nature of the storage medium allows the control strategy to be optimized online according to changes in raw material composition, continuously improving system adaptability.

[0166] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0167] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. A zoned intelligent temperature control system for a lime kiln, characterized in that, include: The data acquisition unit is set up in each area of ​​the lime kiln and is used to collect status data of each area of ​​the lime kiln. The status data includes temperature data, fuel flow rate, air flow rate, current production capacity data and material level height data of each area of ​​the lime kiln. The working condition identification unit identifies the current working condition based on the status data of each area of ​​the lime kiln; The temperature control unit is equipped with a PID control strategy and a predictive control strategy. Based on the current operating conditions, it matches a suitable control strategy to calculate the opening degree of the fuel regulating valve and the air regulating valve in the lime kiln. The unloading control unit is equipped with a balance control strategy, which obtains the current unloading speed by using the current capacity of the lime kiln and the material level in the lime kiln. The execution unit controls the lime kiln according to the opening degree of the fuel regulating valve and air regulating valve and the unloading speed output by the temperature control unit and the unloading control unit.

2. The intelligent temperature control system for a lime kiln zone according to claim 1, characterized in that, The PID control strategy includes: Obtain real-time temperature data for each region; Based on the deviation between real-time temperature data and the set temperature value, the initial control quantity is calculated using a PID control algorithm; Simultaneously, a fuzzy control algorithm is used to dynamically adjust the proportional, integral, and derivative parameters of the PID control algorithm; The adjusted PID control algorithm outputs control quantities to drive the fuel regulating valve and air regulating valve to adjust the combustion intensity and achieve temperature tracking control.

3. The intelligent temperature control system for a lime kiln zone according to claim 2, characterized in that, A fuzzy control algorithm is used to dynamically adjust the parameters of the PID control strategy. The input variable of the fuzzy control algorithm is the temperature deviation. e and the rate of change of deviation Δ e The output variable is the parameter correction coefficient Δ of the PID control strategy. K p Δ K i Δ K d , where Δ K p Δ is the proportional correction coefficient for the PID control strategy. K i Δ is the integral correction factor. K d This is the differential correction coefficient.

4. The intelligent temperature control system for a lime kiln zone according to claim 3, characterized in that, The fuzzy control algorithm is based on temperature deviation. e and the rate of change of deviation Δ e The size is divided into coarse adjustment control, transition control and fine adjustment control.

5. The intelligent temperature control system for a lime kiln zone according to claim 1, characterized in that, The lime kiln is divided into nine independent control zones along the axial preheating zone, calcination zone, and cooling zone, and radially into the center and edge. Each zone is configured with an independent set of PID parameters.

6. The intelligent temperature control system for a lime kiln zone according to claim 5, characterized in that, A coupling coefficient matrix based on temperature change is set between each of the adjacent independent control areas. The parameters in the coupling coefficient matrix correspond to the coupling coefficients between each of the independent control areas. Temperature data of all adjacent areas are fused in real time through the coupling coefficients.

7. The intelligent temperature control system for a lime kiln zone according to claim 1, characterized in that, The predictive control strategy includes: Predict temperature variations in various areas of the lime kiln based on historical temperature, fuel flow rate, and air flow rate; Simultaneously, temperature data from the indoor model is integrated to weight and fuse the predicted temperature changes; Target fuel flow rate and target air flow rate are calculated based on the predicted temperature. The opening degrees of the fuel regulating valve and the air regulating valve are calculated based on the target fuel flow rate and the target air flow rate.

8. The intelligent temperature control system for a lime kiln zone according to claim 1, characterized in that, The predictive control strategy employs a bidirectional LSTM network with an attention mechanism.

9. A zoned intelligent temperature control system for a lime kiln according to claim 8, characterized in that, It also includes a capacity correction strategy, which uses a capacity correction formula to correct the calculated unloading speed when the overall temperature inside the lime kiln deviates from the set value.

10. A storage medium, characterized in that, The system contains a computer program that, when executed by a processor, causes the processor to perform the steps of a zoned intelligent temperature control system for a lime kiln as described in any one of claims 1-9.

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