A pellet induration test system

Abstract

The application discloses a kind of pellet roasting test system, it is related to pellet roasting test technical field, to overcome the defects of test system in prior art, such as actual hot air working condition of belt roaster cannot be accurately simulated, parameter control is not accurate, test result reliability is low, system includes combustion-supporting fan, cooling fan, drum dry hot blast stove, regenerative burner, regenerative chamber, roasting cup and various sensors and controllers Equipment and controller;Before test, heat regenerative chamber to regenerative saturation, supply air temperature control in drum dry, dry preheating roasting soaking, cooling stage, controller is based on belt roaster historical operation data and real-time detection data, searches optimal historical working condition and tracks its parameter curve and controls each equipment.The application can reproduce the full process thermal working condition of belt roaster, improve test stability and data reliability, provide accurate support for pellet roasting process development and raw material adaptation.

Description

Technical Field

[0001] This invention relates to the technical field of pellet roasting. More specifically, this invention relates to a pellet roasting test system. Background Technology

[0002] Belt roasters are core equipment for the large-scale production of iron ore pellets, and the rationality of their process parameters directly determines the quality of the pellets, production efficiency, and energy consumption. To meet the adaptation requirements of different iron ore raw materials and pellet roasting processes, and to promote the development of new processes, major research institutes and metallurgical enterprises have built pellet roasting test lines to explore suitable roasting process parameters and provide technical references for industrial production.

[0003] Regarding the process control and structural optimization of belt calciners, the applicant has conducted a series of technological research and developments. For example, in terms of control methods, patent CN116224945B (Control Method for Thermal System of Belt Calciner Based on Digital Twin) reduces overall production energy consumption by constructing a mechanism model and reducing its order; patent CN120593505B (Refined Control Method for Pellet Sintering of Belt Calciner) further improves control accuracy under multi-variable coupled conditions by utilizing transfer function matrices and feedforward compensation. In terms of equipment structure, patent CN116412665B (Belt Calciner with Staged Cooling and Cascaded Hot Air Utilization) improves energy utilization through three-stage cooling and cascaded hot air utilization; patent CN120557931B (A Ring Calciner and its Working Method) effectively reduces the equipment footprint through the design of a ring-shaped rotary device. These authorized patented technologies have achieved significant results in actual production.

[0004] However, when adapting these advanced control logics and efficient structures to new raw materials and processes, existing pellet roasting test systems revealed significant shortcomings. Existing test lines cannot accurately simulate the actual hot air conditions upon which the aforementioned patented technologies rely, resulting in insufficient reliability of test results and difficulty in effectively supporting subsequent process development. Specifically, in actual industrial production, the hot air system of a belt roaster is highly integrated. The high-temperature hot air discharged from the cooling section has an oxygen content close to that of air. After being transported to the burner through secondary air ducts and mixed with fuel for combustion and heating, the temperature can reach 1300℃, and the oxygen content of the hot air remains above 16%, meeting the core requirements of pellet roasting. In contrast, in existing test lines, when the hot air temperature is raised to approximately 1350℃, required for pellet roasting, the oxygen content drops below 6%, which is significantly inconsistent with the core oxygen content range of 8-18% in actual industrial production. The oxygen content of hot air is a key parameter in pellet roasting, which directly affects the oxidation and consolidation effect of magnetite, the efficiency of fuel combustion, and the energy consumption of the process. Deviations in its value can lead to a disconnect between the experimental process and actual production, thus preventing advanced methods such as the aforementioned digital twin control and refined feedforward compensation from being effectively verified and transferred in the experimental stage.

[0005] Since the roasting process of pellets requires a full oxidation reaction, existing test lines cannot replicate the hot air oxygen content and related thermal conditions in actual production, nor can they replicate the operating conditions of the various high-efficiency, low-consumption roasters developed by the applicant. This severely restricts the adaptation of new raw materials, process optimization, and the development of next-generation technologies. Therefore, it is urgent to improve existing pellet roasting test technology and enhance the matching degree between test conditions and actual advanced production processes. Summary of the Invention

[0006] One objective of this invention is to provide a pellet roasting test system that can reproduce the full thermal operating conditions of a belt roaster, thereby improving test stability and data reliability.

[0007] To achieve these objectives and other advantages of the present invention, according to one aspect of the invention, a pellet roasting test system is provided, comprising a combustion fan, a cooling fan, a forced-drying hot air furnace, a regenerative burner, a regenerator, a roasting cup, a cooling tower, an exhaust fan, an oxygen content sensor, a thermocouple array, an air volume sensor, and a controller. Before conducting the process simulation test, the regenerative burner is activated to heat the regenerator, causing the regenerator pellets inside to reach saturation. During the simulated forced-drying stage, the cooling fan and the forced-drying hot air furnace are activated to supply hot air to the bottom of the roasting cup. The hot air passing through the material layer is cooled by the cooling tower and then discharged by the exhaust fan. During the simulated drying, preheating, roasting, and homogenization stages, the combustion fan is activated to supply cold air to the regenerator. The cold air is heated in the regenerator to form high-temperature hot air. The high-temperature hot air is then conditioned by the regenerative burner to meet the test hot air requirements before being supplied to the top of the roasting cup to treat the pellet material inside. The process involves desiccation, preheating, roasting, and homogenization. During the simulated cooling stage, a cooling fan directly supplies air to the bottom of the roasting cup to cool the roasted pellets. The controller stores a historical operating data sample set for the entire process of the belt roaster, containing hot air temperature, oxygen content, and air volume values ​​for each moment. The controller is configured to retrieve the historical moment closest to the thermal state within the current process stage from the historical operating data sample set, based on the real-time detection data from the oxygen content sensor, thermocouple array, and air volume sensor. It then extracts the hot air temperature change curve, oxygen content change curve, and air volume change curve for the continuous time period following that historical moment as target tracking curves. Based on the target tracking curves, the controller adjusts the speed of the combustion fan, the speed of the cooling fan, the output power of the drying hot air furnace, and the fuel supply of the regenerative burner.

[0008] Furthermore, the controller is configured to: determine the corresponding process mode based on the current process stage, including blast drying mode, desiccation mode, preheating mode, calcination mode, homogenization mode, and cooling mode; for each process mode, a corresponding feedforward control model is preset, which calculates the feedforward control quantities of the corresponding combustion fan speed, cooling fan speed, blast drying hot air furnace output power, and regenerative burner fuel supply based on the expected values ​​of hot air temperature, oxygen content, and air volume at the current moment in the target tracking curve; obtain the current actual hot air temperature, oxygen content, and air volume based on the real-time detection data of the oxygen content sensor, thermocouple array, and air volume sensor, calculate the deviation from the expected values ​​at the corresponding moment in the target tracking curve, and calculate the feedback correction quantity through the PID controller based on the deviation; superimpose the feedforward control quantity and the feedback correction quantity to generate the final control quantity, which is output to the combustion fan, cooling fan, blast drying hot air furnace, and regenerative burner, respectively.

[0009] Furthermore, for each process mode, the historical operating data samples belonging to that process mode are clustered according to the hot air temperature, oxygen content, and air volume values ​​to form multiple operating condition sub-clusters. For each operating condition sub-cluster, the hot air temperature, oxygen content, and air volume values ​​of each sample point within the sub-cluster are used as inputs, and the corresponding combustion fan speed, cooling fan speed, output power of the hot air furnace, and fuel supply of the regenerative burner are used as outputs to establish a local linear regression model. During real-time control, the controller first determines the operating condition sub-cluster based on the expected values ​​of hot air temperature, oxygen content, and air volume at the current moment in the target tracking curve, and then calls the local linear regression model corresponding to the operating condition sub-cluster to calculate the feedforward control quantity.

[0010] Furthermore, the PID controller includes a temperature PID controller, an oxygen content PID controller, and an air volume PID controller. The temperature PID controller outputs a temperature correction coefficient based on the deviation of the hot air temperature, the oxygen content PID controller outputs an oxygen content correction coefficient based on the deviation of the oxygen content, and the air volume PID controller outputs an air volume correction coefficient based on the deviation of the air volume. The controller calls a preset decoupling control matrix according to the current process mode, taking the temperature correction coefficient, oxygen content correction coefficient, and air volume correction coefficient as inputs. Through the decoupling control matrix calculation, it outputs the speed correction amount acting on the combustion fan, the speed correction amount acting on the cooling fan, the output power correction amount acting on the hot air furnace, and the fuel supply correction amount acting on the regenerative burner, respectively, as feedback correction amounts. The matrix elements of the decoupling control matrix are pre-calibrated according to the coupling relationship between the hot air temperature, oxygen content, and air volume and each actuator under each process mode, and dynamically switch with the switching of process modes.

[0011] Furthermore, based on the actual tracking error of the hot air temperature, oxygen content, and air volume relative to the target tracking curve at the current moment, the real-time confidence coefficient of the feedback correction is calculated. The larger the tracking error, the larger the real-time confidence coefficient. The feedforward control quantity is added to the feedback correction quantity multiplied by the real-time confidence coefficient to obtain the final control quantity. The final control quantity is then output to the combustion fan, cooling fan, hot air blower, and regenerative burner, respectively.

[0012] Furthermore, the historical operating data sample set also includes the pellet compressive strength value corresponding to each moment; the controller is configured to retrieve multiple candidate historical moments with the closest thermal state from the historical operating data sample set based on the real-time detection data of the oxygen content sensor, thermocouple array and air volume sensor, and then select the candidate historical moment with the highest corresponding pellet compressive strength value from the multiple candidate historical moments, and use the hot air temperature change curve, oxygen content change curve and air volume change curve in the continuous time period after the candidate historical moment as the target tracking curve.

[0013] Furthermore, the trends of hot air temperature, oxygen content, and air volume changes within a preset time window prior to the current moment are extracted. These trends are then dynamically time-warped to calculate similarity with the trends of the same length within the same time window prior to each historical moment in the historical operating data sample set, yielding a trend similarity index. The weighted Euclidean distance between the current measured values ​​of hot air temperature, oxygen content, and air volume and the values ​​of the same temperature, oxygen content, and air volume at each historical moment is calculated, yielding an instantaneous similarity index. The trend similarity index and the instantaneous similarity index are then weighted and summed according to a preset ratio to obtain a comprehensive similarity index. The historical moments with the lowest comprehensive similarity index are selected as candidate historical moments with the closest thermal state.

[0014] Furthermore, from multiple actual belt roaster production lines, a target belt roaster was selected that matched the target simulation object of the experimental system in terms of raw material type, pellet specifications, and production capacity. During the production and operation of the target belt roaster, the hot air temperature, oxygen content, air volume, and corresponding pellet compressive strength values ​​were collected in real time at a fixed sampling frequency. Simultaneously, the process stage at each moment was recorded. Based on the proportional relationship between the cross-sectional dimensions and material layer thickness of the roasting cup in the experimental system and the cross-sectional dimensions and material layer thickness of the trolley in the target belt roaster, a correlation was established between the experimental system and the target belt roaster. The air volume conversion factor and temperature holding time conversion factor between machines are used. Based on the air volume conversion factor and temperature holding time conversion factor, the collected air volume value of the target belt roaster and the duration of each process section are converted into the target air volume value and target process duration of the corresponding test system. The hot air temperature value and oxygen content value are used as the target hot air temperature value and target oxygen content value of the test system. The converted target hot air temperature value, target oxygen content value, target air volume value, target process duration and the corresponding pellet compressive strength value are classified and stored according to the process section to obtain the historical operation data sample set.

[0015] The present invention has at least the following beneficial effects: This invention effectively solves the technical problem that traditional pellet roasting test systems cannot accurately simulate the actual thermal state of belt roasters, achieving precise simulation testing across the entire process. The system can completely replicate the entire process of drying, preheating, roasting, homogenization, and cooling, closely matching the actual process logic of industrial belt roasters. By matching historical operating data from the belt roaster, it precisely controls the core parameters of hot air temperature, oxygen content, and air volume in real time, completely overcoming the shortcomings of traditional tests where high temperature and oxygen content are mismatched, ensuring that the experimental hot air conditions are highly consistent with industrial production. Relying on real-time detection data to dynamically retrieve the optimal historical operating curve, it achieves precise tracking and automatic adjustment of the thermal state at each process stage, significantly improving test stability and parameter accuracy. The test results are closer to actual production, with significantly improved reliability, providing precise basis for process development and raw material adaptation, effectively shortening the process optimization cycle, reducing R&D costs, and providing reliable support for the precise R&D and industrial implementation of pellet roasting processes.

[0016] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of a pellet roasting test system according to an embodiment of this application. It shows the connection relationship of each component of the system, including a combustion fan, a cooling fan, a forced-drying hot air furnace, a regenerative burner, a regenerator, a roasting cup, a cooling tower, and a flue gas fan. The overall structure is clearly presented. Each component is connected by pipes, providing structural support for the operation of each subsequent process stage.

[0018] Figure 2 This is a schematic diagram of the heat storage preparation stage of one embodiment of this application, showing the equipment operation status during the heat storage preparation stage. It mainly shows the heating process of the heat storage chamber by the heat storage burner. The heat storage chamber is filled with high-alumina ceramic heat storage balls. The controller monitors the temperature of the heat storage chamber in real time until the heat storage balls are saturated with heat, providing a stable heat source for subsequent processes.

[0019] Figure 3 This is a flow diagram of the drying process in one embodiment of this application, showing the airflow transmission path during the drying stage. The cooling fan and the drying hot air furnace work together. Hot air is sent in from the bottom of the roasting cup, passes through the pellet material layer and enters the cooling tower for cooling, and is finally discharged by the exhaust fan, showing the airflow direction of the drying process and the working status of each related equipment.

[0020] Figure 4This is a flow diagram of the drying / preheating / calcination / homogenization process in one embodiment of this application, showing the airflow transmission and heating process in this stage. The combustion fan supplies air to the regenerator, and after being heated in the regenerator and then heated again by the regenerator burner, the high-temperature hot air is sent in from the top of the calcination cup to complete the drying, preheating, calcination and homogenization of the pellets. The flow parameters and equipment operating status of each stage are marked.

[0021] Figure 5 This is a cooling process airflow diagram of one embodiment of the present application, showing the airflow transmission path during the cooling stage. The regenerative burner stops supplying fuel, and the cooling fan supplies air directly to the bottom of the roasting cup. The cold air passes through the roasted pellet layer and carries away the heat, thereby cooling the pellets. The diagram shows the airflow direction and equipment working status during the cooling process. Detailed Implementation

[0022] The present invention will now be described in further detail so that those skilled in the art can implement it based on the description.

[0023] It should be understood that terms such as "having," "comprising," and "including" used in the embodiments of this application do not exclude the presence or addition of one or more other elements or combinations thereof. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of components in a specific posture. If the specific posture changes, the directional indication will also change accordingly. When an element is referred to as "fixed to" or "set on" another element, it can be directly on the other element or may have an intervening element present. When an element is referred to as "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element through an intervening element. Descriptions involving "first," "second," etc., in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features.

[0024] It should be noted that the technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this application.

[0025] In one embodiment, the pellet roasting test system may include a combustion fan 5, a cooling fan 6, a forced-drying hot air furnace 7, a regenerative burner 1, a regenerator 2, a roasting cup 3, a cooling tower 4, an exhaust fan 8, an oxygen content sensor, a thermocouple array, an air volume sensor, and a controller. The combustion fan 5 and cooling fan 6 serve as forced-drying fans, and the exhaust fan 8 serves as an exhaust fan. All three can be Roots blowers, which feature stable airflow and low pressure fluctuations, ensuring stable and controllable air supply and exhaust volume for the test system, providing a reliable air source for stable operation at each process stage. The combustion fan 5 primarily provides a stable source of cold air for the regenerator 2, ensuring its heat exchange efficiency. The cooling fan 6 can provide airflow during both the forced-drying and cooling stages, accommodating both the delivery of forced-drying hot air and the cooling needs of the pellets. The exhaust fan 8 is used to discharge the cooled exhaust gas from the system, maintaining the internal pressure balance. The hot air drying furnace 7 can be an electrically heated hot air furnace, capable of rapidly producing hot air at a stable temperature for drying pellet materials, avoiding excessive moisture content in the materials from affecting subsequent roasting effects; the regenerative burner 1 can be a natural gas regenerative burner 1. To meet the oxygen content requirements of the hot air for preheating and roasting processes in the belt roaster, the system is specially equipped with a regenerative chamber 2. This regenerative chamber 2 can completely simulate the hot air properties of the hot air source (cooling section) of the belt roaster, and its internal hot air oxygen content can reach more than 20%, accurately matching the actual production hot air oxygen content requirements; the regenerative chamber 2 can be filled with high-alumina ceramic regenerative spheres similar in structure to pellets. These regenerative spheres have excellent high-temperature resistance and can operate at temperatures above 1400℃, meeting the hot air heat storage requirements above the pellet roasting temperature, and can quickly absorb and store the heat generated by the regenerative burner 1, providing a stable heat source for subsequent process stages. In addition, for laboratory tests requiring specific oxygen content, the system can adjust the oxygen content through the burner in the upper part of the regenerator 2, flexibly adapting to various test conditions. The system can also simulate special conditions (such as reducing atmosphere) or advanced processes such as sintering hot air ignition, further expanding the test range.The roasting cup 3 can be a high-temperature resistant quartz beaker, which can hold the pellet material to be roasted, serving as the core carrier for pellet roasting. The cross-sectional size of the beaker can be φ300mm, and the material layer thickness can be 400-900mm. The cooling tower 4 can be a spray-type cooling tower, which can quickly reduce the temperature of the hot air discharged after passing through the material layer, preventing high-temperature exhaust gas from damaging the exhaust fan 8. The oxygen content sensor can be installed at the inlet and outlet air ducts of the roasting cup 3 to detect the oxygen content of the hot air in real time, providing accurate oxygen content data for the controller. Its measurement range can be 0%~25%. The thermocouple array can be composed of multiple S-type thermocouples, which are installed inside the roasting cup 3, at the hot air inlet and outlet, respectively, to detect the hot air temperature at multiple points, ensuring the comprehensiveness and accuracy of temperature detection. The air volume sensor can be installed at the outlet of the combustion fan 5 and the cooling fan 6 to detect the air flow rate. Its measurement range can be 0~800Nm. 3 / h; The controller can be a PLC controller, serving as the core control unit of the entire system, responsible for receiving detection data from various sensors, recalling historical operating data, and outputting control commands. Before conducting the process simulation test, the regenerative burner 1 needs to be started to heat the regenerative chamber 2. During the heating process, the controller monitors the internal temperature of the regenerative chamber 2 in real time through the thermocouple array until the heat storage balls in the regenerative chamber 2 absorb heat to saturation. At this point, the internal temperature of the regenerative chamber 2 can be stabilized at a maximum of 1350℃, providing a sufficient and stable heat source for the subsequent preheating, calcination, and homogenization stages. During the simulated drying stage, the controller synchronously starts the cooling fan 6 and the drying hot air furnace 7. The hot air produced by the drying hot air furnace 7 can reach a temperature of 200-400℃. The hot air is transported through pipes to the bottom of the roasting cup 3, and then passes upward through the pellet material layer. During the process of passing through the material layer, the hot air can remove the moisture from the pellet material, completing the drying process. After passing through the material layer, the temperature of the hot air will drop to 80-110℃, and then enter the cooling tower 4 for cooling. After being cooled to below 80℃, it is discharged from the system by the exhaust fan 8. During the simulated desiccation, preheating, roasting, and homogenization stages, the controller starts the combustion fan 5 to supply cold air to the heat storage chamber 2. After being heated by the heat storage pellets in the heat storage chamber 2, the cold air forms a high-temperature hot air with a temperature of 900-1100℃. The oxygen content of this high-temperature hot air is close to that of air. Subsequently, the high-temperature hot air enters the regenerative burner 1. Simultaneously, the controller adjusts the ratio of fuel supply to combustion air (from a portion of the self-fired blower 5 or an independent combustion air duct) in the regenerative burner 1 to perform secondary temperature and oxygen regulation of the hot air: when it is necessary to increase the hot air temperature and appropriately reduce the oxygen content, the fuel supply is increased, utilizing fuel combustion to consume some oxygen, raising the hot air temperature to the target value within the range of 400-1350℃, while precisely controlling the oxygen content within the 8-18% range required by the target process; when it is necessary to maintain a higher oxygen content, the fuel supply can be reduced or suspended, with only the high-temperature hot air from the regenerative chamber 2 directly output, or supplemented by a small amount of combustion air for regulation. Through the above proportional control, the system can independently and continuously regulate the temperature and oxygen content of the hot air throughout the entire process, ensuring that both simultaneously meet the stringent requirements of each stage of drying, preheating, calcination, and homogenization for the hot air conditions, especially overcoming the defect of a sudden drop in oxygen content at high temperatures in traditional experimental systems. Hot air is piped to the top of the calcining cup 3, and from top to bottom, it dries, preheats, calcines, and homogenizes the pellet material inside the calcining cup 3, continuously for the experimental process time. The preheating stage can last for 20 minutes, the calcination stage for 30 minutes, and the homogenization stage for 15 minutes to ensure that the pellet material reacts fully. During the simulated cooling stage, the controller shuts off the fuel supply to the regenerative burner 1, while simultaneously controlling the cooling fan 6 to supply air directly to the bottom of the calcining cup 3. The cold air passes through the pellet material layer, carrying away the heat from the pellet material, thus cooling the pellets until the pellet temperature drops below 80°C.The controller has a pre-stored historical operating data sample set for the entire process of the belt roaster. This sample set comes from the operating data of multiple actual belt roaster production lines, including the hot air temperature, oxygen content, and air volume values ​​at each acquisition moment. The acquisition interval can be 1 minute or 2 minutes. The controller is configured to retrieve the historical moment in the historical operating data sample set that is closest to the thermal state of the current process stage based on the detection data transmitted in real time by the oxygen content sensor, thermocouple array, and air volume sensor. Then, it extracts the hot air temperature change curve, oxygen content change curve, and air volume change curve for the next 10 or 15 minutes as the target tracking curve. The controller then adjusts the corresponding components in the combustion fan 5, cooling fan 6, output power of the hot air furnace 7, and fuel supply of the regenerative burner 1 according to the target tracking curve to ensure that the thermal parameters of the current system are consistent with the target tracking curve, thus ensuring the accuracy of the test.

[0026] In existing technologies, pellet roasting test systems are mostly simple, segmented structures, with each process stage operating independently without a unified control unit. Hot air parameters rely on manual setting, and there is no heat storage or heat exchange structure, resulting in low heating efficiency. Oxygen content cannot be stabilized within the range required for industrial production at high temperatures, and the test conditions deviate significantly from actual belt roaster production. This embodiment integrates various functional devices and detection elements to form a complete full-process test system. A heat storage chamber 2 is introduced to achieve heat recovery and stable supply. Automatic control of thermal parameters is achieved using a controller and historical operating data, accurately replicating the hot air conditions of actual production. This makes the test process more aligned with industrial production logic, improves the reliability of test data, and provides more accurate references for process development.

[0027] In one embodiment, the controller is further configured to determine the corresponding process mode based on the current process stage. The process modes include blast drying, extraction drying, preheating, roasting, homogenization, and cooling. Different process modes correspond to different thermal parameter control logics. The controller can automatically determine the current process stage by detecting the operating status of each device and sensor data, and then switch to the corresponding process mode. For each process mode, the controller has a pre-set feedforward control model. This feedforward control model is a mathematical model established based on the correspondence between process parameters and the control quantities of the actuators. In one possible implementation, the feedforward control model can adopt a linear regression model. Its inputs are the expected values ​​of hot air temperature, oxygen content, and air volume at the current moment in the target tracking curve. The outputs are the feedforward control quantities of the corresponding combustion fan 5 speed, cooling fan 6 speed, blast drying hot air furnace 7 output power, and regenerative burner 1 fuel supply. This feedforward control quantity can predict the changing trend of process parameters in advance, providing basic control commands to the system and reducing the lag in parameter adjustment. Simultaneously, the controller acquires the actual hot air temperature, oxygen content, and air volume in real time through oxygen content sensors, thermocouple arrays, and air volume sensors. It calculates the difference between the actual detected values ​​and the expected values ​​at the corresponding moments of the target tracking curve to obtain the deviation values ​​for each parameter. For example, when the actual hot air temperature is 5°C lower than the expected value, the deviation value is -5°C. Subsequently, the controller inputs this deviation value to the PID controller, which calculates the feedback correction amount based on the deviation value. In one possible implementation, the proportional gain of the PID controller can be 1.0 or 1.2, the integral time can be 10s or 15s, and the derivative time can be 2s or 3s. The PID algorithm adjusts the deviation to obtain the feedback correction amount that corrects the parameter deviation. Finally, the controller superimposes the feedforward control amount and the feedback correction amount to generate the final control amount, which is output to the combustion fan 5, cooling fan 6, hot air furnace 7, and regenerative burner 1, respectively, to achieve precise control of each actuator and ensure that the thermal parameters remain stable within the target range.

[0028] This embodiment uses a combination of feedforward control and PID feedback control. Feedforward control provides the basic control quantity in advance, while feedback control corrects parameter deviations in real time. The two work together to effectively shorten the control response time, reduce parameter fluctuations, improve the accuracy and stability of system control, and make the thermal parameters more closely match the requirements of the target tracking curve.

[0029] In one embodiment, for each process mode, the controller preprocesses the historical operating data sample set, filtering out sample points belonging to that process mode. Then, based on three core parameters—hot air temperature, oxygen content, and air volume—the controller uses a K-means clustering algorithm to cluster the filtered sample points. The number of clusters can be set to 5 or 6, ultimately forming multiple operating condition sub-clusters. Each sub-cluster corresponds to a typical process condition, and sample points within the same sub-cluster have similar thermal parameter characteristics, representing a stable operating state. For each operating condition sub-cluster, the hot air temperature, oxygen content, and air volume of each sample point within the sub-cluster are used as input parameters, and the corresponding rotational speed of the combustion fan 5, the rotational speed of the cooling fan 6, the output power of the hot air furnace 7, and the fuel supply of the regenerative burner 1 are used as output parameters. A local linear regression model is established using the least squares method. This model accurately reflects the correspondence between the thermal parameters and the control quantities of the actuators within the operating condition sub-cluster. During real-time control, the controller first determines the operating condition sub-cluster to which the expected value belongs by calculating the distance based on the expected values ​​of hot air temperature, oxygen content, and air volume at the current moment in the target tracking curve. Then, it calls the local linear regression model corresponding to the operating condition sub-cluster, substitutes the expected value into the model, and calculates the corresponding feedforward control quantity. This feedforward control quantity can better adapt to the current operating condition requirements and avoid control deviations caused by insufficient adaptability of the global model.

[0030] This embodiment divides the operating conditions into sub-clusters using a clustering algorithm and establishes a dedicated local linear regression model for each sub-cluster. This enables the feedforward control model to accurately match the current operating conditions, improves the calculation accuracy of the feedforward control quantity, makes the control commands more consistent with the operating rules of the current operating conditions, and further optimizes the control effect of the system.

[0031] In one embodiment, the PID controller includes a temperature PID controller, an oxygen content PID controller, and an airflow PID controller, which operate independently and are each responsible for adjusting the deviation of their respective parameters. Specifically, the temperature PID controller receives the deviation between the real-time hot air temperature transmitted by the thermocouple array and the target temperature, and outputs a temperature correction coefficient based on this deviation. The oxygen content PID controller receives the deviation between the real-time oxygen content transmitted by the oxygen content sensor and the target oxygen content, and outputs an oxygen content correction coefficient based on this deviation. The airflow PID controller receives the deviation between the real-time airflow transmitted by the airflow sensor and the target airflow, and outputs an airflow correction coefficient based on this deviation. The values ​​of all three correction coefficients can range from 0.8 to 1.2. The controller invokes a preset decoupling control matrix based on the current process mode. This 4×3 matrix has its elements pre-calibrated according to the coupling relationships between hot air temperature, oxygen content, and air volume and the actuators under each process mode. For example, in the roasting mode, the coupling coefficient between temperature and fuel supply to regenerative burner 1 can be 0.7 or 0.8, and the coupling coefficient between oxygen content and combustion fan speed 5 can be 0.6 or 0.7. The decoupling control matrix dynamically switches with the process mode to adapt to the coupling characteristics of different stages. The controller uses the temperature correction coefficient, oxygen content correction coefficient, and air volume correction coefficient as input vectors, substitutes them into the decoupling control matrix for linear calculation, and outputs the speed correction amounts for combustion fan 5, cooling fan 6, output power correction amounts for hot air furnace 7, and fuel supply correction amounts for regenerative burner 1, respectively. These correction amounts are the feedback correction amounts, which can accurately cancel the coupling interference between various parameters.

[0032] This embodiment processes the three types of correction coefficients by decoupling the control matrix, which can effectively reduce the coupling effect between various thermal parameters, allow the feedback correction amount to act accurately on the corresponding actuators, avoid mutual interference of parameter adjustments, and improve the stability and accuracy of system regulation.

[0033] In one embodiment, after calculating the feedback correction, the controller calculates a real-time confidence coefficient for the feedback correction based on the actual tracking error of the current hot air temperature, oxygen content, and airflow relative to the target tracking curve. The larger the tracking error, the larger the real-time confidence coefficient, and vice versa. Optionally, the real-time confidence coefficient can be 0.5 or 1.0. When the tracking error is within ±2%, the confidence coefficient is 0.5; when the tracking error is greater than ±2%, the confidence coefficient is 1.0. In one possible implementation, the real-time confidence coefficient can be calculated using the sum of squares of the tracking errors. The formula is: Confidence coefficient = 1 - exp(-k × sum of squares of errors), where k can be 0.1 or 0.2, and the sum of squares of errors is the sum of squares of the temperature error, oxygen content error, and airflow error. After calculating the real-time confidence coefficient, the controller multiplies the feedback correction by this real-time confidence coefficient to obtain a weighted feedback correction. Then, the weighted feedback correction is added to the feedforward control quantity to obtain the final control quantity. Finally, the controller outputs the final control quantity to the combustion fan 5, cooling fan 6, hot air furnace 7 and regenerative burner 1 respectively, and dynamically adjusts the intensity of feedback correction according to the magnitude of the tracking error, making parameter adjustment more targeted.

[0034] This embodiment dynamically adjusts the strength of the feedback correction by real-time confidence coefficient, which can adapt to the adjustment requirements of different tracking errors. When the error is large, the correction strength is increased, and when the error is small, the correction strength is decreased, making the parameter adjustment more stable, reducing over-adjustment or under-correction, and further improving the control accuracy of the system.

[0035] In one embodiment, the historical operating data sample set also includes the compressive strength value of the pellets at each moment. This compressive strength value can be obtained by testing with a pressure testing machine. During testing, 3 or 5 pellet samples can be selected for testing, and the average value is taken as the compressive strength value at that moment. The testing range can be 1000N / sample to 5000N / sample. This value can intuitively reflect the quality level of the pellets under the corresponding operating conditions. The controller is configured to retrieve multiple candidate historical moments with the closest thermal state from the historical operating data sample set based on the real-time detection data from the oxygen content sensor, thermocouple array, and air volume sensor, using a comprehensive similarity algorithm. The number of candidate historical moments can be 5 or 8. Subsequently, the controller extracts the compressive strength values ​​of the pellets corresponding to these multiple candidate historical moments, selects the candidate historical moment with the highest compressive strength value, and uses the hot air temperature change curve, oxygen content change curve, and air volume change curve in the continuous time period after the candidate historical moment as the target tracking curve. This ensures that the target tracking curve can match the current thermal state and correspond to the production conditions of high-quality pellets, ensuring that the test process can not only reproduce the actual production state but also guarantee the quality of the test pellets.

[0036] Based on thermal state matching, this embodiment introduces the compressive strength of pellets as a quality indicator to screen the target tracking curve. This allows the experimental process parameters to meet the requirements of thermal simulation while better aligning with the production needs of high-quality pellets, thus improving the practicality of the experimental results.

[0037] In one embodiment, the controller first extracts the trends of hot air temperature, oxygen content, and airflow within a preset time window prior to the current moment. The preset time window can be 5 minutes or 8 minutes long. The trends are determined by the parameter differences between adjacent moments; for example, a continuous increase in temperature indicates an upward trend, and a continuous decrease indicates a downward trend. Subsequently, the controller compares this trend with the corresponding parameter trends within the same time window prior to each historical moment in the historical operating data sample set. A dynamic time warping algorithm is used to calculate the similarity. This algorithm stretches or compresses the time series to calculate the minimum distance between the two trend curves. The smaller the distance, the higher the trend similarity, resulting in a trend similarity index. Simultaneously, the controller calculates the weighted Euclidean distance between the current measured values ​​of hot air temperature, oxygen content, and airflow and the corresponding parameter values ​​at each historical moment. The weights for temperature, oxygen content, and airflow can be 0.4, 0.3, and 0.3, respectively. The smaller the weighted Euclidean distance, the higher the instantaneous similarity, resulting in an instantaneous similarity index. The controller weights and sums the trend similarity index and the instantaneous similarity index according to a preset ratio (such as 6:4 or 7:3) to obtain a comprehensive similarity index. The historical moments with the smallest comprehensive similarity index are the candidate historical moments with the closest thermal state. Optionally, the number of candidate historical moments can be 5 or 8.

[0038] In existing technologies, experimental systems only match historical operating conditions based on current instantaneous parameter values, without considering parameter trends. When instantaneous parameters are similar but their trends differ, this leads to deviations in the matching results, resulting in low matching accuracy and affecting the accuracy of the target tracking curve. This embodiment combines instantaneous parameter values ​​with parameter trends, using a comprehensive similarity algorithm to filter candidate historical moments. This enables a more comprehensive and accurate matching of historical thermal states, improving the accuracy of candidate moment selection and providing a more reliable foundation for subsequent parameter control.

[0039] In one embodiment, a target belt roaster is first selected from multiple actual belt roaster production lines to match the target simulation object of the test system in terms of raw material type, pellet size, and production capacity. The raw material type can be magnetite or hematite, the pellet size can be φ8-16mm, and the production capacity can be 300t / h or 600t / h, ensuring that the operating conditions of the target production line are consistent with the simulation requirements of the test system. During the normal production operation of the target belt roaster, the hot air temperature, oxygen content, air volume, and corresponding pellet compressive strength values ​​are collected in real time at a fixed sampling frequency. The sampling frequency can be once per minute or once every two minutes. Simultaneously, the process stage at each moment is recorded in real time to ensure that the collected data corresponds one-to-one with the process stage. Subsequently, based on the proportional relationship between the cross-sectional dimensions and material layer thickness of the roasting cup 3 in the experimental system and the cross-sectional dimensions and material layer thickness of the trolley in the target belt roaster, conversion factors for air volume and temperature holding time were established between the experimental system and the target belt roaster. For example, when the ratio of the cross-sectional dimensions of the roasting cup 3 to the trolley is 1:100, the conversion factor for air volume can be 1 / 100, and the conversion factor for temperature holding time can be 1. According to these conversion factors, the collected air volume values ​​and durations of each process segment of the target belt roaster were converted into the target air volume values ​​and target process durations of the corresponding experimental system. Since the hot air temperature and oxygen content values ​​are not affected by equipment size, they can be directly used as the target hot air temperature and target oxygen content values ​​of the experimental system. Finally, the converted target hot air temperature values, target oxygen content values, target air volume values, target process durations, and corresponding pellet compressive strength values ​​were categorized and stored according to process segment, forming a historical operating data sample set of the experimental system. This ensures that the data in the sample set accurately matches the simulation requirements of the experimental system.

[0040] In existing technologies, historical operating data of experimental systems are mostly derived from theoretical derivations or collected from small-scale experiments, without being integrated with actual industrial production lines. This results in insufficient representativeness and authenticity of the data, limiting the simulation accuracy of the experimental system and preventing accurate reproduction of actual production conditions. This embodiment, however, uses data collected from an actual belt roasting machine production line matched with the experimental system. By converting this data into data suitable for the experimental system through conversion factors, the historical operating data sample set can better reflect actual industrial production, improving the realism and reliability of the experimental system's simulation and providing a more precise basis for subsequent parameter control.

[0041] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A pellet roasting test system, characterized in that, The system includes a combustion blower, a cooling blower, a forced-drying hot air furnace, a regenerative burner, a regenerator chamber, a calcining cup, a cooling tower, an exhaust fan, an oxygen content sensor, a thermocouple array, an airflow sensor, and a controller. Before conducting the process simulation test, the regenerative burner is started to heat the regenerator chamber, causing the heat storage spheres inside to reach saturation. During the simulated forced-drying stage, the cooling blower and the forced-drying hot air furnace are started to supply hot air to the bottom of the calcining cup. The hot air passing through the material layer is cooled by the cooling tower. The exhaust is then discharged by the exhaust fan. During the simulated drying, preheating, roasting, and homogenization stages, the combustion fan is started to supply cold air to the regenerator. The cold air is heated in the regenerator to form high-temperature hot air. After the high-temperature hot air is regulated by the regenerator burner to meet the test hot air requirements, it is supplied to the top of the roasting cup to dry, preheat, roast, and homogenize the pellet material inside the roasting cup. During the simulated cooling stage, the cooling fan supplies air directly to the bottom of the roasting cup to cool the pellet material after roasting. The controller is pre-stored with a historical operating data sample set for the entire process of the belt roaster. This set includes the hot air temperature, oxygen content, and airflow values ​​for each moment. The controller is configured to retrieve the historical moment closest to the thermal state within the current process stage from the historical operating data sample set, based on the real-time detection data from the oxygen content sensor, thermocouple array, and airflow sensor. It then extracts the hot air temperature change curve, oxygen content change curve, and airflow change curve for the continuous time period following that historical moment as target tracking curves. Based on these target tracking curves, it adjusts the speed of the combustion fan, the speed of the cooling fan, the output power of the hot air furnace, and the fuel supply of the regenerative burner. It determines the corresponding process mode based on the current process stage, including hot air drying mode, desiccation mode, preheating mode, roasting mode, homogenization mode, and cooling mode. Finally, it obtains the current actual hot air temperature, oxygen content, and airflow based on the real-time detection data from the oxygen content sensor, thermocouple array, and airflow sensor, and calculates the values ​​relative to the target... The tracking curve tracks the deviation from the expected value at each moment and calculates the feedback correction amount based on the deviation using a PID controller. The PID controller includes a temperature PID controller, an oxygen content PID controller, and an air volume PID controller. The temperature PID controller outputs a temperature correction coefficient based on the deviation of the hot air temperature, the oxygen content PID controller outputs an oxygen content correction coefficient based on the deviation of the oxygen content, and the air volume PID controller outputs an air volume correction coefficient based on the deviation of the air volume. The controller calls a preset decoupling control matrix based on the current process mode, taking the temperature correction coefficient, oxygen content correction coefficient, and air volume correction coefficient as inputs. Through the decoupling control matrix calculation, it outputs the speed correction amount acting on the combustion fan, the speed correction amount acting on the cooling fan, the output power correction amount acting on the hot air furnace, and the fuel supply correction amount acting on the regenerative burner, respectively, as feedback correction amounts. The matrix elements of the decoupling control matrix are pre-calibrated according to the coupling relationship between the hot air temperature, oxygen content, and air volume and each actuator under each process mode, and dynamically switch with the switching of process modes.

2. The pellet roasting test system as described in claim 1, characterized in that, The controller is also configured as follows: For each process mode, there is a corresponding feedforward control model. The feedforward control model calculates the feedforward control quantities of the combustion fan speed, cooling fan speed, output power of the hot air furnace and fuel supply of the regenerative burner based on the expected values ​​of hot air temperature, oxygen content and air volume at the current moment in the target tracking curve. The feedforward control quantity and the feedback correction quantity are superimposed to generate the final control quantity, which is then output to the combustion fan, cooling fan, hot air furnace and regenerative burner, respectively.

3. The pellet roasting test system as described in claim 2, characterized in that, For each process mode, the historical operating data samples are grouped into sample points belonging to that process mode, and clustered according to hot air temperature, oxygen content and air volume values ​​to form multiple operating condition sub-clusters; For each operating condition sub-cluster, the hot air temperature, oxygen content, and air volume of each sample point within the operating condition sub-cluster are used as inputs, and the corresponding combustion fan speed, cooling fan speed, output power of the hot air furnace, and fuel supply of the regenerative burner are used as outputs to establish a local linear regression model. During real-time control, the controller first determines the operating condition sub-cluster based on the expected values ​​of hot air temperature, oxygen content, and air volume at the current moment in the target tracking curve, and then calls the local linear regression model corresponding to the operating condition sub-cluster to calculate the feedforward control quantity.

4. The pellet roasting test system as described in claim 2, characterized in that, Based on the actual tracking error of the hot air temperature, oxygen content, and air volume relative to the target tracking curve at the current moment, calculate the real-time confidence coefficient of the feedback correction amount. The larger the tracking error, the larger the real-time confidence coefficient. The final control quantity is obtained by adding the feedforward control quantity to the feedback correction quantity multiplied by the real-time confidence coefficient. The final control values ​​are output to the combustion fan, cooling fan, hot air blower, and regenerative burner, respectively.

5. The pellet roasting test system as described in claim 1, characterized in that, The historical operational data sample set also includes the compressive strength value of the pellets at each moment; The controller is configured to retrieve multiple candidate historical moments with the closest thermal state from the historical operating data sample set based on real-time detection data from oxygen content sensors, thermocouple arrays, and air volume sensors. Then, it selects the candidate historical moment with the highest corresponding pellet compressive strength value from the multiple candidate historical moments and uses the hot air temperature change curve, oxygen content change curve, and air volume change curve in the continuous time period after the candidate historical moment as the target tracking curve.

6. The pellet roasting test system as described in claim 5, characterized in that, Extract the trends of hot air temperature, oxygen content, and air volume changes within a preset time window before the current moment. Perform dynamic time warping similarity calculations with the trends of hot air temperature, oxygen content, and air volume changes within the same length time window before each historical moment in the historical operation data sample set to obtain trend similarity indices. The instantaneous similarity index is obtained by calculating the weighted Euclidean distance between the current measured values ​​of hot air temperature, oxygen content, and air volume and the values ​​of hot air temperature, oxygen content, and air volume at each historical moment. The trend similarity index and the instantaneous similarity index are weighted and summed according to a preset ratio to obtain the comprehensive similarity index; The historical moments with the lowest comprehensive similarity index are selected as the candidate historical moments with the closest thermal state.

7. The pellet roasting test system as described in claim 1, characterized in that, From multiple actual belt roaster production lines, a target belt roaster that matches the target simulation object of the test system in terms of raw material type, pellet specifications and production capacity was selected. During the production and operation of the target belt roaster, the hot air temperature, oxygen content, air volume and corresponding pellet compressive strength values ​​are collected in real time at a fixed sampling frequency. At the same time, the process segment at each moment is recorded. Based on the proportional relationship between the cross-sectional dimensions and material layer thickness of the calcining cup of the test system and the cross-sectional dimensions and material layer thickness of the trolley of the target belt calciner, the air volume conversion factor and temperature holding time conversion factor between the test system and the target belt calciner are established. According to the air volume conversion factor and the temperature holding time conversion factor, the collected air volume value of the target belt roaster and the duration of each process section are converted into the target air volume value and target process duration of the corresponding test system, and the hot air temperature value and oxygen content value are used as the target hot air temperature value and target oxygen content value of the test system. The converted target hot air temperature, target oxygen content, target air volume, target process duration, and corresponding pellet compressive strength values ​​are classified and stored according to process segments to obtain a historical operation data sample set.

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