Segmented control method for material tube type coal dryer

Abstract

The invention discloses a segmented control method for a material tube type coal dryer, and relates to the technical field of segmented control, and the method comprises the steps: interacting the material characteristics of batch processing coal and the principle of the dryer, and determining a drying processing task; performing hierarchical segmentation on the drying processing task; performing automatic data modeling, and constructing a two-phase flow model; identifying the segmented control chain, and determining a pre-control strategy; based on the pre-control strategy, receiving returned multi-source monitoring data for model response, executing an interactive proofreading decision, and positioning an abnormal control point; introducing a function relationship, and determining a feedback adjustment strategy; and performing feedback regulation and control management based on the feedback regulation strategy. The technical problem of poor product quality caused by low drying efficiency and rough control precision in the prior art is solved, and the technical effects of improving the drying efficiency, optimizing the control precision and guaranteeing the product quality are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of segmented control, and in particular to a segmented control method for a feed pipe type coal dryer. Background Art

[0002] In today's society, where energy demand is constantly increasing, coal, as a vital energy resource, is crucial for its efficient utilization. However, mined coal often contains a high moisture content, which poses numerous challenges to its transportation, storage, and use. Traditional coal processing methods suffer from low efficiency, high energy consumption, and incomplete combustion when dealing with coal with a high moisture content. During transportation, high-moisture coal increases transportation costs and difficulty. During storage, moisture can easily lead to safety hazards such as coal agglomeration and spontaneous combustion. During combustion, moisture not only reduces the calorific value of coal, but also increases pollutant emissions. To overcome these problems, coal dryers were developed. Early coal drying technologies were relatively simple, with limited drying effects and impacting product quality.

[0003] The existing technology has technical problems such as low drying efficiency and rough control precision, which leads to poor product quality. Summary of the Invention

[0004] The present application provides a segmented control method for a feed tube type coal dryer, which is used to solve the technical problems in the prior art such as low drying efficiency and rough control accuracy, which lead to poor product quality.

[0005] In view of the above problems, the present application provides a segmented control method for a feed pipe type coal dryer, the method comprising: Interactive batch processing of coal material properties and dryer principles, determine the drying processing tasks based on the coal calorific value range; hierarchically segment the drying processing tasks, and make decisions to determine the segmented control chain; perform data automatic modeling, and construct a two-phase flow model. The two-phase flow model is constructed by limiting the spatial field and fitting based on the solid phase of the material and the gas phase of the heat flow. The spatial field is determined based on the feed bin and the discharge bin, and the two-phase flow model is a twin network structure; identify the segmented control chain and determine the pre-control strategy. The pre-control strategy corresponds to the stage of the drying processing task; based on the pre-control strategy, initialize the two-phase flow model, receive the returned multi-source monitoring data for model response, perform interactive proofreading decisions, and locate abnormal control points, wherein the abnormal positioning satisfies the control freedom; introduce functional relationships, perform feedback decision analysis on the abnormal control points, and determine the feedback adjustment strategy; based on the feedback adjustment strategy, perform feedback control management of the execution of the drying processing task.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The interactive batch processing method combines the material properties of coal with the dryer principle to determine the drying task based on the coal calorific value range. The drying task is hierarchically segmented and a segmented control chain is determined. Data is automatically modeled to construct a two-phase flow model. The segmented control chain is identified and a pre-control strategy is determined. Based on the pre-control strategy, the two-phase flow model is initialized and multi-source monitoring data is received for model response, executing interactive calibration decisions. Functional relationships are introduced to determine a feedback control strategy. Based on this feedback control strategy, feedback control management of the drying task execution is performed. This method achieves the technical results of improving drying efficiency, optimizing control accuracy, and ensuring product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic flow chart of a segmented control method for a feed pipe type coal dryer provided in an embodiment of the present application; Figure 2 A schematic flow chart of the abnormal control point positioning process of the segmented control method for a feed pipe type coal dryer provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The present application provides a segmented control method for a feed pipe type coal dryer, which is used to solve the technical problems in the prior art such as low drying efficiency and rough control accuracy, which lead to poor product quality.

[0010] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0011] Example 1 like Figure 1 As shown, the present application provides a segmented control method for a feed pipe type coal dryer, the method comprising: Step S100: Interacting the material properties of the batch processed coal and the principle of the dryer to determine the drying task based on the calorific value range of the coal.

[0012] Specifically, alternating batch processing means processing coal in batches that are alternating and interconnected. Comprehensive consideration of the coal's material properties is crucial, including the initial moisture content of the coal. For example, some batches may have a moisture content as high as 20%, while others may have only 5%. Particle size distribution is also crucial; batches may have uniformly sized coal particles, while others may have a mixture of coarse and fine particles. Differences in coal density also affect drying efficiency, with denser coal being more difficult to dry. Chemical composition, such as sulfur and carbon content, also influences the drying process. The dryer's operating principle encompasses many aspects, including heat transfer: whether it relies primarily on conduction, transferring heat from the dryer walls to the coal, or convection, where hot air circulates within the dryer to heat the coal. The airflow within the dryer, whether unidirectional or circulating, is also considered. The coal's calorific value range provides an indirect measure of drying requirements, particularly the calorific value gain. A high calorific value gain indicates a significant increase in calorific value from drying, reflecting a high drying requirement. For example, if a batch of coal has a large calorific value gain and a low initial calorific value, it means that its water content is high. The drying task is to carry out long-term, high-intensity drying to significantly increase its calorific value. On the contrary, if another batch of coal has a small calorific value gain and a relatively high initial calorific value, it indirectly indicates that its water content is low. The drying task is only to make moderate fine-tuning to maintain its calorific value stability and achieve better combustion results. By interactively processing the material characteristics of coal in batches and the principles of the dryer, and determining the drying task according to the coal calorific value range, it is possible to formulate a more targeted drying plan, avoid blind operations, and thus improve overall drying efficiency.

[0013] Step S200: hierarchically segmenting the drying process task and determining a segment control chain.

[0014] Specifically, the drying task is segmented hierarchically. Based on constraints such as coal type (e.g., anthracite, bituminous coal), initial moisture content (e.g., high moisture content, low moisture content), thermal sensitivity (e.g., high thermal sensitivity, low thermal sensitivity), and particle size distribution (e.g., coarse particle size, fine particle size), the task is divided into a preheating phase, a constant drying rate phase, and a decreasing drying rate phase. This represents the first-level segmentation result. Then, by traversing the first-level segmentation results, each phase is further subdivided into lower-level phases based on preset control variables such as drying temperature, wind speed, and drying time, thereby determining the second-level segmentation results. Finally, the first-level and second-level segmentation results are combined to produce the final task segmentation results. The decision-making and segmentation control chain, after completing the hierarchical segmentation, defines specific control links and elements for each phase and organically connects them to form a coherent control chain. By clearly defining the control focus for each phase, fluctuations in the drying process are reduced and consistent coal drying quality is ensured.

[0015] Step S300: Automatically model the data and construct a two-phase flow model. The two-phase flow model is constructed by limiting the spatial field and fitting the solid phase of the material with the heat flow gas phase. The spatial field is determined based on the feed bin and the discharge bin. The two-phase flow model is a twin network structure.

[0016] Specifically, automated data modeling utilizes relevant data processing techniques and algorithms to automatically construct mathematical models to describe and analyze the physical phenomena during the drying process. A two-phase flow model is constructed. The two phases here refer to the solid phase of the material (i.e., the solid state of the coal itself) and the gaseous phase of the heat flow (e.g., hot air). By modeling the flow of these two phases, heat transfer and material transport during the drying process can be more accurately simulated. This model is constructed within a defined spatial domain, defined by the feed bin (where the coal enters the drying equipment) and the discharge bin (where the coal is discharged after drying). This means that the model focuses on the physical changes occurring throughout the entire process, from feeding to discharge. The model is constructed based on a fitting of the solid phase of the material and the gaseous phase of the heat flow. This involves collecting and analyzing data on the solid phase of the material (e.g., coal particle size, density, and shape) and the gaseous phase of the heat flow (e.g., hot air temperature, flow rate, and pressure), identifying relationships and patterns between them, and constructing a model that accurately reflects the actual situation. Furthermore, this two-phase flow model employs a twin network structure. A twin network structure typically features two or more similar, but not identical, subnetworks that can collaborate and compare with each other, thereby improving model performance and accuracy. In this model, the twin network structure better captures and processes the complex interactions and dynamic changes between the solid and gas phases. This allows for accurate prediction of various parameter changes during the drying process, providing a reliable basis for process optimization.

[0017] Step S400: Identify the segmented control chain and determine a pre-control strategy, where the pre-control strategy corresponds to the stage of the drying process task.

[0018] Specifically, when identifying the previously determined segmented control chain, each segment, along with their connections and sequence, is analyzed in detail. This in-depth identification and analysis leads to the development of pre-control strategies, preliminary control plans developed before the actual drying process begins. Crucially, these pre-control strategies closely and clearly correspond to each stage of the drying process. For example, in the early stages of the drying process, the pre-control strategy focuses on precisely controlling the quality and quantity of the incoming coal. This involves rigorously testing coal characteristics such as initial moisture content, particle size distribution, and calorific value. Based on these test results, the feed rate and feeding method are adjusted to ensure uniform coal delivery to the dryer, laying a good foundation for subsequent drying processes. In the mid-stage of the drying process, the pre-control strategy focuses on heat exchange efficiency and airflow distribution within the dryer. This involves fine-tuning the power of the heating elements, adjusting the air speed and volume of the ventilation system, and adjusting the operating speed of the internal agitator to ensure sufficient heating and rapid moisture evaporation. The pre-control strategy corresponding to each stage can set and adjust the control parameters in a targeted manner according to the characteristics and requirements of different stages of the drying process, so as to achieve efficient, stable and high-quality completion of the entire drying process.

[0019] Step S500: Based on the pre-control strategy, the two-phase flow model is initialized, and the multi-source monitoring data transmitted back is received for model response, interactive calibration decision is performed, and abnormal control points are located, wherein the abnormal location satisfies the control freedom.

[0020] Specifically, the two-phase flow model is initialized based on the pre-defined control strategy. This means inputting the relevant control parameters from the pre-control strategy into the model, providing initial conditions for its operation. The model then receives multi-source monitoring data from various monitoring points. This data includes parameters such as temperature, humidity, pressure, and flow rate, originating from different locations and equipment, and collectively reflects the actual drying process. The model responds to this data, simulating the changes in material and heat flow during the drying process. During this process, interactive decision-making is performed, comparing and analyzing the model's response with the actual monitoring data. This interactive verification enables the identification of abnormal control points—locations or links in the drying process where unexpected behavior occurs. Importantly, abnormality location must maintain control freedom. This means that adjustability and controllability must be considered when locating abnormal control points to ensure effective intervention and adjustment.

[0021] Step S600: introducing a functional relationship, performing feedback decision analysis on the abnormal control point, and determining a feedback adjustment strategy.

[0022] Specifically, the entire segmented control chain is first traversed. During this process, linear and nonlinear control nodes are distinguished based on their control characteristics. For linear control nodes, control adjustments are performed based on linear functions. This means that a simple linear relationship exists between the control input and output at the linear control node. For example, an increase or decrease in the control parameter is proportional to the change in control effect. This clear linear function allows for a relatively direct and accurate calculation of the required control adjustment. For nonlinear control nodes, however, proxy functions are introduced for control adjustments. The control input and output relationship of nonlinear control nodes is more complex, not a simple linear relationship. Proxy functions are specifically designed to handle these complex nonlinear relationships. They calculate an appropriate control adjustment strategy based on the characteristics and current state of the nonlinear control node, taking into account various factors. By using different control functions for linear and nonlinear control nodes in this targeted manner, abnormal control points can be addressed more accurately and effectively, ensuring a stable and efficient drying process that achieves the desired results.

[0023] Step S700: Based on the feedback regulation strategy, feedback control management of the execution of the drying process task is performed.

[0024] Specifically, comprehensive feedback control and management of the drying process is implemented based on the previously determined feedback control strategy. This means implementing the feedback control strategy into actual operations and controls. For example, if the feedback control strategy is to increase the heating temperature of the dryer, then in this step, the control parameters of the heating device will be adjusted accordingly to achieve the temperature increase. At the same time, the execution of the drying process is continuously monitored to ensure that the feedback control strategy is effectively implemented and its effect on the drying process is observed. If it is found that the expected improvement is not achieved after the strategy is implemented, or if new problems arise, the feedback control strategy will be re-evaluated and adjusted. In addition, feedback control management also includes coordinating various related equipment and links to ensure that they can work together to optimize the drying process. For example, when adjusting the heating temperature, it is also necessary to adjust the material feed rate or the operating parameters of the ventilation system accordingly to achieve the best overall effect.

[0025] In one possible implementation, step S200 further includes: Step S210: Divide the drying process task into stages and determine a layer of segmentation results, wherein the layer of segmentation results includes a preheating stage, a constant speed drying stage, and a reduced speed drying stage. The division criteria are based on the type of coal, initial moisture content, thermal sensitivity, and particle size distribution.

[0026] Step S220: traverse the first-layer segmentation results, divide the lower stages step by step based on the preset control variables, and determine the second-layer segmentation results.

[0027] Step S230: Fusing the first-layer segmentation result and the second-layer segmentation result to determine a task segmentation result.

[0028] Specifically, a phased division of the drying process is implemented to clarify the results of the first-level segmentation. This division is primarily based on a number of important factors, including differences in coal type. Different types of coal, such as lignite, bituminous coal, and anthracite, have varying physical and chemical properties, which affect the water evaporation rate and heat transfer characteristics during the drying process. Initial moisture content is also a key factor. Coal with a high initial moisture content requires a longer time to remove surface moisture during the initial drying phase; coal with a lower initial moisture content, on the other hand, will dry more quickly into the subsequent stages. Coal's thermal sensitivity is also crucial. Some coals are highly sensitive to temperature. Excessively high temperatures can cause degradation or chemical reactions. Therefore, careful temperature control is required during the drying process, which can influence the stage division. Particle size distribution also affects drying. Coal with larger particles has a longer internal moisture diffusion path, resulting in slower drying. Coal with smaller particles, on the other hand, exhibits different characteristics during the drying process. Taking these constraints into account, the drying process is divided into a preheating phase, a constant-rate drying phase, and a decreasing-rate drying phase. The preheating phase primarily aims to gradually increase the coal temperature to prepare for subsequent moisture evaporation. During the constant-rate drying phase, moisture on the coal surface evaporates at a relatively steady rate, maintaining a constant drying rate. During the decreasing-rate drying phase, as the moisture content within the coal decreases, the diffusion resistance increases, and the drying rate gradually decreases.

[0029] A comprehensive traversal check is performed on the determined first-layer segmentation results. The so-called preset control variables include but are not limited to the temperature variation range during the drying process, the set value of the air flow rate, the humidity control index, etc. The preheating stage in the first-layer segmentation results will be further subdivided according to these preset control variables. Based on different initial temperature ranges or heating rates, the preheating stage will be further subdivided into multiple lower-level stages, such as low-temperature preheating sub-stages and high-temperature preheating sub-stages. In the constant-speed drying stage, different sub-stages are divided according to different air flow rate ranges or specific standards for humidity control, such as high-speed constant-speed drying sub-stages and low-speed constant-speed drying sub-stages. For the deceleration drying stage, it is further subdivided into several lower-level sub-stages according to the temperature drop gradient or the range of residual moisture content. By dividing the lower-level stages based on the preset control variables in this way, the second-layer segmentation results are finally determined. This makes the segmentation of the drying process tasks more refined and specific, and can provide a basis for more precise control and optimization.

[0030] The broad divisions of the preheating phase, constant-rate drying phase, and decreasing-rate drying phase in the first-level segmentation results are integrated with the more detailed sub-phase divisions of each phase in the second-level segmentation results. For example, the preheating phase in the first-level segmentation results is further subdivided into low-temperature preheating and high-temperature preheating sub-phases in the second-level segmentation results. During the fusion process, these sub-phases are merged under the broader framework of the preheating phase. A similar approach is adopted for the constant-rate drying and decreasing-rate drying phases, rationally integrating the more detailed sub-phases in the second-level segmentation into the corresponding phases in the first-level segmentation. During the fusion process, the characteristics, control requirements, and transition relationships of each phase and sub-phase need to be comprehensively considered. Ultimately, this fusion results in a complete and clear task segmentation result that incorporates both a relatively high-level phase division and a more detailed and detailed sub-phase division. This provides a comprehensive, accurate, and hierarchical segmentation plan for the entire drying process, facilitating more effective implementation of drying control strategies and ensuring an efficient, stable, and high-quality drying process.

[0031] In one possible implementation, step S300 further includes: Step S310: The spatial field is used to simulate the heat and mass transfer process, with motion trajectory and velocity distribution as characterization elements.

[0032] Step S320: Based on the intermediate structural layout of the feed bin and the discharge bin, the spatial field is determined, wherein the spatial field is partitioned and constrained by heat and mass transfer characteristics, and the intermediate structural layout includes a rotating cylinder-drying tube-heating chamber.

[0033] Specifically, the defined spatial field has a specific function, which is to simulate the heat and mass transfer process. The heat and mass transfer process is crucial in the drying process, and it directly affects the drying effect and efficiency of the material. The motion trajectory refers to the path of the material moving inside the drying equipment. For example, the material enters the feed bin, passes through specific channels and components, and finally reaches the discharge bin. Understanding the motion trajectory of the material helps to analyze the processing conditions and time it experiences at different locations. The velocity distribution describes the speed of the material at different locations. The speed will affect the contact time of the material with the hot air or heating surface, thereby affecting the heat and mass transfer effect. Using the motion trajectory and velocity distribution as characterization elements means that through detailed description and analysis of these two aspects, we can have a deeper understanding of the heat and mass transfer behavior of the material in the spatial field.

[0034] The spatial field is defined based on the intermediate structural layout between the feed and discharge silos. This intermediate structural layout includes key components such as the rotating drum, drying tube, and heating chamber. The rotating drum drives the material to tumble or move, ensuring uniform heating and drying; the drying tube is the primary channel for material transport and contact with hot air; and the heating chamber provides the heat source. When defining the spatial field, zoning constraints are applied based on heat and mass transfer characteristics. This is because different areas exhibit distinct characteristics and patterns in heat and mass transfer. For example, areas near the heating chamber exhibit stronger heat transfer and faster moisture evaporation from the material. Meanwhile, in certain areas of the drying tube, varying air flow and material distribution lead to varying mass transfer conditions. This zoning constraint based on heat and mass transfer characteristics enables more accurate simulation and analysis of the physical processes within the spatial field. This helps optimize the design and operating parameters of the drying equipment to improve drying efficiency and product quality.

[0035] In one possible implementation, Figure 2 As shown, step S500 further includes: Step S510: Based on the pre-control strategy, the two-phase flow model is initialized, and an initialization branch is determined, where the initialization branch covers the entire cycle of the strategy.

[0036] Step S520: pre-processing and receiving the multi-source monitoring data, mapping it to the two-phase flow model, and determining a real-time response branch.

[0037] Step S530: Laterally interact the initialization branch and the real-time response branch to map, calibrate, and locate abnormal control points.

[0038] Specifically, the initialization process of the two-phase flow model is started based on the previously determined pre-control strategy. The pre-control strategy includes a series of preliminary plans and settings for the drying process task, such as the preset values ​​of parameters such as initial temperature, humidity, and feed rate. By inputting the parameters and rules in these pre-control strategies into the two-phase flow model, the initial state and operating conditions of the model are set. During the initialization process, the determined initialization branch is of great significance. This initialization branch covers the entire cycle of the pre-control strategy, which means that it includes the initial settings of all stages and links from the beginning to the end of the drying process task. For example, if the drying process task is pre-divided into multiple stages, each stage has specific control parameters and targets, then the initialization branch will provide corresponding starting conditions for each stage. Such comprehensive coverage ensures that the two-phase flow model can have a complete framework and expectation for the entire drying process from the beginning, providing a benchmark and reference for subsequent real-time monitoring, comparison and adjustment.

[0039] Monitoring data from multiple sources is preprocessed, including data screening, cleaning, and integration to remove noise, errors, or duplication, ensuring data accuracy and validity. The preprocessed multi-source monitoring data, covering various parameters relevant to the drying process, such as temperature, humidity, pressure, and flow, is then mapped to the two-phase flow model. This involves mapping the actual collected data to the corresponding locations and parameters in the model. This mapping operation determines the real-time response branch, which reflects the model's immediate reaction and changes to the current actual monitoring data. For example, if the monitoring data indicates a higher-than-expected temperature in a certain area, the model's real-time response branch might indicate increased heat transfer in that area or a change in the material drying rate. This real-time response branch provides real-time information on the current state of the drying process, facilitating comparison and analysis with the initialization branch to identify any anomalies.

[0040] The initialization branch and the real-time response branch interact laterally. This interaction involves exchanging, comparing, and integrating information between the two branches. The initialization branch represents the ideal or expected model operating state based on the pre-defined control strategy, while the real-time response branch reflects the actual operating state after inputting actual monitoring data into the model. By comparing and contrasting the information from these two branches, a mapping calibration is performed, comparing the expected and actual values ​​for each location and parameter. This mapping calibration process accurately identifies abnormal control points, which are locations or parameters where the actual operating state deviates significantly from the expected state. For example, if the temperature at a location in the initialization branch is expected to fluctuate steadily within a certain range, but the real-time response branch indicates that the temperature at that location significantly exceeds or falls below this range, this location is identified as an abnormal control point. For another example, if the initialization branch predicts a uniform material flow rate, but the real-time response branch indicates a significant slowdown or acceleration in a certain area, this area is also identified as an abnormal control point. Through such lateral interaction and mapping proofreading, abnormal situations in the drying process can be discovered in a timely and accurate manner, providing a key basis for subsequent adjustments and optimizations.

[0041] In one possible implementation, step S600 further includes: Step S610: traverse the segmented control chain and determine linear control nodes and nonlinear control nodes based on control characteristics.

[0042] Step S620: Based on the linear function, control and adjust the linear control node.

[0043] Step S630: introducing a proxy function to control and adjust the nonlinear control node.

[0044] Specifically, in this step, each node in the segmented control chain is individually examined. The so-called control characteristic refers to how its output responds to a specific control variable input. For example, if increasing the heating power input causes the temperature to rise at a fixed rate, while decreasing the heating power causes the temperature to fall at a fixed rate—that is, if there is a clear, proportional relationship between the input and output—then the node has a linear control characteristic and is classified as a linear control node. For another example, consider the relationship between the material feed rate and drying performance. If the effect of feed rate changes on drying performance is not a simple proportional relationship, but rather a small effect within a certain feed rate range and a large effect within another range, exhibiting a complex, non-linear relationship, then the node is considered a nonlinear control node. By carefully analyzing and evaluating each node in the segmented control chain, linear and nonlinear control nodes can be accurately distinguished based on their different output response patterns to input changes. This distinction is crucial for implementing appropriate control strategies and ensuring more precise and efficient operation of the entire control system.

[0045] A linear function is a simple, straightforward mathematical expression that clearly demonstrates the proportional relationship between input and output. For example, if a linear control node relates a device's operating speed to its production efficiency, the linear function indicates that increasing the speed by a certain amount will result in a corresponding increase in efficiency. To achieve a specific production efficiency target, this linear function can be used to accurately calculate the value to which the device's operating speed should be adjusted. The corresponding settings can then be made to effectively adjust the linear control node to ensure that it operates as expected.

[0046] Nonlinear control nodes are those where the relationship between their input and output is not a simple linear ratio; it is extremely complex and difficult to describe directly using intuitive rules. Proxy functions are employed to address this complexity. They are carefully designed and constructed to capture the various variations in the input variables of nonlinear control nodes and their complex impact on the output. For example, in the drying process, a nonlinear control node involves the relationship between humidity, temperature, and drying time. The proxy function comprehensively considers the interactions between these factors, as well as their nonlinear variation trends. By substituting the current input data into the proxy function for calculation and analysis, it is possible to derive the appropriate adjustment strategy and parameters for the nonlinear control node, thereby achieving accurate control of the nonlinear control node and enabling the entire system to operate more stably and efficiently.

[0047] In one possible implementation, step S630 further includes: Step S631: Identify the nonlinear control node, and perform a big data search based on the conditional logic of upper and lower level control to determine an approximate linear function set.

[0048] Step S632: Based on the control fitness, traverse the approximate linear function set and define a proxy function.

[0049] Specifically, the algorithm identifies control nodes identified as nonlinear and then operates according to the conditional logic of hierarchical and subordinate control. This logic can be understood as logical rules governing the order, priority, or inclusion relationships between different levels of control. This conditional logic is then used to search through large datasets derived from records of similar control processes, experimental data, simulation results, and other sources. By searching and filtering this large dataset, the goal is to find a set of linear functions that can, to a certain extent, approximate the behavior of nonlinear control nodes.

[0050] Control fitness can be understood as a measure of the effectiveness of these approximate linear functions at controlling the current nonlinear control node. Each function in the set of approximate linear functions is then examined one by one. During the traversal process, each function is evaluated according to a predetermined control fitness criterion. This includes examining the function's prediction accuracy, control stability, and response timeliness for the nonlinear control node. Through comprehensive analysis and comparison of these aspects, the function that best meets the control fitness requirements is selected from the set of approximate linear functions, or some of these functions are modified and combined to ultimately define a proxy function that effectively handles the nonlinear control node. For example, for a nonlinear temperature control node, there is a set of approximate linear functions. Through control fitness-based traversal, a function is found that, in most cases, can effectively predict the temperature trend and stably control the temperature within the set range. This function is then defined as the proxy function and used in actual control operations. By identifying nonlinear control nodes, utilizing approximate linear functions, and defining proxy functions, these complex nodes can be more accurately simulated and controlled, thereby improving control precision.

[0051] In one possible implementation, step S700 further includes: Step S710: Obtaining gas phase elements based on the heat flow gas phase, wherein the gas phase elements at least include air volume and temperature.

[0052] Step S720: Determine a decoupling control rule based on the coupling relationship of the gas phase elements, wherein the decoupling control rule is determined based on the coupling degree and the coupling characteristic.

[0053] Step S730: Based on the decoupling control rule, assist in making control decisions and feedback adjustment decisions for the drying process task.

[0054] Specifically, key gas phase elements related to the heat flow gas phase are obtained. The heat flow gas phase describes the flow and state of the gas during the drying process. Among the gas phase elements obtained, air volume is an important aspect. Air volume refers to the volume or mass of gas flow passing through per unit time. A larger air volume can speed up heat transfer and remove moisture, but excessive air volume may also lead to energy waste or excessive disturbance of the material. Temperature is also critical. The temperature of the gas directly affects the effect and efficiency of drying. High temperature helps to accelerate the evaporation of moisture, but too high a temperature may damage the quality of the material or cause safety hazards. In addition to air volume and temperature, in some cases, other related gas phase elements may also be obtained, such as gas humidity, pressure, flow rate distribution, etc.

[0055] During the operation of a tube-type coal dryer, the gas-phase elements, air volume, and temperature, are tightly coupled. For example, increasing air volume rapidly removes heat from the dryer, resulting in a decrease in temperature. Conversely, decreasing air volume causes localized heat accumulation, leading to a temperature increase. This intuitively illustrates the interplay between air volume and temperature. The degree of coupling reflects the closeness of this interaction. If the coupling is high, a mere 10% increase in air volume might result in a temperature drop of as much as 15°C. Meanwhile, a similar increase in air volume might only result in a temperature drop of around 5°C. The coupling characteristics are more complex and diverse, exhibiting nonlinear characteristics. For example, increasing air volume at low values ​​does not significantly reduce temperature, but once it exceeds a certain value, the temperature drops sharply. Furthermore, there may be a time delay, so that after an air volume change, the temperature may not respond immediately, but may take several minutes or even more than ten minutes to show a significant change. Based on an in-depth analysis of these coupling degrees and characteristics, decoupling control rules are established. Suppose that at the feed end of a pipe-type coal dryer, the coupling between air volume and temperature is high, and the coupling characteristics exhibit both nonlinearity and time delay. The decoupling control rule would be as follows: When increasing air volume to increase feed rate, the immediate increase in heating power required to maintain temperature stability is calculated in advance based on the nonlinear relationship. Temperature changes are continuously monitored for a period of time after the air volume adjustment, and the heating power is further fine-tuned based on the actual temperature fluctuations after the delay. For another example, at the dryer's discharge end, if the coupling is relatively low but nonlinearity still exists, the decoupling control rule would be: when adjusting air volume, the temperature trend is estimated based on the nonlinearity, and an initial heating power adjustment is made. Then, based on subsequent actual temperature changes, additional adjustments are made in a timely manner to ensure that the coal dryness meets the requirements at discharge. This carefully determined decoupling control rule, based on the coupling degree and coupling characteristics, enables precise and independent control of the gas-phase elements within the pipe-type coal dryer, ensuring efficient and stable operation of the entire drying process while maximizing energy utilization and drying quality.

[0056] The decoupled control rules defined above provide crucial support for all aspects of the drying process. They assist in control decision-making. This means that the rules provide clear guidance when deciding how to operate the dryer, such as determining initial air volume, temperature settings, and heating power. For example, if the current moisture content of the coal requires a more effective drying effect, the rules will initially increase the air volume to a specific value, while also significantly increasing the heating power accordingly to ensure the temperature remains within the ideal range for rapid and efficient drying. Furthermore, during the drying process, these rules also provide feedback for regulatory decisions. Various parameters and conditions within the dryer are constantly changing. New data, such as real-time changes in coal moisture content and dynamic data on temperature and air volume at different locations within the dryer, are captured by the real-time monitoring system. The decoupled control rules make adjustments based on this feedback. For example, if the temperature in a particular area is detected to be too high while the air volume is relatively low, the rules will reduce the heating power and appropriately increase the air volume to prevent overdrying or quality issues with the coal. Suppose, during the drying process, the coal is found to be drying slower than expected, even though the temperature and air volume are within the set ranges. At this point, the decoupling control rules prompt the user to check for blockages in the air ducts, causing insufficient air volume, or a partial failure in the heating system, leading to uneven temperature distribution. This leads to appropriate equipment inspections and adjustments. By making these control and feedback adjustments based on the decoupling control rules, the pipe-type coal dryer can consistently operate at optimal conditions, improving drying efficiency and ensuring coal drying quality while reducing energy consumption and equipment wear.

[0057] In one possible implementation, step S700 further includes: Step S740: The feed pipe type coal dryer includes a waste heat recovery module, and waste heat recovery has a control priority.

[0058] Specifically, the design of a feed-tube coal dryer includes a key component, the waste heat recovery module. This module collects waste heat generated during the drying process for energy reuse, improving the overall system's energy efficiency. Furthermore, waste heat recovery is assigned control priority, meaning that considerations related to waste heat recovery take precedence when making various control operations and decisions for the dryer. For example, when adjusting dryer operating parameters to meet varying drying requirements, the waste heat recovery module must first be ensured to function properly and efficiently. Only after ensuring sufficient waste heat recovery can other parameters, such as air volume and temperature, be adjusted. If, during dryer operation, a need arises to reduce energy consumption, the waste heat recovery module's operation will be prioritized due to its control priority, such as improving waste heat recovery efficiency and adjusting the flow rate in the recovery pipeline. Energy reduction measures such as reducing heating power or air volume will then be considered. In short, assigning waste heat recovery control priority demonstrates a commitment to energy conservation and efficient utilization, helping to improve the overall performance of the feed-tube coal dryer.

[0059] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0061] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A segmented control method for a feed pipe type coal dryer, characterized in that: The method comprises: Interactive batch processing of coal material characteristics and dryer principles to determine the drying task based on the coal calorific value range; Divide the drying process task into hierarchical segments and make decisions to determine the segment control chain; Automatic data modeling is performed to construct a two-phase flow model. The two-phase flow model is constructed by fitting the solid phase of the material and the heat flow gas phase by limiting the spatial field. The spatial field is determined based on the feed bin and the discharge bin. The two-phase flow model is a twin network structure. Identifying the segmented control chain and determining a pre-control strategy, wherein the pre-control strategy corresponds to an existing stage of the drying process task; Based on the pre-control strategy, the two-phase flow model is initialized, and the multi-source monitoring data transmitted back is received for model response, interactive calibration decision is performed, and abnormal control points are located, wherein the abnormal location satisfies the control freedom degree; Introducing a functional relationship, performing feedback decision analysis on the abnormal control point, and determining a feedback adjustment strategy; Based on the feedback regulation strategy, feedback regulation and management of the execution of the drying process task is performed.

2. The segmented control method for a feed pipe type coal dryer according to claim 1, characterized in that: The drying process tasks are hierarchically segmented, including: The drying process task is divided into stages to determine a layer of segmented results, wherein the layer of segmented results includes a preheating stage, a constant speed drying stage, and a reduced speed drying stage, and the division criteria are based on the coal type, initial moisture content, thermal sensitivity, and particle size distribution. Traversing the first-layer segmentation results, dividing the lower stages step by step based on the preset control variables, and determining the second-layer segmentation results; The first-layer segmentation result and the second-layer segmentation result are integrated to determine a task segmentation result.

3. The segmented control method for a feed pipe type coal dryer according to claim 1, characterized in that: The spatial field is used to simulate the heat and mass transfer process, with motion trajectory and velocity distribution as characterization elements; The spatial field is determined based on the intermediate structural layout of the feed bin and the discharge bin, wherein the spatial field is partitioned and constrained by heat and mass transfer characteristics, and the intermediate structural layout includes a rotating cylinder-drying tube-heating chamber.

4. The segmented control method for a feed pipe type coal dryer according to claim 1, characterized in that: The positioning of abnormal control points includes: Based on the pre-control strategy, the two-phase flow model is initialized, and an initialization branch is determined, where the initialization branch covers the entire cycle of the strategy; Preprocessing and receiving the multi-source monitoring data, mapping it to the two-phase flow model, and determining a real-time response branch; The initialization branch and the real-time response branch interact laterally to map, calibrate, and locate abnormal control points.

5. The segmented control method for a feed pipe type coal dryer according to claim 1, characterized in that: The introducing of the functional relationship and performing feedback decision analysis on the abnormal control point includes: Traversing the segmented control chain, and determining linear control nodes and nonlinear control nodes based on control characteristics; Based on a linear function, controlling and adjusting the linear control node; An agent function is introduced to control and adjust the nonlinear control node.

6. The segmented control method for a feed pipe type coal dryer according to claim 5, characterized in that: The introducing proxy function includes: Identify the nonlinear control node, and perform big data retrieval to determine an approximate linear function set based on the conditional logic of upper and lower control; Based on the control fitness, the approximate linear function set is traversed to define a proxy function.

7. The segmented control method for a feed pipe type coal dryer according to claim 1, characterized in that: The method further comprises: Acquiring gas phase elements based on the heat flow gas phase state, wherein the gas phase elements at least include air volume and temperature; Determining a decoupling control rule based on the coupling relationship of the gas phase elements, wherein the decoupling control rule is determined based on the coupling degree and the coupling characteristics; Based on the decoupling control rule, the control decision and feedback adjustment decision of the drying process task are assisted.

8. The segmented control method for a feed pipe type coal dryer according to claim 1, characterized in that: The feed tube type coal dryer includes a waste heat recovery module, and the recovered waste heat has a control priority.

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