Self-adaptive control multi-field cooperative intelligent drying system and method

Abstract

The invention discloses a self-adaptive control multi-field cooperative intelligent drying system and method, and the system comprises a multi-field cooperative drying host unit which comprises a drying cavity, a vibration drying bed and a vibration excitation mechanism, and the vibration excitation mechanism is used for driving the vibration drying bed to generate vibration; the airflow supply and distribution unit is used for providing temperature and flow adjustable drying gas into the drying cavity; the multi-parameter online detection unit is used for collecting operation signals reflecting the state of the drying process in real time. The data acquisition and processing unit is used for acquiring and processing the operation parameters to form a characteristic parameter group; the drying state identification unit is used for identifying the drying stage of the material and / or predicting the moisture content change trend of the material based on the characteristic parameter group; the adaptive control unit is used for generating a control instruction according to the identification and / or prediction result; and the equipment health state monitoring unit is used for monitoring an equipment structure or a gas distribution state and sending a feedback signal to the self-adaptive control unit when an abnormity is identified.

Description

Technical Field

[0001] This invention relates to the field of material drying and intelligent process control technology, and in particular to an adaptively controlled multi-field collaborative intelligent drying system and an adaptively controlled multi-field collaborative intelligent drying method. Background Technology

[0002] Drying processes are widely used in chemical, food, pharmaceutical, and materials processing industries. They are typical high-energy-consuming unit operations, characterized by complex heat and mass transfer processes, numerous influencing factors, and distinct stage-specific variations in material moisture content. Multi-field synergistic drying equipment, by introducing mechanical vibration to enhance material disturbance and gas-solid contact, can improve heat and mass transfer conditions and shows promising application prospects in the continuous drying of granular and loose materials.

[0003] However, existing vibration drying or fluidized bed drying equipment mostly operates with fixed or empirically set parameters, lacking the ability to dynamically adjust according to changes in the material drying stage, making it difficult to balance drying efficiency, energy consumption, and product quality. Furthermore, existing systems rely heavily on single temperature or humidity signals for monitoring the drying process, failing to comprehensively analyze multiple parameters such as bed temperature distribution, pressure difference signals, and vibration status. This makes it difficult to accurately determine the drying stage and moisture content change trends, resulting in lag in control adjustments.

[0004] In addition, vibration mechanisms and gas distribution components are prone to wear, loosening or blockage during long-term operation. Existing equipment lacks health monitoring and active protection mechanisms linked to process control, which affects operational stability and safety. Summary of the Invention

[0005] This invention aims to at least partially solve one of the technical problems in related technologies. To this end, the first objective of this invention is to propose an adaptive, multi-field collaborative intelligent drying system. This system identifies the drying stage of the material and predicts the trend of moisture content changes by real-time acquisition of material moisture content, bed temperature, pressure difference, exhaust humidity, and vibration signals. It dynamically adjusts vibration intensity, gas temperature, gas flow rate, and material residence time to achieve phased optimized control. Simultaneously, it monitors the health of key structures and gas distribution, automatically reducing load and issuing warnings in case of abnormalities or wear trends. This improves drying efficiency, reduces energy consumption, and enhances operational stability and safety.

[0006] The second objective of this invention is to propose an adaptive, multi-field synergistic intelligent drying method.

[0007] To achieve the above objectives, a first aspect of the present invention proposes an adaptively controlled multi-field cooperative intelligent drying system, comprising: The multi-field coordinated drying host unit includes a drying chamber, a vibrating drying bed, and a vibration excitation mechanism. The vibration excitation mechanism is used to drive the vibrating drying bed to generate vibration, so as to cause the material to tumble and be conveyed. An airflow supply and distribution unit is used to supply dry gas with adjustable temperature and flow rate into the drying chamber; A multi-parameter online detection unit is installed at key locations in the drying chamber and gas flow channel to collect operating signals that reflect the status of the drying process in real time. The data acquisition and processing unit is connected to the multi-parameter online detection unit and is used to acquire and process operating parameters to form a set of characteristic parameters; A drying state identification unit, connected to a data acquisition and processing unit, is used to identify the drying stage of a material and / or predict the trend of material moisture content changes based on a set of characteristic parameters. An adaptive control unit, connected to a dry state identification unit, is used to generate control commands based on the identification and / or prediction results; The execution adjustment unit is connected to the vibration excitation mechanism and the air supply and distribution unit respectively, and is used to adjust the vibration frequency or amplitude of the vibrating drying bed, as well as the temperature and flow rate of the drying gas according to the control command. The equipment health status monitoring unit is used to monitor the equipment structure or gas distribution status and send feedback signals to the adaptive control unit when an abnormality is detected. The aforementioned units together constitute a closed-loop intelligent control system for the drying process.

[0008] In addition, the adaptively controlled multi-field cooperative intelligent drying system according to the above embodiments of the present invention may also have the following additional technical features: According to one embodiment of the present invention, the airflow supply and distribution unit includes: A blower installed at the front end of a gas delivery pipeline; The gas storage tank located downstream of the blower is used for temporary storage and pressure stabilization of the gas. A heating device is installed downstream of the gas supply and storage tank to heat the gas to a preset drying temperature. Solenoid valves installed in gas delivery pipelines are used to control the start and stop of gas supply. The gas flow meter, located downstream of the solenoid valve, is used to monitor the gas flow in real time and transmit the monitoring signal to the data acquisition and processing unit. A flow regulating valve located downstream of the gas flow meter is used to precisely regulate the gas flow rate according to control commands; and The gas recovery storage tank, connected to the exhaust duct via a gas recovery pipeline, is used to recover part of the dried exhaust gas and resend it to the gas supply system. After mixing with the fresh gas entering the system, the gas is reheated by the heating device, thus realizing the recycling of the gas.

[0009] According to one embodiment of the present invention, the multi-field collaborative drying host unit includes: Drying chamber; A vibrating drying bed is installed inside the drying chamber; A vibration excitation mechanism connected to the vibrating dryer is used to provide periodic excitation force to the vibrating dryer; The drying bed damper, installed between the vibrating drying bed and the crossbeam, is used to absorb vibration impact. A feeder is installed at the upper feeding position of the drying chamber; The guide plate, located in the feeding area of ​​the vibrating dryer, is used to guide and disperse the material. The exhaust duct is located at the top of the drying chamber; The unloader is installed at the discharge end of the vibrating dryer; An air inlet hood is located at the bottom of the drying chamber; And a first air inlet and a second air inlet are disposed between the air inlet hood and the drying chamber; The dry gas entering through the air inlet hood enters the drying chamber through the first air inlet and the second air inlet, forming a multi-regional airflow distribution below the vibrating drying bed. This airflow is coupled with the vibration of the vibrating drying bed, creating a mixed-flow drying state of vibration-airflow coupling inside the drying chamber.

[0010] According to one embodiment of the present invention, the multi-parameter online detection unit includes at least one of the following: a multi-point temperature sensor array disposed at different height positions in the drying chamber, a differential pressure sensor disposed between the gas inlet and outlet, a humidity sensor disposed in the exhaust channel, and an acceleration sensor installed on the vibration excitation mechanism; the operating signals include at least two of the following: material moisture content, bed temperature, bed pressure difference, exhaust humidity, and vibration state signals.

[0011] According to one embodiment of the present invention, the drying state identification unit is used to divide the material drying process into at least two stages, namely, a heating and preheating stage, a constant-rate drying stage, a falling-rate drying stage, and a final moisture approximation stage, and to identify and predict these stages using a mechanistic model, an empirical correlation model, or a data-driven model, specifically including: The drying state identification unit uses a preset model based on a mechanistic model or an empirical model to make a preliminary prediction of the moisture content of coal particles; the preliminary prediction result is used as one of the input features and is input into the data-driven model together with the drying process operating parameters; the preliminary prediction result is dynamically corrected by the data-driven model to obtain the final prediction result of the moisture content of coal particles, and the drying state is determined accordingly.

[0012] According to one embodiment of the present invention, the adaptive control unit is configured to: increase the gas flow rate and / or vibration intensity during the constant-rate drying phase, and decrease the gas temperature and / or vibration intensity during the deceleration drying phase.

[0013] According to one embodiment of the present invention, the adaptive control unit further includes a drying rate prediction module, which is used to predict the material moisture content within a preset time period based on the changing trends of exhaust humidity, bed temperature and material moisture content, and adjust the control parameters in advance based on the prediction results.

[0014] According to one embodiment of the present invention, the equipment health status monitoring unit includes a vibration sensor arranged on the outer wall of the drying chamber or on the supporting structure, for judging the structural status based on the changes in the vibration spectrum; the equipment health status monitoring unit is configured to judge the status of the gas distribution device based on the long-term trend or fluctuation characteristics of the bed pressure difference signal; and when the equipment health status monitoring unit identifies an abnormality, the adaptive control unit is used to reduce the vibration intensity and / or gas flow rate, and output a maintenance warning signal.

[0015] According to one embodiment of the present invention, the regulating unit includes a frequency converter connected to the vibration excitation mechanism and a regulating controller connected to the blower and the heating device, so that when the moisture content at the drying endpoint reaches a preset target value, the gas supply is automatically reduced or the discharge mechanism is started.

[0016] To achieve the above objectives, a second aspect of the present invention proposes a multi-field collaborative intelligent drying method based on adaptive control, applied to the aforementioned adaptive control multi-field collaborative drying system. The method includes the following steps: Step S1: Use a multi-parameter online detection unit to collect multi-parameter operating signals that reflect the drying status of the material; Step S2: The data acquisition and processing unit processes the operating signal and extracts characteristic parameters representing the current drying state. Step S3: Using the drying state identification unit, based on the feature parameters and a preset model, identify the current drying stage of the material and / or predict the trend of the material's moisture content change. Step S4: Using the adaptive control unit, control commands are generated based on the identified drying stage and / or prediction results; Step S5: Using the execution adjustment unit, the operating parameters of the multi-field collaborative drying system are adjusted according to the control command. The operating parameters include one or more of the following: vibration frequency, vibration amplitude, gas temperature, gas flow rate, and material residence time, so as to realize adaptive closed-loop control of the drying process.

[0017] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: (1) Enhance gas-solid contact and heat and mass transfer process: Through the vibration-airflow coupling mixed flow drying structure, the drying gas forms a multi-directional flow in the bed, thereby improving the gas-solid contact efficiency and enhancing the heat and mass transfer capacity.

[0018] (2) Achieve automatic identification and dynamic adjustment of the drying stage: The drying stage is accurately identified through multi-parameter fusion analysis, and the system operating parameters are adaptively adjusted according to the identification results to improve the control accuracy of the drying process.

[0019] (3) Reduce energy consumption and improve efficiency: Reduce ineffective heating and excessive ventilation by predictive control and phased adjustment, thereby reducing energy consumption per unit output.

[0020] (4) Improve equipment operation stability and safety: Through the linkage mechanism of equipment health status monitoring and process control, the abnormal status of equipment can be identified in advance and actively protected, thereby extending the service life of equipment and improving the reliability of system operation.

[0021] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] Figure 1 This is a block diagram of an adaptively controlled multi-field collaborative intelligent drying system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of an adaptively controlled multi-field collaborative intelligent drying system according to an embodiment of the present invention.

[0023] Figure label: 100. Multi-field collaborative drying main unit; 200. Airflow supply and distribution unit; 300. Multi-parameter online detection unit; 400. Data acquisition and processing unit; 500. Drying status identification unit; 600. Adaptive control unit; 700. Execution and adjustment unit; 800. Equipment health status monitoring unit; 101. Drying chamber; 102. Vibrating drying bed; 103. Vibration excitation mechanism; 104. Drying bed shock absorber; 105. Crossbeam; 106. Guide plate; 107. Feeder; 108. Exhaust duct; 109. Unloader; 110. Air inlet hood; 111. First air inlet; 112. Second air inlet; 201. Blower; 202. Gas supply and storage tank; 203. Heating device; 204. Solenoid valve; 205. Gas flow meter; 206. Flow regulating valve; 207. Recovery storage tank; 301. Bed temperature sensor; 302. Bed differential pressure sensor; 303. Exhaust air humidity sensor; 304. Vibration status sensor; 401. PLC electrical control cabinet. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0025] The adaptive control multi-field collaborative intelligent drying system and the adaptive control multi-field collaborative intelligent drying method proposed in the embodiments of the present invention are described below with reference to the accompanying drawings.

[0026] like Figure 1 and Figure 2 As shown, the adaptive control multi-field collaborative intelligent drying system of this invention includes: a multi-field collaborative drying host unit 100, an airflow supply and distribution unit 200, a multi-parameter online detection unit 300, a data acquisition and processing unit 400, a drying status identification unit 500, an adaptive control unit 600, an execution adjustment unit 700, and an equipment health status monitoring unit 800.

[0027] The multi-field collaborative drying host unit 100 includes a drying chamber 101, a vibrating drying bed 102, and a vibration excitation mechanism 103. The vibration excitation mechanism 103 drives the vibrating drying bed 102 to vibrate, thereby causing the material to tumble and be conveyed. An airflow supply and distribution unit 200 provides adjustable temperature and flow rate drying gas to the drying chamber 101. A multi-parameter online detection unit 300, located at key positions in the drying chamber 101 and gas flow channels, collects operating signals reflecting the drying process status in real time. A data acquisition and processing unit 400, connected to the multi-parameter online detection unit 300, collects and processes operating parameters to form a set of characteristic parameters. A drying status identification unit 500 is connected to the data acquisition and processing unit. Unit 400 is connected to identify the drying stage of the material and / or predict the trend of material moisture content change based on the characteristic parameter set; Adaptive control unit 600 is connected to drying state identification unit 500 and is used to generate control commands based on the identification and / or prediction results; Execution adjustment unit 700 is connected to vibration excitation mechanism 103 and airflow supply and distribution unit 200 respectively, and is used to adjust the vibration frequency or amplitude of vibration drying bed 102, as well as the temperature and flow rate of drying gas according to the control commands; Equipment health status monitoring unit 800 is used to monitor the equipment structure or gas distribution status and send feedback signals to adaptive control unit 600 when abnormalities are detected; wherein, the above units together constitute a closed-loop intelligent control system for the drying process.

[0028] Specifically, the core principle of this invention lies in constructing an efficient gas-solid contact and heat / mass transfer environment through the synergistic coupling of vibration and airflow. Specifically, the vibration excitation mechanism 103 in the multi-field synergistic drying host unit 100 drives the vibrating drying bed 102 to generate periodic vibrations, causing the material to tumble, jump, and be conveyed on the bed, thereby effectively reducing the density of the material layer and forming a loose structure. Simultaneously, the airflow supply and distribution unit 200 provides drying gas with adjustable temperature and flow rate to the drying chamber 101, and through the gas distribution device (i.e., air inlet hood 110, first air inlet 111, and second air inlet 112) located below the vibrating drying bed 102, the drying gas enters the material layer in multiple directions. Under the combined influence of the vibration and airflow, a mixed flow state is formed inside the material layer, where local upward airflow and lateral turbulent airflow are coupled, significantly enhancing the contact efficiency and heat / mass transfer capacity between the gas and solid phases, providing a good physical basis for subsequent intelligent control.

[0029] During the drying process, the multi-parameter online detection unit 300 collects key operating parameters reflecting the drying status in real time, including material moisture content, bed temperature distribution, bed pressure difference, exhaust humidity, and vibration signals. These signals are filtered, fused, and feature extracted by the data acquisition and processing unit 400 to form a set of characteristic parameters representing the current drying status. The drying status identification unit 500 analyzes and processes the above-mentioned characteristic parameter set based on a preset model to accurately determine the current drying stage of the material (such as the preheating stage, constant-rate drying stage, falling-rate drying stage, or final moisture approximation stage) or predict the trend of material moisture content change. The adaptive control unit 600 generates corresponding control commands based on the identification and prediction results, and dynamically adjusts operating parameters such as vibration frequency and amplitude, gas temperature and flow rate, and material residence time through the execution adjustment unit 700, so that the system's operating status always matches the current drying stage of the material, thereby realizing real-time closed-loop intelligent control of the drying process.

[0030] Meanwhile, the equipment health status monitoring unit 800 monitors the structural status of the drying host unit and the working status of the gas distribution device in real time through vibration sensors arranged on key structural components or based on the long-term trend of bed pressure difference signals. When abnormal vibration, structural wear trend or abnormal gas distribution status is detected, the unit sends a feedback signal to the adaptive control unit 600. The adaptive control unit 600 automatically reduces the vibration intensity or gas flow according to the preset safety strategy, so that the system enters the unloaded operation state, and simultaneously outputs the equipment maintenance early warning signal.

[0031] Therefore, through the linkage mechanism of process control and equipment health monitoring, the present invention not only realizes the phased optimization control of the drying process, improves drying efficiency and reduces energy consumption, but also effectively enhances the stability and safety of equipment operation, providing a complete technical solution for the intelligent operation of multi-field collaborative drying equipment.

[0032] According to one embodiment of the present invention, the airflow supply and distribution unit 200 includes: a blower 201 disposed at the front end of the gas delivery pipeline; a gas supply storage tank 202 disposed downstream of the blower 201 for temporarily storing and stabilizing the gas; a heating device 203 disposed downstream of the gas supply storage tank 202 for heating the gas to a preset drying temperature; a solenoid valve 204 disposed in the gas delivery pipeline for controlling the start and stop of the gas supply; a gas flow meter 205 disposed downstream of the solenoid valve 204 for real-time monitoring of the gas flow rate and transmitting the monitoring signal to the data acquisition and processing unit 400; a flow regulating valve 206 disposed downstream of the gas flow meter 205 for precisely regulating the gas flow rate according to control commands; and a recovery storage tank 207 connected to the exhaust pipe 108 via a gas recovery pipeline for recovering part of the drying exhaust gas and re-delivering it to the gas supply system, mixing it with the fresh gas entering the system, and then re-entering the heating device 203 for heating, thereby realizing the recycling of the gas.

[0033] Specifically, the blower 201 is located at the front end of the gas conveying pipeline to provide the power required for conveying dry gas, so that external air or circulating gas can enter the gas conveying system.

[0034] In this embodiment, the gas storage tank 202 is located downstream of the blower 201 to temporarily store and stabilize the gas delivered by the blower 201, thereby reducing the flow fluctuations generated during the operation of the blower 201 and improving the stability of the gas supply in the system.

[0035] Furthermore, the heating device 203 is located after the gas supply and storage tank 202 to heat the gas entering the system, so that the gas reaches a preset drying temperature before entering the drying main unit. Understandably, the heating device 203 can be an electric heater, a steam heat exchanger, or other form of gas heating device 203.

[0036] In this embodiment, the solenoid valve 204 is installed in the gas delivery pipeline to control the opening or closing of the gas delivery pipeline, thereby realizing the start and stop control of the gas supply of the system.

[0037] Specifically, the gas flow meter 205 is installed on the gas delivery pipeline to monitor the gas flow rate entering the drying host unit in real time and transmit the monitoring signal to the data acquisition and processing unit 400 to realize real-time monitoring of gas flow rate.

[0038] Furthermore, the flow regulating valve 206 is located downstream of the gas flow meter 205 and is used to regulate the gas flow in the gas delivery pipeline according to the control command, thereby achieving precise control of the dry gas flow.

[0039] In this embodiment, the gas recovery storage tank 207 is installed on the gas recovery pipeline connected to the exhaust duct 108, and is used to recover and temporarily store part of the exhaust gas discharged during the drying process. Preferably, the gas recovery storage tank 207 can re-send the high-temperature drying exhaust gas to the gas supply system, mix it with the gas newly entering the system, and then re-enter the heating device 203 for heating, thereby realizing the recycling of gas.

[0040] With the above structural configuration, during equipment operation, the blower 201 delivers gas to the gas supply and storage tank 202 for pressure stabilization. The gas is then heated by the heating device 203 to form dry gas, which enters the drying main unit through the solenoid valve 204, gas flow meter 205, and flow regulating valve 206. After entering the drying chamber 101, the dry gas passes through the material layer and undergoes heat and mass transfer with the material, thereby removing moisture from the material and forming hot, humid exhaust gas. This hot, humid exhaust gas is discharged through the exhaust system, with a portion of it entering the recovery storage tank 207 for recycling and then re-entering the gas supply system for reuse.

[0041] By recovering and reusing the drying exhaust gas, system heat loss can be reduced and energy utilization efficiency can be improved, while the overall energy consumption of the drying system can be reduced.

[0042] According to an embodiment of the present invention, a multi-field co-operated drying host unit 100 includes: a drying chamber 101; a vibrating drying bed 102 disposed inside the drying chamber 101; a vibration excitation mechanism 103 connected to the vibrating drying bed 102 for providing periodic excitation force to the vibrating drying bed 102; a drying bed shock absorber 104 disposed between the vibrating drying bed 102 and the crossbeam 105 for absorbing vibration impact; a feeder 107 disposed at the upper feeding position of the drying chamber 101; a guide plate 106 disposed in the feeding area of ​​the vibrating drying bed 102 for guiding and dispersing the material; and a drying chamber 101... The upper exhaust duct 108; the unloader 109 located at the discharge end of the vibrating drying bed 102; the air inlet hood 110 located at the bottom of the drying chamber 101; and the first air inlet 111 and the second air inlet 112 located between the air inlet hood 110 and the drying chamber 101; wherein, the drying gas entering through the air inlet hood 110 enters the interior of the drying chamber 101 through the first air inlet 111 and the second air inlet 112 respectively, forming a multi-region airflow distribution below the vibrating drying bed 102, and coupling with the vibration of the vibrating drying bed 102, forming a vibration-airflow coupled mixed flow drying state inside the drying chamber 101.

[0043] Specifically, in this embodiment, the vibrating drying bed 102 works in conjunction with the multi-air intake structure to create a mixed-flow drying environment with vibration-airflow coupling inside the drying chamber 101. A drying bed shock absorber 104 is disposed between the vibrating drying bed 102 and the crossbeam 105 to absorb the vibration impact generated by the vibrating drying bed 102 during operation, thereby reducing the transmission of vibration to the main structure of the drying chamber 101, improving the stability of equipment operation, and extending the service life of the equipment.

[0044] Specifically, the vibration excitation mechanism 103 provides periodic excitation force to the vibrating drying bed 102, causing the vibrating drying bed 102 to vibrate at a set frequency and amplitude. Under this vibration, the bed material continuously loosens, tumbles, and jumps, forming more gap structures between material particles, thereby reducing the packing density of the material layer and increasing the permeability of gas passing through the material layer.

[0045] Specifically, the feeder 107 is located at the upper feeding position of the drying chamber 101 to convey the material to be dried to the feeding end of the vibrating drying bed 102. The guide plate 106 is located in the feeding area of ​​the vibrating drying bed 102 to guide and disperse the material entering the drying chamber 101, so that the material can form a uniformly distributed material layer on the vibrating drying bed 102.

[0046] Meanwhile, the drying gas entering through the air inlet hood 110 enters the drying chamber 101 through the first air inlet 111 and the second air inlet 112, forming a multi-regional airflow distribution below the vibrating drying bed 102. Due to the differences in the gas flow direction of the multiple air inlets, the drying gas forms a flow state in which local upward airflow and lateral turbulent airflow are superimposed inside the material layer.

[0047] Preferably, by setting multiple air inlets, the drying gas can form airflow channels in different areas of the material layer, thereby improving the uniformity of gas distribution in the material layer and enhancing the contact efficiency between the gas and solid phases.

[0048] Furthermore, the exhaust duct 108 is located at the upper part of the drying chamber 101 and communicates with the internal space of the drying chamber 101 to exhaust the hot and humid gas generated during the drying process.

[0049] In this embodiment, the unloader 109 is disposed at the discharge end of the vibrating drying bed 102 and is used to discharge the dried material from the drying chamber 101 so as to achieve continuous or intermittent discharge of the material.

[0050] Understandably, during equipment operation, after the material to be dried enters the drying chamber 101 through the feeder 107, it gradually moves towards the discharge end along the bed surface under the vibration conveying action of the vibrating drying bed 102. At the same time, the drying gas entering through the air inlet hood 110, the first air inlet 111, and the second air inlet 112 passes through the material layer and fully contacts the material, thereby realizing the evaporation and removal of moisture from the material. Finally, the dried material is discharged from the equipment through the unloader 109.

[0051] Through the synergistic effect of vibration conveying, multi-zone air intake, and material tumbling, a mixed-flow drying state with vibration-airflow coupling can be formed inside the drying chamber 101, thereby improving gas-solid contact efficiency, enhancing heat and mass transfer processes, and improving material drying efficiency and uniformity. This structural design ensures that the material can fully contact the drying gas during vibration conveying, thus improving drying efficiency and material drying uniformity, while reducing localized over-drying or agglomeration of the material.

[0052] According to one embodiment of the present invention, the multi-parameter online detection unit 300 includes at least one of the following: a multi-point temperature sensor array disposed at different height positions of the drying chamber 101, a differential pressure sensor disposed between the gas inlet and outlet, a humidity sensor disposed in the exhaust channel, and an acceleration sensor mounted on the vibration excitation mechanism 103; the operating signals include at least two of the following: material moisture content, bed temperature, bed differential pressure, exhaust humidity, and vibration state signals.

[0053] Specifically, the multi-parameter online detection unit 300 is installed at key locations in the drying chamber 101 and the gas flow channel to collect operating parameters during the drying process in real time, including bed temperature signals, exhaust humidity signals, bed pressure difference signals, and vibration status signals. The multi-parameter online detection unit 300 transmits the collected operating parameters to the data acquisition and processing unit 400, which performs data processing and feature extraction on each sensor signal to form a set of characteristic parameters representing the current drying state.

[0054] In a preferred embodiment of the present invention, all types of sensors in the multi-parameter online detection unit 300 adopt a protective mounting structure suitable for multi-field collaborative drying environment, so as to avoid the influence of material impact, wear and dust deposition while ensuring measurement accuracy.

[0055] The bed temperature sensor 301 preferably adopts a thermocouple or resistance temperature detector structure with a wear-resistant metal protective sleeve. It is inserted into the edge area of ​​the bed through the side wall of the drying chamber 101. The sensor axis is set at an angle to the direction of material movement, so that the end of the sensor avoids the core area where the material is violently churning, thereby reducing direct impact and wear of particles and improving long-term operational reliability.

[0056] The pressure taps of the bed differential pressure sensor 302 are respectively located on the side wall of the air inlet chamber below the gas distribution device and on the side wall of the free space above the bed. The pressure taps are equipped with an anti-clogging filter structure and can be connected to a backflushing gas interface for periodic cleaning to prevent dust accumulation from causing measurement inaccuracies.

[0057] The exhaust humidity sensor 303 is preferably installed in the middle and upper part of the exhaust duct 108 using a side-insertion method. A dustproof filter cap is set at the front end of the sensor, and a heat tracing or insulation structure can be matched to prevent the condensation of high humidity gas from affecting the detection accuracy, while avoiding the sensor being directly exposed to the frontal scouring area of ​​the high-speed dust-laden airflow.

[0058] The vibration state sensor 304 is installed on the base of the vibration excitation mechanism 103 and is rigidly fixed by bolts to accurately reflect the overall vibration characteristics of the equipment, while avoiding measurement errors caused by loose parts.

[0059] With the above installation method, each sensor can maintain a stable and reliable operating state under vibration, gas-solid two-phase flow and dust environments, thereby ensuring the long-term accuracy of multi-parameter detection in the drying process and the stability of system control.

[0060] According to one embodiment of the present invention, the drying state identification unit 500 is used to divide the material drying process into at least two stages, namely, a heating and preheating stage, a constant-rate drying stage, a falling-rate drying stage, and a final moisture approximation stage, and to identify and predict the drying state using a mechanistic model, an empirical correlation model, or a data-driven model. Specifically, the drying state identification unit uses a preset model to make a preliminary prediction of the moisture content of coal particles based on a mechanistic model or an empirical model; the preliminary prediction result is used as one of the input features and input into the data-driven model along with the drying process operating parameters; the preliminary prediction result is dynamically corrected by the data-driven model to obtain the final prediction result of the moisture content of coal particles, and the drying state is determined accordingly.

[0061] According to one embodiment of the present invention, the adaptive control unit 600 further includes a drying rate prediction module, which is used to predict the material moisture content within a preset time period based on the changing trends of exhaust humidity, bed temperature and material moisture content, and adjust the control parameters in advance based on the prediction results.

[0062] Specifically, in this embodiment, the drying state identification unit 500 identifies the state of the drying process based on a set of characteristic parameters to determine the current drying stage of the material or predict the trend of material moisture content change. Understandably, in the initial stage of drying, the material surface has a high moisture content and the exhaust humidity is high; as the drying process progresses, the internal moisture of the material gradually migrates to the surface, the exhaust humidity gradually decreases, while the bed temperature gradually increases. By comprehensively analyzing the trends of these parameter changes, the drying stage can be identified.

[0063] Specifically, in the preheating stage: the bed temperature rises rapidly while the exhaust humidity is low; in the constant-rate drying stage: the exhaust humidity remains high and fluctuates smoothly, and the evaporation rate per unit time is basically constant; in the deceleration drying stage: the exhaust humidity begins to decrease, and the bed temperature gradually increases; in the final moisture approach stage: the exhaust humidity is at a low level, and the moisture content of the material is close to the target value. In a preferred embodiment, the preset model used by the drying state identification unit 500 may be, but is not limited to, the following types: 1) Empirical / semi-empirical models: such as the Page model or the Midilli model. By determining the model parameters through nonlinear fitting, the current moisture ratio can be predicted based on the drying time, and then the moisture content of the material can be inferred.

[0064] In a preferred embodiment, the empirical / semi-empirical model is used to describe the change in moisture ratio over time during the coal particle drying process.

[0065] The moisture ratio is defined as: in, Let be the moisture content at time t. This is the initial moisture content. To balance the moisture content.

[0066] Furthermore, the empirical model includes, but is not limited to, the following forms: (1) Page model: (2) Midilli model: in, These are the model parameters obtained by fitting historical drying experimental data.

[0067] Using the above model, the moisture ratio can be calculated based on the current drying time, and the real-time moisture content of coal particles can be obtained through inversion, thereby realizing the identification of the drying status.

[0068] 2) Mechanism model: For example, the Reaction Engineering Pathway (REA) model, which solves the drying rate equation using real-time temperature and humidity data.

[0069] As a preferred embodiment, the mechanism model adopts the Reaction Engineering Pathway (REA) model to describe the heat and mass transfer coupling effect during coal particle drying.

[0070] The drying rate satisfies the following relationship: in, It is the apparent activation energy, and is a function of moisture content. Absolute temperature is the gas constant.

[0071] Furthermore, the apparent activation energy varies with moisture content according to a functional relationship: By collecting temperature and humidity parameters in real time during the drying process and combining them with the above model for numerical solution, the dynamic change process of coal particle moisture content can be obtained, and continuous identification of the drying state can be achieved.

[0072] 3) Data-driven model: For example, a multilayer perceptron (MLP) neural network is used, with real-time collected multiple parameters (such as bed temperature T, exhaust humidity RH, bed pressure difference ΔP, vibration frequency f, etc.) as input, and the drying stage or predicted moisture content as output. The network weights are trained through historical data to achieve real-time intelligent identification of the state. In a preferred embodiment, the data-driven model employs a multilayer perceptron neural network (MLP) model to establish a nonlinear mapping relationship between multiple parameter inputs and the drying state.

[0073] The model inputs include, but are not limited to: bed temperature. Exhaust humidity Bed pressure difference and vibration frequency The output is either the moisture content of coal particles or the drying stage category.

[0074] The neural network model can be represented as: in, For the input vector, This is the weight matrix. For bias terms, This is the activation function.

[0075] The input vector of the model is defined as: X=[T,RH,ΔP,f] Furthermore, for the input vector Normalization or feature scaling can be performed to eliminate the impact of differences in the dimensions of different physical quantities on model training.

[0076] Furthermore, to improve prediction accuracy, the data-driven model is used to correct the prediction results of the empirical model or the mechanistic model.

[0077] Specifically: The moisture content prediction value obtained from the empirical model or the mechanistic model is used as an additional input feature, and together with the operating parameters, an extended input vector is constructed. This vector is then input into the data to drive the model to perform calculations, thereby obtaining the corrected moisture content prediction result.

[0078] By incorporating the prediction results of mechanistic or empirical models into the data-driven model as input features, collaborative modeling of physical mechanism constraints and data-driven correction is achieved, thereby improving the accuracy and stability of dry state identification.

[0079] By training with historical drying data and optimizing network weight parameters, real-time intelligent identification of the drying status of coal particles can be achieved.

[0080] As a preferred embodiment, the drying state identification method based on the above model includes the following steps: S101: Data Acquisition Sensors installed in the drying equipment are used to collect temperature, humidity, pressure difference and vibration parameters in real time during the coal particle drying process; S102: Model Selection or Fusion Choose one or more of the following models based on the current operating conditions: empirical model, mechanistic model, or data-driven model. S103: Moisture Content Prediction The collected data is input into the preset model to calculate the current moisture content or moisture ratio of the coal particles; S104: Drying State Judgment Based on the predicted moisture content or its rate of change, the drying process is divided into a constant-rate drying stage, a falling-rate drying stage, or an equilibrium stage. S105: Output Results Output the drying status identification results and use them to guide the adjustment of the drying system's operating parameters.

[0081] According to another embodiment of the present invention, in order to realize real-time monitoring of the material drying process, an online detection device for material moisture content is set in a multi-field collaborative drying system.

[0082] Specifically, a material moisture content sensor is installed near the discharge end of the multi-field collaborative drying device. The material moisture content sensor is used to detect the moisture content of the dried material in real time and transmit the detection signal to the data acquisition and processing unit 400.

[0083] In this embodiment, the material moisture content sensor can be installed in the discharge area of ​​the vibrating drying bed 102, at the unloader 109, or on the discharge conveying channel, so as to measure the moisture content of the material online when it is discharged from the drying device.

[0084] Furthermore, the material moisture content sensor can be a near-infrared moisture detector, a microwave moisture detector, or a capacitive moisture detector, or other moisture content detection equipment capable of online detection.

[0085] It should be noted that after the material moisture content detection signal is transmitted to the data acquisition and processing unit 400, it is processed and then input to the adaptive control unit 600 to determine the current drying state of the material. Based on the detection results, the unit adjusts the gas temperature, gas flow rate, or vibration intensity, thereby achieving intelligent control of the drying process. By setting up an online material moisture content detection device, the endpoint of the drying process can be determined in real time, thus avoiding over-drying or under-drying of the material and improving the control accuracy of the drying process.

[0086] According to one embodiment of the present invention, the adaptive control unit 600 is configured to: increase the gas flow rate and / or vibration intensity during the constant-rate drying phase, and decrease the gas temperature and / or vibration intensity during the deceleration drying phase.

[0087] Specifically, the adaptive control unit 600 generates control commands based on the recognition results output by the drying state recognition unit 500, and dynamically adjusts the equipment operating parameters through the execution adjustment unit 700. The execution adjustment unit 700 is connected to the vibration excitation mechanism 103 and the airflow supply system, respectively, and is used to adjust the vibration frequency or amplitude of the vibrating drying bed 102, as well as the temperature and flow rate of the drying gas.

[0088] Under the above control strategy, when the system identifies that the material is in the constant-rate drying stage, the gas flow rate or vibration intensity can be appropriately increased to enhance gas-solid contact and increase the drying rate; when the system identifies that the material has entered the deceleration drying stage, the gas temperature or vibration intensity can be reduced to reduce energy consumption and prevent the material from becoming too dry.

[0089] According to one embodiment of the present invention, the equipment health status monitoring unit 800 includes a vibration sensor disposed on the outer wall of the drying chamber 101 or on the supporting structure, for judging the structural status based on the changes in the vibration spectrum; the equipment health status monitoring unit 800 is configured to judge the status of the gas distribution device based on the long-term trend or fluctuation characteristics of the bed pressure difference signal; when the equipment health status monitoring unit 800 identifies an abnormality, the adaptive control unit 600 is used to reduce the vibration intensity and / or gas flow rate, and output a maintenance warning signal.

[0090] Specifically, the equipment health status monitoring unit 800 is used to monitor the operating status of key components of the equipment in real time, such as the vibration characteristics of the vibrating drying bed 102 or the trend of bed pressure difference changes. When the equipment health status monitoring unit 800 detects abnormal vibration or structural wear trends in the equipment, it can feed back the relevant signals to the adaptive control unit 600. The adaptive control unit 600 reduces the vibration intensity or gas flow rate according to a preset safety strategy and outputs an equipment maintenance early warning signal, thereby improving the safety and reliability of equipment operation.

[0091] According to one embodiment of the present invention, the execution adjustment unit 700 includes a frequency converter connected to the vibration excitation mechanism 103 and an adjustment controller connected to the blower 201 and the heating device 203, so that when the moisture content at the drying endpoint reaches a preset target value, the gas supply is automatically reduced or the discharge mechanism is started.

[0092] According to one embodiment of the present invention, the drying state identification unit 500, the adaptive control unit 600, the execution adjustment unit 700, etc. can be integrated into a PLC control cabinet 401 device for identification and control system, so as to realize the intelligentization and automation of the drying process.

[0093] Corresponding to the above embodiments, the present invention also proposes a multi-field collaborative intelligent drying method based on adaptive control. The adaptive control-based multi-field synergistic intelligent drying method of this invention is applied to the above-mentioned adaptive control-based multi-field synergistic drying system, and the method includes the following steps: Step S1: Use the multi-parameter online detection unit 300 to collect multi-parameter operating signals that reflect the drying state of the material; Step S2: The data acquisition and processing unit 400 processes the operating signal and extracts characteristic parameters representing the current drying state. Step S3: Using the drying state identification unit 500, based on the feature parameters and a preset model, the current drying stage of the material is identified and / or the trend of the material's moisture content change is predicted. Step S4: Using the adaptive control unit 600, control commands are generated based on the identified drying stage and / or prediction results. Step S5: Using the execution adjustment unit 700, the operating parameters of the multi-field collaborative drying system are adjusted according to the control command. The operating parameters include one or more of the following: vibration frequency, vibration amplitude, gas temperature, gas flow rate, and material residence time, so as to realize adaptive closed-loop control of the drying process.

[0094] Specifically, referring to the figure, the material to be dried is continuously or intermittently fed into the multi-field co-operated drying bed by the feeder 107. At the same time, the blower 201, the heating device 203, and the vibration excitation mechanism 103 are started to put the drying system into operation. The vibration excitation mechanism 103 drives the vibrating drying bed 102 to generate vibrations at a preset frequency and amplitude, so that the material entering the drying chamber 101 forms a loose and uniform bed structure, providing good contact conditions for the subsequent gas-solid heat and mass transfer process.

[0095] Blower 201 delivers gas to gas supply and storage tank 202 for pressure stabilization, then heats it through heating device 203 to form dry gas. The gas flow rate is regulated by gas flow meter 205 and flow regulating valve 206. Subsequently, the dry gas enters drying chamber 101 through air inlet hood 110, and then enters the multi-field co-current drying bed through first air inlet 111 and second air inlet 112, forming a uniform airflow distribution at the bottom of the bed. Through the above gas supply process, the gas entering the drying bed has a stable temperature and flow rate, providing a continuous heat source for material drying.

[0096] The vibration excitation mechanism 103 drives the vibrating drying bed 102 to generate periodic vibrations, causing the material in the bed to tumble, jump, and mix. Under the combined influence of vibration and airflow, the material forms a vibrating mixed flow state inside the bed, thereby enhancing the heat and mass transfer process between the material and the drying gas. During this process, the moisture in the material continuously evaporates to form hot, humid gas, which is then discharged from the drying system through the exhaust duct 108 with the airflow.

[0097] During the drying process, the multi-parameter online detection unit 300 monitors the operating status of the drying process in real time. Specifically: multiple temperature sensors are arranged axially at different heights in the drying chamber 101 to obtain the bed temperature distribution; a differential pressure sensor is installed between the air inlet and outlet of the drying chamber 101 to monitor the bed pressure drop and its fluctuation signal; a humidity sensor is installed in the exhaust duct 108 to detect the humidity change of the discharged gas; preferably, an online material moisture content detection device is installed in the material discharge area to obtain the material moisture content in real time; and an acceleration sensor is installed at the bottom of the vibration excitation mechanism 103 to monitor the vibration frequency and vibration intensity. The above sensor signals are continuously transmitted to the data acquisition and processing unit 400.

[0098] The data acquisition and processing unit 400 filters, denoises, and extracts features from the acquired sensor signals, and inputs the extracted feature parameters to the drying state identification unit 500. The drying state identification unit 500 determines the current drying stage of the material based on information such as temperature change trends, exhaust humidity change rates, pressure difference fluctuation characteristics, and material moisture content. The drying process can be divided into at least three of the following stages: Preheating stage: bed temperature rises rapidly while exhaust humidity is low; Constant-rate drying stage: exhaust humidity remains high and fluctuates smoothly, with a relatively constant evaporation rate per unit time; Decreasing-rate drying stage: exhaust humidity begins to decrease, and bed temperature gradually increases; Final moisture approximation stage: exhaust humidity is at a low level, and the material moisture content approaches the target value. State identification can be based on empirical thresholds or on multi-parameter fusion based on mechanistic models or data-driven models. For example, it can be jointly identified using temperature change rates, humidity gradients, and pressure difference fluctuation spectrum characteristics to improve accuracy.

[0099] The adaptive control unit 600 generates control commands based on the drying stage identification results and the material moisture content change trend, and dynamically adjusts the system operating parameters through the execution adjustment unit 700. The adjustment parameters include: vibration frequency and amplitude; drying gas temperature; drying gas flow rate; and material residence time in the drying chamber 101. For example, in the constant-rate drying stage, the gas flow rate and vibration intensity are increased to maintain a higher evaporation rate; in the falling-rate drying stage, the gas temperature or vibration intensity is gradually reduced; and in the final moisture approach stage, the airflow is reduced and vibration is weakened to ensure a smooth transition to the drying process at the end.

[0100] The control unit 700 adjusts the speed of the blower 201, the power of the heating device 203, and the drive frequency of the vibration motor according to the control commands. The new operating status data is then fed back to the multi-parameter online detection unit 300, thus forming a closed-loop control system.

[0101] When the moisture content of the material is detected or predicted to reach the preset target value, the adaptive control unit 600 executes a termination control strategy, including: reducing the temperature and flow rate of the drying gas supply; reducing the vibration intensity; and starting the unloader 109 to discharge the dried material from the drying system.

[0102] The following section details the multi-field collaborative intelligent drying method based on multi-parameter sensing and adaptive control, according to specific experimental data.

[0103] A specific embodiment of the present invention, such as Figure 2 As shown, a multi-field collaborative intelligent drying method based on multi-parameter sensing and adaptive control is disclosed. The above-mentioned multi-field collaborative intelligent drying system is used to conduct drying experiments on a certain fine coal material.

[0104] I. Experimental Conditions and Initial Parameters In this embodiment, lignite with a particle size of 0–50 mm was selected as the material to be dried. The initial moisture content of the coal sample was 27.4%–31.4%, with an average moisture content of 29.4%. The net calorific value on an as-received basis was 18761.65–19841.64 kJ / kg, classifying it as low-ash lignite. To ensure the reliability of the experimental results, at least three repeated experiments were conducted under each operating condition. The following data are the average values ​​obtained from the repeated experiments. The initial operating parameters of the system were set as follows: Vibration frequency: 40 Hz; (comparative operating conditions are 30 Hz and 45 Hz) Vibration amplitude: 2 mm (comparison conditions set to 1 mm and 3 mm); Dry gas temperature: 100 ℃ (comparative operating condition is 150 ℃); Apparent gas velocity: 0.165 m / s (comparative condition: 0.060 m / s); Initial bed height: 150 mm; Coal feed rate: 160 t / h (comparative operating conditions are 100 t / h and 200 t / h); Dryer hot air volume: 2×10 5 m³ / h.

[0105] Under the vibration frequency conditions, the bed porosity is not less than 97%, which is used to achieve uniform gas penetration into the material layer and enhance gas-solid contact.

[0106] II. Dynamic Changes in the Drying Process (1) Changes in moisture content over time Under optimized operating conditions (gas velocity 0.165 m / s, particle size 0–6 mm): Within 0–10 minutes, the moisture content decreased from 29.4% to approximately 23.2%; within 10–20 minutes, it decreased to approximately 20.6%; and within 20–30 minutes, the moisture content entered a slow decline phase. Under particle size conditions of 25–50 mm, the moisture content decrease was only 0.2%–0.8% within the same time period, indicating that particle size has a significant impact on the drying rate. Under low gas velocity (0.060 m / s), the moisture content remained at 23.01% after 20 minutes of drying, indicating that gas flow rate has a significant impact on drying efficiency.

[0107] (2) Changes in drying rate The drying rate exhibits distinct phased characteristics: In the initial stage, the drying rate rapidly increased, exhibiting a brief acceleration. During the constant-rate stage, the drying rate remained at approximately 0.7% / min. In the deceleration stage, the drying rate gradually decreased, primarily controlled by the moisture diffusion mechanism. At an amplitude of 3 mm, the maximum drying rate increased by more than 15%, and the duration of the constant-rate stage was prolonged, indicating that vibration enhancement significantly improved heat and mass transfer efficiency. The drying rate of coal samples with particle sizes in the range of 0–6 mm was much greater than that in the ranges of 6–13 mm, 13–25 mm, and 25–50 mm. Smaller particle sizes resulted in a larger specific surface area for lignite gasification, significantly accelerating the drying process.

[0108] (3) Temperature and humidity distribution characteristics During the drying process: During the constant-rate stage, the exhaust air humidity is close to saturation, with a relative humidity approaching 100%. During the decreasing-rate stage, the exhaust air humidity drops rapidly, while the bed temperature gradually increases in the later stages. Simultaneously, the larger the amplitude, the more uniform the temperature and humidity distribution, and the smaller the temperature difference between different heights of the bed. When the inlet air temperature is increased from 100 ℃ to 150 ℃, the drying rate increases significantly. The constant-rate stage curve matches the actual drying rate curve, indicating that increasing the hot air temperature can effectively enhance heat and mass transfer.

[0109] (4) Pressure difference and fluidization characteristics The variation pattern of bed pressure difference is as follows: Initially, the pressure differential increases with increasing gas velocity; after fluidization stabilizes, the pressure differential remains essentially constant, with the internal resistance of the dryer ranging from 700 to 1300 Pa; in the later stages of drying, the pressure differential decreases slightly, corresponding to a reduction in material mass. The amplitude of pressure differential fluctuations is directly related to the fluidization quality of the material and is used to determine changes in the bed state.

[0110] III. Implementation Process of Intelligent Control In this embodiment, the system performs adaptive control based on real-time multi-parameter data, and the specific process is as follows: (1) Control of constant rate drying stage Identification characteristics: Exhaust air humidity remains high, and the drying rate is basically stable. Control strategy: Increase the gas flow rate to 0.165 m / s, increase the amplitude to 2-3 mm, and maintain a relatively high gas temperature (100-150 ℃). This control strategy is used to maintain the maximum evaporation rate. Experiments show that the highest water removal rate and the largest increase in calorific value are achieved under the conditions of particle size 0-6 mm and coal feed rate of 100 t / h.

[0111] (2) Control of the falling-rate drying stage Identification characteristics: Exhaust air humidity begins to decrease, and bed temperature rises. Control strategy: Reduce gas temperature by 10–20°C and appropriately reduce vibration intensity. This control strategy is used to prevent excessive drying of the material surface and reduce energy waste. Experiments show that the decrease in drying rate during the falling-rate stage is mainly affected by changes in pore structure and shrinkage of lignite particles; reducing the heat load can avoid over-drying.

[0112] (3) Control of final moisture approximation stage Identification characteristics: Moisture content is close to the target value (approximately 10%), and the humidity signal tends to stabilize. Control strategy: Reduce gas flow rate, decrease vibration, and shorten residence time. This control strategy is used to smoothly end the drying process. Experiments show that when the coal feed rate increases from 100 t / h to 200 t / h, the drying rate decreases due to the unchanged unit heat supply. Reducing energy input in advance during the final moisture approach stage can avoid over-drying.

[0113] IV. Predictive Control The system establishes a predictive model based on the rate of change of humidity, the rate of change of temperature, and the decreasing trend of moisture content within a continuous 10-minute time window. The predictive model is preferably a mechanistic or empirical model based on historical operating data, used to predict the drying endpoint based on the rate of change of exhaust humidity, the rate of change of bed temperature, and the trend of moisture content change. A closed-loop control model is constructed with the target moisture content W* as the control objective, and feedback adjustment is performed through the deviation ΔW between the real-time moisture content W(t) and the target value. Experimental results show that the predictive model can predict the drying endpoint 3–5 minutes in advance and reduce energy input in advance, thereby avoiding over-drying. Drying characteristic data under different coal feed rates (100 t / h, 160 t / h, 200 t / h) and different particle sizes (0–6 mm, 6–13 mm, 13–25 mm, 25–50 mm) verify the effectiveness of the predictive model. The system can adjust operating parameters in advance according to the trend of drying rate changes, realizing intelligent control of the drying process.

[0114] In this embodiment, the adaptive control unit further includes a control parameter calculation module, which constructs a mapping relationship between the control quantity and the drying state based on multi-parameter features, specifically including: (1) Vibration parameter adjustment model The vibration amplitude A, drying rate dW / dt, and bed pressure difference ΔP satisfy the following relationship: A = A0 + k1·(dW / dt) + k2·ΔP Where A0 is the basic amplitude, and k1 and k2 are adjustment coefficients.

[0115] (2) Gas temperature regulation model The gas temperature T is dynamically adjusted based on the moisture content deviation ΔW = W - W*. T = T0 - k3·ΔW Where T0 is the basic amplitude and k3 is the adjustment coefficient.

[0116] (3) Gas flow rate regulation model The apparent gas velocity u and the rate of change of exhaust humidity dH / dt satisfy the following relationship: u = u0 + k4·(dH / dt) Where u0 is the basic amplitude and k4 is the adjustment coefficient.

[0117] (4) Closed-loop feedback control The system uses the target moisture content W* as the control objective and constructs a closed-loop feedback regulation mechanism. By collecting the moisture content W(t) in real time, it calculates the deviation ΔW and adjusts the vibration parameters, gas temperature and flow rate in a coordinated manner.

[0118] (5) Predictive control model The prediction model establishes a moisture content prediction function W(t+Δt) based on time series data, and uses a regression model or a data-driven model to predict the moisture content change in the next 3 to 5 minutes, and adjusts the control parameters in advance.

[0119] Based on the above embodiments, in order to verify the technical effect of the multi-field collaborative intelligent drying method based on multi-parameter sensing and adaptive control of the present invention compared with the traditional control method, a comparative experiment was set up for verification.

[0120] I. Comparative Experiment Design Comparison using the same materials and operating conditions: Material: Lignite with a particle size of 0–50 mm Initial moisture content: 29.4% Bed height: 150 mm Hot air volume: 2×10 5 m³ / h Configure the following two operating modes: (1) Control group (traditional control method) The comparison group represents the fixed-parameter operation mode commonly used in current engineering: Vibration frequency: 40 Hz (constant) Amplitude: 2 mm (constant) Gas temperature: 120 ℃ (constant) Gas flow rate: 0.120 m / s (constant) Identification and dynamic adjustment of no drying stage (2) Implementation Group (Method of the Invention) The adaptive control method of this invention is adopted: Multi-parameter fusion identification based on temperature, humidity, pressure difference, and vibration signals. Dynamically adjust according to the drying stage: Constant speed stage: Increase air volume (0.165 m / s) and amplitude (2-3 mm); Deceleration phase: Temperature decreases (by 10-20°C); Final stage: Reduce airflow and vibration intensity; Predictive control is introduced, and parameters are adjusted 3 to 5 minutes in advance.

[0121] II. Comparison of Experimental Results (1) Comparison of drying performance (2) Energy consumption comparison Note: This invention reduces ineffective heating by providing energy in stages and utilizing exhaust gas.

[0122] (3) Comparison of control precision Note: This invention significantly improves control accuracy through closed-loop control and prediction mechanisms.

[0123] (4) Comparison of adaptability under different working conditions III. Analysis of Key Technology Effects (1) Mechanism for improving drying efficiency This invention is achieved through: Coordinated adjustment of vibration parameters and airflow parameters Precise identification of the drying stage This improves gas-solid contact efficiency and prolongs the duration of the constant-rate phase, thereby: The drying rate is increased by approximately 20% or more.

[0124] (2) Energy-saving mechanism pass: Reduce temperature during deceleration phase Reduce air volume in advance at the final stage Exhaust gas recycling Reduce ineffective energy consumption and achieve: Energy consumption per unit is reduced by approximately 10% to 15%.

[0125] (3) Control precision improvement mechanism pass: Multi-parameter fusion (temperature + humidity + pressure difference + vibration) Moisture content trend forecast accomplish: The moisture content control error was reduced from ±2.5% to ±1.0%.

[0126] (4) Improved equipment stability pass: differential pressure fluctuation monitoring Vibration spectrum analysis accomplish: Early detection of anomalies Automatic load reduction protection The failure rate has been reduced and the operational stability has been significantly improved.

[0127] IV. Quantitative Conclusions on the Invention's Effectiveness Based on the above comparative experimental results, the present invention has at least the following beneficial effects compared to the prior art: 1. Drying efficiency is increased by approximately 15% to 25%; 2. Unit energy consumption is reduced by approximately 10% to 15%; 3. Moisture content control accuracy improved to within ±1%; 4. Drying time is reduced by approximately 15% to 20%; 5. The system possesses excellent adaptability and stability under various operating conditions; Therefore, the method of the present invention can realize the transformation of the multi-field collaborative drying process from "experience control" to "multi-parameter sensing + adaptive intelligent control".

[0128] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0129] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0130] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0131] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An adaptively controlled multi-field collaborative intelligent drying system, characterized in that, include: The multi-field coordinated drying host unit includes a drying chamber, a vibrating drying bed, and a vibration excitation mechanism. The vibration excitation mechanism is used to drive the vibrating drying bed to vibrate, so as to cause the material to tumble and be conveyed. An airflow supply and distribution unit is used to supply dry gas with adjustable temperature and flow rate into the drying chamber; A multi-parameter online detection unit is set at key locations in the drying chamber and gas flow channel to collect operating signals that reflect the status of the drying process in real time. The data acquisition and processing unit is connected to the multi-parameter online detection unit and is used to acquire and process the operating parameters to form a set of feature parameters; A drying state identification unit, connected to the data acquisition and processing unit, is used to identify the drying stage of the material and / or predict the trend of material moisture content change based on the set of characteristic parameters. An adaptive control unit, connected to the dry state identification unit, is used to generate control commands based on the identification and / or prediction results; An adjustment unit is connected to the vibration excitation mechanism and the air supply and distribution unit, respectively, and is used to adjust the vibration frequency or amplitude of the vibrating drying bed, as well as the temperature and flow rate of the drying gas, according to the control command. The equipment health status monitoring unit is used to monitor the equipment structure or gas distribution status and send a feedback signal to the adaptive control unit when an abnormality is detected. The aforementioned units together constitute a closed-loop intelligent control system for the drying process.

2. The adaptively controlled multi-field collaborative intelligent drying system according to claim 1, characterized in that, The airflow supply and distribution unit includes: A blower installed at the front end of a gas delivery pipeline; The gas storage tank located downstream of the blower is used for temporary storage and pressure stabilization of the gas. A heating device located downstream of the gas supply and storage tank is used to heat the gas to a preset drying temperature. Solenoid valves installed in gas delivery pipelines are used to control the start and stop of gas supply. A gas flow meter located downstream of the solenoid valve is used to monitor the gas flow in real time and transmit the monitoring signal to the data acquisition and processing unit. A flow regulating valve located downstream of the gas flow meter is used to precisely regulate the gas flow rate according to the control command; and The gas recovery storage tank, connected to the exhaust duct via a gas recovery pipeline, is used to recover part of the dried exhaust gas and resend it to the gas supply system. After mixing with the fresh gas entering the system, the gas is re-entered into the heating device for heating, thus realizing the recycling of the gas.

3. The adaptively controlled multi-field collaborative intelligent drying system according to claim 1, characterized in that, The multi-field coordinated drying host unit includes: Drying chamber; A vibrating drying bed is installed inside the drying chamber; The vibration excitation mechanism connected to the vibrating drying bed is used to provide periodic excitation force to the vibrating drying bed; The drying bed damper, installed between the vibrating drying bed and the crossbeam, is used to absorb vibration impact. A feeder is installed at the upper feeding position of the drying chamber; The guide plate installed in the feeding area of ​​the vibrating dryer is used to guide and disperse the material; An exhaust duct is installed at the upper part of the drying chamber; A discharge device is installed at the discharge end of the vibrating dryer; An air inlet hood is located at the bottom of the drying chamber; And a first air inlet and a second air inlet disposed between the air inlet hood and the drying chamber; The drying gas entering through the air inlet hood enters the drying chamber through the first air inlet and the second air inlet, forming a multi-regional airflow distribution below the vibrating drying bed, and coupling with the vibration of the vibrating drying bed to form a mixed flow drying state of vibration-airflow coupling inside the drying chamber.

4. The adaptively controlled multi-field collaborative intelligent drying system according to claim 3, characterized in that, The multi-parameter online detection unit includes at least one of the following: a multi-point temperature sensor array set at different heights in the drying chamber, a differential pressure sensor set between the gas inlet and outlet, a humidity sensor set in the exhaust channel, and an acceleration sensor installed on the vibration excitation mechanism; the operating signals include at least two of the following: material moisture content, bed temperature, bed pressure difference, exhaust humidity, and vibration status signals.

5. The adaptively controlled multi-field collaborative intelligent drying system according to claim 1, characterized in that, The drying state identification unit is used to divide the material drying process into at least two stages, namely, a heating preheating stage, a constant-rate drying stage, a falling-rate drying stage, and a final moisture approximation stage, and to identify and predict these stages using a mechanistic model, an empirical correlation model, or a data-driven model. Specifically, this includes: The drying state identification unit uses a preset model based on a mechanistic model or an empirical model to make a preliminary prediction of the moisture content of coal particles; the preliminary prediction result is used as one of the input features and is input into the data-driven model together with the drying process operating parameters; the preliminary prediction result is dynamically corrected by the data-driven model to obtain the final prediction result of the moisture content of coal particles, and the drying state is determined accordingly.

6. The adaptively controlled multi-field collaborative intelligent drying system according to claim 1, characterized in that, The adaptive control unit is configured to increase gas flow rate and / or vibration intensity during the constant-rate drying phase, and decrease gas temperature and / or vibration intensity during the deceleration drying phase.

7. The adaptively controlled multi-field collaborative intelligent drying system according to claim 1, characterized in that, The adaptive control unit also includes a drying rate prediction module, which is used to predict the material moisture content within a preset time period based on the changing trends of exhaust humidity, bed temperature and material moisture content, and adjust the control parameters in advance based on the prediction results.

8. The adaptively controlled multi-field collaborative intelligent drying system according to claim 1, characterized in that, The equipment health status monitoring unit includes vibration sensors arranged on the outer wall of the drying chamber or on the supporting structure, used to determine the structural status based on changes in the vibration spectrum; the equipment health status monitoring unit is configured to determine the status of the gas distribution device based on the long-term trend or fluctuation characteristics of the bed pressure difference signal; and when the equipment health status monitoring unit detects an abnormality, the adaptive control unit is used to reduce the vibration intensity and / or gas flow rate, and output a maintenance warning signal.

9. The adaptively controlled multi-field collaborative intelligent drying system according to claim 1, characterized in that, The execution adjustment unit includes a frequency converter connected to the vibration excitation mechanism and an adjustment controller connected to the blower and heating device, so that when the moisture content at the drying endpoint reaches the preset target value, the gas supply is automatically reduced or the discharge mechanism is started.

10. A multi-field synergistic intelligent drying method based on adaptive control, characterized in that, The method, applied to the adaptively controlled multi-field synergistic drying system as described in any one of claims 1-9, comprises the following steps: Step S1: Use the multi-parameter online detection unit to collect multi-parameter operating signals reflecting the drying state of the material; Step S2: The data acquisition and processing unit processes the operating signal to extract characteristic parameters representing the current drying state; Step S3: Using the drying state identification unit, based on the feature parameters and a preset model, identify the current drying stage of the material and / or predict the trend of the material's moisture content change. Step S4: Using the adaptive control unit, control commands are generated based on the identified drying stage and / or prediction results; Step S5: Using the execution adjustment unit, the operating parameters of the multi-field collaborative drying system are adjusted according to the control command. The operating parameters include one or more of vibration frequency, vibration amplitude, gas temperature, gas flow rate and material residence time, so as to realize adaptive closed-loop control of the drying process.

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