Method and system for controlling mulberry branch bio-pellet fuel production system

Abstract

This application discloses a control method and system for the production of whole mulberry branch bio-pellet fuel, relating to the field of biomass energy production control technology. The disclosed control method and system for the production of whole mulberry branch bio-pellet fuel, through the collaboration of the edge control layer and the cloud platform monitoring and management layer, collects and analyzes data in real time, dynamically adjusts the parameters of the dryer, feeder and pellet mill, and combines equipment interlocking control logic to solve the problems of inaccurate material moisture control, low pellet mill operating efficiency and high equipment safety risks in traditional systems. It can improve the accuracy of material moisture control, ensure that the pellet mill operates at full load without overload, realize equipment interlocking start and stop control, and optimize the dynamic adjustment of process parameters.

Description

Technical Field

[0001] This application relates to the field of biomass energy production control technology, and in particular to a control method and system for the production of whole mulberry branch biomass pellet fuel. Background Technology

[0002] Traditional bio pellet fuel production often suffers from low production efficiency, unstable product quality, and excessive energy consumption due to the lack of efficient and intelligent control systems. Specifically, traditional systems rely heavily on manual experience for material moisture control, making precise moisture control difficult and consequently affecting pellet quality and combustion efficiency. Furthermore, the lack of effective real-time monitoring and adjustment mechanisms for pellet mill operating current can lead to underloading or overloading, impacting efficiency and potentially damaging equipment. In addition, traditional systems typically employ sequential control for equipment start-up and shutdown, lacking interlocking protection mechanisms. If one piece of equipment unexpectedly stops, it cannot promptly stop preceding equipment, potentially causing safety accidents. Existing systems also generally suffer from incomplete data collection and insufficient cloud-based analysis capabilities, hindering dynamic optimization of process parameters and restricting the improvement of intelligent production processes.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a control method and system for the production of whole mulberry branch biomass pellet fuel, which aims to improve the accuracy of material moisture control and optimize the dynamic adjustment of process parameters.

[0005] To achieve the above objectives, this application proposes a control method for a whole mulberry branch biomass pellet fuel production system. The whole mulberry branch biomass pellet fuel production system includes an equipment execution layer, an edge control layer, and a cloud platform monitoring and management layer. The equipment execution layer includes sensors and execution devices. The edge control layer includes a programmable logic controller and an industrial computer. The execution devices include a dryer, a pelletizer, and a feeder.

[0006] The method includes:

[0007] Real-time data is acquired through sensors in the device execution layer and output to the edge control layer and cloud platform monitoring and management layer; the real-time data includes material moisture data, working current data of the pellet mill main motor, equipment status signal data, temperature data, humidity data, pressure data and vibration data;

[0008] The cloud platform monitoring and management layer stores and analyzes the real-time data, calculates the optimal process parameter data for each process step, and sends the optimal process parameter data to the edge control layer; the optimal process parameter data includes target moisture value data and ideal operating current value data;

[0009] In the edge control layer, the material moisture data is compared with the target moisture value data to obtain moisture deviation data. Based on the moisture deviation data, a control signal data is generated by a PID control algorithm and output to the dryer and the feeder to keep the material moisture stable within a predetermined range.

[0010] In the edge control layer, the operating current data of the main motor of the pellet mill is compared with the ideal operating current value data to obtain current deviation data, and speed adjustment command data is generated based on the current deviation data and output to the feeder so that the pellet mill operates under full load and without overload.

[0011] At the edge control layer, interlocking control command data is generated based on the device status signal data and the preset sequential start-stop control logic, and the interlocking control command data is output to the relevant execution devices to control the relevant execution devices to perform start-stop operations in a preset sequence.

[0012] In one embodiment, the step of generating control signal data based on the moisture deviation data using a PID control algorithm and outputting it to the dryer and the feeder to stabilize the material moisture content within a predetermined range includes:

[0013] The moisture deviation data is input into the PID control algorithm to calculate the control signal data.

[0014] The control signal data is converted into commands to adjust the opening of the hot air valve of the dryer and to adjust the feeding speed of the feeder.

[0015] The hot air valve opening adjustment command and the feeding speed adjustment command are executed to adjust the working parameters of the dryer and the feeder so that the moisture content of the material is stabilized within a predetermined range.

[0016] In one embodiment, the step of inputting the moisture deviation data into a PID control algorithm to calculate the control signal data includes:

[0017] Calculate the proportional components of the moisture deviation data and generate proportional output data;

[0018] Calculate the integral component of the moisture deviation data and generate integral output data;

[0019] Calculate the differential component of the moisture deviation data and generate differential output data;

[0020] The control signal data is generated by summing the proportional output data, integral output data, and derivative output data.

[0021] In one embodiment, the step of generating speed adjustment command data based on the current deviation data and outputting it to the feeder to ensure that the pellet mill operates at full load without overload includes:

[0022] When the current deviation data indicates that the operating current is higher than the ideal operating current value, a command to reduce the feeder speed is generated.

[0023] When the current deviation data indicates that the operating current is lower than the ideal operating current value, a command to increase the feeder speed is generated.

[0024] The command data for reducing the feeder speed or increasing the feeder speed is output to the feeder motor to perform the speed adjustment operation.

[0025] In one embodiment, the executing equipment further includes a packaging machine, a cooling machine, a crusher, and a belt conveyor; the step of outputting interlocking control command data to the relevant executing equipment to control the relevant executing equipment to perform start-stop operations in a preset sequence includes:

[0026] The interlocking control command data is output to the relevant execution equipment to start the packaging machine, cooler, granulator, feeder, dryer, crusher and belt conveyor in reverse material flow during the start-up process, or to stop the belt conveyor, crusher, dryer, feeder, granulator, cooler and packaging machine in forward material flow during the stop process.

[0027] In one embodiment, the method further includes:

[0028] When an unexpected shutdown signal is detected in the execution device, a cascading shutdown command is generated to stop all preceding devices of the execution device.

[0029] In one embodiment, the step of generating a cascading shutdown command to stop all preceding devices when an unexpected shutdown signal of the execution device is detected includes:

[0030] Obtain unexpected shutdown signal data of the execution equipment through equipment execution layer status monitoring;

[0031] Based on the unexpected shutdown signal data, determine the range of execution equipment that requires interlocking shutdown;

[0032] The target execution device is determined based on the scope of the execution devices;

[0033] Generate equipment shutdown command data and output it to the target execution device.

[0034] In one embodiment, the steps of storing and analyzing the real-time data, calculating the optimal process parameter data for each process step, and sending the optimal process parameter data to the edge control layer include:

[0035] The real-time data is preprocessed, and energy consumption data is calculated based on the equipment running time data and the working current data of the pellet mill main motor in the equipment status signal data. Production data is calculated based on the pellet mill start-stop signal and running time data in the equipment status signal data. Key process index data are formed by combining temperature data and material moisture data.

[0036] Based on historical data and a preset algorithm model, the key process index data are analyzed to generate target moisture value data and ideal operating current value data.

[0037] Data is sent to the edge control layer via a communication gateway.

[0038] In one embodiment, the step of analyzing the key process indicator data based on historical data and a preset algorithm model to generate target moisture value data and ideal operating current value data includes:

[0039] A statistical analysis model is used to process the energy consumption data and output data in the key process indicators, and combined with historical data to generate process optimization feature vector data.

[0040] The process optimization feature vector data is fused and analyzed with the material moisture data and the temperature data, and the target moisture value data and ideal operating current value data are dynamically generated through the algorithm model.

[0041] Furthermore, to achieve the above objectives, this application also proposes a control system for a whole mulberry branch biomass pellet fuel production system. The control system includes a memory, a processor, and a control program for the whole mulberry branch biomass pellet fuel production system stored in the memory and executable on the processor. The control program for the whole mulberry branch biomass pellet fuel production system is configured to implement the steps of the control method for the whole mulberry branch biomass pellet fuel production system.

[0042] The control method and system for the production of whole mulberry branch bio-pellet fuel proposed in this application, through the collaboration of the edge control layer and the cloud platform monitoring and management layer, collects and analyzes data in real time, dynamically adjusts the parameters of the dryer, feeder and pellet mill, and combines equipment interlocking control logic, solves the problems of inaccurate material moisture control, low pellet mill operating efficiency and high equipment safety risks in traditional systems. It can improve the accuracy of material moisture control, ensure that the pellet mill operates at full load without overload, realize equipment interlocking start and stop control, and optimize the dynamic adjustment of process parameters. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A schematic flowchart of an embodiment of the control method for producing whole mulberry branch biomass pellet fuel according to this application;

[0046] Figure 2 This is a schematic diagram of the structure of a control system for the production system of whole mulberry branch biomass pellet fuel according to this application.

[0047] Explanation of icon numbers:

[0048] 10. Memory; 20. Processor.

[0049] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0051] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0052] In existing technologies, the bio pellet fuel production process often suffers from frequent production problems due to insufficient control system efficiency. Traditional methods rely on manual experience to adjust material moisture content, making precise control difficult and affecting pellet quality and combustion efficiency. The pellet mill's operating current lacks real-time monitoring and adjustment mechanisms, causing the equipment to operate under sub-full load or overload conditions, reducing production efficiency and increasing equipment wear and tear. Equipment start-up and shutdown control uses a simple sequential approach, lacking interlocking protection mechanisms, failing to promptly stop preceding equipment in case of unexpected shutdowns, posing safety hazards. For example, in one production line, an overloaded pellet mill caused the motor to burn out; the failure to stop preceding equipment in time led to material accumulation and equipment failure.

[0053] To address these issues, a system capable of automatically adjusting key parameters and implementing equipment interlocking control is needed. Traditional manual adjustments suffer from lag and cannot respond to process changes in real time. Integrating real-time data acquisition, cloud analytics, and edge execution through a layered architecture becomes crucial. Considering that material moisture content and granulation current directly affect product quality and equipment load, a closed-loop control mechanism is required. The design of equipment start-up and shutdown sequences and safety interlocking mechanisms must balance production process requirements with equipment protection needs.

[0054] Based on this, this application provides a control method for a whole mulberry branch biomass pellet fuel production system. The whole mulberry branch biomass pellet fuel production system includes an equipment execution layer, an edge control layer, and a cloud platform monitoring and management layer. The equipment execution layer includes sensors and execution devices; the edge control layer includes a programmable logic controller and an industrial computer; the execution devices include a dryer, a pelletizer, and a feeder; see reference... Figure 1 The control method for the production system of whole mulberry branch biomass pellet fuel includes steps S100 to S500, wherein:

[0055] Step S100: Real-time data is acquired through sensors in the device execution layer and output to the edge control layer and cloud platform monitoring and management layer; the real-time data includes material moisture data, working current data of the pellet mill main motor, equipment status signal data, temperature data, humidity data, pressure data and vibration data;

[0056] Step S200: In the cloud platform monitoring and management layer, the real-time data is stored and analyzed, the optimal process parameter data for each process step is calculated, and the optimal process parameter data is sent down to the edge control layer; the optimal process parameter data includes target moisture value data and ideal operating current value data;

[0057] Step S300: In the edge control layer, the material moisture data is compared with the target moisture value data to obtain moisture deviation data. Based on the moisture deviation data, a control signal data is generated by a PID control algorithm and output to the dryer and the feeder to keep the material moisture stable within a predetermined range.

[0058] Step S400: In the edge control layer, the working current data of the main motor of the pellet mill is compared with the ideal working current value data to obtain current deviation data, and speed adjustment command data is generated based on the current deviation data and output to the feeder so that the pellet mill can operate under full load and without overload.

[0059] In step S500, at the edge control layer, interlocking control command data is generated based on the device status signal data and the preset sequential start-stop control logic, and the interlocking control command data is output to the relevant execution devices to control the relevant execution devices to perform start-stop operations in a preset sequence.

[0060] In this embodiment, the equipment execution layer includes execution units such as a dryer, granulator, and feeder. Specifically, a temperature sensor can be used to detect the temperature of the material at the dryer outlet, and a moisture sensor can be used to detect the moisture content of the material online. The programmable logic controller (PLC) of the edge control layer is responsible for executing the control algorithm, and the industrial computer can be configured as a data preprocessing node, such as filtering the sensor signals. The cloud platform monitoring and management layer analyzes historical production data through machine learning models to dynamically optimize the target moisture value and ideal current value. The PID control algorithm calculates the adjustment amount through proportional, integral, and derivative steps, and can be implemented as a discrete digital control program. The sequential start-stop control logic designs the equipment start priority according to the material flow direction; for example, starting against the material flow can avoid the equipment from idling.

[0061] In this embodiment, the sensor network collects real-time production site data and transmits it to the edge layer and cloud platform via industrial communication protocols. The cloud platform performs fusion analysis on historical and real-time data to establish a process parameter optimization model, such as calculating the optimal moisture control range by combining energy consumption data and output data. After receiving the target parameters, the edge controller compares the measured material moisture value with the target value and dynamically adjusts the opening of the dryer's hot air valve and the feeder's feeding speed using a PID algorithm. The pellet mill current monitoring module continuously compares the actual current with the ideal value, and automatically reduces the feeder speed to prevent overload when the current exceeds the limit. The equipment start-stop control module generates an equipment operation sequence according to preset logic and immediately cuts off the power supply to the preceding equipment when an abnormal shutdown signal is detected.

[0062] In this embodiment, the solution achieves collaborative operation of data acquisition, analysis, decision-making, and execution through a three-layer architecture. Automatic closed-loop control replaces traditional manual adjustment, improving the accuracy of material moisture control. Cloud platform data analysis capabilities enable dynamic optimization of process parameters, making it more adaptable to changes in raw material characteristics compared to fixed parameter settings. The equipment interlocking control mechanism enhances system safety while ensuring production continuity, avoiding the risk of equipment failure propagation in traditional sequential control. Thus, this application achieves stable control of material moisture, ensuring consistent pellet quality. The pellet mill operates within its optimal load range, improving equipment utilization and preventing motor overload damage. Automated equipment start-up and shutdown operations reduce manual intervention, and the interlocking shutdown mechanism in abnormal situations effectively prevents safety accidents. The collaborative work of each system level improves overall production efficiency, providing a reliable control solution for the large-scale production of biomass pellet fuel.

[0063] In one feasible implementation, the step of generating control signal data based on the moisture deviation data using a PID control algorithm and outputting it to the dryer and the feeder to stabilize the material moisture content within a predetermined range includes: inputting the moisture deviation data into the PID control algorithm to calculate the control signal data; converting the control signal data into a command to adjust the opening degree of the hot air valve of the dryer and a command to adjust the feeding speed of the feeder; executing the command to adjust the opening degree of the hot air valve and the feeding speed to adjust the operating parameters of the dryer and the feeder to stabilize the material moisture content within the predetermined range.

[0064] In this embodiment, the PID control algorithm refers to a closed-loop control algorithm that uses a linear combination of proportional, integral, and derivative components to correct deviation data in real time. Specifically, it can be implemented using incremental or positional algorithms to dynamically adjust the control signal based on moisture deviation. The hot air valve opening adjustment command refers to a valve opening percentage adjustment command generated based on the control signal data. Specifically, it can drive the actuator through analog or digital pulse signals to adjust the hot air flow rate inside the dryer. The feed speed adjustment command refers to a feeder motor speed adjustment command generated based on the control signal data. Specifically, it can adjust the motor speed through a frequency converter or servo controller to control the rate at which material enters the dryer.

[0065] In this embodiment, during the material moisture control process, real-time collected material moisture data is compared with the target moisture value to generate moisture deviation data. This deviation data is input into a PID control algorithm, which generates control signal data by calculating the weighted sum of proportional, integral, and derivative components. The control signal data is then decomposed into two independent instructions: adjusting the opening of the dryer's hot air valve to change the hot air supply, and adjusting the feeder's feeding speed to control the material's residence time in the dryer. The coordinated execution of these two instructions enables the material to dynamically balance the moisture evaporation rate and the material supply rate during the drying process, thereby stabilizing the final material moisture content within a predetermined range.

[0066] In this embodiment, the solution achieves closed-loop automatic control through a PID algorithm, which can sense moisture deviation in real time and generate precise adjustment commands. At the same time, the dual-variable collaborative control mechanism effectively eliminates system oscillations that may be caused by a single adjustment method, significantly improving control stability and response speed. This solves the technical problem of insufficient material moisture control precision in traditional bio-pellet fuel production, realizes fully automatic closed-loop control of the drying process, avoids control errors caused by manual intervention, and ensures that the material moisture is always within the reasonable range required by the process, providing stable raw materials for subsequent pelleting processes.

[0067] In one feasible implementation, the step of inputting the moisture deviation data into a PID control algorithm to calculate control signal data includes: calculating the proportional component of the moisture deviation data to generate proportional output data; calculating the integral component of the moisture deviation data to generate integral output data; calculating the derivative component of the moisture deviation data to generate derivative output data; and summing the proportional output data, integral output data, and derivative output data to generate the control signal data.

[0068] In this embodiment, the proportional component refers to the product of the moisture deviation data and a preset proportional coefficient. Specifically, it can be achieved by multiplying the real-time deviation value by the proportional gain parameter, used to quickly respond to changes in the current moisture deviation. The integral component refers to the product of the cumulative value of the moisture deviation data over time and a preset integral coefficient. Specifically, it can be calculated by accumulating historical deviations using an integrator module and combining it with the integral time parameter, used to eliminate long-term steady-state errors. The derivative component refers to the product of the rate of change of the moisture deviation data and a preset derivative coefficient. Specifically, it can be calculated by differentiating the deviation change rate using a differentiator module and combining it with the derivative time parameter, used to predict future deviation trends and adjust the control quantity in advance.

[0069] In this embodiment, during the moisture control process, real-time collected material moisture data is compared with the target moisture value to generate moisture deviation data. This deviation data is input into a PID control algorithm to calculate three components: proportional, integral, and derivative. The proportional component directly reflects the magnitude of the current deviation, the integral component eliminates steady-state errors by accumulating historical deviations, and the derivative component predicts the adjustment direction based on the rate of deviation change. The three components are weighted and summed to generate the final control signal data, which is converted into adjustment commands for the opening of the dryer's hot air valve and the feeder's feeding speed. Through the synergistic effect of these three components, the system can quickly respond to the current deviation while eliminating long-term accumulated errors and suppressing system oscillations, thereby ensuring that the material moisture content remains stable within a predetermined range.

[0070] In this embodiment, the proposed solution eliminates persistent deviations by introducing integral components and predicts trends using derivative components, forming a closed-loop control system. This significantly improves the accuracy and dynamic response capability of moisture control, solving the problem of insufficient material moisture control accuracy in traditional bio-pellet fuel production. It achieves precise adjustment of the drying process and feeding speed. Through the synergistic effect of the three components in the PID algorithm, the lag of manual adjustment is effectively avoided, reducing the impact of moisture fluctuations on pellet forming quality and ensuring that the moisture content of the finished fuel remains stable within the process requirements.

[0071] In one feasible implementation, the step of generating speed adjustment command data based on the current deviation data and outputting it to the feeder to enable the pellet mill to operate under full load and without overload includes: generating feeder speed reduction command data when the current deviation data indicates that the operating current is higher than the ideal operating current value; generating feeder speed increase command data when the current deviation data indicates that the operating current is lower than the ideal operating current value; and outputting the feeder speed reduction command data or feeder speed increase command data to the feeder motor to perform speed adjustment operation.

[0072] In this embodiment, the current deviation data refers to the difference between the measured value of the main motor's operating current and the ideal operating current value. Specifically, this can be achieved by using a current sensor to collect real-time operating current data and calculating the difference between this value and the ideal value sent from the cloud platform, which is used to determine the granulator's load status. The speed adjustment command data refers to the control signal used to adjust the feeder's feeding speed. Specifically, this can be achieved by a frequency converter receiving the command and changing the motor's drive frequency, thereby indirectly controlling the granulator's load by adjusting the feed rate. The feeder motor refers to the power unit driving the material conveying device; specifically, a three-phase asynchronous motor combined with a frequency converter can be used to achieve the speed adjustment function.

[0073] In this embodiment, when the operating current of the pellet mill's main motor exceeds the ideal value, it indicates that the material supply is too large, causing the equipment to be at risk of overload. At this time, the material supply is reduced by decreasing the feeder speed, bringing the main motor's operating current back to a safe range. When the operating current is below the ideal value, it indicates that the pellet mill's capacity is not saturated. The material supply is increased by increasing the feeder speed, allowing the equipment to operate at full load. This adjustment process uses an edge control layer to monitor current data in real time and dynamically generate control commands, forming a closed-loop adjustment mechanism.

[0074] In this embodiment, the proposed solution establishes a real-time correlation mechanism between current deviation and feeding speed, enabling the pellet mill to automatically maintain operation within its optimal load range. This avoids the lag of manual intervention and overcomes the inflexibility of fixed threshold control, achieving automatic optimization control of the pellet mill's workload. It effectively prevents motor overload damage caused by excessive material supply while ensuring continuous and stable operation at maximum capacity. This control method, by precisely matching the feeding speed with the pellet mill's processing capacity, solves the technical problems of low equipment utilization and safety hazards inherent in traditional control methods.

[0075] In one feasible implementation, the executing equipment further includes a packaging machine, a cooler, a crusher, and a belt conveyor; the step of outputting interlocking control command data to the relevant executing equipment to control the relevant executing equipment to perform start-stop operations in a preset sequence includes: outputting interlocking control command data to the relevant executing equipment to start the packaging machine, cooler, granulator, feeder, dryer, crusher, and belt conveyor sequentially against the material flow during the start-up process, or to stop the belt conveyor, crusher, dryer, feeder, granulator, cooler, and packaging machine sequentially along the material flow during the stop-down process.

[0076] In this embodiment, the packaging machine refers to equipment used for automatically packaging the formed pellet fuel. Specifically, it can be an automated packaging machine with a weighing module, for example, triggering the sealing operation by setting a packaging weight threshold. The cooler refers to equipment used to cool the high-temperature material after pelleting, for example, using an air-cooled or water-cooled system for heat dissipation. The crusher refers to equipment used to crush raw materials, for example, by using rotating blades to crush the material. The belt conveyor refers to a continuous conveying device used for material transport, for example, a rubber belt structure driven by a variable frequency motor. Reverse material flow start-up refers to starting the equipment sequentially in the opposite order to the material flow direction during the production start-up phase; for example, if the material ultimately flows to the packaging machine, then the packaging machine is started first. Forward material flow stop refers to shutting down the equipment sequentially in the material flow direction during the production stop phase; for example, stopping the belt conveyor first.

[0077] In this embodiment, when the system starts, the packaging machine, cooler, granulator, feeder, dryer, crusher, and belt conveyor are started sequentially against the material flow direction. For example, the material is ultimately transported to the packaging machine by the belt conveyor for packaging; therefore, the packaging machine is started first to ensure the end-point equipment is ready, followed by the sequential start of the cooler, granulator, and other upstream equipment, and finally the belt conveyor to begin material transport. When the system stops, the belt conveyor, crusher, dryer, feeder, granulator, cooler, and packaging machine are shut down sequentially in the material flow direction. For example, the belt conveyor is stopped first to cut off the material supply, followed by the sequential shutdown of upstream equipment to prevent material residue inside the equipment. This start-stop sequence prevents material blockage or equipment idling caused by disordered equipment start-up timing, while also reducing energy waste.

[0078] Compared to existing technologies, traditional equipment start-up and shutdown control typically employs a fixed sequence or manual operation, neglecting the dependency between material flow direction and equipment. For example, starting the front-end equipment before the back-end equipment may lead to material accumulation in the unready conveying stages. This solution ensures the back-end equipment is ready first by starting with reverse material flow and ensures the front-end equipment is powered off first by stopping with forward material flow, effectively avoiding material stagnation and equipment overload. Furthermore, automated interlocking control replaces manual operation, improving system reliability. Thus, this application solves the problem of material blockage and equipment damage caused by unreasonable equipment start-up and shutdown sequences in traditional systems. Reverse material flow start-up ensures that subsequent processing units are ready when each piece of equipment starts, preventing material accumulation at unstarted equipment; forward material flow stop-up ensures that all material is discharged before shutting down the equipment, preventing residual material from affecting the next start-up. For example, in the shutdown process, stopping the belt conveyor first immediately cuts off the material supply, and subsequent equipment processes the remaining material before shutting down, protecting the equipment's mechanical structure and reducing energy consumption.

[0079] In one feasible implementation, the method further includes: when an unexpected shutdown signal of the execution device is detected, generating a cascading shutdown command to stop all preceding devices of the execution device.

[0080] In this embodiment, the unexpected shutdown signal of the execution equipment refers to the abnormal interruption signal of the execution equipment's operating status collected in real time by the equipment execution layer status monitoring module. Specifically, it can be implemented using current sensors, vibration sensors, or fault alarm signals output by the equipment controller, and is used to identify unplanned equipment shutdown events. The interlocking shutdown command refers to the equipment control command generated according to a preset shutdown logic relationship. Specifically, it can be implemented through the logic judgment module built into the programmable logic controller, and is used to trigger the preceding equipment to stop operating in reverse sequence of the process flow. For example, when the crusher triggers a shutdown signal due to motor overload, the status monitoring module transmits this signal to the edge control layer. The logic judgment module determines, based on the equipment process flow, that the preceding equipment of the crusher includes a belt conveyor, a dryer, and a feeder, and then generates a shutdown command and sends it to the controller of the corresponding equipment. The belt conveyor first stops operating to cut off the material supply, the dryer then shuts down the hot air system, and finally the feeder stops feeding, forming a reverse shutdown sequence. This process uses an industrial bus communication protocol to transmit commands, ensuring that equipment linkage control is completed within millisecond-level response time.

[0081] In this embodiment, the solution achieves intelligent identification and interlocking control of equipment shutdown events through automated status monitoring and preset logic judgment, effectively shortening fault response time and eliminating human operation errors. This enables the application to automatically identify abnormal equipment shutdown events and quickly cut off the operation of preceding equipment, preventing materials from accumulating at the faulty equipment and causing blockages, avoiding energy waste caused by equipment idling, and eliminating the risk of secondary faults caused by continuous equipment operation, thus ensuring the safe and stable operation of the production system.

[0082] In one feasible implementation, when an unexpected shutdown signal of an execution device is detected, the step of generating a cascading shutdown command to stop all preceding devices of the execution device includes: acquiring unexpected shutdown signal data of the execution device through device execution layer status monitoring; determining the range of execution devices that need to be cascaded shut down based on the unexpected shutdown signal data; determining the target execution device based on the range of execution devices; generating device shutdown command data and outputting it to the target execution device.

[0083] In this embodiment, unexpected shutdown signal data refers to unplanned shutdown signals triggered by faults or anomalies during equipment operation. These signals can be collected in real-time by sensors or the input module of a programmable logic controller (PLC) to promptly identify abnormal equipment states. The scope of the cascading shutdown execution equipment refers to the set of preceding equipment that has material conveying or process associations with the faulty equipment. This is specifically determined based on the process flow topology, such as all equipment upstream of the faulty equipment in the material flow direction. The target execution equipment refers to the specific equipment object requiring a shutdown operation, located using equipment numbers or address mapping tables. Equipment shutdown command data refers to signals that control the equipment to cut off power or stop operation, transmitted to the controller of the execution equipment using Modbus TCP or OPC UA communication protocols.

[0084] In this embodiment, when the dryer experiences a motor overload leading to an unexpected shutdown, the status monitoring module acquires a shutdown signal via sensors and uploads it to the edge control layer. Based on the preset material flow path, the preceding equipment requiring interlocking shutdown is determined to include the feeder, crusher, and belt conveyor. After matching the target execution equipment through the equipment address table, a data packet containing the equipment shutdown command is generated and sent to the controller of each target device via industrial Ethernet, triggering its immediate cessation of operation.

[0085] In this embodiment, the solution utilizes automated status monitoring and interlocking control logic to achieve rapid location of faulty equipment and synchronous shutdown of upstream equipment, effectively preventing safety accidents. Through the above technical solution, this application can detect abnormal equipment status in real time and automatically trigger interlocking shutdown protection, preventing material blockage or equipment damage to the entire production line due to a single point of failure. By accurately identifying the range of upstream equipment, excessive shutdowns are avoided, preventing disruption to the operation of unrelated equipment and reducing production downtime and energy waste.

[0086] In one feasible implementation, the steps of storing and analyzing the real-time data, calculating the optimal process parameter data for each process step, and sending the optimal process parameter data to the edge control layer include: preprocessing the real-time data; calculating energy consumption data based on equipment runtime data and pellet mill main motor operating current data in the equipment status signal data; calculating output data based on pellet mill start / stop signals and runtime data in the equipment status signal data; and forming key process indicator data by combining temperature data and material moisture data; analyzing the key process indicator data based on historical data and a preset algorithm model to generate target moisture value data and ideal operating current value data; and sending the data to the edge control layer through a communication gateway.

[0087] In this embodiment, preprocessing refers to cleaning, denoising, and format conversion of the collected raw data. Specifically, filtering algorithms can be used to remove outliers, and data normalization methods can be used to unify the dimensions, thereby ensuring the accuracy of subsequent analysis. Energy consumption data is calculated by multiplying the equipment running time by the main motor current. Specifically, an integral algorithm can be used to accumulate the current data and combine it with the running time to generate an energy consumption index per unit output, which is used to evaluate the equipment's energy efficiency. Output data is obtained based on the start / stop signals and running time statistics of the pellet mill. Specifically, effective running time can be recorded by a timer, and the actual output can be calculated by combining it with a preset capacity coefficient, providing a basis for process optimization. Key process indicator data refers to a multidimensional dataset that integrates temperature, material moisture, and output. Specifically, data fusion technology can be used to correlate parameters of different dimensions to form a feature matrix reflecting the process status. Historical data refers to equipment operation records and process parameters stored on the cloud platform. Specifically, database technology can be used to classify and store historical operating conditions, providing samples for model training. The preset algorithm model refers to an optimization model built based on machine learning or statistical analysis methods. Specifically, regression analysis can be used to establish the correlation between process parameters and energy consumption and output, and dynamically adjust the target parameters. The target moisture content data refers to the material moisture content setpoint dynamically generated based on real-time operating conditions. Specifically, it can be predicted through models to assess the impact of different moisture levels on molding quality and output the optimal control target. The ideal operating current data refers to the reference current value of the pellet mill's main motor under full load. Specifically, it can be calculated based on the equipment's rated power and load characteristics to determine the safe operating range and avoid overload risks. The communication gateway is the transmission module that enables data interaction between the cloud platform and the edge control layer. Specifically, it can use the industrial Ethernet protocol to encapsulate data packets, ensuring real-time command delivery.

[0088] In this embodiment, in the cloud platform monitoring and management layer, real-time data, after preprocessing, includes equipment runtime and main motor current used to calculate energy consumption per unit time. For example, the energy consumption value is obtained by integrating the current over time using an integral algorithm and multiplying it by the voltage. The start / stop signals and runtime of the pellet mill are parsed into effective production time, and combined with the preset hourly benchmark output, the current output data can be calculated. The dryer hot air temperature collected by the temperature sensor and the moisture content data detected by the material moisture sensor, together with the above indicators, constitute the key process indicator dataset. Furthermore, the operating data under different seasonal conditions stored in the historical database over the past three months are retrieved, and a regression analysis model is used to establish a nonlinear relationship between temperature, material moisture, energy consumption, and output. For example, when an increase in ambient humidity is detected, the model automatically lowers the target moisture value to compensate for the decrease in drying efficiency; when the pellet mill load fluctuates, the model dynamically adjusts the ideal current threshold based on the historical current trend. The finally generated target moisture value and ideal current value are transmitted to the PLC of the edge control layer via the communication gateway using the Modbus TCP protocol, realizing closed-loop optimization of process parameters.

[0089] In this embodiment, the proposed solution establishes a dynamic model by integrating multi-dimensional data, which can automatically adapt to changes in raw material characteristics and environmental interference. Furthermore, based on historical data analysis of load patterns, this solution can generate ideal current reference values ​​that change with raw material characteristics. For example, when an increase in mulberry fiber content is detected, the upper limit of the current threshold is automatically increased to ensure the equipment always operates within a high-efficiency range. Through the above technical solution, this application solves the energy waste problem caused by the rigidity of process parameters in traditional bio-pellet fuel production. The pre-processing stage eliminates sensor noise interference, ensuring the reliability of data analysis; accurate calculation of energy consumption and output provides a quantitative basis for optimization; multi-dimensional integration of key process indicators overcomes the limitations of single-parameter control, such as simultaneously considering the coupled influence of temperature and moisture on molding quality; the combination of historical data and algorithm models enables adaptive parameter adjustment, such as automatically reducing the target moisture value during the rainy season to compensate for the influence of environmental humidity; the communication gateway ensures real-time transmission of optimized parameters, avoiding control delays. Therefore, the moisture content control accuracy of the material drying process is improved, the pellet mill load rate is stabilized within a safe range, and the energy consumption per unit output is reduced. At the same time, the synergistic effect of equipment start-stop interlocking control and parameter optimization further reduces the risk of unexpected shutdowns.

[0090] In one feasible implementation, the steps of analyzing the key process indicator data based on historical data and a preset algorithm model to generate target moisture value data and ideal operating current value data include: using a statistical analysis model to process the energy consumption data and output data in the key process indicator data, and combining historical data to generate process optimization feature vector data; fusing the process optimization feature vector data with the material moisture data and the temperature data, and dynamically generating target moisture value data and ideal operating current value data through the algorithm model.

[0091] In this embodiment, the statistical analysis model refers to the mathematical modeling method used to process energy consumption and output data. Specifically, it can be implemented using linear regression, principal component analysis, or machine learning models to extract the correlation between process parameters and energy consumption and output from historical operating data. Process optimization feature vector data refers to the multi-dimensional dataset formed after model processing. Specifically, feature engineering methods can be used to convert energy consumption, output, temperature, and moisture data into standardized vectors to characterize optimization features under different process conditions. Fusion analysis refers to the integration and processing of data from different dimensions. Specifically, it can be implemented using data fusion algorithms or neural network models to comprehensively analyze the dynamic relationship between material moisture, temperature, and process optimization feature vectors. The algorithm model dynamically generates target parameters, which is an adaptive adjustment method based on real-time data and historical features. Specifically, it can be implemented using online learning or feedback control algorithms to optimize the target moisture value and operating current in real time according to the current operating conditions.

[0092] In this embodiment, within the cloud platform monitoring and management layer, preprocessed real-time data is used to calculate key process indicators based on energy consumption and output. A statistical analysis model performs pattern mining on historical data regarding energy consumption, output, temperature, and moisture content, generating a process optimization feature vector. This feature vector is integrated with current material moisture and temperature data through a fusion analysis module. The algorithm model dynamically adjusts the target moisture value and ideal operating current value based on the integrated data. For example, when fluctuations in material moisture lead to increased energy consumption, the model matches the feature vector to historical optimal operating conditions and dynamically reduces the target moisture value to balance energy consumption and molding quality. When increased temperature causes changes in the pellet mill load, the model adjusts the ideal current threshold based on historical operating current data.

[0093] In some specific implementations, the statistical analysis model can use time series analysis methods to process data from continuous production batches, generating feature vectors that include energy efficiency and output volatility. The fusion analysis module can use a weighted average algorithm to adjust and optimize the input ratio of the feature vectors based on the weight of the influence of temperature data on material moisture content. The algorithm model can use a gradient descent-based optimization algorithm to iteratively update the target parameters based on real-time feedback.

[0094] Compared to existing technologies, traditional methods rely on manual experience to set fixed process parameters, which cannot be dynamically adjusted according to real-time operating conditions. This solution, through a data-driven statistical analysis model and fusion analysis mechanism, can automatically identify the complex relationships between process parameters and energy consumption and output, and dynamically generate target parameters adapted to different production conditions. For example, traditional methods require manual recalibration of the moisture target value when the ambient temperature changes, while this solution, by fusing temperature data with historical feature vectors, can automatically compensate for the impact of temperature on the material drying process. Through the above technical solution, this application solves the problems of high energy consumption and large equipment load fluctuations caused by static setting of process parameters in traditional bio-pellet fuel production. By dynamically generating target moisture values ​​and ideal operating current values, it is possible to optimize energy consumption during the drying process and precisely control the pellet mill load, avoiding over-drying of materials or equipment overload caused by human experience bias, while also improving consistency between production batches.

[0095] In the embodiments of this application, the control method for the production system of whole mulberry branch bio-pellet fuel, through the collaboration of the edge control layer and the cloud platform monitoring and management layer, collects and analyzes data in real time, dynamically adjusts the parameters of the dryer, feeder and pellet mill, and combines equipment interlock control logic, solves the problems of inaccurate material moisture control, low pellet mill operating efficiency and high equipment safety risks in traditional systems, and can improve the accuracy of material moisture control, ensure that the pellet mill operates at full load without overload, realize equipment interlock start and stop control and optimize the dynamic adjustment of process parameters.

[0096] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the control method of the whole mulberry branch bio-pellet fuel production system of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0097] This application also provides a control system for a production system of all-mulberry branch biomass pellet fuel, see reference. Figure 2 The control system for the whole mulberry branch biomass pellet fuel production system includes: a memory 10, a processor 20, and a whole mulberry branch biomass pellet fuel production system control program stored in the memory 10 and executable on the processor 20. The whole mulberry branch biomass pellet fuel production system control program is configured to implement the steps of the whole mulberry branch biomass pellet fuel production system control method.

[0098] The control system for the production system of whole mulberry branch bio-pellet fuel provided in this application adopts the control method for the production system of whole mulberry branch bio-pellet fuel in the above embodiments, which can improve the accuracy of material moisture control and optimize the dynamic adjustment of process parameters. Compared with the prior art, the beneficial effects of the control system for the production system of whole mulberry branch bio-pellet fuel provided in this application are the same as the beneficial effects of the control method for the production system of whole mulberry branch bio-pellet fuel provided in the above embodiments, and other technical features in the control system for the production system of whole mulberry branch bio-pellet fuel are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0099] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0100] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A control method for a system for producing whole mulberry branch biomass pellet fuel, characterized in that, The whole mulberry branch bio-pellet fuel production system includes an equipment execution layer, an edge control layer, and a cloud platform monitoring and management layer. The equipment execution layer includes sensors and execution devices, and the edge control layer includes a programmable logic controller and an industrial computer. The execution devices include a dryer, a pelletizer, and a feeder. The method includes: Real-time data is acquired through sensors in the device execution layer and output to the edge control layer and cloud platform monitoring and management layer; the real-time data includes material moisture data, working current data of the pellet mill main motor, equipment status signal data, temperature data, humidity data, pressure data and vibration data; The cloud platform monitoring and management layer stores and analyzes the real-time data, calculates the optimal process parameter data for each process step, and sends the optimal process parameter data to the edge control layer; the optimal process parameter data includes target moisture value data and ideal operating current value data; In the edge control layer, the material moisture data is compared with the target moisture value data to obtain moisture deviation data. Based on the moisture deviation data, a control signal data is generated by a PID control algorithm and output to the dryer and the feeder to keep the material moisture stable within a predetermined range. In the edge control layer, the operating current data of the main motor of the pellet mill is compared with the ideal operating current value data to obtain current deviation data, and speed adjustment command data is generated based on the current deviation data and output to the feeder so that the pellet mill operates under full load and without overload. At the edge control layer, interlocking control command data is generated based on the device status signal data and the preset sequential start-stop control logic, and the interlocking control command data is output to the relevant execution devices to control the relevant execution devices to perform start-stop operations in a preset sequence. The steps of storing and analyzing the real-time data, calculating the optimal process parameter data for each process step, and sending the optimal process parameter data to the edge control layer include: The real-time data is preprocessed, and energy consumption data is calculated based on the equipment running time data and the working current data of the pellet mill main motor in the equipment status signal data. Production data is calculated based on the pellet mill start-stop signal and running time data in the equipment status signal data. Key process index data are formed by combining temperature data and material moisture data. Based on historical data and a preset algorithm model, the key process index data are analyzed to generate target moisture value data and ideal operating current value data. Data is sent to the edge control layer via the communication gateway; The steps for generating target moisture content data and ideal operating current value data based on historical data and preset algorithm models to analyze the key process index data include: A statistical analysis model is used to process the energy consumption data and output data in the key process indicators, and combined with historical data to generate process optimization feature vector data. The process optimization feature vector data is fused and analyzed with the material moisture data and the temperature data, and the target moisture value data and ideal operating current value data are dynamically generated through the algorithm model.

2. The control method for the production system of whole mulberry branch bio-pellet fuel as described in claim 1, characterized in that, The steps of generating control signal data based on the moisture deviation data using a PID control algorithm and outputting it to the dryer and the feeder to stabilize the material moisture content within a predetermined range include: The moisture deviation data is input into the PID control algorithm to calculate the control signal data. The control signal data is converted into commands to adjust the opening of the hot air valve of the dryer and to adjust the feeding speed of the feeder. The hot air valve opening adjustment command and the feeding speed adjustment command are executed to adjust the working parameters of the dryer and the feeder so that the moisture content of the material is stabilized within a predetermined range.

3. The control method for the production system of whole mulberry branch biomass pellet fuel as described in claim 2, characterized in that, The steps of inputting the moisture deviation data into the PID control algorithm to calculate the control signal data include: Calculate the proportional components of the moisture deviation data and generate proportional output data; Calculate the integral component of the moisture deviation data and generate integral output data; Calculate the differential component of the moisture deviation data and generate differential output data; The control signal data is generated by summing the proportional output data, integral output data, and derivative output data.

4. The control method for the production system of whole mulberry branch biomass pellet fuel as described in claim 1, characterized in that, The steps of generating speed adjustment command data based on the current deviation data and outputting it to the feeder to ensure that the pellet mill operates at full load without overload include: When the current deviation data indicates that the operating current is higher than the ideal operating current value, a command to reduce the feeder speed is generated. When the current deviation data indicates that the operating current is lower than the ideal operating current value, a command to increase the feeder speed is generated. The command data for reducing the feeder speed or increasing the feeder speed is output to the feeder motor to perform the speed adjustment operation.

5. The control method for the production system of whole mulberry branch bio-pellet fuel as described in claim 1, characterized in that, The executing equipment also includes a packaging machine, a cooler, a crusher, and a belt conveyor; the steps of outputting interlocking control command data to the relevant executing equipment to control the relevant executing equipment to perform start-stop operations in a preset sequence include: The interlocking control command data is output to the relevant execution equipment to start the packaging machine, cooler, granulator, feeder, dryer, crusher and belt conveyor in reverse material flow during the start-up process, or to stop the belt conveyor, crusher, dryer, feeder, granulator, cooler and packaging machine in forward material flow during the stop process.

6. The control method for the production system of whole mulberry branch biomass pellet fuel as described in claim 5, characterized in that, The method further includes: When an unexpected shutdown signal is detected in the execution device, a cascading shutdown command is generated to stop all preceding devices of the execution device.

7. The control method for the production system of whole mulberry branch biomass pellet fuel as described in claim 6, characterized in that, When an unexpected shutdown signal of the executing device is detected, the step of generating a cascading shutdown command to stop all preceding devices of the executing device includes: Obtain unexpected shutdown signal data of the execution equipment through equipment execution layer status monitoring; Based on the unexpected shutdown signal data, determine the range of execution equipment that requires interlocking shutdown; The target execution device is determined based on the scope of the execution devices; Generate equipment shutdown command data and output it to the target execution device.

8. A control system for a production system of whole mulberry branch biomass pellet fuel, characterized in that, The control system for the whole mulberry branch biomass pellet fuel production system includes: a memory, a processor, and a control program for the whole mulberry branch biomass pellet fuel production system stored in the memory and executable on the processor. The control program for the whole mulberry branch biomass pellet fuel production system is configured to implement the steps of the control method for the whole mulberry branch biomass pellet fuel production system as described in any one of claims 1 to 7.

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