Feeding metering device for a biomass power plant

Abstract

The application discloses a biomass power plant feeding metering device, comprising: a data acquisition module including a weighing sensor group, a screw conveyor encoder, and a humidity detector; a PID control submodule; a fuzzy correction submodule including an error change rate calculation unit and a rule inference unit; a composite output module; and an actuator. Through deep integration of PID control and fuzzy logic, a composite control framework of dynamic response and steady-state accuracy collaborative optimization is constructed, and fuzzy rules are used to correct PID parameters in real time, so that the controller can adapt to changes of non-linear factors such as material humidity and density, and the stability of the system under complex working conditions is significantly improved. Through linkage design of the humidity detector and the fuzzy correction submodule, rapid sensing and parameter adjustment of material property mutation are realized, and overshoot and undershoot phenomena are effectively inhibited.

Description

Technical Field

[0001] This invention relates to the field of biomass power plant technology, and more specifically to a feeding and metering device for biomass power plants. Background Technology

[0002] As an important form of renewable energy utilization, the operating efficiency of biomass power plants is highly dependent on the precise control of the feeding system.

[0003] However, biomass materials exhibit significant nonlinear characteristics: firstly, the material moisture content fluctuates widely (20%–60%), leading to dynamic changes in the friction coefficient and making it difficult to stabilize the transmission efficiency between the feeding screw and the material; secondly, the material density is low (0.1–0.5 t / m³). 3 Furthermore, uneven distribution of feed materials can easily lead to bridging and blockages during the material feeding process, further exacerbating system inertia. Thirdly, real-time changes in boiler load require rapid tracking of the feed rate, but traditional PID control, with its fixed parameters, struggles to adapt to dynamic disturbances in parameters such as humidity and density, easily resulting in overshoot or steady-state errors. While single fuzzy control can handle nonlinear problems, it lacks sufficient steady-state accuracy due to the absence of a precise mathematical model. In addition, existing technologies lack a rapid adaptive parameter adjustment mechanism when dealing with sudden changes in material humidity, leading to control lag or decreased accuracy, becoming a key bottleneck restricting the combustion efficiency and operational stability of biomass power plants. Summary of the Invention

[0004] To address this issue, the present invention provides a feeding and metering device for biomass power plants, which solves the problem of the disconnect between humidity detection and control strategies in the prior art. This disconnect causes the system to be unable to adjust parameters in a timely manner when humidity changes abruptly, becoming a key bottleneck restricting the combustion efficiency and operational stability of biomass power plants.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A feeding and metering device for biomass power plants includes the following modules;

[0007] Data acquisition module: includes a weighing sensor group, a screw conveyor encoder, and a humidity detector, which are used to acquire the real-time weight of the silo G(t), the screw motor speed c(t), and the material humidity H(t), respectively;

[0008] PID control submodule: Generates control quantity based on the error e(t) = V_set - V_act between the set feed rate V_set and the actual feed rate V_act.

[0009]

[0010] Where μ(t): controller output;

[0011] e(t): Flow error, in tons per hour;

[0012] Kp', Ki', Kd': proportional, integral, and differential coefficients after fuzzy correction;

[0013] The fuzzy correction submodule includes an error change rate calculation unit and a rule reasoning unit. The error change rate calculation unit generates de(t) / dt based on e(t). The rule reasoning unit takes the error e(t), the error change rate de(t) / dt, and the material humidity H(t) as inputs, dynamically adjusts the PID parameters through a preset fuzzy rule table, and outputs the parameter correction amounts ΔKp, ΔKi, and ΔKd.

[0014] Composite output module: Kp'=Kp0+ΔKp, Ki'=Ki0+ΔK, Kd'=Kd0+ΔKd, and then substitute the new parameters into the standard PID formula to calculate the control quantity;

[0015] Actuator: Includes screw conveyor and its frequency conversion drive unit, which adjusts the rotation speed according to μ(t) to achieve closed-loop control of feeding rate.

[0016] Preferably, in the rule reasoning unit of the fuzzy correction submodule, the fuzzy rule table is as follows:

[0017] The fuzzy controller outputs three correction coefficients ΔKp, ΔKi, and ΔKd based on the real-time humidity H(t), error e(t), and its rate of change de / dt, to perform online tuning of the PID parameters:

[0018] When the system is in a state of large deviation |e(t)| is large or the response lag de / dt>0 and deviates from the set value: the fuzzy rule outputs a positive ΔKp, increases the proportional gain Kp, so as to speed up the system response and reduce the rise time;

[0019] When the system is in a small deviation but rapidly approaches the setpoint |e(t)|, which is small and de / dt<0, or when overshoot is predicted: the fuzzy rule outputs a negative ΔKp and increases ΔKd, decreases the proportional gain Kp and increases the derivative gain Kd to suppress overshoot and increase system damping;

[0020] When the system has a steady-state error that changes slowly: adjust ΔKi appropriately to eliminate steady-state error while avoiding integral saturation.

[0021] Preferably, the humidity detector uses a capacitive humidity sensor, and its installation location meets the following conditions:

[0022] Located in the material flow channel between the silo outlet and the screw conveyor inlet, the vertical distance from the center line of the screw shaft is 0.2 to 0.5 m;

[0023] The sensor probe surface is covered with a porous stainless steel filter screen with a pore size of 0.5–1 mm to prevent biomass particles from clogging it.

[0024] The sampling frequency of the humidity detector is synchronized with the PID control cycle, and the median filtering is performed on 10 consecutive humidity measurements in each control cycle. After removing outliers, the average value is taken as H(t).

[0025] When the H(t) mutation rate is detected to exceed 5% / s, an abnormal alarm signal is triggered in the data acquisition module, and the parameter adjustment function of the fuzzy correction submodule is suspended until the humidity returns to stability.

[0026] Preferably, the composite output module further includes a control quantity limiting unit, the limiting rule of which is:

[0027] When μ(t) > 0.8·c_max, the output μ(t) = 0.8·c_max is forced, where c_max is the maximum speed of the screw motor;

[0028] When μ(t) < -0.5·c_min, the output μ(t) = -0.5·c_min is forced, where c_min is the minimum speed of the screw motor;

[0029] When |μ(t)|≤0.1·c_nom, the current rotational speed remains unchanged, where c_nom is the rated rotational speed;

[0030] The limiting unit is implemented through a hardware comparator circuit, with a response time of less than 10ms.

[0031] Preferably, the data acquisition module further includes a temperature compensation unit, the compensation method of which is:

[0032] A PT100 temperature sensor is embedded inside the weighing sensor housing to monitor the ambient temperature T(t) in real time;

[0033] Based on the temperature-sensitivity curve The sensor output signal is corrected, where S0 is the nominal sensitivity at 25℃, and α is the temperature coefficient, with a value range of -0.02% to -0.05% / ℃.

[0034] Preferably, the actuator further includes a fault diagnosis unit, the diagnosis logic of which is:

[0035] If the actual speed of the screw motor deviates from the theoretical speed corresponding to the output frequency of the frequency converter by more than 10% for an extended period, it is determined to be a slippage fault in the transmission chain.

[0036] If the output signal of the weighing sensor fluctuates by more than ±3% within 3 consecutive control cycles, it is determined to be a material bridging fault.

[0037] When a fault occurs, the fault code is displayed through the human-machine interface, and the system automatically switches to manual control mode.

[0038] Preferably, the set feed rate V_set is dynamically generated by the host computer according to the boiler load command, and the generation method is as follows:

[0039] The boiler steam pressure P(t) and main steam flow rate Q(t) are obtained through the feedforward compensation module;

[0040] According to the formula The calculations are performed, where K1, K2, and K3 are boiler characteristic coefficients, determined through regression analysis of historical data.

[0041] The present invention has the following advantages: By deeply integrating PID control and fuzzy logic, the present invention constructs a composite control framework that coordinates the optimization of dynamic response and steady-state accuracy. By using fuzzy rules to correct PID parameters in real time, the controller can adapt to changes in nonlinear factors such as material humidity and density, which significantly improves the stability of the system under complex working conditions.

[0042] The linkage design of the humidity detector and the fuzzy correction submodule enables rapid sensing and parameter adjustment of sudden changes in material properties, effectively suppressing overshoot and undershoot phenomena.

[0043] The actuator integrates fault diagnosis and amplitude limiting protection functions, which not only avoids interference with control accuracy caused by abnormal working conditions such as transmission chain slippage and material bridging, but also ensures the safe operation of the system through a hardware-level response mechanism. Attached Figure Description

[0044] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0045] Figure 1 A module diagram of a feeding and metering device for a biomass power plant provided in an embodiment of this application. Detailed Implementation

[0046] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these embodiments are merely for further explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Technical engineers in the field can make some non-essential improvements and adjustments to the present invention based on the above-described content. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see Figure 1 A feeding and metering device for biomass power plants, comprising the following modules;

[0048] Data acquisition module: includes a weighing sensor group, a screw conveyor encoder, and a humidity detector, which are used to acquire the real-time weight of the silo G(t), the screw motor speed c(t), and the material humidity H(t), respectively;

[0049] PID control submodule: Generates control quantity based on the error e(t) = V_set - V_act between the set feed rate V_set and the actual feed rate V_act.

[0050]

[0051] Where μ(t): controller output;

[0052] e(t): Flow error, in tons per hour;

[0053] Kp', Ki', Kd': proportional, integral, and differential coefficients after fuzzy correction;

[0054] The fuzzy correction submodule includes an error change rate calculation unit and a rule reasoning unit. The error change rate calculation unit generates de(t) / dt based on e(t). The rule reasoning unit takes the error e(t), the error change rate de(t) / dt, and the material humidity H(t) as inputs, dynamically adjusts the PID parameters through a preset fuzzy rule table, and outputs the parameter correction amounts ΔKp, ΔKi, and ΔKd.

[0055] Composite output module: Kp'=Kp0+ΔKp, Ki'=Ki0+ΔK, Kd'=Kd0+ΔKd, and then substitute the new parameters into the standard PID formula to calculate the control quantity;

[0056] Actuator: Includes screw conveyor and its frequency conversion drive unit, which adjusts the rotation speed according to μ(t) to achieve closed-loop control of feeding rate.

[0057] In some embodiments, it has:

[0058] Data acquisition module

[0059] Weighing sensor group: It adopts three Mettler Toledo SBH series cantilever beam sensors with a range of 5t and an accuracy of ±0.05%, which are installed at the bottom support point of the silo;

[0060] Screw conveyor encoder: Omron E6B2-CWZ6C incremental encoder, resolution 1000P / R, connected to the screw shaft via a coupling;

[0061] Humidity detector: Vaisala HMD60Y capacitive humidity sensor, range 0~100%RH, accuracy ±2%RH, installed 0.3m below the silo outlet;

[0062] PID control submodule

[0063] The system uses a Siemens S7-1200 PLC (CPU 1214C) with a built-in PID instruction block. The initial parameters are set to Kp=0.8, Ki=0.05, and Kd=0.1.

[0064] Fuzzy Correction Submodule

[0065] Error change rate calculation unit: It obtains encoder pulse signals through a PLC high-speed counter and calculates the speed change rate;

[0066] Rule-based reasoning unit: Based on the .fis file generated by the MATLAB Fuzzy Logic Toolbox, it communicates with the PLC via the OPC UA protocol;

[0067] Composite output module

[0068] The Advantech UNO-2184G industrial computer was used to run a LabVIEW program to realize control quantity synthesis and amplitude limiting processing.

[0069] Executive agency

[0070] Screw conveyor: SEW-EURODRIVE M3AA132S4 variable frequency motor, power 11kW, rated speed 1450rpm;

[0071] Inverter: Danfoss FC302 series, supports Modbus RTU protocol, and receives 0-10V control signals.

[0072] In the rule reasoning unit of the fuzzy correction submodule, the fuzzy rule table is as follows:

[0073] The fuzzy controller outputs three correction coefficients ΔKp, ΔKi, and ΔKd based on the real-time humidity H(t), error e(t), and its rate of change de / dt, to perform online tuning of the PID parameters:

[0074] When the system is in a state of large deviation |e(t)| is large or the response lag de / dt>0 and deviates from the set value: the fuzzy rule outputs a positive ΔKp, increases the proportional gain Kp, so as to speed up the system response and reduce the rise time;

[0075] When the system is in a small deviation but rapidly approaches the setpoint |e(t)|, which is small and de / dt<0, or when overshoot is predicted: the fuzzy rule outputs a negative ΔKp and increases ΔKd, decreases the proportional gain Kp and increases the derivative gain Kd to suppress overshoot and increase system damping;

[0076] When the system has a steady-state error that changes slowly: adjust ΔKi appropriately to eliminate steady-state error while avoiding integral saturation.

[0077] The humidity detector uses a capacitive humidity sensor, and its installation location meets the following conditions:

[0078] Located in the material flow channel between the silo outlet and the screw conveyor inlet, the vertical distance from the center line of the screw shaft is 0.2 to 0.5 m;

[0079] The sensor probe surface is covered with a porous stainless steel filter screen with a pore size of 0.5–1 mm to prevent biomass particles from clogging it.

[0080] The sampling frequency of the humidity detector is synchronized with the PID control cycle, and the median filtering is performed on 10 consecutive humidity measurements in each control cycle. After removing outliers, the average value is taken as H(t).

[0081] When the H(t) mutation rate is detected to exceed 5% / s (or the humidity change exceeds 5% within 3 consecutive sampling periods), an abnormal alarm signal is triggered in the data acquisition module, and the parameter adjustment function of the fuzzy correction submodule is suspended until the humidity returns to stability.

[0082] The composite output module also includes a control quantity limiting unit, whose limiting rule is as follows:

[0083] When μ(t) > 0.8·c_max, the output μ(t) = 0.8·c_max is forced, where c_max is the maximum speed of the screw motor;

[0084] When μ(t) < -0.5·c_min, the output μ(t) = -0.5·c_min is forced, where c_min is the minimum speed of the screw motor;

[0085] When |μ(t)|≤0.1·c_nom, the current rotational speed remains unchanged, where c_nom is the rated rotational speed;

[0086] The limiting unit is implemented through a hardware comparator circuit, with a response time of less than 10ms.

[0087] The data acquisition module also includes a temperature compensation unit, the compensation method of which is as follows:

[0088] A PT100 temperature sensor is embedded in the housing of the weighing sensor to monitor the ambient temperature T(t) in real time;

[0089] Based on the temperature-sensitivity curve The sensor output signal is corrected, where S0 is the nominal sensitivity at 25℃, and α is the temperature coefficient, with a value range of -0.02% to -0.05% / ℃.

[0090] The PLC reads T(t) every 500ms and corrects the weighing signal using a formula; for example, when T(t) = 40℃, S(T) = 1 × (1 - 0.0003 × 15) = 0.9955, and the weighing value is corrected to 3.2t × 0.9955 = 3.1856t.

[0091] By monitoring the operating environment temperature of the weighing sensor in real time using a temperature sensor, and dynamically correcting the weighing signal using the above algorithm, the sensor output drift caused by temperature fluctuations (such as changes in ambient temperature or heat generated by material friction) is eliminated. This avoids measurement errors of more than ±0.5% caused by temperature changes, ensures the accuracy of the feeding rate calculation, and guarantees the long-term reliability of the device.

[0092] The actuator also includes a fault diagnosis unit, whose diagnostic logic is as follows:

[0093] If the actual speed of the screw motor deviates from the theoretical speed corresponding to the output frequency of the frequency converter by more than 10% for an extended period, it is determined to be a slippage fault in the transmission chain.

[0094] If the output signal of the weighing sensor fluctuates by more than ±3% within 3 consecutive control cycles, it is determined to be a material bridging fault.

[0095] When a fault occurs, the fault code is displayed through the human-machine interface, and the system automatically switches to manual control mode.

[0096] By fusing data from multiple sensors (such as screw motor speed, weighing fluctuation, and inverter current) to monitor the system status in real time, and by using preset logic rules (such as speed difference threshold and weight fluctuation frequency), the system can quickly and accurately determine faults such as transmission chain slippage and material bridging, and trigger protection actions (such as switching to a backup motor and starting vibration to break the arch) to avoid equipment damage or production interruption.

[0097] The set feed rate V_set is dynamically generated by the host computer based on the boiler load command, and its generation method is as follows:

[0098] The boiler steam pressure P(t) and main steam flow rate Q(t) are obtained through the feedforward compensation module;

[0099] According to the formula The calculations are performed, where K1, K2, and K3 are boiler characteristic coefficients, determined through regression analysis of historical data.

[0100] In some embodiments, the following features are included: using historical boiler operating data (1000 samples of steam pressure P, flow rate Q, and feed rate V) to perform multiple linear regression, determining coefficients K1=0.5, K2=0.3, and K3=2.0, and generating the formula V_set=0.5P+0.3Q+2.0; when the boiler load suddenly increases from 80% to 90%, P increases from 4.2MPa to 4.5MPa, and Q increases from 25t / h to 28t / h; V_set is dynamically calculated to be 0.5×4.5+0.3×28+2.0=13.85t / h, and the feed rate is adjusted 5 seconds in advance.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A feeding and metering device for biomass power plants, characterized in that, Includes the following modules; Data acquisition module: includes a weighing sensor group, a screw conveyor encoder, and a humidity detector, which are used to acquire the real-time weight of the silo G(t), the screw motor speed c(t), and the material humidity H(t), respectively; PID control submodule: Generates control quantity based on the error e(t) = V_set - V_act between the set feed rate V_set and the actual feed rate V_act. Where μ(t): controller output; e(t): Flow error, in tons per hour; Kp', Ki', Kd': proportional, integral, and differential coefficients after fuzzy correction; The fuzzy correction submodule includes an error change rate calculation unit and a rule reasoning unit. The error change rate calculation unit generates de(t) / dt based on e(t). The rule reasoning unit takes the error e(t), the error change rate de(t) / dt, and the material humidity H(t) as inputs, dynamically adjusts the PID parameters through a preset fuzzy rule table, and outputs the parameter correction amounts ΔKp, ΔKi, and ΔKd. Composite output module: Kp'=Kp0+ΔKp, Ki'=Ki0+ΔK, Kd'=Kd0+ΔKd, and then substitute the new parameters into the standard PID formula to calculate the control quantity; Actuator: Includes screw conveyor and its frequency conversion drive unit. The screw conveyor speed is adjusted according to μ(t) to achieve closed-loop control of the feeding rate.

2. The feeding and metering device for a biomass power plant according to claim 1, characterized in that, In the rule reasoning unit of the fuzzy correction submodule, the fuzzy rule table is as follows: The fuzzy controller outputs three correction coefficients ΔKp, ΔKi, and ΔKd based on the real-time humidity H(t), error e(t), and its rate of change de / dt, to perform online tuning of the PID parameters: When the system is in a state of large deviation |e(t)| is large or the response lag de / dt>0 and deviates from the set value: the fuzzy rule outputs a positive ΔKp, increases the proportional gain Kp, so as to speed up the system response and reduce the rise time; When the system is in a small deviation but rapidly approaches the setpoint |e(t)|, which is small and de / dt<0, or when overshoot is predicted: the fuzzy rule outputs a negative ΔKp and increases ΔKd, decreases the proportional gain Kp and increases the derivative gain Kd to suppress overshoot and increase system damping; When the system has a steady-state error that changes slowly: adjust ΔKi appropriately to eliminate steady-state error while avoiding integral saturation.

3. The feeding and metering device for a biomass power plant according to claim 2, characterized in that, The humidity detector uses a capacitive humidity sensor, and its installation location meets the following conditions: Located in the material flow channel between the silo outlet and the screw conveyor inlet, the vertical distance from the center line of the screw shaft is 0.2 to 0.5 m; The sensor probe surface is covered with a porous stainless steel filter screen with a pore size of 0.5–1 mm; The sampling frequency of the humidity detector is synchronized with the PID control cycle, and the median filtering is performed on 10 consecutive humidity measurements in each control cycle. After removing outliers, the average value is taken as H(t). When the H(t) mutation rate is detected to exceed 5% / s, an abnormal alarm signal is triggered in the data acquisition module, and the parameter adjustment function of the fuzzy correction submodule is suspended until the humidity returns to stability.

4. The feeding and metering device for a biomass power plant according to claim 3, characterized in that, The composite output module also includes a control quantity limiting unit, whose limiting rule is as follows: When μ(t) > 0.8·c_max, the output μ(t) = 0.8·c_max is forced, where c_max is the maximum speed of the screw motor; When μ(t) < -0.5·c_min, the output μ(t) = -0.5·c_min is forced, where c_min is the minimum speed of the screw motor; When |μ(t)|≤0.1·c_nom, the current rotational speed remains unchanged, where c_nom is the rated rotational speed; The limiting unit is implemented through a hardware comparator circuit, with a response time of less than 10ms.

5. The feeding and metering device for a biomass power plant according to claim 1, characterized in that, The data acquisition module also includes a temperature compensation unit, the compensation method of which is as follows: A PT100 temperature sensor is embedded in the housing of the weighing sensor to monitor the ambient temperature T(t) in real time; Based on the temperature-sensitivity curve The sensor output signal is corrected, where S0 is the nominal sensitivity at 25℃, and α is the temperature coefficient, with a value range of -0.02% to -0.05% / ℃.

6. The feeding and metering device for a biomass power plant according to claim 1, characterized in that, The actuator also includes a fault diagnosis unit, whose diagnostic logic is as follows: If the actual speed of the screw motor deviates from the theoretical speed corresponding to the output frequency of the frequency converter by more than 10% for an extended period, it is determined to be a slippage fault in the transmission chain. If the output signal of the weighing sensor fluctuates by more than ±3% within 3 consecutive control cycles, it is determined to be a material bridging fault. When a fault occurs, the fault code is displayed through the human-machine interface, and the system automatically switches to manual control mode.

7. The feeding and metering device for a biomass power plant according to claim 1, characterized in that, The set feed rate V_set is dynamically generated by the host computer based on the boiler load command, and its generation method is as follows: The boiler steam pressure P(t) and main steam flow rate Q(t) are obtained through the feedforward compensation module; According to the formula The calculations are performed, where K1, K2, and K3 are boiler characteristic coefficients, determined through regression analysis of historical data.

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