Storage tank pressurization control method and system based on LNG filling station fuzzy self-adaptive PID control

Abstract

The invention discloses a storage tank pressurization control method and system based on LNG gas station fuzzy self-adaptive PID control, and aims to solve the technical problems that a traditional pressurization control method of an LNG storage tank is low in control precision, poor in stability and rapid in equipment abrasion. According to the control method, dynamic compensation for the target pressure is introduced, so that the system can aim at a more accurate control target in the initial stage, the steady-state control deviation is reduced, and the control precision is improved. And static pressure head change disturbance caused by liquid level change is overcome through dynamic compensation, the adjusting burden of the controller is reduced, the controller can focus on processing other dynamic disturbance, and therefore the control process is more stable, and overshoot is smaller. Through the design of a dynamic compensation mechanism and a special fuzzy rule base, the system can automatically adapt to various complex working conditions, and fast, stable and overshoot-free pressure control is always kept. Therefore, frequent and large-amplitude action and surge phenomena of the valve are avoided, and the energy consumption of an executing mechanism is reduced.

Description

Technical Field

[0001] This invention relates to the field of LNG refueling technology, specifically to a tank pressurization control method and system based on fuzzy adaptive PID control for LNG refueling stations. Background Technology

[0002] LNG (liquefied natural gas) is a clean and efficient energy source formed by liquefying natural gas (mainly methane) through cryogenic processes. In LNG refueling stations, the LNG in the storage tanks needs to maintain a certain pressure (typically 0.4–0.8 MPa) to ensure that the cryogenic submersible pump has sufficient net positive suction head to smoothly and efficiently deliver the LNG to the refueling machine for vehicle refueling. Therefore, when the tank pressure decreases due to unloading of tank trucks or increased refueling volume, it is necessary to promptly pressurize the tanks.

[0003] Traditional LNG tank pressurization methods primarily rely on single-value control or PID control. For value control (i.e., on / off control), upper and lower pressure limits are set. When the pressure falls below the lower limit, the pressurization valve opens; when the pressure exceeds the upper limit, the valve closes. This pressurization control method results in large pressure fluctuations and frequent valve actuations, easily leading to equipment fatigue damage. Furthermore, unstable pressure control affects refueling efficiency and the lifespan of the submersible pump.

[0004] Traditional PID control adjusts the pressure booster valve opening based on the deviation between the pressure setpoint and the actual pressure value, using proportional, integral, and derivative operations. However, LNG tank pressurization is a nonlinear thermodynamic process with significant time lag, and its dynamic characteristics change significantly with variations in liquid level, ambient temperature, and medium composition. Therefore, fixed PID parameters are insufficient to achieve ideal control across the entire operating range, easily leading to overshoot, oscillation, or slow response.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the background technology of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] In view of at least one of the above technical problems, this disclosure provides a tank pressurization control method and system based on fuzzy adaptive PID control for LNG refueling stations, aiming to solve the technical problems of low control accuracy, poor stability and rapid equipment wear of traditional pressurization control methods for LNG storage tanks.

[0007] According to one aspect of this disclosure, a tank pressurization control method based on fuzzy adaptive PID control for LNG refueling stations is provided, comprising the following steps: (1) Real-time acquisition of the current pressure of the LNG storage tankP actual and current liquid level H actual ; (2) Set the target pressure of the LNG storage tank according to the current liquid level. P set ; (3) Calculate the pressure deviation between the target pressure and the current pressure. e and pressure deviation change rate ec ;in, e= P set -P actual ; ec = de / dt ; (4) Based on the pressure deviation e and the rate of change of pressure deviation ec As input variables of the fuzzy inference engine, based on the fuzzy rule base preset by the fuzzy inference engine, the input variables are... e and ec After fuzzification, fuzzy inference, and defuzzification, the corresponding output is used in real time to adjust the parameters of the PID controller. ΔK p , ΔK i , ΔK d ; (5) Initial parameters of the PID controller based on parameter correction amount K p0 , K i0 , K d0 To perform online self-tuning, the adaptive PID parameters for the current time step are calculated as follows: K p = K p0 + ΔK p ; K i = K i0 + ΔK i ; K d = K d0 + ΔK d ; (6) The PID controller is based on the current pressure deviation. e and adaptive parametersK p , K i , K d The corresponding calculation output control signal U ( t ); (7) The control signal U ( t After being converted into an analog signal of 4-20mA, it drives and regulates the pressure regulating valve of the LNG storage tank.

[0008] In some embodiments of this disclosure, in step (2), the current liquid level of the LNG storage tank is determined. H actual Corresponding to dynamic compensation of the target pressure P set : P set = P base + f ( H actual ); in, P base The base pressure is designed to match the requirements of the submersible pump. f ( H actual ) is the liquid level compensation function that is inversely proportional to the liquid level height.

[0009] In some embodiments of this disclosure, in step (2), the expression for the liquid level compensation function is: ; in, H threshold The set liquid level threshold for initiating liquid level compensation; ΔH To compensate for the liquid level interval; K This represents the compensation pressure value corresponding to each liquid level interval.

[0010] In some embodiments of this disclosure, in step (4), the input variables are... e and ec When performing fuzzification, first set the input variables... e and ec The corresponding fuzzy sets are divided into several fuzzy sets, including negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB. Then, the triangular membership function or Gaussian membership function is used to quantize each fuzzy set.

[0011] In some embodiments of this disclosure, in step (4), each output variable ΔK p , ΔK i , ΔK d Use with input variables e and ec They use the same fuzzy set partitioning method and membership function, and the universe of discourse of each output variable represents the relative change with respect to the current PID control parameters. 。

[0012] In some embodiments of this disclosure, step (4) of the fuzzy inference includes the following sub-steps: (41) For each fuzzy rule in the fuzzy rule base, calculate the input variable by taking the minimum value. e and ec The degree of matching with the preconditions; (42) Apply the matching degree of each fuzzy rule to the output variable of its conclusion. ΔK p , ΔK i , ΔK d The membership function is used to form the output fuzzy set corresponding to the fuzzy rule by truncating or scaling; (43) Aggregate the output fuzzy sets of all fuzzy rules corresponding to the same output variable to obtain the comprehensive output fuzzy set of each output variable.

[0013] In some embodiments of this disclosure, in step (4), the defuzzification is used to convert the fuzzy output into a precise correction value: ; in, ΔK This refers to the parameter correction amount corresponding to the PID controller. z i To output the sampling points in the universe of discourse, μ ( z i ) is the sampling point z i The corresponding membership function.

[0014] According to another aspect of this disclosure, a tank pressurization control system based on fuzzy adaptive PID control of an LNG refueling station is provided to implement the above-mentioned tank pressurization control method. The system includes an LNG tank, a controller for executing the LNG tank pressurization control method, a sensor group communicatively connected to the controller and used for real-time monitoring of the pressure and liquid level inside the LNG tank, a pressurization regulating valve connected to the outlet of the LNG tank and communicatively connected to the controller, and a pressurization vaporizer connected between the pressurization regulating valve and the gas phase space of the LNG tank.

[0015] One or more technical solutions provided in the embodiments of this application have at least one of the following technical effects or advantages: 1. By using dynamic compensation for the target pressure as a feedforward control mechanism, the control system can aim at a more accurate control target in the initial control phase, thereby reducing steady-state control deviation and improving control accuracy. Furthermore, dynamic compensation overcomes disturbances caused by static head changes due to liquid level variations, reducing the controller's adjustment burden and allowing it to focus on handling other dynamic disturbances such as sudden changes in gas flow rate and environmental heat leakage, resulting in a smoother control process with less overshoot.

[0016] 2. Through a dynamic compensation mechanism and a special fuzzy rule base, the system can automatically adapt to various complex operating conditions such as liquid level changes, sudden temperature drops after unloading, ambient temperature fluctuations, and sudden changes in gas flow, while maintaining rapid, stable, and overshoot-free pressure control. This avoids frequent large-amplitude valve movements and surge phenomena, which helps reduce the energy consumption of the actuator.

[0017] 3. Precise pressure control avoids unnecessary over-pressurization, reducing the amount of BOG flash generation at the source, thereby directly reducing the energy consumption for reliquefaction or the loss of safe emissions required to treat these BOGs.

[0018] 4. Smooth control significantly reduces mechanical wear on the pressure regulating valve and actuator; and the stable pump inlet pressure ensures that the cryogenic submersible pump always operates under optimal conditions, effectively preventing cavitation and helping to extend the service life of the equipment. Attached Figure Description

[0019] Figure 1 The pressure control curves show a comparison between the control method disclosed in this application and traditional control methods.

[0020] Figure 2 This is a structural principle block diagram of a tank pressurization system in one embodiment of this application. Detailed Implementation

[0021] The programs involved or relied upon in the following embodiments are all conventional or simple programs in this technical field, and those skilled in the art can make conventional selections or adaptive adjustments according to specific application scenarios. Unless otherwise specified, the devices involved in the following embodiments are all conventional commercially available products.

[0022] To better understand the technical solution of this application, the above technical solution will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] To address the issues of large pressure fluctuations and frequent valve actuation caused by conventional customized switch control for LNG tank pressurization, and the problems of overshoot, oscillation, or slow response that easily occur with traditional setpoint PID control, this paper discloses a tank pressurization control method based on fuzzy adaptive PID control for LNG refueling stations. The method specifically includes the following steps: (1) Real-time acquisition of the current pressure of the LNG storage tank P actual and current liquid level H actual .

[0024] To maintain the required pressure within the LNG storage tank, timely and effective monitoring and feedback of the LNG's current state are necessary to inform control measures. Specifically, in this embodiment, pressure and liquid level information within the LNG storage tank are collected in real time during the control process.

[0025] (2) Set the target pressure of the LNG storage tank according to the current liquid level. P set .

[0026] During LNG refueling, the LNG in the storage tank needs to maintain a certain pressure to ensure that the cryogenic submersible pump inside the tank has sufficient net positive suction head. This ensures that the submersible pump can smoothly and efficiently deliver LNG to the refueling machine for vehicle refueling. Therefore, in this embodiment, a target pressure is set for the LNG storage tank as a control objective. Based on the current pressure and level of the LNG storage tank, adjustments are made to maintain the set target pressure within the LNG storage tank to meet refueling requirements.

[0027] Specifically, the normal operation of a cryogenic submersible pump requires a net positive suction head as a pressure margin. This net positive suction head is actually determined by the tank's gas phase pressure and the liquid static pressure head. P Jointly provided by: P = ρgh ; in, ρ For the density of the liquid, g It is the acceleration due to gravity. h This refers to the liquid level height.

[0028] When the liquid level decreases, the corresponding liquid static pressure head will decrease linearly. If the gas phase pressure in the LNG storage tank remains unchanged at this time (i.e., the target pressure is set to a fixed value), then the total net positive suction head cannot meet the normal working requirements of the submersible pump.

[0029] Therefore, in this embodiment, based on the current liquid level of the LNG storage tank... H actual The set value for the target pressure P set Dynamic compensation is performed, specifically: P set = P base + f ( H actual ); in, P base The base pressure set according to the operating requirements of the submersible pump; f ( H actual The compensation function is inversely proportional to the liquid level. The lower the liquid level in the LNG tank, the greater the compensation value brought by the compensation function. This is equivalent to actively increasing the gas phase pressure to compensate when the static head decreases, thereby maintaining the basic stability of the total effective net positive suction head and overcoming the decrease in the liquid static head in the LNG tank when the liquid level is low.

[0030] In this embodiment, a liquid level compensation function is defined. f ( H actual (Based on liquid level threshold) H threshold The piecewise linear function is expressed as follows: ;(1)。

[0031] in, H threshold The set liquid level threshold for initiating liquid level compensation (e.g., set to 50%); that is, when the actual liquid level is the current liquid level. H actual When the level falls below this threshold, linear compensation is activated, and when the actual liquid level... H actual When the threshold is reached or exceeded, the compensation value is zero; ΔH For compensation liquid level interval (e.g., set to 10%). K This is the compensation pressure value corresponding to each liquid level interval (e.g., set to 0.025 MPa / 10%). Therefore, the above liquid level compensation function...f ( H actual This can be represented as: ;(2)。

[0032] This liquid level compensation function ensures that the system can automatically and dynamically increase the target pressure under low liquid level conditions, effectively counteracting the decrease in liquid static head caused by LNG refueling and other operations, thereby ensuring the stable operation of the submersible pump. It fundamentally eliminates the problem of excessively low submersible pump inlet pressure caused by low LNG tank levels, thus effectively preventing cavitation problems caused by LNG vaporization within the submersible pump. Cavitation severely reduces the efficiency and lifespan of the submersible pump, leading to vibration, noise, and impeller damage. Furthermore, the stable operation of the submersible pump also ensures continuous and efficient LNG refueling, avoiding problems such as refueling interruptions and unstable flow rates caused by pressure fluctuations or cavitation.

[0033] From a control perspective, dynamic compensation for the target pressure of the LNG storage tank can serve as a form of feedforward control, optimizing the existing control loop and improving the overall quality and stability of pressure control. Specifically, in traditional control systems that use a set value as the control target without compensation, the controller (such as a PID controller) needs to continuously output a positive deviation to attempt to increase the LNG storage tank pressure in order to maintain the normal operation of the submersible pump under low liquid level conditions. However, in this example, with the introduction of liquid level compensation, the target pressure setpoint is raised in advance, allowing the control system to aim at a more accurate control target in the initial control phase. This reduces steady-state control deviation and improves control accuracy. Furthermore, by setting a more reasonable control target, drastic pressure fluctuations caused by large adjustments by the controller at low liquid levels are avoided, and the frequency and amplitude of the pressure regulating valve's operation are reduced, which helps extend the service life of system components. Furthermore, in traditional setpoint control, the controller also needs to overcome external disturbances such as changes in static head caused by liquid level variations, resulting in a heavy adjustment burden on the controller. In this example, by introducing liquid level compensation, the dynamic compensation of the target pressure preemptively offsets this major disturbance, allowing the controller to focus more on handling other dynamic disturbances (such as sudden changes in gas flow rate and environmental heat leakage), thus making the control process smoother and reducing overshoot. Additionally, since the liquid level in the LNG storage tank is a continuously changing variable throughout its operating cycle, the dynamic compensation mechanism for the target pressure proposed in this example frees the control system from the constraints of traditional, simplistic operation at a fixed setpoint. It can then adaptively adjust the control target based on the sensed state (current liquid level), improving the system's intelligence and reliability.

[0034] For example, if the current liquid level H actualSet the base pressure to 35%. P base The pressure is 0.65 MPa, which can be calculated based on equation (2): f (35)=0.125-0.0025×35=0.0375MPa; Therefore, the target pressure after dynamic compensation at this time P set = P base + f ( 35 =0.65 + 0.0375 = 0.6875 MPa; For ease of control, the target pressure is rounded to 0.69 MPa in this example.

[0035] (3) Calculate the pressure deviation between the target pressure and the current pressure. e and pressure deviation change rate ec.

[0036] In this embodiment, the pressure deviation is calculated according to equations (3) and (4) respectively. e and pressure deviation change rate ec : e = P set -P actual ;(3)。

[0037] ec = de / dt ;(4)。

[0038] If the current pressure is monitored P actual =0.58MPa, then according to equation (3), we can calculate: e = P set -P actual =0.69-0.58=0.11MPa.

[0039] Furthermore, by continuously sampling and calculating, the rate of change of pressure deviation can be obtained. ec A value of -0.005 MPa / s indicates that the pressure is slowly decreasing.

[0040] (4) Based on pressure deviation e and the rate of change of pressure deviation ec As input variables of the fuzzy inference engine, based on the fuzzy rule base pre-set by the fuzzy inference engine, the input variables are... e and ec After fuzzification, fuzzy inference, and defuzzification, the corresponding output is used in real time to adjust the parameters of the PID controller. ΔKp , ΔK i , ΔK d .

[0041] The dynamic adjustment mechanism based on feedforward compensation of the target pressure alone cannot achieve the desired control effect, and there are still problems such as overshoot, oscillation or response lag. Therefore, in this embodiment, the control parameters of the PID controller based on fuzzy inference mechanism are modified on the basis of dynamic compensation of the target pressure.

[0042] Specifically, in this embodiment, pressure deviation is used as a reference. e and the rate of change of pressure deviation ec As input variables for the fuzzy inference engine, the input variables are first... e and ec Fuzzification is performed, which involves converting precise input values ​​into fuzzy linguistic values ​​and their membership degrees. In this example, the input variables are... e and ec The corresponding division is into several fuzzy sets, including negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB), which represent pressure deviations. e The corresponding fuzzy set is: {Negative Large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Large (PB)}; Pressure deviation change rate ec The corresponding fuzzy set is: {Negative Large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Large (PB)}.

[0043] Furthermore, triangular or Gaussian membership functions are used to quantize each fuzzy set, thereby describing the degree to which a precise value of an input variable belongs to a particular fuzzy set. For example, for pressure deviation... e The fuzzy set “Zhengda (PB)” is defined as: when e When it approaches the upper limit of the set range (e.g., 0.1 MPa), its membership degree is 1; when... e When the membership degree deviates from this value, it gradually decreases to 0.

[0044] In this embodiment, fuzzy sets corresponding to triangular membership functions are used for quantization. In this example, pressure deviation... e The parameters of the corresponding triangular membership function are shown in Table 1 (unit: MPa), and the pressure deviation change rate is also shown. ec The membership function structure is similar, but its dimension is MPa / s.

[0045] .

[0046] Therefore, the pressure deviation calculated according to step (3) e =0.11MPa, pressure deviation change rate ec =-0.005MPa / s, and then based on the preset triangle membership function in this example, the precise value is... e =0.11MPa and ec =-0.005MPa / s is converted into a fuzzy quantity. Specifically, in this embodiment, the pressure deviation... e =0.11MPa was determined to have a membership degree of 0.7 for "Zhengda (PB)" and 0.3 for "Zhengzhong (PM)"; pressure deviation change rate ec =0.005MPa / s is determined to have a membership degree of 0.6 for "Negative Small (NS)" and 0.4 for "Zero (ZO)".

[0047] In addition, in this embodiment, the fuzzy rules in the fuzzy rule base adopt the IF-THEN form, which is formatted as: "IF e is A AND ec is B THEN ΔK p is C, ΔK i is D, ΔK d "is E". Here, A, B, C, D, and E are fuzzy sets. Specifically, in this embodiment, considering the "nonlinear and large hysteresis" dynamic characteristics of the LNG storage tank, the conventional fuzzy PID rule base cannot be applied to the specific controlled object of the LNG storage tank. Therefore, the fuzzy rules in the fuzzy rule base were redesigned in this example, and the following fuzzy description principles are followed when presetting the fuzzy rules in the fuzzy rule base: When | e When the value is larger, take the larger value. K p and smaller K d To improve response speed while ignoring K i To prevent integral saturation, this addresses the need for rapid response under large deviations, while also avoiding severe overshoot caused by large inertia (large lag).

[0048] When | e When in a moderate situation, choose the appropriate amount. K p Smaller K i and moderate Kd This achieves a smooth transition and reduces overshoot.

[0049] When | e When the value is smaller, take the larger value. K p and K i At the same time, maintain appropriate K d To improve steady-state accuracy and anti-interference capability, and overcome static pressure fluctuations and external thermal interference.

[0050] For specific details, please refer to some of the fuzzy rules listed in Table 2.

[0051] .

[0052] In this embodiment, the output variables shown in Table 2 are... ΔK p , ΔK i , ΔK d The fuzzy set is also defined as: {Negative Large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Large (PB)}; and uses the same membership function as the input variables. Furthermore, in this example, the universe of discourse (i.e., the range of all possible values ​​of the output variable) represents the relative change (e.g., -30% to 30%) relative to the current PID control parameters. 。

[0053] Therefore, through the design of a special fuzzy PID rule base, it is adapted to LNG storage tanks with nonlinear and large hysteresis dynamic characteristics, realizing precise control of LNG storage tank pressure changes, and effectively suppressing overshoot and oscillation, so that the optimal balance can be achieved between response speed, transition smoothness and steady-state accuracy.

[0054] Then, fuzzy reasoning is performed. In this example, fuzzy reasoning includes the following sub-steps: (41) For each fuzzy rule in the fuzzy rule base, calculate the input variable by taking the minimum value. e and ec Degree of matching with preconditions μ ( rule )=min( μ A( e ), μ B( ec ), where A and B are fuzzy sets.

[0055] For example, in this embodiment, we assume a pressure deviation. eMembership degrees corresponding to 0.11 MPa: PB (0.7), PM (0.3); Pressure deviation change rate ec The membership degrees corresponding to -0.005 MPa / s are: ZO (0.4), NS (0.6); the fuzzy inference engine activates the following two core rules: Rule 1: "IF" e is PB AND ec is ZO THEN ΔK p is PB, ΔK i is ZO. ΔK d If the rule is NS, then the matching strength (activation strength) of the rule is min(0.7, 0.4) = 0.4.

[0056] Rule 2: "IF" e is PM AND ec is NS THEN ΔK p is PM. ΔK i is ZO. ΔK d If the rule is NM, then the matching strength (activation strength) of the rule is min(0.3, 0.6) = 0.3.

[0057] (42) Apply the matching degree of each fuzzy rule to the output variable of its conclusion. ΔK p , ΔK i , ΔK d The membership function is truncated or scaled to form the output fuzzy set corresponding to the fuzzy rule. In this embodiment, the membership function is truncated at the activation intensity level using the minimum value method.

[0058] (43) Aggregate the output fuzzy sets of all fuzzy rules corresponding to the same output variable to obtain the comprehensive output fuzzy set of each output variable. In this embodiment, the aggregation is achieved by taking the maximum value of the membership degree of each fuzzy set at each universe point, thereby forming a new fuzzy set, namely the comprehensive output fuzzy set.

[0059] Further defuzzification processing is performed. In this embodiment, the centroid method is used to convert the fuzzy output into a precise correction value: ;(5)。

[0060] in, ΔK This refers to the parameter correction amount corresponding to the PID controller. zi To output the sampling points in the universe of discourse, μ ( z i ) is the sampling point z i The corresponding membership function. In this embodiment, after synthesizing the outputs of all activated rules and using the centroid method for defuzzification, the precise PID parameter correction is obtained as follows: ΔK p =+24% ΔK i =0% ΔK d =-15%.

[0061] (5) Initial parameters of the PID controller based on parameter correction amount K p0 , K i0 , K d0 Perform online self-tuning.

[0062] In this embodiment, the initial control parameters of the PID controller are assumed to be: K p0 =2.0、 K i0 =0.05、 K d0 If = 1.0, then the adaptive PID parameters at the current moment are: K p = K p0 + ΔK p =2.0 + 2.0 × 24% = 2.48; K i = K i0 + ΔK i =0.05 + 0 = 0.05; K d = K d0 + ΔK d =1.0 + 1.0 × (-15%) = 0.85.

[0063] (6) The PID controller adjusts the pressure deviation based on the current pressure deviation. e and adaptive parameters K p , K i , Kd The corresponding calculation output control signal U ( t ).

[0064] In this embodiment, the PID controller is based on the current pressure deviation e=0.11 and the tuned PID controller parameters: K p =2.48、 K i =0.05、 K d =0.85, according to the standard output expression of a PID controller: ;(6)。

[0065] The corresponding control signals are calculated. U ( t =58%.

[0066] (7) Control signal U ( t After being converted into an analog signal of 4-20mA, it drives and controls the opening of the LNG storage tank pressurization regulating valve.

[0067] To verify the effectiveness of the tank pressurization control method proposed in this application, a simulation experiment was conducted, and the control method of this application was compared with traditional on-off control and traditional PID control. The results are shown in [reference needed]. Figure 1 Under the control method disclosed herein, simulations based on the above data show that with the booster valve open at 58%, LNG flows through the booster vaporizer at a stable flow rate of 45 kg / min. The vaporized LNG returns to the LNG storage tank, and the pressure inside the tank rises at a steady rate of 0.014 MPa / min, avoiding pressure shocks and overshoot, and rapidly and smoothly approaching the target pressure of 0.69 MPa. Figure 1 As can be seen, traditional switching control and traditional PID control suffer from severe oscillations and significant overshoot, while the control method disclosed herein can reach the set pressure smoothly at a faster speed and with less overshoot, and maintain stability.

[0068] Furthermore, this example also discloses a tank pressurization control system based on fuzzy adaptive PID control for LNG refueling stations, used to implement the aforementioned tank pressurization control method. See also Figure 2The system includes an LNG storage tank, which is equipped with a sensor array for real-time monitoring of the gas phase pressure and liquid level within the tank. Specifically, in this example, the sensor array includes a pressure transmitter and a level gauge. To implement the LNG storage tank pressurization control method disclosed in this application, the LNG storage tank pressurization system also includes a controller. In this example, the controller is specifically a station control PLC, and the pressure transmitter and level gauge are electrically connected to the input ports of the PLC to allow the PLC to acquire the pressure and liquid level data of the LNG storage tank in real time. In addition, a pressure regulating valve is connected to the outlet of the LNG storage tank via a pipeline, and a pressure boosting vaporizer is also connected between the pressure regulating valve and the gas phase space of the LNG storage tank via a pipeline. Therefore, in situations such as outputting LNG liquid, the opening of the pressure regulating valve is adjusted by the PLC controller according to the LNG storage tank pressure boosting control method disclosed herein, so as to adjust the amount of LNG entering the pressure boosting vaporizer. Thus, after the LNG undergoes liquid-gas conversion at the pressure boosting vaporizer, it re-enters the LNG storage tank, thereby achieving the purpose of adjusting the gas phase pressure in the storage tank and maintaining the stability of the tank pressure.

[0069] Although some preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0070] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A tank pressurization control method based on fuzzy adaptive PID control for LNG refueling stations, characterized in that, Includes the following steps: (1) Real-time acquisition of the current pressure of the LNG storage tank P actual and current liquid level H actual ; (2) Set the target pressure of the LNG storage tank according to the current liquid level. P set ; (3) Calculate the pressure deviation between the target pressure and the current pressure. e and pressure deviation change rate ec ;in, e=P set - P actual ; ec=de / dt ; (4) Based on the pressure deviation e and the rate of change of pressure deviation ec As input variables of the fuzzy inference engine, based on the fuzzy rule base preset by the fuzzy inference engine, the input variables are... e and ec After fuzzification, fuzzy inference, and defuzzification, the corresponding output is used in real time to adjust the parameters of the PID controller. ΔK p , ΔK i , ΔK d ; (5) Initial parameters of the PID controller based on parameter correction amount K p0 , K i0 , K d0 To perform online self-tuning, the adaptive PID parameters for the current time step are calculated as follows: K p = K p0 + ΔK p ； K i = K i0 + ΔK i ； K d = K d0 + ΔK d ； (6) The PID controller is based on the current pressure deviation. e and adaptive parameters K p , K i , K d The corresponding calculation output control signal U ( t ); (7) The control signal U ( t After being converted into an analog signal of 4-20mA, it drives and regulates the pressure regulating valve of the LNG storage tank.

2. The tank pressurization control method according to claim 1, characterized in that, In step (2), based on the current liquid level of the LNG storage tank... H actual Corresponding to dynamic compensation of the target pressure P set : P set = P base + f ( H actual )； in, P base The base pressure is designed to match the requirements of the submersible pump. f ( H actual ) is the liquid level compensation function that is inversely proportional to the liquid level height.

3. The tank pressurization control method according to claim 2, characterized in that, In step (2), the expression for the liquid level compensation function is: ； in, H threshold The set liquid level threshold for initiating liquid level compensation; ΔH To compensate for the liquid level interval; K This represents the compensation pressure value corresponding to each liquid level interval.

4. The tank pressurization control method according to claim 1, characterized in that, In step (4), the input variables are... e and ec When performing fuzzification, first set the input variables... e and ec The corresponding fuzzy sets are divided into several fuzzy sets, including negative large NB, negative medium NM, negative small NS, zero ZO, positive small PS, positive medium PM, and positive large PB. Then, the triangular membership function or Gaussian membership function is used to quantize each fuzzy set.

5. The tank pressurization control method according to claim 4, characterized in that, In step (4), each output variable ΔK p , ΔK i , ΔK d Use with input variables e and ec They use the same fuzzy set partitioning method and membership function, and the universe of discourse of each output variable represents the relative change with respect to the current PID control parameters. 。 6. The tank pressurization control method according to claim 4, characterized in that, In step (4), the fuzzy reasoning includes the following sub-steps: (41) For each fuzzy rule in the fuzzy rule base, calculate the input variable by taking the minimum value. e and ec The degree of matching with the preconditions; (42) Apply the matching degree of each fuzzy rule to the output variable of its conclusion. ΔK p , ΔK i , ΔK d The membership function is used to form the output fuzzy set corresponding to the fuzzy rule by truncating or scaling; (43) Aggregate the output fuzzy sets of all fuzzy rules corresponding to the same output variable to obtain the comprehensive output fuzzy set of each output variable.

7. The tank pressurization control method according to claim 6, characterized in that, In step (4), the defuzzification is used to convert the fuzzy output into a precise correction value: ； in, ΔK This refers to the parameter correction amount corresponding to the PID controller. z i To output the sampling points in the universe of discourse, μ ( z i ) is the sampling point z i The corresponding membership function.

8. A tank pressurization control system based on fuzzy adaptive PID control for LNG refueling stations, used to implement the tank pressurization control method described in claim 1, characterized in that, The system includes an LNG storage tank, a controller for executing the LNG storage tank pressurization control method, a sensor group that is communicatively connected to the controller and used for real-time monitoring of the pressure and liquid level inside the LNG storage tank, a pressurization regulating valve that is connected to the liquid outlet of the LNG storage tank and communicatively connected to the controller, and a pressurization vaporizer that connects the pressurization regulating valve to the gas phase space of the LNG storage tank.

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