An adaptive PID crude oil processing liquid inlet steady flow control method

Abstract

The invention belongs to the technical field of oil and gas field development, and discloses an adaptive PID crude oil processing liquid inlet steady flow control method, comprising: determining the control logic of inner and outer loops, the outer loop uses the target liquid level and target pressure of a pressure buffer tank as input and output, and the inner loop uses the target pressure of the pressure buffer tank and the opening of an air supply valve as input and output; establishing inner and outer loop domain adjusters and fuzzy controllers, determining their input parameters, domain correction coefficients and PID initial parameters, and optimizing the domain and PID parameters by using a genetic algorithm; determining a rule table, a membership function and a defuzzification method of the fuzzy controller by fuzzification processing, and performing fuzzy reasoning and clear output; and continuously optimizing the domain range of errors and error change rates and PID control parameters under the joint action of the domain adjuster and the fuzzy controller to realize steady flow control, improve the stability of crude oil inlet flow, and meet the processing capacity of the process back end and light load operation.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas field development, and in particular relates to an adaptive PID crude oil processing liquid inlet steady flow control method. Background Art

[0002] The produced crude oil is transported through pipelines and gathered at the injection and transportation joint station. After being diverted to three-row horizontal pressure buffer tanks through the manifold, it is transported by the lift pump to the subsequent process for separation, purification and other operations. The flow rate of produced crude oil varies greatly. If the inlet flow rate is too low, the pump will stop due to empty pumping. If the inlet flow rate is too high, the buffer tank will overflow, causing a production stoppage accident.

[0003] The current method of using conventional PID control loops to control crude oil production fluids generally has disadvantages such as low control accuracy, slow response to changes, and poor control adaptability. It requires frequent manual intervention and adjustment, and untimely control can easily lead to production stoppage accidents. Summary of the invention

[0004] The present invention aims to solve the technical problems existing in the prior art and provide an adaptive PID crude oil processing inlet steady flow control method, which can greatly improve the stability of the crude oil inlet flow rate, ensure that the processing capacity of the process back-end device is met, and ensure the light load operation of the pressure buffer tank, and effectively solve the problem of rapid and accurate control of pipeline flow when the crude oil inlet is unstable.

[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0006] An adaptive PID crude oil processing inlet steady flow control method specifically comprises the following steps:

[0007] Step S1: First, in the incoming liquid buffer crude oil processing system, flow meters and flow control valves are respectively set at the front ends of the three transmission pipelines; crude oil level detection sensors and pressure sensors are respectively installed in the three horizontal pressure buffer tanks; a gas flow rate sensor is set in the gas supply valve transmission pipeline of each pressure buffer tank; by analyzing the field test data, it is concluded that the gas pressure in the pressure buffer tank should be maintained within 1.2~1.5 MPa;

[0008] Step S2: According to the actual needs of the crude oil collection pipeline workers, the target liquid level in the pressure buffer tank is set to ;

[0009] Step S3: Use a flow meter to measure the incoming liquid flow in the crude oil pipeline in real time ;

[0010] Step S4: According to The target pressure value required in the pressure buffer tank can be calculated by , the calculation method is as follows;

[0011] Step S5: ;in, is the liquid density; is the pipe radius;

[0012] Step S6: Use the fuzzy cascade PID control system to adjust the flow control valve at the front end of the delivery pipeline so that the target pressure value is calculated by step S5. ;

[0013] Step S7: The fuzzy cascade PID control system mainly includes a liquid level measurement sensor, a tank pressure sensor, a flow rate sensor, an outer loop PID controller and an inner loop PID controller. The outer loop PID controller is composed of an outer loop PID domain regulator, an outer loop PID fuzzy controller and a position flow valve PID controller with adjustable parameters; the inner loop PID controller is composed of an inner loop PID domain regulator, an inner loop PID fuzzy controller and an incremental air supply valve PID controller with adjustable parameters;

[0014] Step S8: Target tank liquid level Real-time tank liquid level Deviation and the rate of change of the deviation As the input of the outer loop PID controller, the parameters are optimized through the outer loop PID domain regulator and , as the input of the outer loop PID fuzzy controller; after fuzzification, fuzzy reasoning and defuzzification, the correction values ​​of the three PID basic control coefficients are obtained , , And these correction values ​​are transmitted to the position flow valve PID controller for calculation;

[0015] Step S9: Apply the output of the outer loop PID controller to the flow valve and calculate the target pressure value according to the calculation formula of step S5. , which is used as the set value of the inner loop PID controller, and the real-time pressure value measured by the pressure sensor is used as the feedback value of the inner loop PID controller ;

[0016] Step S10: Target tank pressure value And real-time tank pressure value Deviation and the rate of change of the deviation As the input of the inner loop PID controller, the parameters are optimized through the inner loop PID domain regulator and , as the input of the inner loop PID fuzzy controller; after fuzzification, fuzzy reasoning and defuzzification, the correction values ​​of the three PID basic control coefficients are obtained. , , And these correction values ​​are transmitted to the incremental air supply valve PID controller for calculation;

[0017] Step S11: , , ;in, is the critical pressure ratio, is the adiabatic index, and They are respectively the inlet and outlet pressures of the air supply valve, is the flow coefficient of the air supply valve, and is a constant, is the gas volume flow rate under standard conditions, is the relative density of the gas, is the absolute temperature of the air supply valve inlet, It is the pressure difference at both ends of the air supply valve.

[0018] Step S12: The opening value of the air supply valve calculated by step S11 is ;

[0019] Step S13: converting the target pressure value in the tank output by the inner loop PID controller into the opening value of the air supply valve, which is used to adjust the opening of the air supply valve to achieve a stable state of gas-liquid mixing in the tank, thereby achieving the function of steady flow control of crude oil processing liquid inlet.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] The present invention provides an adaptive PID crude oil processing liquid inlet steady flow control method. The method uses a genetic algorithm to adjust the input deviation of the inner and outer loop PID controllers. , Deviation Change Rate And PID control coefficient , , Optimize to determine the optimal domain range and PID control coefficient so that and The mapping is more accurate, avoiding the problem of rigid parameter adjustment caused by fixed domain. Subsequently, the optimized input is fuzzified, fuzzy inferenced and defuzzified. Among them, the accurate division of membership function can reduce the nonlinear mismatch problem. Therefore, the output value of the fuzzy controller can realize the precise adjustment of the inner and outer loop PID control loops. This method effectively solves the problem of steady flow of the inlet pipeline of the incoming liquid buffer device in the crude oil processing system, reduces pipeline vibration, and realizes the rapid and accurate control of the flow of the inlet pipeline of the incoming liquid buffer device.

[0022] The on-site practice of the adaptive PID control loop in the operation of the crude oil stabilization device shows that this method can greatly improve the stability of the crude oil inlet flow rate, ensuring that both the processing capacity of the process back-end device is met and the light-load operation of the pressure buffer tank is guaranteed. It effectively solves the problem of rapid and accurate control of pipeline flow when the crude oil inlet is unstable, and realizes the autonomous, stable and reliable operation of the crude oil stabilization device under different operating conditions. It can also be applied to the input flow control of other processes with unstable inlet volume and high gas content. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an adaptive PID crude oil processing liquid inlet steady flow control method according to an embodiment of the present invention;

[0024] Figure 2 It is a fuzzy cascade PID control flow chart of an embodiment of the present invention;

[0025] Figure 3 A logical diagram of a domain adjuster according to an embodiment of the present invention;

[0026] Figure 4 This is a diagram of the optimization of the fuzzy PID structure by the genetic algorithm of an embodiment of the present invention;

[0027] Figure 5 This is a genetic algorithm optimization flow chart of an embodiment of the present invention;

[0028] Figure 6 For the embodiment of the present invention , , Membership function graph;

[0029] Figure 7 For the embodiment of the present invention , Membership function graph;

[0030] Figure 8 This is a flow chart of the liquid buffering process of the crude oil processing system according to an embodiment of the present invention;

[0031] Fig. 9 A fuzzy cascade PID control loop control logic diagram of an embodiment of the present invention;

[0032] Fig.10 This is a schematic diagram of instantaneous flow fluctuations of three crude oil liquid pressure buffer tanks before and after the method is adopted in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Combination Figure 1 , Figure 2 As shown, this embodiment provides an adaptive PID crude oil processing liquid inlet steady flow control method, the method comprising the following steps:

[0035] Step S1: determining a control logic of a fuzzy cascade PID control loop, wherein the fuzzy cascade PID control loop includes an outer control loop and an inner control loop;

[0036] For the outer control loop: the input is the target liquid level setting value of the pressure buffer tank, the feedback is the actual value of the pressure buffer tank liquid level, and the output is the target pressure value entering the pressure buffer tank;

[0037] For the inner control loop: the input is the target pressure value of the pressure buffer tank, the feedback is the actual pressure value of the pressure buffer tank, and the output is the opening of the pressure buffer tank air supply valve;

[0038] Step S2: Determine the input of the inner and outer loop domain regulators in the fuzzy cascade PID control loop , and PID control coefficient , , The initial domain and domain correction coefficient , Initial value; the initial domain of the fuzzy controller input and output parameters, and fuzzification processing;

[0039] For the domain adjuster and , , Perform genetic algorithm optimization:

[0040] The domain regulator and fuzzy PID structure optimization of this design are as follows Figure 3 and Figure 4 As shown. and the rate of change of deviation Input to the domain adjuster corresponding to the inner and outer loops, and dynamically adjust the domain correction coefficient , To flexibly optimize the domain range of input variables, thereby further refining the domain division and optimizing , , The value of enhances the ability of the fuzzy cascade PID control loop to adjust the slight deviation in the crude oil processing liquid inlet steady flow control system. The genetic algorithm optimization flow chart is as follows Figure 5 shown.

[0041] Among them, the binary encoding format is adopted, according to , Initial value and , , The initial domain, randomly generate a population of N = 30, and set the maximum genetic generation =100; Crossover probability =0.8; the mutation probability is dynamically adjusted according to formula (1); the error of each individual is calculated by the fuzzy cascade PID control system , and then calculate its fitness value according to formula (2), The larger the value, the better the individual. Then, the fitness is normalized using formula (3) to determine the probability of each individual being selected. Then, the next generation of population is generated through selection, crossover, and mutation operations. Repeat the above process until the maximum genetic generation is reached. ; Finally, the individual with the highest fitness is selected as the optimal solution, and the corresponding , , , , parameter.

[0042] (1)

[0043] in, is the mutation probability, =0.1 is the initial mutation probability, is the current iteration number;

[0044] (2)

[0045] In formula (2), the integral absolute error is used. and As the comprehensive performance objective function of the system; For the The fitness of an individual =10 For response time, and For the The individual error, =0.5, =0.5 is the weight of the outer and inner rings;

[0046] (3)

[0047] in, For the The normalized fitness of each individual, For the The fitness of an individual is the total fitness of the entire population;

[0048] Specifically, taking the outer loop fuzzy cascade PID control loop as an example: Determine the deviation The initial domain is [-10,10] and the rate of change of deviation The basic domain of is [-0.2,0.2], , , , when the actual deviation and the rate of change of deviation When the dynamic change exceeds the fixed domain, the fixed domain cannot correctly describe the relationship between input and output, which leads to the failure of fuzzy rules. Figure 5 Search for the optimal solution of the crude oil processing liquid inlet steady flow control system and dynamically adjust the domain correction coefficient , as well as , , parameters to obtain the optimal value , , , , ,Will , Substitute into formula (11) and formula (12) to change , The initial domain of the system is optimized under different working conditions. The domain change formulas are shown in formula (4), formula (5), formula (6) and formula (7).

[0049] (4)

[0050] (5)

[0051] (6)

[0052] (7)

[0053] For the outer control loop:

[0054] Pressure buffer tank real-time level and target level Deviation , and the rate of change of the outer ring deviation After being optimized by the outer loop PID domain controller, it is used as the input value of the outer loop PID fuzzy controller, and the outer loop proportional coefficient increment , Increment of the outer loop integral coefficient , Outer loop differential coefficient increment As the output value of the outer loop PID fuzzy controller;

[0055] error The basic domain of is [-10,10], and the error change rate The basic domain of is [-0.2,0.2];

[0056] Assume that the basic domain of the flow change increment is [-2%, 2%];

[0057] According to the regulation rule of the outer loop PID, take The basic domain of is [-20,20], The basic domain of is [-10,10], The basic domain of is [-4,4];

[0058] The output of the position flow valve PID controller of the outer loop is:

[0059] (8)

[0060] In the above formula (8), is the output of the PID controller of the outer loop; for t Outer ring error of time; is the outer ring proportional coefficient; is the outer loop integral coefficient; is the outer loop differential coefficient.

[0061] For the inner control loop:

[0062] Target pressure value of the pressure buffer tank The actual value of the pressure buffer tank pressure Deviation 、 and the rate of change of the inner ring deviation After being optimized by the inner loop PID domain controller, it is used as the input value of the inner loop PID fuzzy controller, and the inner loop proportional coefficient increment , Inner loop integral coefficient increment , Inner loop differential coefficient increment As the output value of the inner loop PID fuzzy inference engine;

[0063] error The initial domain is [-0.02, 0.02], and the error change rate The initial domain of is [-0.1, 0.1];

[0064] Assume that the basic domain of the increment of pressure change is [-10%, 10%];

[0065] According to the regulation rule of inner loop PID, take The basic domain of is [-50,50], The basic domain of is [-25,25], The basic domain of is [-10,10];

[0066] The output of the incremental air supply valve PID controller of the inner loop is:

[0067] (9)

[0068] In the above formula (9), is the output of the PID controller of the inner loop; for t The inner loop error at the moment; for t-1 The inner loop error at the moment; for t-2 The inner loop error at the moment; is the inner ring proportional coefficient; is the inner loop integral coefficient; is the inner loop differential coefficient.

[0069] For blurring:

[0070] The fuzzification process specifically includes: fuzzification of the fuzzy PID controller of the outer control loop and fuzzification of the fuzzy PID controller of the inner control loop;

[0071] Specifically, the fuzzification process of the fuzzy PID controller of the outer control loop is:

[0072] Step S201: The fuzzy controller adopts the Mamdani model and follows the following principles when designing control rules: for larger errors, the selection of control actions gives priority to quickly eliminating the errors; for small errors, the control actions give priority to stability to prevent system overshoot.

[0073] Step S202: For input variables and , use the domain correction coefficient to change its initial domain, so as to transform it into the domain range that the fuzzy controller can handle;

[0074] Since the input and output variables , , The basic domain of is an exact value continuous set. In order to convert it into a fuzzy subset domain that can be processed by the fuzzy controller without distortion, it is necessary to introduce a quantization factor to fuzzify the input and output variables. The quantization factor conversion formula is:

[0075] (10)

[0076] In the above formula (10), is the quantitative factor; n is the boundary of the fuzzy subset domain; a is the minimum value of the basic domain; b is the maximum value of the basic domain;

[0077] Step S203: Divide the fuzzy subsets of the input variables and the output variables into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium and positive large. NB, NM, NS, ZO, PS, PM, PB express;

[0078] Step S204: Divide the fuzzy subset domain of the input variables and the output variables into 13 levels, namely: {-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6}.

[0079] The fuzzification process of the fuzzy PID controller of the inner control loop is:

[0080] Step S211: For input variables and , use the domain correction coefficient to change the initial domain, so as to transform it into the domain range that the fuzzy controller can handle. The quantitative factor conversion formula is the above formula (10);

[0081] Step S212: Divide the fuzzy subsets of the input variables and the output variables into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium and positive large, represented by NB, NM, NS, ZO, PS, PM and PB;

[0082] Step S213: Combine the input variable and the output variable , , The fuzzy subset domain is divided into 13 levels, namely: {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}.

[0083] Step S3: Determine the fuzzy rule table, membership function, and defuzzification processing method of the inner and outer loop fuzzy controllers in the fuzzy cascade PID control loop, and perform fuzzy reasoning and clarification;

[0084] Among them, according to the expert experience, the fuzzy control rules of the inner and outer loop fuzzy controllers of the fuzzy cascade PID control loop are formulated as follows:

[0085] (1) If When the change is significant, choose the larger and smaller , , to ensure the stability of the crude oil processing liquid inlet steady flow system and effectively control the instantaneous deviation;

[0086] (2) If , If the change is small, it can be appropriately reduced according to system requirements. , select the appropriate , , to ensure the minimum overshoot and effectively shorten the system response time;

[0087] (3) When When the change is minimal, increase and The value of Adjustment of changes For smaller , choose the larger ; Larger Choose the smaller ;

[0088] (4) When and When the error changes in the same direction and increases, On the contrary, reduce .

[0089] The fuzzy control rule table of the fuzzy controller of the outer control loop is formulated as follows:

[0090] Table 1 Fuzzy control rule table

[0091]

[0092] Table 2 Fuzzy control rule table

[0093]

[0094] Table 3 Fuzzy rule table

[0095]

[0096] The fuzzy control rule table of the fuzzy controller of the inner control loop is formulated as follows:

[0097] Table 4 Fuzzy Controller

[0098]

[0099] Table 5 Fuzzy control rule table

[0100]

[0101] Table 6 Fuzzy control rule table

[0102]

[0103] Fuzzy cascade PID control loop inner and outer loop fuzzy controller , , , , The membership functions are as follows: Figure 6 , Figure 7 As shown; among them, NB and PB adopt normal distribution; NM, NS, ZO, PS, and PM adopt triangular distribution.

[0104] (11)

[0105] Formula (11) is the normal membership function, For input value or , is the central value of the fuzzy set, is the standard deviation of the normal distribution; the normal membership function can smoothly and continuously describe the gradual change characteristics of the liquid level deviation and pressure deviation at both ends in the pressure buffer tank;

[0106] (12)

[0107] Formula (12) is a triangular membership function. For input value or , , , are the left endpoint, vertex and right endpoint of the triangle; the triangle membership function is used to describe NM, NS, ZO, PS and PM to quickly respond to the occurrence of errors and improve the system control efficiency.

[0108] The fuzzy controllers of the inner and outer loops of the fuzzy cascade PID control loop select the centroid method as the defuzzification method, which can more accurately convert fuzzy information into specific clear values. , as shown in formula (13).

[0109] (13)

[0110] Formula (13) is the centroid method formula for defuzzification processing. , is the value range of the output variable, is the output variable, is the membership function.

[0111] Step S4: setting the target liquid level height value of the pressure buffer tank as the input of the outer loop, determining the initial parameters of the inner and outer loop PID controllers of the fuzzy cascade PID control loop, and continuously optimizing the domain range of the error and the error change rate and the PID control parameters under the joint action of the domain adjuster and the fuzzy controller, so as to perform steady flow control on the crude oil processing liquid inlet;

[0112] Among them, the initial parameters of the inner and outer loop PID controllers of the fuzzy cascade PID control loop are determined, including:

[0113] According to expert experience, when selecting the optimization range of the initial parameters of the inner and outer loop PID controllers, The range is set to [0, 10], The range is set to [0, 5], The range is set to [0, 2].

[0114] Correct the outer loop PID controller based on fuzzy control, the correction formula is as follows:

[0115] (14)

[0116] The initial parameters of the outer loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters and the parameter increments derived from fuzzy reasoning:

[0117] (15)

[0118] In the above formulas (14)-(15), for t The output of the PID controller of the outer loop at the moment; for t The outer ring proportionality factor of the moment; is the outer ring proportional coefficient at the previous moment; is the increment of the outer ring proportional coefficient; for tThe outer loop integral coefficient at time; is the outer loop integral coefficient at the previous moment; is the outer loop integral coefficient increment; to this end t The outer loop differential coefficient at time; is the outer loop differential coefficient at the previous moment; is the increment of the outer loop differential coefficient; for t The output of the outer loop PID controller at that moment; for t Outer ring error of time; is the outer ring error at the previous moment; for i The outer loop error at time, i [0, t ].

[0119] Determine the initial parameters of the inner loop fuzzy PID controller of the fuzzy cascade PID control loop, including:

[0120] Correct the inner loop PID controller based on fuzzy control, the correction formula is as follows:

[0121] (16)

[0122] The initial parameters of the inner loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters and the parameter increments derived from fuzzy reasoning:

[0123] (17)

[0124] In the above formulas (16)-(17), for t The output of the PID controller of the inner loop at the moment; for t The inner loop proportionality factor of the moment; is the inner ring proportional coefficient of the previous moment; is the flow rate proportional coefficient increment; for t The inner loop integral coefficient at time; is the flow integration coefficient at the previous moment; is the increment of the inner loop integral coefficient; to this end t The inner loop differential coefficient at time; is the inner loop differential coefficient of the previous moment; is the increment of the inner loop differential coefficient; for t The inner loop error at the moment; is the inner loop error at the previous moment; for i The inner loop error at time, .

[0125] The crude oil processing liquid inlet steady flow control method based on genetic algorithm optimized fuzzy cascade PID in the embodiment of the present invention is suitable for steady flow of the inlet pipeline of the liquid buffer device of the crude oil processing system, and meets the safety and stable regulation requirements of the liquid buffer device.

[0126] Figure 8 The figure shows a crude oil processing system inlet buffering process flow chart of an embodiment of the present invention, which includes 3 parallel process flow lines. The pressure buffer tanks on each process flow line adopt the above-mentioned crude oil processing inlet steady flow control method based on fuzzy cascade PID for steady flow control. Figure 8 The control logic diagram of the fuzzy cascade PID control loop corresponding to the pressure buffer tank on each process line is as follows: Fig. 9 shown.

[0127] The method of the embodiment of the present invention is applied to Figure 8 The crude oil processing system liquid buffer process shown in the figure shows that after the on-site inlet pipeline stabilizes the flow, the instantaneous flow fluctuations of the three pressure buffer tanks before and after the use of this method are as follows Fig.10 As shown in the figure (A, B, and C are the fluctuation curves of the three columns of pressure tanks respectively), the instantaneous flow fluctuation range of the incoming liquid buffer device is obviously converged, and the instantaneous flow fluctuation amplitude is reduced by 80%-90% on average. The corresponding pressure fluctuation amplitude of the incoming liquid buffer tank is reduced by 65%-75% on average. The regulation is safe, stable and reliable, which can ensure the automatic operation of the on-site crude oil incoming liquid buffer device.

[0128] The above description is only an embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement and improvement made within the application scope of the present invention shall be included in the protection scope of the present invention.

Claims

1. An adaptive PID crude oil processing inlet steady flow control method, characterized in that: The following steps are involved: Step S1: determining a control logic of a fuzzy cascade PID control loop, wherein the fuzzy cascade PID control loop includes an outer control loop and an inner control loop; For the outer control loop: the input is the target liquid level setting value of the pressure buffer tank, the feedback is the actual value of the pressure buffer tank liquid level, and the output is the target pressure value entering the pressure buffer tank; For the inner control loop: the input is the target pressure value of the pressure buffer tank, the feedback is the actual pressure value of the pressure buffer tank, and the output is the opening of the pressure buffer tank air supply valve; Step S2: Determine the input of the inner and outer loop domain regulators in the fuzzy cascade PID control loop , and PID control coefficient , , The initial domain and domain correction coefficient , Initial value; the initial domain of the fuzzy controller input and output parameters, and fuzzification processing; In step S2, it is characterized in that the outer control loop: Pressure buffer tank real-time level and target level Deviation , and the rate of change of the outer ring deviation After being optimized by the outer loop PID domain controller, it is used as the input value of the outer loop PID fuzzy controller, and the outer loop proportional coefficient increment , Outer loop integral coefficient increment , Outer loop differential coefficient increment As the output value of the outer loop PID fuzzy controller; error The basic domain of is [-10,10], and the error change rate The basic domain of is [-0.2,0.2]; Assume that the basic domain of the flow change increment is [-2%, 2%]; According to the regulation rule of the outer loop PID, take The basic domain of is [-20,20], The basic domain of is [-10,10], The basic domain of is [-4,4]; The output of the position flow valve PID controller of the outer loop is: In the above formula, is the output of the PID controller of the outer loop; is the outer loop error at time t; is the outer ring proportional coefficient; is the outer loop integral coefficient; is the differential coefficient of the outer loop; In step S2, it is characterized in that the inner control loop: Target pressure value of the pressure buffer tank The actual value of the pressure buffer tank pressure Deviation , and the rate of change of the inner ring deviation After being optimized by the inner loop PID domain controller, it is used as the input value of the inner loop PID fuzzy controller, and the inner loop proportional coefficient increment , Inner loop integral coefficient increment , Inner loop differential coefficient increment As the output value of the inner loop PID fuzzy inference engine; error The initial domain is [-0.02, 0.02], and the error change rate The initial domain of is [-0.1, 0.1]; Assume that the basic domain of the increment of pressure change is [-10%, 10%]; According to the regulation rule of inner loop PID, take The basic domain of is [-50,50], The basic domain of is [-25,25], The basic domain of is [-10,10]; The output of the incremental air supply valve PID controller of the inner loop is: In the above formula, is the output of the PID controller of the inner loop; is the inner loop error at time t; is the inner loop error at time t-1; is the inner loop error at time t-2; is the inner ring proportional coefficient; is the inner loop integral coefficient; is the inner loop differential coefficient; Step S3: Determine the fuzzy rule table, membership function, and defuzzification processing method of the inner and outer loop fuzzy controllers in the fuzzy cascade PID control loop, and perform fuzzy reasoning and clarification; Step S4: Set the target liquid level height value of the pressure buffer tank as the input of the outer loop, determine the initial parameters of the inner and outer loop PID controllers of the fuzzy cascade PID control loop, and continuously optimize the domain range of the error and the error change rate and the PID control parameters under the joint action of the domain adjuster and the fuzzy controller, so as to perform steady flow control on the crude oil processing liquid inlet.

2. The adaptive PID crude oil processing inlet steady flow control method according to claim 1 is characterized in that: In the step S2, the fuzzification processing includes: fuzzification of the fuzzy PID controller of the outer control loop and fuzzification of the fuzzy PID controller of the inner control loop.

3. The adaptive PID crude oil processing inlet steady flow control method according to claim 2 is characterized in that: The fuzzification process of the fuzzy PID controller of the outer control loop is: Step S201. For input variables and , using the domain correction factor , Change its initial domain, so as to transform it into the domain range that the fuzzy controller can handle; input variable and , output variables , and , using quantization factors to transform into a discrete set that can be processed by the fuzzy controller; Step S202. Divide the fuzzy subsets of the input variables and the output variables into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium and positive large. NB, NM, NS, ZO, PS, PM, PB express; Step S203. Divide the fuzzy domain of the input variables and the output variables into 13 levels, namely: {-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6}.

4. The adaptive PID crude oil processing inlet steady flow control method according to claim 2 is characterized in that: The fuzzification process of the fuzzy PID controller of the inner control loop is: Step S211. For input variables and , using the domain correction factor , Change its initial domain, so as to transform it into the domain range that the fuzzy controller can handle; input variable and , output variables , and , using quantization factors to transform into a discrete set that can be processed by the fuzzy controller; Step S212. Divide the fuzzy subsets of the input variables and the output variables into seven levels: negative large, negative medium, negative small, zero, positive small, positive medium and positive large, represented by NB, NM, NS, ZO, PS, PM and PB; Step S213. Divide the fuzzy subset domain of the input variables and the output variables into 13 levels, namely: {-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6}.

5. The adaptive PID crude oil processing inlet steady flow control method according to claim 3 is characterized in that: The quantitative factor conversion formula is: In the above formula, is the quantitative factor; n is the boundary of the fuzzy subset domain; a is the minimum value of the basic domain; b is the maximum value of the basic domain.

6. The adaptive PID crude oil processing inlet steady flow control method according to claim 1 is characterized in that: In step S4, the initial parameters of the outer loop fuzzy PID controller of the fuzzy cascade PID control loop are determined, specifically including: Correct the outer loop PID controller based on fuzzy control, the correction formula is as follows: The initial parameters of the outer loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters and the parameter increments derived from fuzzy reasoning: In the above formula, is the output of the PID controller of the outer loop at time t; is the outer ring proportional coefficient at time t; is the outer ring proportional coefficient at the previous moment; is the outer ring proportional coefficient increment; is the outer loop integral coefficient at time t; is the outer loop integral coefficient at the previous moment; is the outer loop integral coefficient increment; This is the outer loop differential coefficient at time t; is the outer loop differential coefficient at the previous moment; is the increment of the outer loop differential coefficient; is the output of the outer loop PID controller at time t; is the outer loop error at time t; is the outer ring error at the previous moment; is the outer loop error at time i, .

7. The adaptive PID crude oil processing inlet steady flow control method according to claim 1 is characterized in that: In step S4, the initial parameters of the inner loop fuzzy PID controller of the fuzzy cascade PID control loop are determined, specifically including: Correct the inner loop PID controller based on fuzzy control, the correction formula is as follows: The initial parameters of the inner loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters and the parameter increments derived from fuzzy reasoning: In the above formula, is the output of the PID controller of the inner loop at time t; is the inner loop proportional coefficient at time t; is the inner ring proportional coefficient of the previous moment; is the flow rate proportional coefficient increment; is the inner loop integral coefficient at time t; is the flow integration coefficient at the previous moment; is the increment of the inner loop integral coefficient; This is the inner loop differential coefficient at time t; is the inner loop differential coefficient of the previous moment; is the increment of the inner loop differential coefficient; is the inner loop error at time t; is the inner loop error at the previous moment; is the inner loop error at time i, .

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