Crude oil treatment liquid inlet steady flow control method based on fuzzy cascade PID (Proportion Integration Differentiation)

Abstract

The invention belongs to the technical field of oil and gas field development, and discloses a crude oil treatment inlet liquid steady flow control method based on fuzzy cascade PID, which comprises the following steps: determining fuzzy cascade PID regulation loop control logic of an inner loop control loop and an outer loop control loop, the input and output of the outer loop control loop are respectively a pressure buffer tank target liquid level and an inlet pressure buffer tank flow control valve opening; the input and the output of the inner ring control loop are respectively the opening degree of a flow control valve of an inlet pressure buffer tank and the opening degree of a gulp valve; determining initial domains of discourse of input and output parameters of a fuzzy inference engine, and performing fuzzification processing; determining fuzzy rule tables of the inner and outer ring fuzzy inference engines; the target liquid level of the pressure buffer tank serves as loop input, initial parameters of an inner ring PID controller and an outer ring PID controller are determined, the parameters are continuously optimized under the action of a fuzzy inference engine, and steady flow control is achieved. The stability of crude oil liquid inlet flow can be greatly improved, the processing capacity of a process rear-end device is met, and light-load operation of the pressure buffer tank is guaranteed.

Description

A method for steady-flow control of crude oil feed fluid based on fuzzy cascade PID Technical Field

[0001] This invention belongs to the field of oil and gas field development technology, specifically relating to a crude oil processing influent steady flow control method based on fuzzy cascade PID. Background Technology

[0002] Crude oil produced fluid is transported via pipeline to the injection and transportation station, where it is then diverted through a manifold to three horizontal pressure buffer tanks for buffering before being pumped to subsequent processes for separation, purification, and other operations. The flow rate of the crude oil produced fluid varies significantly; excessively low influent flow will cause the pumps to shut down due to lack of power, while excessively high influent flow will cause the buffer tanks to overflow, leading to a production shutdown.

[0003] The conventional PID control loop method currently used to control crude oil produced fluid generally suffers from drawbacks such as low control accuracy, slow response to changes, and poor adaptability. It requires frequent manual intervention and adjustment, and untimely control can easily lead to production stoppages. Summary of the Invention

[0004] This invention aims to address the technical problems existing in the prior art by providing a crude oil inlet flow stabilization control method based on fuzzy cascade PID, which can significantly improve the stability of crude oil inlet flow rate, ensuring that the processing capacity of the downstream equipment is met while the pressure buffer tank operates under light load, and effectively solving the problem of rapid and accurate control of pipeline flow when the crude oil inlet is unstable.

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

[0006] A crude oil processing feed flow stabilization control method based on fuzzy cascade PID includes the following steps:

[0007] Step S1: Determine the control logic of the fuzzy cascade PID control loop; the fuzzy cascade PID control loop includes an outer loop control loop and an inner loop control loop;

[0008] For the outer loop control loop: the input is the target liquid level of the pressure buffer tank, and the output is the opening degree of the flow control valve for the pressure buffer tank;

[0009] For the inner loop control circuit: the input is the opening degree of the inlet pressure buffer tank flow control valve, and the output is the opening degree of the pressure buffer tank air supply valve;

[0010] Step S2: Determine the initial universe of discourse for the input and output parameters of the fuzzy inference engine in the fuzzy cascade PID control loop, and perform fuzzification processing;

[0011] Step S3: Determine the fuzzy rule tables for the inner and outer loop fuzzy inference engines of the fuzzy cascade PID control loop;

[0012] Step S4: Set the target liquid level height of the pressure buffer tank as the loop input, determine the initial parameters of the inner and outer loop PID controllers of the fuzzy cascade PID control loop, and continuously optimize the PID controller parameters under the action of the fuzzy inference engine, thereby achieving steady flow control of the crude oil processing feed liquid.

[0013] Furthermore, in step S2, for the outer loop control loop:

[0014] The deviation e1 between the real-time liquid level H-(Ob) of the pressure buffer tank and the target liquid level HO, and the rate of change ec1 of the outer loop deviation are used as the input values ​​of the outer loop PID fuzzy inference engine; the increment Δk of the outer loop proportional coefficient. p1 Increment Δk of the outer loop integral coefficient i1 Increment Δk of the outer ring differential coefficient d1 As the output value of the outer-loop PID fuzzy inference engine;

[0015] The fundamental universe of discourse for error e1 is [-0.02, 0.02], and the fundamental universe of discourse for the rate of change of error ec1 is [-0.1, 0.1].

[0016] Let the fundamental domain of discourse for the increment of pressure change be [-10%, 10%];

[0017] Based on the adjustment law of the outer loop PID, take Δk p1 The fundamental domain of discourse is [-50, 50], Δk i1 The fundamental domain of discourse is [-25, 25], Δk d1 The fundamental domain of discourse is [-10, 10];

[0018] The output of the PID controller in the outer loop is:

[0019] u1 = k p1 ×e1(t)+k i1 ×∫[0,t]e1(t)dt+k d1 de1(t) / dt (1)

[0020] In equation (1) above, u1 is the output of the PID controller in the outer loop; k p1 e1(t) is the outer loop proportionality coefficient; e1(t) is the outer loop error at time t; k i1 k represents the outer loop integral coefficient. d1 is the outer ring differential coefficient.

[0021] Furthermore, in step S2, for the inner loop control loop:

[0022] The deviation e2 between the opening degree F-(Ob) of the inlet pressure buffer tank flow control valve and the target control valve opening FO, and the rate of change of the inner loop deviation ec2 are used as the input values ​​of the inner loop PID fuzzy inference engine; the inner loop proportional coefficient increment Δkp2 Increment Δk of the inner loop integral coefficient i2 Increment of the inner loop differential coefficient Δk d2 As the output value of the inner-loop PID fuzzy inference engine;

[0023] The fundamental universe of discourse for the error e² is [-10, 10], and the fundamental universe of discourse for the rate of change of error ec² is [-0.2, 0.2].

[0024] Let the fundamental universe of discourse for the increment of flow change be [-2%, 2%];

[0025] Based on the adjustment law of the inner loop PID, take Δk p2 The fundamental domains are [-20, 20] and Δk. i2 The fundamental domain of discourse is [-10, 10], Δk d2 The fundamental universe of discourse is [-4,4]; the output of the PID controller in the inner loop is:

[0026] u2=k p2 ×e2(t)+k i2 ×∫[0,t]e2(t)dt+k d2 de2(t) / dt (2)

[0027] In equation (2) above, u2 is the output of the PID controller in the inner loop; k p2 e2(t) is the inner loop proportionality coefficient; e2(t) is the inner loop error at time t; k i2 k represents the inner loop integral coefficient. d2 is the inner loop differential coefficient.

[0028] Furthermore, in step S2, the fuzzification process specifically includes: fuzzification of the fuzzy PID controller in the outer loop control loop and fuzzification of the fuzzy PID controller in the inner loop control loop.

[0029] Furthermore, the fuzzification process of the fuzzy PID controller in the outer loop control loop is as follows:

[0030] Step S211. For input variables e1 and ec1, and output variable Δk p1 Δk i1 and Δk d1 The quantization factor is used to transform it into a discrete set that can be processed by a fuzzy controller.

[0031] Step S212. Divide the fuzzy subsets of the input and 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;

[0032] Step S313. Divide the fuzzy universe of discourse of the input and output variables into 13 levels, namely: {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}.

[0033] Furthermore, the fuzzification process of the fuzzy PID controller in the inner loop control loop is as follows:

[0034] Step S221. For input variables e2 and ec2, and output variable Δk p2 Δk i2 and Δk d2 The quantization factor is used to transform it into a discrete set that can be processed by a fuzzy controller.

[0035] Step S222. Divide the fuzzy subsets of the input and 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;

[0036] Step S223. Divide the fuzzy universe of discourse of the input and output variables into 13 levels, namely: {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}.

[0037] 10. The crude oil processing feed steady flow control method based on fuzzy cascade PID according to claim 5 or 6, characterized in that the quantization factor conversion formula is:

[0038]

[0039] In equation (3) above, k is the quantization factor; n is the absolute value of the discretized universe boundary; a is the minimum value of the basic universe; and b is the maximum value of the basic universe.

[0040] Further, in step S4, determining the initial parameters of the outer loop fuzzy PID controller of the fuzzy cascade PID control loop specifically includes:

[0041] The outer-loop PID controller based on fuzzy control is modified, and the modified formula is as follows:

[0042]

[0043] The initial parameters of the outer-loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters with the parameter increments derived from fuzzy inference:

[0044]

[0045] In equations (4)-(5) above, u1(k) is the output of the PID controller in the outer loop at time k; k p1 (k) is the outer ring scaling factor at time k; k p1 (k-1) is the outer ring scaling factor at the previous time step; Δk p1 The increment of the outer ring proportional coefficient; k i1 (k) represents the outer loop integral coefficient at time k; k i1 (k-1) represents the outer loop integral coefficient at the previous time step; Δk i1 k represents the increment of the outer loop integral coefficients. d1 (k) represents the outer ring differential coefficient at time k; k d1 (k-1) represents the outer ring differential coefficients at the previous time step; Δk d1 The outer loop differential coefficient increment is u1(k); the outer loop PID controller output at time k is e1(k); the outer loop error at time k is e1(k-1); the outer loop error at the previous time is e1(i); and the outer loop error at time i is e1(i), where i∈[0, k].

[0046] Furthermore, in step S4, determining the initial parameters of the inner loop fuzzy PID controller of the fuzzy cascade PID control loop specifically includes:

[0047] The inner-loop PID controller based on fuzzy control is modified, and the modified formula is as follows:

[0048]

[0049] The initial parameters of the inner-loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters with the parameter increments derived from fuzzy inference:

[0050]

[0051] In equations (6)-(7) above, u2(k) is the output of the PID controller in the inner loop at time k; k p2 (k) is the inner loop scaling factor at time k; k p2 (k-1) is the inner loop scaling factor of the previous time step; Δk p2 For the increase in the flow ratio coefficient; k i2 (k) represents the inner loop integral coefficient at time k; k i2 (k-1) is the flow integral coefficient of the previous time step; Δk i2 k represents the increment of the inner loop integral coefficients. d2 (k) represents the inner loop differential coefficient at time k; k d2 (k-1) represents the inner loop differential coefficients of the previous time step; Δk d2u2(k) is the inner loop differential coefficient increment; u2(k) is the output of the inner loop PID controller at time k; e2(k) is the inner loop error at time k; e2(k-1) is the inner loop error at the previous time; e2(i) is the inner loop error at time i, i∈[0,k].

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] The crude oil processing feed flow stabilization control method based on fuzzy cascade PID provided by this invention obtains the inner and outer loop Δk by performing fuzzification, fuzzy inference, and defuzzification processing on the input value deviation and deviation change rate of the inner and outer loop PID control loops. p Δk i Δk d Three output values ​​are used to reasonably control the inner and outer loop PID control loops, effectively solve the problem of stable flow in the inlet pipeline of the crude oil processing system's liquid buffer device, reduce pipeline vibration, and achieve rapid and accurate control of the flow rate in the inlet pipeline of the liquid buffer device.

[0054] Meanwhile, field practice of fuzzy cascade PID control loop in the control and operation of crude oil stabilization unit shows that this method can significantly improve the stability of crude oil inlet flow rate, ensuring that it meets the processing capacity of downstream units while guaranteeing light-load operation of pressure buffer tank. It effectively solves the problem of rapid and accurate control of pipeline flow rate when crude oil inlet is unstable, realizing autonomous, stable and reliable operation of crude oil stabilization unit under different operating conditions. It can also be applied to the input flow control of other processes with unstable inlet flow rate and high gas content. Attached Figure Description

[0055] Figure 1 is a flowchart of the crude oil processing inlet steady flow control method based on fuzzy cascade PID according to an embodiment of the present invention;

[0056] Figure 2 is a flow chart of the crude oil processing system for liquid buffering according to an embodiment of the present invention.

[0057] Figure 3 is a control logic diagram of a fuzzy cascaded PID control loop according to an embodiment of the present invention;

[0058] Figure 4 is a schematic diagram showing the instantaneous flow fluctuation of the three crude oil inlet pressure buffer tanks before and after applying this method according to an embodiment of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0060] Referring to Figure 1, this embodiment provides a crude oil processing feed flow stabilization control method based on fuzzy cascade PID, the method including the following steps:

[0061] Step S1: Determine the control logic of the fuzzy cascade PID control loop, wherein the fuzzy cascade PID control loop includes an outer loop control loop and an inner loop control loop;

[0062] For the outer loop control loop: the input is the target liquid level of the pressure buffer tank, and the output is the opening degree of the flow control valve for the pressure buffer tank;

[0063] For the inner loop control circuit: the input is the opening degree of the flow control valve of the pressure buffer tank, and the output is the opening degree of the air replenishment valve of the pressure buffer tank.

[0064] Step S2: Determine the initial universe of discourse for the input and output parameters of the fuzzy inference engine in the fuzzy cascade PID control loop, and perform fuzzification processing;

[0065] For the outer loop control loop:

[0066] The deviation e1 between the real-time liquid level H-(Ob) of the pressure buffer tank and the target liquid level HO, and the rate of change ec1 of the outer loop deviation are used as the input values ​​of the outer loop PID fuzzy inference engine; the increment Δk of the outer loop proportional coefficient. p1 Increment Δk of the outer loop integral coefficient i1 Increment Δk of the outer ring differential coefficient d1 As the output value of the outer-loop PID fuzzy inference engine;

[0067] The fundamental universe of discourse for error e1 is [-0.02, 0.02], and the fundamental universe of discourse for the rate of change of error ec1 is [-0.1, 0.1].

[0068] Let the fundamental domain of discourse for the increment of pressure change be [-10%, 10%];

[0069] Based on the adjustment law of the outer loop PID, take Δk p1 The fundamental domain of discourse is [-50, 50], Δk i1 The fundamental domain of discourse is [-25, 25], Δk d1 The fundamental domain of discourse is [-10, 10];

[0070] The output of the PID controller in the outer loop is:

[0071] u1 = k p1 ×e1(t)+k i1 ×∫[0,t]e1(t)dt+k d1 de1(t) / dt (1)

[0072] In equation (1) above, u1 is the output of the PID controller in the outer loop; k p1 e1(t) is the outer loop proportionality coefficient; e1(t) is the outer loop error at time t; k i1 k represents the outer loop integral coefficient. d1 is the outer ring differential coefficient.

[0073] For the inner loop control loop:

[0074] The deviation e2 between the opening degree F-(Ob) of the inlet pressure buffer tank flow control valve and the target control valve opening FO, and the rate of change of the inner loop deviation ec2 are used as the input values ​​of the inner loop PID fuzzy inference engine; the inner loop proportional coefficient increment Δk p2 Increment Δk of the inner loop integral coefficient i2 Increment of the inner loop differential coefficient Δk d2 As the output value of the inner-loop PID fuzzy inference engine;

[0075] The fundamental universe of discourse for the error e² is [-10, 10], and the fundamental universe of discourse for the rate of change of error ec² is [-0.2, 0.2].

[0076] Let the fundamental universe of discourse for the increment of flow change be [-2%, 2%];

[0077] Based on the adjustment law of the inner loop PID, take Δk p2 The fundamental domains are [-20, 20] and Δk. i2 The fundamental domain of discourse is [-10, 10], Δk d2 The fundamental universe of discourse is [-4,4]; the output of the PID controller in the inner loop is:

[0078] u2=k p2 ×e2(t)+k i2 ×∫[0,t]e2(t)dt+k d2 de2(t) / dt (2)

[0079] In equation (2) above, u2 is the output of the PID controller in the inner loop; k p2 e2(t) is the inner loop proportionality coefficient; e2(t) is the inner loop error at time t; k i2 k represents the inner loop integral coefficient. d2 is the inner loop differential coefficient.

[0080] Specifically, the fuzzification process includes: fuzzification of the fuzzy PID controller in the outer loop control loop and fuzzification of the fuzzy PID controller in the inner loop control loop.

[0081] The fuzzification process of the fuzzy PID controller in the outer loop control loop is as follows:

[0082] Step S211. For input variables e1 and ec1, and output variable Δkp1 Δk i1 and Δk d1 The quantization factor is used to transform it into a discrete set that can be processed by a fuzzy controller.

[0083] Since the fundamental universes of discourse for both input and output variables are continuous sets, in order to transform them into discrete sets that the fuzzy controller can process without distortion, a quantization factor needs to be introduced to convert the input and output variables into discrete sets that the fuzzy controller can process. The quantization factor conversion formula is as follows:

[0084]

[0085] In equation (3) above, k is the quantization factor; n is the absolute value of the discretized universe boundary; a is the minimum value of the basic universe; and b is the maximum value of the basic universe.

[0086] Step S212. Divide the fuzzy subsets of the input and 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;

[0087] Step S213. Divide the fuzzy universe of discourse of the input and output variables into 13 levels, namely: {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}.

[0088] The fuzzification process of the fuzzy PID controller in the inner loop control loop is as follows:

[0089] Step S321. For input variables e2 and ec2, and output variable Δk p2 Δk i2 and Δk d2 The quantization factor is used to transform the discrete set that the fuzzy controller can process. The quantization factor transformation formula is the above formula (3).

[0090] Step S322. Divide the fuzzy subsets of the input and 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;

[0091] Step S323. Divide the fuzzy universe of discourse of the input and output variables into 13 levels, namely: {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}.

[0092] Step S3: Determine the fuzzy rule tables for the inner and outer loop fuzzy inference engines of the fuzzy cascade PID control loop;

[0093] The fuzzy control rule table for the outer loop fuzzy inference engine of the fuzzy cascade PID control loop is defined as follows:

[0094] Table 1 Δk p1 Fuzzy control rule table

[0095]

[0096] Table 2 Δk i1 Fuzzy control rule table

[0097]

[0098] Table 3 Δk d1 Fuzzy control rule table

[0099]

[0100] The fuzzy control rule table for the inner loop fuzzy inference engine of the fuzzy cascade PID control loop is defined as follows:

[0101] Table 4 Δk p2 Fuzzy control rule table

[0102]

[0103] Table 5 Δk i2 Fuzzy control rule table

[0104]

[0105] Table 6 Δk d2 Fuzzy control rule table

[0106]

[0107]

[0108] Step S4: Set the target liquid level height of the pressure buffer tank as the loop input, determine the initial parameters of the inner and outer loop PID controllers of the fuzzy cascade PID control loop, and continuously optimize the PID controller parameters under the action of the fuzzy inference engine, thereby achieving steady flow control of the crude oil processing feed liquid;

[0109] Specifically, determining the initial parameters of the outer loop fuzzy PID controller in the fuzzy cascade PID control loop includes:

[0110] The outer-loop PID controller based on fuzzy control is modified, and the modified formula is as follows:

[0111]

[0112] The initial parameters of the outer-loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters with the parameter increments derived from fuzzy inference:

[0113]

[0114] In equations (4)-(5) above, u1(k) is the output of the PID controller in the outer loop at time k; k p1 (k) is the outer ring scaling factor at time k; k p1 (k-1) is the outer ring scaling factor at the previous time step; Δk p1 The increment of the outer ring proportional coefficient; k i1 (k) represents the outer loop integral coefficient at time k; k i1 (k-1) represents the outer loop integral coefficient at the previous time step; Δk i1 k represents the increment of the outer loop integral coefficients. d1 (k) represents the outer ring differential coefficient at time k; k d1 (k-1) represents the outer ring differential coefficients at the previous time step; Δk d1 The outer loop differential coefficient increment is u1(k); the outer loop PID controller output at time k is e1(k); the outer loop error at time k is e1(k-1); the outer loop error at the previous time is e1(i); and the outer loop error at time i is e1(i), where i∈[0, k].

[0115] Determine the initial parameters of the inner loop fuzzy PID controller in the fuzzy cascade PID control loop, specifically including:

[0116] The inner-loop PID controller based on fuzzy control is modified, and the modified formula is as follows:

[0117]

[0118] The initial parameters of the inner-loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters with the parameter increments derived from fuzzy inference:

[0119]

[0120] In equations (6)-(7) above, u2(k) is the output of the PID controller in the inner loop at time k; k p2 (k) is the inner loop scaling factor at time k; k p2 (k-1) is the inner loop scaling factor of the previous time step; Δk p2 For the increase in the flow ratio coefficient; ki2 (k) represents the inner loop integral coefficient at time k; k i2 (k-1) is the flow integral coefficient of the previous time step; Δk i2 k represents the increment of the inner loop integral coefficients. d2 (k) represents the inner loop differential coefficient at time k; k d2 (k-1) represents the inner loop differential coefficients of the previous time step; Δk d2 u2(k) is the inner loop differential coefficient increment; u2(k) is the output of the inner loop PID controller at time k; e2(k) is the inner loop error at time k; e2(k-1) is the inner loop error at the previous time; e2(i) is the inner loop error at time i, i∈[0,k].

[0121] The crude oil inlet flow stabilization control method based on fuzzy cascade PID in this invention is applicable to the flow stabilization of the inlet pipeline of the crude oil processing buffer device, and meets the safety and stability control requirements of the inlet buffer device.

[0122] Figure 2 shows the flow chart of the crude oil processing system's influent buffer process according to an embodiment of the present invention. It includes three parallel process flow lines. The pressure buffer tanks on each process flow line are controlled by the aforementioned crude oil influent steady flow control method based on fuzzy cascade PID. Figure 3 shows the schematic diagram of the fuzzy cascade PID control loop corresponding to the pressure buffer tanks on each process flow line in Figure 2.

[0123] After applying the method of this invention to the inlet pipeline of the crude oil processing system shown in Figure 2 to stabilize the flow, the instantaneous flow fluctuation of the three pressure buffer tanks before and after adopting this method is shown in Figure 4. The instantaneous flow fluctuation range of the inlet buffer device is significantly reduced, and the instantaneous flow fluctuation amplitude is reduced by an average of about 80%-90%. The corresponding pressure fluctuation amplitude of the inlet buffer tank is reduced by an average of about 65%-75%. The regulation is safe, stable and reliable, and can ensure the automatic operation of the crude oil inlet buffer device on site.

[0124] The above description is merely an embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for steady-flow control of crude oil processing feed fluid based on fuzzy cascade PID, characterized in that, Includes the following steps: Step S1: Determine the control logic of the fuzzy cascade PID control loop; the fuzzy cascade PID control loop includes an outer loop control loop and an inner loop control loop; for the outer loop control loop: the input is the target liquid level of the pressure buffer tank, and the output is the opening degree of the flow control valve for the pressure buffer tank; for the inner loop control loop: the input is the opening degree of the flow control valve for the pressure buffer tank, and the output is the opening degree of the air supply valve for the pressure buffer tank; Step S2: Determine the initial universe of discourse of the input and output parameters of the fuzzy inference engine in the fuzzy cascade PID control loop, and perform fuzzification processing; Step S3: Determine the fuzzy rule tables of the inner and outer loop fuzzy inference engines of the fuzzy cascade PID control loop; Step S4: Set the target liquid level height of the pressure buffer tank as the loop input, determine the initial parameters of the inner and outer loop PID controllers of the fuzzy cascade PID control loop, and continuously optimize the PID controller parameters under the action of the fuzzy inference engine, thereby achieving steady flow control of the crude oil processing feed.

2. The crude oil processing feed flow stabilization control method based on fuzzy cascade PID according to claim 1, characterized in that, In step S2, for the outer loop control loop: the deviation e1 between the real-time liquid level H-(Ob) of the pressure buffer tank and the target liquid level HO, and the rate of change of the outer loop deviation ec1 are used as the input values ​​of the outer loop PID fuzzy inference engine. Increment Δk of the outer ring proportional coefficient p1 Increment Δk of the outer loop integral coefficient i1 Increment Δk of the outer ring differential coefficient d1 As the output value of the outer-loop PID fuzzy inference engine; the fundamental universe of discourse for error e1 is [-0.02, 0.02], and the fundamental universe of discourse for the rate of change of error ec1 is [-0.1, 0.1]; let the fundamental universe of discourse for the increment of pressure change be [-10%, 10%]; according to the adjustment law of the outer-loop PID, take Δk p1 The fundamental domain of discourse is [-50, 50], Δk i1 The fundamental domain of discourse is [-25, 25], Δk d1 The fundamental universe of discourse is [-10, 10]; the output of the PID controller in the outer loop is: u1 = k p1 ×e1(t)+k i1 ×∫[0,t]e1(t)dt+k d1 In equation (1) above, u1 is the output of the PID controller in the outer loop; k p1 e1(t) is the outer loop proportionality coefficient; e1(t) is the outer loop error at time t; k i1 k represents the outer loop integral coefficient. d1 is the outer ring differential coefficient.

3. The crude oil processing feed flow stabilization control method based on fuzzy cascade PID according to claim 2, characterized in that, In step S2, for the inner loop control loop: the deviation e2 between the opening degree F-(Ob) of the inlet pressure buffer tank flow control valve and the target control valve opening FO, and the rate of change of the inner loop deviation ec2 are used as the input values ​​of the inner loop PID fuzzy inference engine; the inner loop proportional coefficient increment Δk p2 Increment Δk of the inner loop integral coefficient i2 Increment of the inner loop differential coefficient Δk d2 As the output value of the inner-loop PID fuzzy inference engine; the fundamental universe of discourse for error e2 is [-10, 10], and the fundamental universe of discourse for the rate of change of error ec2 is [-0.2, 0.2]; let the fundamental universe of discourse for the increment of flow change be [-2%, 2%]; according to the adjustment law of the inner-loop PID, take Δk p2 The fundamental domains are [-20, 20] and Δk. i2 The fundamental domain of discourse is [-10, 10], Δk d2 The fundamental universe of discourse is [-4,4]; the output of the PID controller in the inner loop is: u2 = k p2 ×e2(t)+k i2 ×∫[0,t]e2(t)dt+k d2 In equation (2) above, u2 is the output of the PID controller in the inner loop; k p2 e2(t) is the inner loop proportionality coefficient; e2(t) is the inner loop error at time t; k i2 k represents the inner loop integral coefficient. d2 is the inner loop differential coefficient.

4. The crude oil processing feed flow stabilization control method based on fuzzy cascade PID according to claim 3, characterized in that, In step S2, the fuzzification process specifically includes: fuzzification of the fuzzy PID controller in the outer loop control loop and fuzzification of the fuzzy PID controller in the inner loop control loop.

5. The crude oil processing feed flow stabilization control method based on fuzzy cascade PID according to claim 4, characterized in that, The fuzzification process of the fuzzy PID controller in the outer loop control loop is as follows: Step S211. For input variables e1 and ec1, and output variable Δk p1 Δk i1 and Δk d1 Step S212. The fuzzy subsets of the input and output variables are divided 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, respectively. Step S313. The fuzzy universes of discourse of the input and output variables are divided into 13 levels: {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}.

6. The crude oil processing feed flow stabilization control method based on fuzzy cascade PID according to claim 4, characterized in that, The fuzzification process of the fuzzy PID controller in the inner loop control loop is as follows: Step S221. For input variables e2 and ec2, and output variable Δk p2 Δk i2 and Δk d2 Step S222. The fuzzy subsets of the input and output variables are divided 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, respectively. Step S223. The fuzzy universes of discourse of the input and output variables are divided into 13 levels: {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}.

7. The crude oil processing feed flow stabilization control method based on fuzzy cascade PID according to claim 5 or 6, characterized in that, The formula for converting quantification factors is: In equation (3) above, k is the quantization factor; n is the absolute value of the discretized universe boundary; a is the minimum value of the basic universe; and b is the maximum value of the basic universe.

8. The crude oil processing feed flow stabilization control method based on fuzzy cascade PID according to claim 1, characterized in that, In step S4, determining the initial parameters of the outer loop fuzzy PID controller of the fuzzy cascade PID control loop specifically includes: correcting the outer loop PID controller based on fuzzy control, with the following correction formula: The initial parameters of the outer-loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters with the parameter increments derived from fuzzy inference: In equations (4)-(5) above, u1(k) is the output of the PID controller in the outer loop at time k; k p1 (k) is the outer ring scaling factor at time k; k p1 (k-1) is the outer ring scaling factor at the previous time step; Δk p1 The increment of the outer ring proportional coefficient; k i1 (k) represents the outer loop integral coefficient at time k; k i1 (k-1) represents the outer loop integral coefficient at the previous time step; Δk i1 k represents the increment of the outer loop integral coefficients. d1 (k) represents the outer ring differential coefficient at time k; k d1 (k-1) represents the outer ring differential coefficients at the previous time step; Δk d1 The outer loop differential coefficient increment is u1(k); the outer loop PID controller output at time k is e1(k); the outer loop error at time k is e1(k-1); the outer loop error at the previous time is e1(i); and the outer loop error at time i is e1(i), where i∈[0, k].

9. The crude oil processing feed flow stabilization control method based on fuzzy cascade PID according to claim 8, characterized in that, In step S4, determining the initial parameters of the inner loop fuzzy PID controller of the fuzzy cascade PID control loop specifically includes: correcting the inner loop PID controller based on fuzzy control, with the following correction formula: The initial parameters of the inner-loop fuzzy PID controller are formed by weighting the current corrected PID controller parameters with the parameter increments derived from fuzzy inference: In equations (6)-(7) above, u2(k) is the output of the PID controller in the inner loop at time k; k p2 (k) is the inner loop scaling factor at time k; k p2 (k-1) is the inner loop scaling factor of the previous time step; Δk p2 For the increase in the flow ratio coefficient; k i2 (k) represents the inner loop integral coefficient at time k; k i2 (k-1) is the flow integral coefficient of the previous time step; Δk i2 k represents the increment of the inner loop integral coefficients. d2 (k) represents the inner loop differential coefficient at time k; k d2 (k-1) represents the inner loop differential coefficients of the previous time step; Δk d2 u2(k) is the inner loop differential coefficient increment; u2(k) is the output of the inner loop PID controller at time k; e2(k) is the inner loop error at time k; e2(k-1) is the inner loop error at the previous time; e2(i) is the inner loop error at time i, i∈[0,k].

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