PID pressure controller optimization method

Abstract

The invention relates to the technical field of pressure controller optimization methods, provides a PID (Proportion Integration Differentiation) pressure controller optimization method, and solves the problem of pressure fluctuation in a methanol supply system. The method comprises the following steps: S100, establishing a process dynamic model of the marine methanol fuel supply system, and determining key equipment and operation conditions; s200, constructing a fitness function according to an integral value of an error between a set value SP and a process value PV of a PID pressure controller of the methanol fuel supply system along with time; s300, taking a proportionality coefficient and an integral coefficient of a PID pressure controller of the methanol fuel supply system as optimization variables, and forming a search space of two dimensions in a particle swarm algorithm; s400, performing multi-working-condition dynamic simulation on the methanol supply system process, and optimizing the performance of a PID pressure controller of the marine methanol fuel supply system under different working conditions by using a particle swarm algorithm; adjusting controller parameters through a multi-objective optimization algorithm; and S500, adjustment is carried out based on an actual application scene, pressure fluctuation is reduced, and the energy efficiency and the system stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pressure controller optimization method, in particular, to a PID pressure controller optimization method for a methanol fuel supply system of a ship. BACKGROUND

[0002] In recent years, the field of ships has been committed to using clean fuel to replace traditional high-pollution fuel. Methanol has a unique advantage of normal-temperature storage among many clean fuels, and its application in the field of ships is becoming more and more widespread. During the operation of a methanol-powered ship, when the operating conditions of the ship change, the load of the engine will change, the supply flow will change, and the system pressure will fluctuate, which will disturb the stable operation of the engine.

[0003] Therefore, to ensure the stable navigation of a methanol-powered ship, the performance of the methanol supply system determines the stable and reliable operation of the engine. In the prior art, the control of the system pressure is usually completed by a PID controller. The PID controller resists the pressure fluctuation caused by the flow fluctuation by automatically adjusting the speed of the fuel pump in the supply system. The optimization of the parameters of the PID controller is the best method to improve the control effect. In order to optimize the pressure control performance of the methanol supply system and solve the problem of pressure fluctuation during operation, the control parameters of the PID controller can be optimized by using an optimization algorithm to improve the stability of the system pressure.

[0004] The above-mentioned PID (Proportional-Integral-Derivative) control is a linear control composed of a proportional unit, an integral unit and a differential unit. It forms a control amount by linear combination of the proportional , integral and differential of the deviation between the set value SP and the process value PV, and controls the controlled object. The mathematical expression of the PID control is as follows: ; Therefore, the performance of the PID controller depends on the setting of the three control parameters of the proportional coefficient , the integral coefficient and the differential coefficient . At present, the control parameters of the equipment with the PID controller are fixedly set when the equipment is shipped, which results in great fluctuation of the performance of the PID controller in some scenes. Especially in the condition of the methanol supply system which is operated in multiple conditions, the PID parameters need to be adjusted and optimized for different conditions to ensure the continuous and stable operation of the methanol supply system. SUMMARY

[0005] ​One of the purposes of the present application is to provide a PID pressure controller optimization method to solve the problem of pressure fluctuation in the methanol supply system.

[0006] The technical solutions of the present application are as follows: A PID pressure controller optimization method, comprising the following steps: S100: Establish a dynamic model of the marine methanol fuel supply system process, and clearly define the key equipment and operating conditions; S200: The integral value of the error of the set value SP and the process value PV of the PID pressure controller of the methanol fuel supply system with respect to time is constructed to build a fitness function for evaluating the stability and response performance of the system; S300: The proportional coefficient and the integral coefficient of the PID pressure controller of the methanol fuel supply system are taken as optimization variables to form the search space of two dimensions in the particle swarm algorithm; S400: Perform dynamic simulation of the methanol supply system process under multiple working conditions, and use the particle swarm algorithm to optimize the PID pressure controller performance of the marine methanol fuel supply system under different working conditions according to the optimization variables and the fitness function; Adjust the controller parameters through a multi-objective optimization algorithm to ensure smooth operation of the system under different loads and speed changes; S500: Obtain the optimal PID pressure controller parameters, and adjust them based on the actual application scenario to reduce pressure fluctuations and improve energy efficiency and system stability.

[0007] Further, the fitness function in step S200 is as follows: ; Wherein, represents the current deviation, represents the start time of simulation, represents the end time of simulation.

[0008] Further, the particle swarm algorithm optimization in step S400 uses Matlab software, and connects the simulation software Aspen HYSYS and Matlab to realize real-time data interaction between Aspen HYSYS and Matlab, and the steps are as follows: S410: Initialize the parameters of the particle swarm algorithm; S420: Randomly initialize the particle swarm; S430: Calculate the fitness value corresponding to each particle; S440: Find the individual and group optimal values; S450: Update the speed and position of the example; S460: Determine whether the termination condition is met; S470: if the termination condition is met, output the optimization result; If the termination condition is not met, return to step S420.

[0009] Further, step S410 comprises: S411: open the HYSYS specific file through Matlab software; S412: set the PID controller parameter value in HYSYS in the Matlab software; S413: start the HYSYS dynamic simulation; S414: judge whether the simulation is ended; S415: if the simulation is not ended, stop; If the simulation is ended, Matlab reads the HYSYS error integral value; S416: close the HYSYS file and do not need to save; S417: stop and enter step S420.

[0010] Further, the constraint condition in step S415 is as follows: Take the end time of the integrator set in the simulation software Aspen HYSYS as the constraint condition, if the particle parameter causes the system to terminate the simulation in advance, use the penalty function to eliminate the population and the objective function value which ends the simulation in advance due to unreasonable parameters provided by the particle swarm algorithm. And

[0011] Further, the penalty function formula is: ; Wherein, is the time of simulation end, is the objective function value after penalty.

[0012] Further, the multiple working conditions in step S400 comprise several variable load working conditions, such as acceleration, deceleration, emergency braking and cruising.

[0013] The beneficial effects of the present application are: 1. The simulation method is verified, and the particle swarm algorithm (PSO) is used to optimize the control parameters of the PID controller, so as to improve the stability and response performance of the system pressure regulation.

[0014] ‌2. The tedious process of traditional manual setting can be avoided, and the control system regulation ability and response speed can be improved.

[0015] 3. The optimization model established has strong universality and expansibility, can be flexibly adapted according to different objective functions and variable dimensions, so as to meet the design requirements of various marine fuel system control strategies.​ Attached Figure Description

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0017] Figure 1 This is a flowchart of Example 1; Figure 2 This is a schematic diagram of the process flow simulation for Example 1; Figure 3 This is a logical framework diagram of the optimization process in Example 1.

[0018] Figure 2 middle: 0: Methanol at atmospheric pressure in the fuel tank; 1: Error integral value; 2: Centrifugal pump outlet logistics 3: Heat exchanger tube-side outlet logistics 4: Heat exchanger shell-side outlet logistics 5: Flow of materials at the heat exchanger shell-side inlet 6: Input flow rate without reflux before the centrifugal pump 7: Flow rate after the throttle valve of the centrifugal pump 8: Flow path after the throttle valve after the heat exchanger 9: The material flowing back to the centrifugal pump inlet via the reflux valve after the heat exchanger. 10: Logistics before throttling at the main unit 11: Centrifugal pump inlet total flow 12: Flow after reflux valve P-100: A centrifugal pump for pressurizing methanol; E-100: Heat exchanger for methanol heating; Q-100: Centrifugal pump power MIX-100: Rectifier TEE-100: Diverter VLV-10: Throttling valve from heat exchanger outlet to main unit VLV-100: Throttling valve before initial methanol inlet to the centrifugal pump; VLV-101: Throttling valve at the centrifugal pump outlet. VLV-102: Heat exchanger outlet throttle valve VLV-103: Return valve on the return line; OUTLET: Methanol at the main unit inlet; PIC-100: Pressure controller, connected to the inverter before the outlet and centrifugal pump; TRF-1: Frequency converter; TRF-2: Error Integrator; SPRDSHT-1: A spreadsheet for transferring data with Matlab. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 like Figures 1 to 3 As shown, this embodiment provides a PID pressure controller optimization method, including the following steps: S100: Establish a dynamic process model for the marine methanol fuel supply system and clarify the key equipment and operating conditions. In step S100, the dynamic process model of the methanol fuel supply system mainly consists of methanol inlet flow, methanol outlet flow, fuel pump, fuel pump frequency converter, valves, and pressure controller. The process variable source of the pressure controller is the pressure at the methanol outlet. The pressure setpoint SP (Set point) is given according to the corresponding operating conditions. The output target object is the fuel pump frequency converter. The system pressure is controlled by adjusting the speed of the centrifugal pump through the frequency converter. The load change of the host machine can be simulated by defining the flow rate change function at the methanol outlet. Different operating conditions can be tested by changing the function.

[0021] S200: The fitness function is constructed by integrating the error of the set point (SP) and process value (PV) of the PID pressure controller of the methanol fuel supply system over time, in order to evaluate the system stability and response performance. The fitness function is as follows: ; in, Indicates the current deviation. Indicates the simulation start time. This indicates the simulation end time.

[0022] S300: Proportional coefficient of the PID pressure controller for the methanol fuel supply system. and integral coefficient As optimization variables, they constitute the two-dimensional search space in the particle swarm optimization algorithm; The objective function is the integral of the difference between the setpoint and the actual value of the PID pressure controller in the methanol supply system over time. The smaller the integral, the better the pressure control effect corresponding to the PID controller parameters set by the particle swarm optimization algorithm.

[0023] Optimization variable scale factor And integral coefficient The upper and lower limits of the above are as follows:

[0024] S400: Dynamic simulation of the methanol supply system process under multiple working conditions, including but not limited to acceleration, deceleration, emergency braking, cruising and several variable load conditions; According to the optimization variable (scale factor And integral coefficient ) and the fitness function, the PID pressure controller performance of the marine methanol fuel supply system under different working conditions is optimized by using the particle swarm algorithm; At the same time, through the multi-objective optimization algorithm to adjust the controller parameters, ensure the smooth running of the system under different load and speed change.

[0025] By initializing a number of particles, each particle represents a set of PID parameters, and the system error integral value returned by Aspen HYSYS is used as the particle fitness. The particle updates its speed and position according to its historical optimal position and the global optimal position of the group, and iterates the parameters. Set the maximum number of iterations as the termination condition, and search for the optimal solution.

[0026] The particle swarm optimization algorithm described above uses Matlab software, and the steps are as follows: S410: Parameter initialization of particle swarm algorithm.

[0027] Specifically, step S410 includes: S411: Open the HYSYS specific file through the Matlab software; S412: Set the PID controller parameter value in HYSYS in the Matlab software; S413: Start HYSYS dynamic simulation; S414: Determine whether the simulation is finished; Specifically, the integrator integration time, i.e. the simulation duration, needs to be set in the dynamic model of HYSYS. According to different working conditions, set the simulation duration and termination time, and when the set termination time is reached, the integrator stops, i.e. the simulation is finished; If the given parameters are unreasonable or the system fails during the HYSYS model calculation, the simulation will be terminated early, i.e. it is determined as a fault group, and the corresponding data group will be discarded through the penalty function of step S415.

[0028] S415: If the simulation is not finished, stop; If the simulation is finished, Matlab reads the HYSYS error integral value; The constraint conditions in the above step S415 are as follows: With the end time of the integrator setting in the simulation software Aspen HYSYS as a constraint condition, if the particle parameters cause the system to terminate the simulation in advance, the penalty function is used to eliminate the population and the objective function value that terminates the simulation in advance due to unreasonable parameters provided by the particle swarm algorithm. and The population and the objective function value that terminates the simulation in advance are eliminated.

[0029] Specifically, the penalty function formula is: ; Among them, is the time when the simulation ends, is the objective function value after the penalty.

[0030] S416: Close the HYSYS file and do not need to save; S417: Stop and enter step S420.

[0031] S420: Randomly initialize the particle swarm; S430: Calculate the fitness value corresponding to each particle; S440: Find the individual and group optimal values; S450: Update the speed and position of the example; S460: Determine whether the termination condition is met; Specifically, the particle swarm algorithm will determine the value range of the optimization variable according to the operator setting, and finally stop the process when the minimum error of the fitness value is within the specified value through the basic setting of the particle swarm algorithm, that is, the termination condition is met.

[0032] S470: If the termination condition is met, output the optimization result; If the termination condition is not met, return to step S420.

[0033] S500: Obtain the optimal PID pressure controller parameter, and adjust based on the actual application scene to reduce pressure fluctuation and improve energy efficiency and system stability.

[0034] Figure 2 is the source model established in Aspen HYSYS, mainly used to show the optimized target methanol supply system process model, PIC-100 is a pressure controller, TRF-1 is a centrifugal pump frequency converter, P-100 is a centrifugal pump controlled by the controller, and OUTLET is a methanol stream to the engine. PIC-100 monitors the pressure of the OUTLET stream, transmits the control signal to the centrifugal pump through TRF-1 to complete the pressure control of the system, and the present application optimizes the control parameters of PIC-100 to realize the optimization of the control effect, reflecting the compatibility of the optimization algorithm and the methanol supply system dynamic model.

[0035] Example 2

[0036] The specific use method of the above step S400 is as follows: In use, the simulation software Aspen HYSYS and Matlab are connected to realize real-time data interaction between Aspen HYSYS and Matlab, and Matlab can read and assign values to Aspen HYSYS.

[0037] The handle of Matlab calling Aspen HYSYS is as follows: Hysys = actxserver('Hysys.Application.V11.0') The handle of obtaining folder information is as follows: [stat, mess] = fileattrib('F:\1\2\') The handle of activating HYSYS simulation is as follows: simcase.invoke('Activate') The handle of Matlab connecting the spreadsheet of Aspen HYSYS is as follows: spread = hyspread(hy, 'SPRDSHT-1') The handle of setting the PID controller parameters of Aspen HYSYS by controlling the spreadsheet is as follows: c = hycell(spread, {'A2', 'A3'}) hyset(c{1}, x(1)) hyset(c{2}, x(2)) The handle of Matlab calling the particle swarm algorithm is as follows: [x, y] = particleswarm(@TEST, 2, lb, ub, options); The handle of the fitness value of the particle swarm algorithm through the calculated value in the Aspen HYSYS spreadsheet is as follows: cellB5 = hycell(spread, {'B5'}) The handle of the penalty function of the particle swarm algorithm is as follows: t = HySolver.Integrator.CurrentTime.value; if HySolver.Integrator.CurrentTime.value<50 y = (51 - t) * 1000.

[0038] The above merely provides the preferred embodiments of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A PID pressure controller optimization method, characterized in that, Includes the following steps: S100: Establish a dynamic process model for the marine methanol fuel supply system and clarify the key equipment and operating conditions. S200: The fitness function is constructed by integrating the error of the setpoint SP and process value PV of the PID pressure controller of the methanol fuel supply system over time, in order to evaluate the system stability and response performance. S300: Proportional coefficient of the PID pressure controller for the methanol fuel supply system. and integral coefficient As optimization variables, they constitute the two-dimensional search space in the particle swarm optimization algorithm; S4 00: Dynamic simulation of the methanol supply system process under multiple operating conditions was performed. Based on the optimization variables and fitness function, the performance of the PID pressure controller of the marine methanol fuel supply system under different operating conditions was optimized using the particle swarm optimization algorithm. The controller parameters are adjusted by a multi-objective optimization algorithm to ensure the smooth operation of the system under different load and speed changes; S500: Obtains optimal PID pressure controller parameters and adjusts them based on actual application scenarios to reduce pressure fluctuations and improve energy efficiency and system stability.

2. The PID pressure controller optimization method according to claim 1, characterized in that, The fitness function in step S200 is as follows: ； in, Indicates the current deviation. Indicates the simulation start time. Indicates the simulation end time.

3. The PID pressure controller optimization method according to claim 2, characterized in that, The particle swarm optimization in step S400 uses Matlab software, and the simulation software Aspen HYSYS is connected to Matlab to achieve real-time data interaction between Aspen HYSYS and Matlab. The steps are as follows: S410: Particle swarm optimization algorithm parameter initialization; S420: Randomly initialize the particle swarm; S430: Calculate the fitness value for each particle; S440: Find the optimal values ​​for individuals and groups; S450: Update the speed and location of the example; S460: Determine whether the termination condition is met; S470: If the termination condition is met, output the optimization result; If the termination condition is not met, return to step S420.

4. The PID pressure controller optimization method according to claim 3, characterized in that, Step S410 includes: S411: Open a specific HYSYS file using Matlab software; S412: Set the PID controller parameter values ​​in HYSYS in Matlab software; S413: Start HYSYS dynamic simulation; S414: Determine if the simulation has ended; S415: Stop if the simulation does not end; If the simulation ends, Matlab will read the HYSYS error integral value; S416: Close the HYSYS file without saving; S417: Stop and proceed to step S420.

5. The PID pressure controller optimization method according to claim 4, characterized in that, The constraints in step S415 are as follows: Using the integrator's end time setting in the Aspen HYSYS simulation software as a constraint, if particle parameters cause the system to terminate the simulation prematurely, a penalty function is used to compensate for the unreasonable parameters provided by the particle swarm optimization algorithm. and The population and objective function value that end the simulation early are eliminated.

6. The PID pressure controller optimization method according to claim 5, characterized in that, The penalty function formula is: ； in, The simulation ends at time. This is the value of the objective function after penalty.

7. The PID pressure controller optimization method according to claim 1, characterized in that, The multiple operating conditions in step S400 include several variable load operating conditions, such as acceleration, deceleration, emergency braking, and cruise.

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