CN113176767B - Propylene recovery control system - Google Patents
Propylene recovery control system Download PDFInfo
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- CN113176767B CN113176767B CN202110479750.1A CN202110479750A CN113176767B CN 113176767 B CN113176767 B CN 113176767B CN 202110479750 A CN202110479750 A CN 202110479750A CN 113176767 B CN113176767 B CN 113176767B
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41845—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by system universality, reconfigurability, modularity
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- G05B2219/33—Director till display
- G05B2219/33273—DCS distributed, decentralised controlsystem, multiprocessor
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention relates to a propylene recovery control system and a propylene recovery control method. The propylene recovery control system comprises a data memory, a propylene recovery observer and a multivariable model predictive controller. The data memory is used for acquiring propylene recovery data; the propylene recovery observer is connected with the data storage and is used for determining the liquid level rate in the propylene recovery system according to the propylene recovery data; the multivariable model pre-estimation controller is respectively connected with the data memory and the propylene recovery observer, is used for generating operation variables according to the liquid level rate and the propylene recovery data, and is used for carrying out constraint processing on the operation variables by adopting a single-value pre-estimation control algorithm. By adopting the multivariable model pre-estimation controller implanted with the single-value pre-estimation adsorption control method, the generated operating variables can be restrained in real time, so that the problem that the temperature, pressure, liquid level and concentration parameters influence each other in the prior art is solved, and the control effect of the traditional PID of the propylene recovery system is further improved.
Description
Technical Field
The invention relates to the field of control of propylene recovery systems, in particular to a propylene recovery control system.
Background
In the petrochemical production process, the application of a proportional-integral-derivative control (PID control for short) strategy is relatively wide, and more than 70 percent of automatic control loops adopt the PID control strategy. The PID control has the advantages of simple structure and relatively convenient parameter adjustment. However, the conventional PID controller cannot solve the problems of nonlinearity, large hysteresis, multivariable, strong coupling and the like, and meanwhile, the adaptability to the operation condition is poor;
advanced control is a generic term for control strategies that differ from conventional single-loop control and have better control effectiveness than conventional PID control. Advanced control is designed to address the problem of complex multivariable control that does not perform well, or even control, using conventional control. By implementing advanced control, the dynamic control performance of the process can be improved, the fluctuation of process variables can be reduced, the process variables are closer to an optimization target, the production device is pushed closer to a constraint boundary, and finally the stability and the safety of the operation of the device are enhanced.
In the global polypropylene production process, the Spheripol process of Basell company is dominant, and the polypropylene yield accounts for about 36.8% of the global total amount, and the Spheripol process of Dow Chemical company, the Innovene process of BP company, the Novolen process of NTH company, the Hypol kettle type body process of Mitsui petrochemical company and the like are followed. The Unipol process polypropylene recovery system is a very important unit of a polypropylene device, and the successful application of advanced control on the Unipol process polypropylene recovery system is not reported at home and abroad.
Traditional PID control is to setting proportion (P), integral (I), three parameter of differentiation (D) to PID control circuit in the DCS system, reduce the fluctuation range of parameters such as device temperature, pressure, flow, liquid level and concentration to a certain extent, but PID control belongs to single-in single-out control, can't overall consider the mutual influence between a plurality of variables, just adjust when the deviation takes place, the interference killing feature is limited, to some nonlinearity, big hysteresis, multivariable, the production process of strong coupling simultaneously, control effect is unsatisfactory.
The propylene recovery system of the Unipol polypropylene production process is a typical continuous flow industrial process, and belongs to a typical multi-parameter coordination system. Among these, heat exchanger E-5231 temperature, de-separator C-5236 secondary temperature, separator C-5204 pressure, separator C-5204 liquid level, and nitrogen concentration are typical multivariable control issues. In the process of realizing automatic operation by adopting a Distributed Control System (DCS), PID control is a main control strategy, but when a plurality of controlled variables CV need to meet certain control precision requirements at the same time, because the PID control is a single-loop controller, the algorithm of the PID control cannot well meet the control requirements, the control precision is reduced, the process requirements cannot be met or are difficult to meet, at the moment, the operator needs to frequently carry out manual intervention, and the labor intensity of the operator is obviously increased.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a propylene recovery control system.
In order to achieve the purpose, the invention provides the following scheme:
a propylene recovery control system comprising:
the data memory is used for acquiring propylene recovery data; the propylene recovery data includes: temperature, pressure, flow, liquid level height, density, and concentration;
the propylene recovery observer is connected with the data storage and used for determining the liquid level rate in the propylene recovery system according to the propylene recovery data;
and the multivariable model pre-estimation controller is respectively connected with the data memory and the propylene recovery observer, is used for generating an operation variable according to the liquid level rate and the propylene recovery data, and is used for carrying out constraint processing on the operation variable by adopting a single-value pre-estimation control algorithm.
Preferably, the method further comprises the following steps:
the key parameter optimizer is respectively connected with the multivariate model pre-estimation controller and the data memory and is used for optimizing key parameters; the key parameters include: optimizing direction, optimizing coefficient and optimizing period.
Preferably, the data storage comprises:
the data acquisition module is used for acquiring the propylene recovery data in real time;
the data filtering module is connected with the data acquisition module and is used for filtering the propylene recovery data;
the range conversion module is connected with the data acquisition module and is used for carrying out range conversion processing on the propylene recovery data;
the historical data storage module is respectively connected with the data filtering module and the range conversion module and is used for storing the filtered propylene recovery data and the propylene recovery data subjected to range conversion in real time;
and the data statistics query module is respectively connected with the historical data storage module and the propylene recovery observer and is used for transmitting the processed propylene recovery data to the propylene recovery observer.
Preferably, the multivariate model predictive controller comprises:
the operation variable generation module is connected with the data storage and the propylene recovery observer and is used for generating operation variables by adopting a VSUPCC controller algorithm according to the liquid level rate and the propylene recovery data;
and the constraint processing module is connected with the operating variable generation module and is used for carrying out constraint processing on the operating variables by adopting a single-value estimation control algorithm.
Preferably, the constraint processing module includes:
an adsorption region acquisition unit for acquiring a preset adsorption region;
the single-value pre-estimation control unit is connected with the adsorption area acquisition unit and is used for judging whether the contact constraint of the operation variable is carried out according to the preset adsorption area; when the operation variable is located in the preset adsorption area, the constraint state of the operation variable is unchanged; and when the operation variable is positioned outside the preset adsorption area and is a specific distance away from the boundary upper limit or the boundary lower limit of the preset adsorption area, releasing the constraint state of the operation variable.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the propylene recovery control system provided by the invention comprises a data memory, a propylene recovery observer and a multivariable model estimation controller. By adopting the multivariable model estimation controller implanted with the single-value estimation adsorption control method, the generated operating variables can be restrained in real time, so that the problem that the temperature, pressure, liquid level and concentration parameters influence each other in the prior art is solved, the problem that the traditional PID control effect of the propylene recovery system is not good is well solved, and the control effect of the propylene recovery system is further improved.
Corresponding to the propylene recovery control system provided above, the invention also provides the following control method:
a propylene recovery control process comprising:
acquiring propylene recovery data; the propylene recovery data includes: temperature, pressure, flow, liquid level height, density, and concentration;
determining a liquid level rate in a propylene recovery system from the propylene recovery data;
and generating an operation variable according to the liquid level rate and the propylene recovery data, and performing constraint processing on the operation variable by adopting a single-value estimation control algorithm.
Preferably, the method further comprises the following steps:
optimizing the key parameters; the key parameters include: optimizing direction, optimizing coefficient and optimizing period.
Preferably, the acquiring propylene recovery data further comprises:
processing the propylene recovery data; the processing comprises filtering processing and range conversion processing;
and storing the filtered propylene recovery data and the propylene recovery data subjected to range conversion in real time.
Preferably, generating the operating variables from the liquid level rate and the propylene recovery data comprises:
the liquid level rate and the propylene recovery data are generated into manipulated variables using VSUPCC controller algorithms.
Preferably, the constraint processing of the manipulated variables by using the single-valued predictive control algorithm includes:
acquiring a preset adsorption area;
judging whether contact constraint is carried out on the operation variable according to the preset adsorption area; when the operation variable is located in the preset adsorption area, the constraint state of the operation variable is unchanged; and when the operation variable is positioned outside the preset adsorption area and is a specific distance away from the boundary upper limit or the boundary lower limit of the preset adsorption area, releasing the constraint state of the operation variable.
Since the technical effect achieved by the propylene recovery control method provided by the invention is the same as the technical purpose achieved by the propylene recovery control system provided by the invention, the details are not repeated herein.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic structural view of a propylene recovery control system according to the present invention;
FIG. 2 is a schematic diagram of a data memory according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating the relationship between the manipulated variables and the predetermined adsorption area according to an embodiment of the present invention; wherein, fig. 3a is a diagram of the upper limit position of the manipulated variable within the preset adsorption region; FIG. 3b is a position diagram of the manipulated variable located outside the predetermined adsorption zone and at a specified distance from the upper limit thereof; FIG. 3c is a graph of the position of the manipulated variable out of the predetermined adsorption zone;
FIG. 4 is a graph showing the operational results of the main parameters of the non-APC operation of the propylene recovery system according to the embodiment of the present invention;
FIG. 5 is a graph showing the operational results of the main parameters of APC used in the propylene recovery system according to the embodiment of the present invention;
FIG. 6 is a block diagram of a second order process with skew according to an embodiment of the present invention;
FIG. 7 is a graph illustrating comparison results of tracking of a given value during model matching according to an embodiment of the present invention;
FIG. 8 is a graph illustrating the comparative results of the interference test when the models provided by the embodiments of the present invention are matched;
FIG. 9 is a graph illustrating comparative results of tracking of a given value when a model is mismatched according to an embodiment of the present invention;
FIG. 10 is a graph illustrating comparative results of interference testing during model mismatch, according to an embodiment of the present invention;
FIG. 11 is a flow chart of a propylene recovery control method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a propylene recovery control system, which can well solve the problem of poor control effect of the traditional PID (proportion integration differentiation) of a propylene recovery system and further improve the control effect of the propylene recovery system.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the propylene recovery control system provided by the present invention includes: a data memory 1, a propylene recovery observer 2 and a multivariable model predictive controller 3.
The data memory 1 is used for acquiring propylene recovery data. Propylene recovery data included: temperature, pressure, flow, level height, density, and concentration.
A propylene recovery observer 2 is connected to the data storage 1 and is mainly used for determining the liquid level rate in the propylene recovery system from the propylene recovery data. The propylene recovery observer 2 observes and calculates the speed of the liquid level 300LICA52043.PV of the separation tank C-5204 through a mechanism model, and the obtained calculation result is used by the multivariable model estimation controller 3 and is provided for operators to refer.
The liquid level rate is calculated as follows:
in the formula, Lv-liquid level rate (Kg/H), H-liquid level height, rho-density, S-liquid level range, A-cross sectional area of a separation tank, k-current time, Ts-calculation period, N, L-adjustable parameters (positive integer). i is a positive integer, the value is from 1, and L is taken as the maximum.
The multivariable model pre-estimation controller 3 is respectively connected with the data memory 1 and the propylene recovery observer 2, is used for generating operation variables according to the liquid level rate and the propylene recovery data, and is used for carrying out constraint processing on the operation variables by adopting a single-value pre-estimation control algorithm.
Further, in order to facilitate data interaction between the propylene recovery control system provided by the present invention and the DCS distributed control system of the propylene recovery system, a real-time database is embedded in the data storage 1 provided by the present invention. The real-time database is responsible for real-time communication with the DCS so as to implement data input and output and fault diagnosis, is used for acquiring real-time data required by calculation of the propylene recovery system from the DCS and outputting results of the observer, the controller and the optimizer to the DCS, mainly refers to process parameters such as temperature, pressure, flow, liquid level and concentration, and the main parameters are shown in a table 1:
TABLE 1
The present invention performs first order filtering for the pressures and flows in table 1 above, with filter coefficients typically set in the range of 0.6, 0.95.
Based on this, as shown in fig. 2, the data storage 1 provided in the above of the present invention includes: the device comprises a data acquisition module 11, a data filtering module 12, a range conversion module 13, a historical data storage module 14 and a data statistics query module 15.
The data acquisition module 11 is used for acquiring propylene recovery data in real time.
The data filtering module 12 is connected to the data acquisition module and is mainly used for filtering the propylene recovery data.
The range conversion module 13 is connected with the data acquisition module and is mainly used for performing range conversion processing on propylene recovery data.
The historical data storage module 14 is connected to the data filtering module and the range conversion module, and is mainly used for storing the filtered propylene recovery data and the propylene recovery data after range conversion in real time.
The data statistics query module 15 is respectively connected with the historical data storage module and the propylene recovery observer, and is mainly used for transmitting the processed propylene recovery data to the propylene recovery observer.
Further, in order to solve the problem of mutual influence of temperature, pressure, liquid level and concentration parameters in the prior art, the invention provides a process for generating control variables by using a VSUPCC controller algorithm in the multivariate model predictive controller 3, please refer to the invention patent 99105546.2 general multivariate model predictive coordination control method.
Specifically, the multivariate model predictive controller 3 includes: the device comprises an operation variable generation module and a constraint processing module.
The operation variable generation module is connected with the data memory 1 and the propylene recovery observer 2 and is used for generating operation variables by adopting VSUPCC controller algorithm liquid level rate and propylene recovery data.
And the constraint processing module is connected with the operation variable generation module and is used for carrying out constraint processing on the operation variables by adopting a single-value estimation control algorithm.
The state variables of the multivariable model predictive controller 3 are shown in table 2:
TABLE 2 state variable table (SV) of recovery system controller
| Sequence of steps | Name (R) | Bit number | Description of the |
| Number | |||
| 1 | Heat exchanger E5231 temperature | 300TICA52042.PV | E5231 temperature as state, incorporated into |
| 2 | Separator C5204 pressure | 300PICA52041.PV | C5204 pressure formingState, |
| 3 | Separator C5204 liquid level | 300LICA52043.PV | C5204 liquid level making state, and bringing into |
| 4 | Temperature of propylene recovery | 300TIA52311.PV | The temperature of the recycled propylene is taken as the state and brought into the |
| 5 | E5231 De-C5236 temperature | 300TIC52314.PV | Removing C5236 temperature as state and introducing into controller model |
| 6 | Secondary temperature of de-C5236 | 300TICA52312.PV | Removing the C5236 secondary temperature as the state and bringing the state into the controller model |
| 7 | Concentration of N2 in the recycle gas | 300AIC400118.PV | The N2 concentration in the circulating gas is taken into a controller model as a state |
| 8 | High pressure of refined nitrogen flow | 300FRC40014.PV | Making high pressure refined nitrogen flow into controller mouldModel (III) |
The operating variables of the multivariable model predictive controller 3 are shown in table 3 below:
TABLE 3 recovery system controller operation variable table (MV)
| Serial number | Name (R) | Number of bits | Description of the |
| 1 | Heat exchanger E5231 temperature setting | TICA52042.SV | Controlling the temperature of the separating tank and keeping the separating tank operating stably |
| 2 | Separator C5204 pressure give | PICA52041.SV | Controlling the pressure of the separator and keeping the pressure of the separator stable |
| 3 | Separator C5204 level assignment | LICA52043.SV | Controlling the liquid level of the separator and keeping the liquid level of the separator stable |
| 4 | High pressure refined nitrogen flow rate setting | FRC40014.SV | By adjusting the flow of refined nitrogen, the concentration of nitrogen is ensured to be stable |
The controlled variables of the multivariable model predictive controller 3 are shown in the following table 4:
TABLE 4 controlled variable table of recovery system Controller (CV)
| Serial number | Name(s) | Number of bits | Description of the |
| 1 | Heat exchanger E5231 temperature | TICA52042.PV | Controlling the temperature of the separating tank and keeping the separating tank operating stably |
| 2 | Secondary temperature of de-C5236 | TICA52312.PV | Controlling the temperature of C5236 secondary stage to keep the temperature stable |
| 3 | Separator C5204 pressure | PICA52041.PV | Controlling the pressure of the separator and keeping the pressure of the separator stable |
| 4 | Separator C5204 liquid level | LICA52043.PV | Controlling the liquid level of the separator and keeping the liquid level of the separator stable |
| 5 | Separator C5204 liquid level Rate | LVC52043.PV | Controlling the liquid level of the separator and keeping the liquid level of the separator stable |
| 6 | Concentration of N2 in the recycle gas | AIC400118.PV | Controlling the nitrogen concentration in the reactor to be stable |
The feedforward variables of the multivariable model predictive controller are shown in table 5 below:
TABLE 5 recovery System controller feedforward variable Table (FV)
| Sequence of steps | Name (R) | Number of bits | Description of the |
| Number | |||
| 1 | Boundary zone nitrogen bus temperature | 300TIA98001.PV | Nitrogen bus temperature is used as feedforward and is brought into |
| 2 | Buffer tank C5229 inlet pressure | 300PICA52291.PV | C5229 Inlet pressure feed Forward, incorporation into |
| 3 | Cooler E5230 temperature | 300TIA52302.PV | E5230 temperature as feedforward and incorporated into controller model |
The single-value estimation control method is the core of the universal multivariable model estimation coordination control method, and the algorithm is widely applied in recent 20 years, but has limitations. There is a problem in application that when both CV and MV are in the constraint boundary, the control action of the controller output fluctuates up and down due to interference and noise at the moment of the production process that affects the CV estimate. In order to solve the problem, the single-value estimation control method adopted by the invention is essentially a boundary adsorption control method, after the control action, namely the increment (delta MV) of the operation variable is obtained, when the increment (delta MV) of the operation variable is output to the given value of a conventional control loop or an adjusting valve in the actual process, the actual upper limit and the actual lower limit of CV and MV are automatically set to be a small adsorption area, and if the variable is in the adsorption area, the constraint state of the variable is considered to be unchanged. The constraint state is released only after the variable leaves the adsorption zone, so that frequent operation fluctuation of the controller on the constraint boundary is avoided.
Based on the working principle, the constraint processing module provided by the invention comprises: an absorption area acquisition unit and a single value estimation control unit.
The adsorption area acquisition unit is used for acquiring a preset adsorption area.
The single-value pre-estimation control unit is connected with the adsorption area acquisition unit and used for judging whether the contact constraint is carried out on the operation variable according to the preset adsorption area. And when the operation variable is positioned in the preset adsorption area, the constraint state of the operation variable is unchanged. And when the operation variable is positioned outside the preset adsorption area and is a specific distance away from the boundary upper limit or the boundary lower limit of the preset adsorption area, removing the constraint state of the operation variable.
The specific implementation process of the constraint processing module is as follows:
step 1: when the single-value estimation control unit works, it is judged that the operation variable is in the upper limit or the lower limit of the boundary and is restricted, and cannot be adjusted upwards or downwards, but can be adjusted in the opposite direction, that is, the operation variable is in the upper limit and cannot be adjusted upwards any more, and the operation variable is in the lower limit and cannot be adjusted downwards any more, as shown in fig. 3 a.
Step 2: when the production conditions change and the manipulated variable is adjusted in the opposite direction in order to ensure that the manipulated variable is prevented from being adjusted repeatedly on the boundary, the constrained state of the manipulated variable is not released immediately, but is released only when the manipulated variable is apart from the boundary by a certain specific distance (the difference between the solid line and the dotted line at the upper limit position in fig. 3 b) according to the model single-value estimation boundary adsorption control method. The magnitude of this particular distance is automatically scaled based on the range of the particular manipulated variable. That is, the boundary value is contracted in this proportion as if the upper limit (lower limit) actually functioning is lowered (raised) by the manipulated variable adsorber, as shown in fig. 3 b.
And step 3: only if the manipulated variable is out of the adsorption zone (shown in dashed lines in fig. 3 c) can the upper limit constraint allow an upward or lower limit constraint allow a downward adjustment. At which point the boundaries automatically revert to the original settings.
In order to further improve the control effect of the conventional PID of the propylene recovery system, the propylene recovery control system provided by the present invention preferably further comprises: a critical parameter optimizer 4.
The key parameter optimizer 4 is respectively connected with the multivariate model predictive controller 3 and the data memory 1 and is used for optimizing the key parameters. The key parameters include: optimizing direction, optimizing coefficient and optimizing period.
The key parameters used here are mostly controlled variables, and in order to implement real-time optimization of the key parameters, the key parameter optimizer needs to be set as shown in table 6:
TABLE 6 Key parameters real-time optimization table
For example, the present invention sets 3 controlled variables in table 4 as key parameters, including: the temperature of the heat exchanger E-5231, the pressure of the separator C-5204 and the concentration of N2 in the recycle gas, and the other 3 controlled variables were not optimized and are detailed in Table 7.
TABLE 7 recovery System controller Key parameter control Range and optimization
Taking the N2 concentration in the recycle gas in table 7 as an example, the multivariate model predictive controller is used to stably control the N2 concentration in the recycle gas within the control range of [9.5,10.5] to meet the process control requirement for the N2 concentration in the recycle gas. Once the nitrogen concentration is set as the key parameter optimizer, the optimization is performed to the upper control limit of 10.5 (i.e., +1 direction) by high pressure refined nitrogen flow given frc40014.sv optimized once every five minutes for the optimization cycle, according to the settings of table 7.
The advantages of the propylene recovery control system provided by the present invention will be described below using specific application examples.
The data (5/8/00/2020/5/16/00/2020) of 216 hours which are not used by the polypropylene device recovery unit and run stably and intelligently control the APC system (i.e. the propylene recovery control system provided by the invention) are selected, and the operation curve is shown in FIG. 4. It can be seen from fig. 4 that with conventional PID control systems, all controlled variable curves are less smooth.
Data (13/00/6/2020/6/21/24/00/21/2020) of 216 hours after the recovery unit of the polypropylene device operates stably and the APC system (i.e., the propylene recovery control system) is intelligently controlled to be put into service and run-in, and the operating curve is shown in FIG. 5. As can be seen by comparing FIG. 4 with FIG. 5, by adopting the propylene recovery control system provided by the invention, all controlled variable curves are very stable, and the problem of poor control effect of the traditional PID of the propylene recovery system is well solved.
The control effect of the propylene recovery control system according to the present invention will be described below with reference to specific examples.
Example one
The simulation comparison of a single-value pre-estimated boundary adsorption control method with a Dynamic Matrix Control (DMC) calculation method and a PID cascade method is carried out, wherein the overall situation is divided into two cases: model matching and model mismatching, when the parameter is epsilon 1 ＝ε 2 When 0, the model is matched. When parameter epsilon 1 ＝10,ε 2 At 30, the model is mismatched. In each case, both a tracking test for the set point and a test for the external disturbance were performed. And analyzing the advantages of the single-value estimation control through the test result. Typical examples are: a second order process with a time lag.
Shown in FIG. 6 is a second order skew process object, where x 1 、x 2 Representing state variables, u representing manipulated variables, v representing feed forward variables, f 1 Representing a feedforward coefficient, a correlation parameter a 11 ＝1/90(s -1 )＝b 1 ，a 22 ＝1/150(s -1 )＝a 21 ，τ 1 ＝10(s)，τ 2 ＝30(s)
To illustrate the singular value predictionThe control effect of the boundary adsorption control method is compared with two methods, namely a Dynamic Matrix Control (DMC) calculation method and a PID cascade method, wherein the overall method is divided into two cases: model matching and model mismatching, when the parameter is epsilon 1 ＝ε 2 When 0, the model is matched. When parameter ε 1 ＝10,ε 2 At 30, the model is mismatched. In each case, both a tracking test for the set point and a test for the external disturbance were performed. And analyzing the advantages of the single-value estimation control through the test result. The following relevant 4 test results were obtained:
the result is as follows: set point tracking during model matching (set point ε) 1 ＝ε 2 ＝0)
The relevant parameters of the three methods are set as follows:
SPC: the referred estimated time domain P150
DMC: the related estimated time domain P is 80, and the control time domain M is 2
PID cascade: sub-controller related parameters: proportional gain KP is 4 and integral gain KI is 0.04
Main controller related parameters: proportional gain KP is 1.2 and integral gain KI is 0.0144
Wherein, the Matlab programming is realized when SPC and DMC, PID cascade is built by Simulink, and the obtained comparison result is shown in FIG. 7: from the comparison of fig. 7, it can be seen that the tracking of given values of SPC and DMC is better than the tracking of PID cascade when the models match.
And a second result: disturbance testing during model matching (disturbance epsilon) 1 ＝ε 2 ＝0)
The relevant parameters of the three methods are set as follows:
SPC: the referred estimated time domain P-40
DMC: the related estimated time domain P is 40, and the control time domain M is 2
PID cascade: sub-controller related parameters: proportional gain KP is 4 and integral gain KI is 0.04
Main controller related parameters: proportional gain KP is 1.2 and integral gain KI is 0.0144
The results obtained are shown in FIG. 8: from the comparison of fig. 8, it can be seen that SPC has a desirable effect on both rapidity and stability compared to DMC and PID cascades when applied to an applied disturbance and observed back to a given value in the case of model matching.
And a third result: set point tracking during model mismatch (set point ε) 1 ＝10,ε 2 ＝30)
The relevant parameters of the three methods are set as follows:
SPC: the referred estimated time domain P is 100
DMC: the related estimated time domain P is 50, and the control time domain M is 2
PID cascade: sub-controller related parameters: proportional gain KP is 4 and integral gain KI is 0.04
Main controller related parameters: proportional gain KP is 1.2 and integral gain KI is 0.0144
The comparative results obtained are shown in FIG. 9: from the comparison of fig. 9, it can be seen that SPC is superior to DMC and PID cascades when observing given value tracking in case of model mismatch, whether stability or rapidity.
And a fourth result: disturbance testing during model mismatch (disturbance epsilon) 1 ＝ε 2 ＝0)
The relevant parameters of the three methods are set as follows:
SPC: the referred estimated time domain P-80
DMC: the related estimated time domain P is 80, and the control time domain M is 2
PID cascade: sub-controller related parameters: proportional gain KP is 4 and integral gain KI is 0.04
Main controller related parameters: proportional gain KP is 1.2 and integral gain KI is 0.0144
The results obtained are shown in FIG. 10: from the comparison of the graphs, it can be seen that SPC is better than DMC in rapidity and PID cascade in stability when observing the return to the given value given an applied disturbance in case of model mismatch.
In addition, the present invention also provides a propylene recovery control method corresponding to the propylene recovery control system provided above, as shown in fig. 11, the control method including:
step 100: propylene recovery data was obtained. Propylene recovery data included: temperature, pressure, flow, level height, density, and concentration.
Step 101: the liquid level rate in the propylene recovery system is determined from the propylene recovery data.
Step 102: and generating an operation variable according to the liquid level rate and the propylene recovery data, and performing constraint processing on the operation variable by adopting a single-value estimation control algorithm.
Further, in order to provide a control effect, the propylene recovery control method according to the present invention further includes:
and optimizing the key parameters. The key parameters include: optimizing direction, optimizing coefficient and optimizing period.
Further to improve the accuracy of data calculation, after step 100, the method further includes:
and processing propylene recovery data. The processing includes filtering processing and span conversion processing.
And storing the filtered propylene recovery data and the propylene recovery data subjected to range conversion in real time.
Further, the manipulated variable generation process in step 102 is primarily generated using VSUPCC controller algorithm liquid level rate and propylene recovery data.
Further, the step 102 of performing constraint processing on the manipulated variables by using the single-valued predictive control algorithm includes:
and acquiring a preset adsorption area.
And judging whether contact constraint is carried out on the operation variable according to a preset adsorption area. And when the operation variable is positioned in the preset adsorption area, the constraint state of the operation variable is unchanged. And when the operation variable is positioned outside the preset adsorption area and is a specific distance away from the boundary upper limit or the boundary lower limit of the preset adsorption area, removing the constraint state of the operation variable.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The method disclosed by the embodiment corresponds to the system disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the system part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (7)
1. A propylene recovery control system, comprising:
the data memory is used for acquiring propylene recovery data; the propylene recovery data includes: temperature, pressure, flow, liquid level height, density, and concentration;
the propylene recovery observer is connected with the data storage and used for determining the liquid level rate in the propylene recovery system according to the propylene recovery data;
the multivariable model pre-estimation controller is respectively connected with the data memory and the propylene recovery observer, is used for generating an operation variable according to the liquid level rate and the propylene recovery data, and is used for carrying out constraint processing on the operation variable by adopting a single-value pre-estimation control algorithm;
the multivariate model predictive controller comprises:
the operation variable generation module is connected with the data storage and the propylene recovery observer and is used for generating operation variables by adopting a VSUPCC controller algorithm according to the liquid level rate and the propylene recovery data;
the constraint processing module is connected with the operating variable generation module and is used for carrying out constraint processing on the operating variables by adopting a single-value estimation control algorithm;
the constraint processing module includes:
an adsorption area acquisition unit for acquiring a preset adsorption area;
the single-value pre-estimation control unit is connected with the adsorption area acquisition unit and is used for judging whether the constraint on the operation variable is removed or not according to the preset adsorption area; when the operation variable is located in the preset adsorption area, the constraint state of the operation variable is unchanged; and when the operation variable is positioned outside the preset adsorption area and is a specific distance away from the boundary upper limit or the boundary lower limit of the preset adsorption area, removing the constraint state of the operation variable, wherein the specific distance is automatically calculated according to the specific range of the operation variable in proportion.
2. The propylene recovery control system of claim 1, further comprising:
the key parameter optimizer is respectively connected with the multivariate model pre-estimation controller and the data memory and is used for optimizing key parameters; the key parameters include: optimizing direction, optimizing coefficient and optimizing period.
3. The propylene recovery control system of claim 1, wherein the data storage comprises:
the data acquisition module is used for acquiring the propylene recovery data in real time;
the data filtering module is connected with the data acquisition module and is used for filtering the propylene recovery data;
the range conversion module is connected with the data acquisition module and is used for carrying out range conversion processing on the propylene recovery data;
the historical data storage module is respectively connected with the data filtering module and the range conversion module and is used for storing the filtered propylene recovery data and the propylene recovery data subjected to range conversion in real time;
and the data statistics query module is respectively connected with the historical data storage module and the propylene recovery observer and is used for transmitting the processed propylene recovery data to the propylene recovery observer.
4. A propylene recovery control method, comprising:
acquiring propylene recovery data; the propylene recovery data includes: temperature, pressure, flow, liquid level height, density, and concentration;
determining a liquid level rate in a propylene recovery system from the propylene recovery data;
generating an operation variable according to the liquid level rate and the propylene recovery data, and performing constraint processing on the operation variable by adopting a single-value estimation control algorithm;
the method for carrying out constraint processing on the operation variables by adopting a single-value estimation control algorithm comprises the following steps:
acquiring a preset adsorption area;
judging whether the operating variable is free from constraint according to the preset adsorption area; when the operation variable is located in the preset adsorption area, the constraint state of the operation variable is unchanged; and when the operation variable is positioned outside the preset adsorption area and is a specific distance away from the boundary upper limit or the boundary lower limit of the preset adsorption area, removing the constraint state of the operation variable, wherein the specific distance is automatically calculated in proportion according to the specific range of the operation variable.
5. The propylene recovery control method according to claim 4, further comprising:
optimizing the key parameters; the key parameters include: optimizing direction, optimizing coefficient and optimizing period.
6. The propylene recovery control method according to claim 4, wherein the acquiring propylene recovery data further comprises:
processing the propylene recovery data; the processing comprises filtering processing and range conversion processing;
and storing the filtered propylene recovery data and the propylene recovery data subjected to range conversion in real time.
7. The propylene recovery control process of claim 4, wherein generating operating variables from the liquid level rate and the propylene recovery data comprises:
the liquid level rate and the propylene recovery data are generated into manipulated variables using VSUPCC controller algorithms.
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