CN107807528A - A kind of plug flow tubular reactor optimal control system based on adaptive congestion control algorithm node - Google Patents
A kind of plug flow tubular reactor optimal control system based on adaptive congestion control algorithm node Download PDFInfo
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- CN107807528A CN107807528A CN201711115246.3A CN201711115246A CN107807528A CN 107807528 A CN107807528 A CN 107807528A CN 201711115246 A CN201711115246 A CN 201711115246A CN 107807528 A CN107807528 A CN 107807528A
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- G—PHYSICS
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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
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention discloses a kind of plug flow tubular reactor optimal control system based on adaptive congestion control algorithm node, the system is by plug flow tubular reactor body, the liquid phase flowmeter at plug flow tubular reactor end and temperature sensor, analog-digital converter, fieldbus networks, DCS, master control room coolant flow speed and temperature of reactor are shown, the digital analog converter at flow control valve end, flow control valve are formed.After specifying production process duration and coolant flow speed control requirement, DCS obtains flow control strategy and is converted to the opening degree instruction of flow control valve, the digital analog converter at flow control valve end is sent to by fieldbus networks, make flow control valve according to the control instruction corresponding actions received, liquid phase flowmeter, temperature sensor gather the coolant flow speed of plug flow tubular reactor, temperature and be passed back to DCS in real time respectively.The present invention can maximize the concentration of target product in plug flow tubular reactor, realize enhancing efficiency by relying on tapping internal latent power.
Description
Technical field
The present invention relates to reactor control field, mainly a kind of plug flow pipe based on adaptive congestion control algorithm node
Reactor optimal control system.The system can carry out automatic optimum control to plug flow tubular reactor coolant flow speed, with
Improve the concentration of target product.
Background technology
Plug flow tubular reactor is that biological chemical field applies a kind of quite varied reactor.In actual production,
After the factors such as material initial concentration, reactor volume, production time determine, the most critical factor for influenceing product yield is exactly temperature
Degree, and temperature is typically controlled by coolant flow speed.Because the manufacturing technique requirent of different product is different, so by production work
It is significant that skill requirement automatically carries out coolant flow speed optimum control to plug flow tubular reactor.Current state inner carrier
Seldom the theory of optimal control and corresponding method are used in the control method of flow tubular reactor, and the parameter in controller is often with
There is experience setting.After method for optimally controlling, the concentration of target product further increases in plug flow tubular reactor, real
Enhancing efficiency by relying on tapping internal latent power is showed.
The content of the invention
In order to improve the concentration of target product in plug flow tubular reactor, the invention provides one kind based on adaptive excellent
Change the plug flow tubular reactor optimal control system of control node, the system is by realizations of the DCS as method for optimally controlling
Carrier.
The purpose of the present invention is achieved through the following technical solutions：A kind of work based on adaptive congestion control algorithm node
Plug flow tubular reactor optimal control system, automatic optimum control can be carried out to plug flow tubular reactor coolant flow speed,
To improve the concentration of target product.By plug flow tubular reactor body, plug flow tubular reactor end liquid phase flowmeter and
Temperature sensor, analog-digital converter, fieldbus networks, DCS, master control room coolant flow speed and temperature of reactor are shown, flow
Digital analog converter, the flow control valve at control valve end are formed.The running of the system includes：
Step A1：Control room engineer sets the production process duration and coolant flow speed control requires；
Step A2：Adaptive congestion control algorithm node method for optimally controlling inside DCS execution, it is dense to calculate target product of sening as an envoy to
Spend maximized coolant flow speed control strategy；
Step A3：DCS is converted to obtained coolant flow speed control strategy the opening degree instruction of flow control valve, passes through
Fieldbus networks are sent to the digital analog converter of flow control valve, flow control valve is done according to the control instruction received
Go out corresponding actions；
Step A4：Liquid phase flowmeter, the temperature sensor at plug flow tubular reactor end gather piston flow tube in real time respectively
The coolant flow speed of formula reactor, temperature, DCS is passed back to fieldbus networks after analog-digital converter, and in master control room
Interior display, control room engineer is set to grasp production process at any time.
Described DCS, including information acquisition module, initialization module, Restriction condition treat module, dominant vector parametrization
It is module, Non-Linear Programming (Nonlinear Programming, NLP) problem solver module, end condition judge module, adaptive
Answer control node distribute module, time scale modular converter, control instruction output module.
The production process of target product can be described as in plug flow tubular reactor：
Wherein t represents time, t0Represent production process time started, tfRepresent the production process end time；X (t)=[x1
(t),x2(t),...,It is referred to as state variable, represents material concentration or related ginseng in plug flow tubular reactor
Number, x0It is its initial value,It is its first derivative；U (t) represents the coolant flow speed of plug flow tubular reactor, ul、uuRespectively
For its lower limit and higher limit；It is the differential equation group according to conservation of matter, conservation of energy foundation；It is raw
The constraints established during production to material concentration or relevant parameter, coolant flow speed.nx,ngIt is state variable peace treaty respectively
The quantity of beam.
Assuming that with Φ [x (tf)] represent target product ultimate density, then make the maximized mathematical modeling of the product design
It is represented by：
Wherein J [u (t)] represents control targe, is determined by coolant flow speed u (t).It is an optimal control in the question essence
Problem processed.
Technical scheme is used by the present invention solves the problem：Adaptive congestion control algorithm node is integrated with dcs most
Excellent control method, and a set of optimal control system is constructed based on this.The structure of the control system includes piston flow tube
Formula reactor body, the liquid phase flowmeter at plug flow tubular reactor end and temperature sensor, analog-digital converter, fieldbus network
Network, DCS, master control room coolant flow speed and temperature of reactor are shown, the digital analog converter at flow control valve end, flow control valve
Door.
The running of the system is as follows：
Step C1：Control room engineer sets the production process duration and coolant flow speed control requires；
Step C2：Adaptive congestion control algorithm node method for optimally controlling inside DCS execution, it is dense to calculate target product of sening as an envoy to
Spend maximized coolant flow speed control strategy；
Step C3：DCS is converted to obtained coolant flow speed control strategy the opening degree instruction of flow control valve, passes through
Fieldbus networks are sent to the digital analog converter of flow control valve, flow control valve is done according to the control instruction received
Go out corresponding actions；
Step C4：Liquid phase flowmeter, the temperature sensor at plug flow tubular reactor end gather piston flow tube in real time respectively
The coolant flow speed of formula reactor, temperature, DCS is passed back to fieldbus networks after analog-digital converter, and in master control room
Interior display, control room engineer is set to grasp production process at any time.
The DCS for being integrated with adaptive congestion control algorithm node method for optimally controlling is that its inside of the core of the present invention includes information
Acquisition module, initialization module, Restriction condition treat module, dominant vector parameterized module, Non-Linear Programming (Nonlinear
Programming, NLP) problem solver module, end condition judge module, Self Adaptive Control node distribution module, time scale
Modular converter, control instruction output module.
Information acquisition module includes the collection of production process duration, coolant flow speed control requires two submodules of collection
Block.
Restriction condition treat module is used to handle the constraints in mathematical modeling (2)Can be by mathematical modeling (2)
Be converted to：
Wherein, Gi(i=1,2 ..., ng) beI-th of component, ρ >=0 is penalty factor, δ ＞ 0 for it is smooth because
Son, and
Introduce new state variableMake its satisfaction
And then mathematical modeling (3) can be converted into：
Wherein,For the state variable of augmentation,For its initial value, For the differential equation group of augmentation.
Dominant vector parameterized module realizes coolant flow speed control using piece-wise constant strategy, specific as follows：
Assuming that entirely control time domain [t0,tf] it is divided into [t between p (p ＞ 0) individual control work zonek-1,tk) (k=1,2 ...,
P), and
t0＜ t1＜ ... ＜ tp-1＜ tp=tf (7)
So, u (t) is represented by：
Wherein,For constant, u (t) [t between control work zone is representedk-1,tk) in parameter value, χk(t) letter is switched for unit
Number, it is defined as follows：
So as to which coolant flow speed control parameter can be by vectorRepresent.
NLP problem solver modules include SQP (Sequential Quadratic Programming, SQP)
Solve, simultaneous differential equations solves two submodules.Simultaneous differential equations includes equation group (37)
With equation group (38)
Wherein,
Simultaneous differential equations (10), (11) are solved using Runge-Kutta algorithm, mathematical modeling can be obtained
(6) First-order Gradient information of the target function value and object function to control parameter vector：
Self Adaptive Control node distribution module provides a kind of strategy of self-adjusted block control node, specific as follows：
Assuming that by the l times iteration, obtained object function optimal value is J*l, optimal control parameter is It is corresponding to control the grid to beBy by ΔlIn each control work zone between carry out
Halve, obtain controlling gridAnd initial control parameter
ForIn current value beParametersIn order to assess it to object function J
The influence of slippage, definitionIt is relative to J sensitivity：
Wherein,Represent the maximum integer no more than (j+1)/2.
Assuming that in control intervalIt is interior,Obtained for respectively the l-1 times and the l times optimal
Control parameter value.If following condition meets：
Wherein, εuv＞ 0 is given threshold value, then makes
s2k-1=0 and s2k=0 (17)
For Δl' in control nodeIf in next iteration retained, need to meet：
Or
Wherein, rsu＞ 0 is given coefficient,For average sensitivity, it is defined as follows：
If formula (18) is unsatisfactory for, the control node is eliminated.
Work as control nodeWithWhen being all eliminated, if following condition meets
And
Wherein, coefficient r is givensl∈(0,rsu]、εuh＞ 0, then control nodeAlso should be eliminated.
The steps such as control grid is halved more than and control node eliminates, control grid Δl' can be used as next time
The control grid Δ of iterationl+1。
Time scale modular converter is that current mathematical modeling (6) is transformed into a new time scale, in order to right
The control grid that Self Adaptive Control node distribution module obtains optimizes, specific as follows：
For a control node newly insertedIf meet
Or
Wherein, coefficient r is givenss≥rsu, then the control node is considered as important control node.So, between control work zoneWithIt is considered as that its length can be used as variable and optimize between important control work zone, to find control
NodeOptimal location.
Assuming that after the adjustment of Self Adaptive Control node distribution module, whole control time domain is present between P control work zone
[tk-1,tk) (k=1,2 ..., P), the length θ between each control work zonekRepresent, and its initial value is
For between insignificant control work zone therein, its length is definite value, it is not necessary to is optimized.For important control therein
Section, according to its continuous situation, it is assumed that R (R >=1) part can be divided into, r (1≤r≤R) partly includes nr(nr>=2) it is individual continuous
Between control work zone, and the length between its all control work zone meets：
It is as follows to introduce time scale transfer function：
Wherein, τ is new time variable,Represent the maximum integer no more than τ.So, mathematical modeling (6) is new
It can be converted into time scale：
Wherein,
The process that the DCS produces flow control valve opening degree instruction is as follows：
Step D1：Information acquisition module obtains production process duration and the coolant flow speed control that engineer specifies
It is required that；
Step D2：Initialization module is run, set initial control lattice number p, coolant flow speed control strategy it is initial
Conjecture valueSet constant value ρ >=0, δ ＞ 0, εuv＞ 0, εuh＞ 0, rsu＞ 0, rsl∈(0,rsu]、rss≥rsu, set maximum to change
Generation number lmax>=1 and termination error tolJ＞ 0, and make iteration count l=0；
Step D3：Mathematical modeling (2) is converted to mathematical modeling (6) by Restriction condition treat module；
Step D4：Dominant vector parameterized module represents coolant flow speed controlling curve using piece-wise constant strategy, such as
Fruit l=0, then control time domain is divided into p sections and obtains currently controlling grid, and make all control parameter values beOtherwise, adopt
Use ΔlAs current control grid, the parameter value in each control work zone is in corresponding control time domainValue；
Step D5：SQP in NLP problem solver modules solves module operation, and is solved by simultaneous differential equations
Module obtains the First-order Gradient information of target function value and object function to control parameter vector, finally gives current control net
Object function optimal value J under lattice*lAnd corresponding optimal control parameter
Step D6：End condition judge module is run, for l ＞ 0, if l=lmaxOr
Step D10 is then performed, otherwise, performs step D7；
Step D7：Self Adaptive Control node distribution module is run, and obtains new control grid Δl+1；
Step D8：Iteration count l=l+1 is made, if l=lmax, then step D9 is performed, otherwise, goes to step D4；
Step D9：Time scale modular converter is converted to mathematical modeling (6) mathematical modeling (25) in new time scale,
Go to step D4；
Step D10：Control instruction output module exports the optimal coolant flow speed control strategy of acquisition.
Beneficial effects of the present invention are mainly manifested in：Plug flow tubular reactor based on adaptive congestion control algorithm node is most
Excellent control system, the coolant flow speed optimal control policy of plug flow tubular reactor can be calculated, is adapted to problem
Optimum control curve, the discontinuity point of problem is particularly found, higher precision can be obtained；Plan is analyzed using adaptive wavelet
After slightly, the initial estimate of next sub-optimal control curve is the optimal curve of current iteration, it is possible thereby to obtain faster
Convergence rate, reduce the calculating time of coolant flow speed optimal control policy.The present invention can maximize plug flow pipe reaction
The concentration of target product in device, realize and excavate synergy.
Brief description of the drawings
Fig. 1 is the functional schematic of the present invention；
Fig. 2 is the structural representation of the present invention；
Fig. 3 is DCS internal modules structure chart of the present invention；
Fig. 4 is the coolant flow speed control strategy figure obtained to embodiment 1；
Fig. 5 is the target product change in concentration figure corresponding to coolant flow speed control strategy in Fig. 4；
Fig. 6 is the temperature variation corresponding to coolant flow speed control strategy in Fig. 4.
Embodiment
As shown in figure 1, the production process of target product can be described as in plug flow tubular reactor：
Wherein t represents time, t0Represent production process time started, tfRepresent the production process end time；X (t)=[x1
(t),x2(t),...,It is referred to as state variable, represents material concentration or related ginseng in plug flow tubular reactor
Number, x0It is its initial value,It is its first derivative；U (t) represents the coolant flow speed of plug flow tubular reactor, ul、uuPoint
Wei not its lower limit and higher limit；It is the differential equation group according to conservation of matter, conservation of energy foundation；It is
The constraints established in production process to material concentration or relevant parameter, coolant flow speed.nx,ngBe respectively state variable and
The quantity of constraint.
Assuming that with Φ [x (tf)] represent target product ultimate density, then make the maximized mathematical modeling of the product design
It is represented by：
Wherein J [u (t)] represents control targe, is determined by coolant flow speed u (t).It is an optimal control in the question essence
Problem processed.
Technical scheme is used by the present invention solves the problem：Adaptive congestion control algorithm node is integrated with dcs most
Excellent control method, and a set of optimal control system is constructed based on this.The structure of the control system is as shown in Fig. 2 bag
Include plug flow tubular reactor body, the liquid phase flowmeter at plug flow tubular reactor end and temperature sensor, analog-digital converter,
Fieldbus networks, DCS, master control room coolant flow speed and temperature of reactor are shown, the digital analog converter at flow control valve end,
Flow control valve.
The running of the system is as follows：
Step C5：Control room engineer sets the production process duration and coolant flow speed control requires；
Step C6：Adaptive congestion control algorithm node method for optimally controlling inside DCS execution, it is dense to calculate target product of sening as an envoy to
Spend maximized coolant flow speed control strategy；
Step C7：DCS is converted to obtained coolant flow speed control strategy the opening degree instruction of flow control valve, passes through
Fieldbus networks are sent to the digital analog converter of flow control valve, flow control valve is done according to the control instruction received
Go out corresponding actions；
Step C8：Liquid phase flowmeter, the temperature sensor at plug flow tubular reactor end gather piston flow tube in real time respectively
The coolant flow speed of formula reactor, temperature, DCS is passed back to fieldbus networks after analog-digital converter, and in master control room
Interior display, control room engineer is set to grasp production process at any time.
The DCS for being integrated with adaptive congestion control algorithm node method for optimally controlling is the core of the present invention, as shown in figure 3, its
Inside includes information acquisition module, initialization module, Restriction condition treat module, dominant vector parameterized module, non-linear rule
Draw (Nonlinear Programming, NLP) problem solver module, end condition judge module, Self Adaptive Control node distribution
Module, time scale modular converter, control instruction output module.
Information acquisition module includes the collection of production process duration, coolant flow speed control requires two submodules of collection
Block.
Restriction condition treat module is used to handle the constraints in mathematical modeling (2)Can be by mathematical modeling (2)
Be converted to：
Wherein, Gi(i=1,2 ..., ng) beI-th of component, ρ >=0 is penalty factor, δ ＞ 0 for it is smooth because
Son, and
Introduce new state variableMake its satisfaction
And then mathematical modeling (3) can be converted into：
Wherein,For the state variable of augmentation,For its initial value, For the differential equation group of augmentation.
Dominant vector parameterized module realizes coolant flow speed control using piece-wise constant strategy, specific as follows：
Assuming that entirely control time domain [t0,tf] it is divided into [t between p (p ＞ 0) individual control work zonek-1,tk) (k=1,2 ...,
P), and
t0＜ t1＜ ... ＜ tp-1＜ tp=tf (34)
So, u (t) is represented by：
Wherein,For constant, u (t) [t between control work zone is representedk-1,tk) in parameter value, χk(t) letter is switched for unit
Number, it is defined as follows：
So as to which coolant flow speed control parameter can be by vectorRepresent.
NLP problem solver modules include SQP (Sequential Quadratic Programming, SQP)
Solve, simultaneous differential equations solves two submodules.Simultaneous differential equations includes equation group (37)
With equation group (38)
Wherein,
Simultaneous differential equations (10), (11) are solved using Runge-Kutta algorithm, mathematical modeling can be obtained
(6) First-order Gradient information of the target function value and object function to control parameter vector：
Self Adaptive Control node distribution module provides a kind of strategy of self-adjusted block control node, specific as follows：
Assuming that by the l times iteration, obtained object function optimal value is J*l, optimal control parameter is It is corresponding to control the grid to beBy by ΔlIn each control work zone between carry out
Halve, obtain controlling gridAnd initial control parameter
ForIn current value beParametersIn order to assess it to object function J
The influence of slippage, definitionIt is relative to J sensitivity：
Wherein,Represent the maximum integer no more than (j+1)/2.
Assuming that in control intervalIt is interior,Obtained for respectively the l-1 times and the l times optimal
Control parameter value.If following condition meets：
Wherein, εuv＞ 0 is given threshold value, then makes
s2k-1=0 and s2k=0 (44)
For Δl' in control nodeIf in next iteration retained, need to meet：
Or
Wherein, rsu＞ 0 is given coefficient,For average sensitivity, it is defined as follows：
If formula (18) is unsatisfactory for, the control node is eliminated.
Work as control nodeWithWhen being all eliminated, if following condition meets
And
Wherein, coefficient r is givensl∈(0,rsu]、εuh＞ 0, then control nodeAlso should be eliminated.
The steps such as control grid is halved more than and control node eliminates, control grid Δl' can be used as next time
The control grid Δ of iterationl+1。
Time scale modular converter is that current mathematical modeling (6) is transformed into a new time scale, in order to right
The control grid that Self Adaptive Control node distribution module obtains optimizes, specific as follows：
For a control node newly insertedIf meet
Or
Wherein, coefficient r is givenss≥rsu, then the control node is considered as important control node.So, between control work zoneWithIt is considered as that its length can be used as variable and optimize between important control work zone, to find control
NodeOptimal location.
Assuming that after the adjustment of Self Adaptive Control node distribution module, whole control time domain is present between P control work zone
[tk-1,tk) (k=1,2 ..., P), the length θ between each control work zonekRepresent, and its initial value is
For between insignificant control work zone therein, its length is definite value, it is not necessary to is optimized.For important control therein
Section, according to its continuous situation, it is assumed that R (R >=1) part can be divided into, r (1≤r≤R) partly includes nr(nr>=2) it is individual continuous
Between control work zone, and the length between its all control work zone meets：
It is as follows to introduce time scale transfer function：
Wherein, τ is new time variable,Represent the maximum integer no more than τ.So, mathematical modeling (6) is new
It can be converted into time scale：
Wherein,
The process that the DCS produces flow control valve opening degree instruction is as follows：
Step D11：Information acquisition module obtains production process duration and the coolant flow speed control that engineer specifies
System requires；
Step D12：Initialization module is run, set initial control lattice number p, coolant flow speed control strategy it is initial
Conjecture valueSet constant value ρ >=0, δ ＞ 0, εuv＞ 0, εuh＞ 0, rsu＞ 0, rsl∈(0,rsu]、rss≥rsu, set maximum to change
Generation number lmax>=1 and termination error tolJ＞ 0, and make iteration count l=0；
Step D13：Mathematical modeling (2) is converted to mathematical modeling (6) by Restriction condition treat module；
Step D14：Dominant vector parameterized module represents coolant flow speed controlling curve using piece-wise constant strategy, such as
Fruit l=0, then control time domain is divided into p sections and obtains currently controlling grid, and make all control parameter values beOtherwise, adopt
Use ΔlAs current control grid, the parameter value in each control work zone is in corresponding control time domainValue；
Step D15：SQP in NLP problem solver modules solves module operation, and is solved by simultaneous differential equations
Module obtains the First-order Gradient information of target function value and object function to control parameter vector, finally gives current control net
Object function optimal value J under lattice*lAnd corresponding optimal control parameter
Step D16：End condition judge module is run, for l ＞ 0, if l=lmaxOr
Step D20 is then performed, otherwise, performs step D17；
Step D17：Self Adaptive Control node distribution module is run, and obtains new control grid Δl+1；
Step D18：Iteration count l=l+1 is made, if l=lmax, then step D19 is performed, otherwise, goes to step D14；
Step D19：Time scale modular converter is converted to mathematical modeling (6) mathematical modeling in new time scale
(25) step D14, is gone to；
Step D20：Control instruction output module exports the optimal coolant flow speed control strategy of acquisition.
Embodiment 1
A reversible exothermic reaction in certain plug flow tubular reactor be presentTo make product B concentration maximum
Change, it is necessary to study the optimal control policy of coolant flow speed.Its mathematical modeling is as follows：
Wherein, x1(t)、x2(t) the product B of standardization concentration, temperature (K) is represented respectively, and u (t) represents the cold of standardization
But agent flow velocity.
Control room engineer is by the duration t of this production processf=5min, coolant flow speed 0≤u of require information
(t)≤0.5 input in DCS information acquisition module.DCS immediately begins to run adaptive congestion control algorithm node optimum control side
Method, its running is as shown in figure 3, be：
Step E1：Initialization module 2 is run, and sets initial control lattice number p=16, coolant flow speed control strategy
Initial guessSet constant value ρ=0.1, δ=10-16、εuv=10-6、εuh=10-4、rsu=0.5, rsl=0.2,
rss=0.5, maximum iteration l is setmax=3 and termination error tolJ=10-6, and make iteration count l=0；
Step E2：Restriction condition treat module 3 is run, and mathematical modeling (55) is converted to the form of mathematical modeling (6)；
Step E3：Dominant vector parameterized module 4 represents coolant flow speed controlling curve using piece-wise constant strategy, such as
Fruit l=0, then control time domain is divided into p sections and obtains currently controlling grid, and make all control parameter values beOtherwise, adopt
Use ΔlAs current control grid, the parameter value in each control work zone is in corresponding control time domainValue；
Step E4：SQP in NLP problem solver modules 5 solves module operation, and is solved by simultaneous differential equations
Module obtains the First-order Gradient information of target function value and object function to control parameter vector, finally gives current control net
Object function optimal value J under lattice*lAnd corresponding optimal control parameter
Step E5：End condition judge module 6 is run, for l ＞ 0, if l=lmaxOr
Step E9 is then performed, otherwise, performs step E6；
Step E6：Self Adaptive Control node distribution module 7 is run, and obtains new control grid Δl+1；
Step E7：Iteration count l=l+1 is made, if l=lmax, then step E8 is performed, otherwise, goes to step E3；
Step E8：Time scale modular converter 8 is converted to mathematical modeling (6) mathematical modeling in new time scale
(25) step E3, is gone to；
Step E9：Control instruction output module 8 exports the optimal coolant flow speed control strategy of acquisition.
The optimal coolant flow speed controlling curve that Self Adaptive Control node method for optimally controlling obtains is as shown in figure 4, completely
Meet that the coolant flow speed control of setting requires.Fig. 5 shows the concentration curve of product B in plug flow tubular reactor,
It can be seen that product B concentration is ever-increasing in production process, maximum is obtained at the end of production process.Meanwhile such as
Shown in Fig. 6, the temperature of reactor also meets to require.
Finally, the flow control valve opening degree instruction of DCS outputs is output to flow control valve by fieldbus networks
The digital analog converter at end, make flow control valve according to the control instruction corresponding actions received, while with liquid phase flowmeter, temperature
Sensor gathers coolant flow speed, the temperature of plug flow tubular reactor in real time respectively, through analog-digital converter, fieldbus networks
DCS is transmitted back to, and is shown in master control room.
Above-described embodiment is used for illustrating the present invention, rather than limits the invention, the present invention spirit and
In scope of the claims, to any modifications and changes of the invention made, protection scope of the present invention is both fallen within.
Claims (1)
1. a kind of plug flow tubular reactor optimal control system based on adaptive congestion control algorithm node, can be to piston flow tube
Formula reactor coolant flow velocity carries out automatic optimum control, to improve the concentration of target product.It is characterized in that：By piston flow tube
Formula reactor body, the liquid phase flowmeter at plug flow tubular reactor end and temperature sensor, analog-digital converter, fieldbus network
Network, DCS, master control room coolant flow speed and temperature of reactor are shown, the digital analog converter at flow control valve end, flow control valve
Door is formed.The running of the system includes：
Step A1：Control room engineer sets the production process duration and coolant flow speed control requires；
Step A2：Adaptive congestion control algorithm node method for optimally controlling inside DCS execution, calculating send as an envoy to target product concentration most
The coolant flow speed control strategy changed greatly；
Step A3：DCS is converted to obtained coolant flow speed control strategy the opening degree instruction of flow control valve, passes through scene
Bus network is sent to the digital analog converter of flow control valve, flow control valve is made phase according to the control instruction received
It should act；
Step A4：Collection plug flow pipe is anti-in real time respectively for liquid phase flowmeter, the temperature sensor at plug flow tubular reactor end
Coolant flow speed, the temperature of device are answered, is passed back to DCS with fieldbus networks after analog-digital converter, and show in master control room
Show, control room engineer is grasped production process at any time.
Described DCS, including information acquisition module, initialization module, Restriction condition treat module, dominant vector parametrization mould
It is block, Non-Linear Programming (Nonlinear Programming, NLP) problem solver module, end condition judge module, adaptive
Control node distribute module, time scale modular converter, control instruction output module.
The production process of target product can be described as in plug flow tubular reactor：
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Wherein t represents time, t0Represent production process time started, tfRepresent the production process end time； It is referred to as state variable, represents material concentration or relevant parameter in plug flow tubular reactor, x0It is that its is initial
Value,It is its first derivative；U (t) represents the coolant flow speed of plug flow tubular reactor, ul、uuRespectively its lower limit and
Higher limit；It is the differential equation group according to conservation of matter, conservation of energy foundation；It is to material in production process
The constraints that concentration or relevant parameter, coolant flow speed are established.
Assuming that with Φ [x (tf)] represent target product ultimate density, then the maximized mathematical modeling of the product design is represented
For：
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Wherein J [u (t)] represents control targe, is determined by coolant flow speed u (t).
Information acquisition module includes the collection of production process duration, coolant flow speed control requires two submodules of collection.
Restriction condition treat module is used to handle the constraints in mathematical modeling (2)Can be by model conversion：
Wherein, Gi(i=1,2 ..., ng) beI-th of component, ρ >=0 is penalty factor, and δ ＞ 0 are smoothing factor, and
And
Introduce new state variableMake its satisfaction
And then mathematical modeling (3) can be converted into：
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Wherein,For the state variable of augmentation,For its initial value, For the differential equation group of augmentation.
Dominant vector parameterized module realizes coolant flow speed control using piece-wise constant strategy, specific as follows：
Assuming that entirely control time domain [t0,tf] it is divided into [t between p (p ＞ 0) individual control work zonek-1,tk) (k=1,2 ..., p),
And
t0＜ t1＜ ... ＜ tp-1＜ tp=tf (7)
So, u (t) is represented by：
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Wherein,For constant, u (t) [t between control work zone is representedk-1,tk) in parameter value, χk(t) it is unit switch function,
It is defined as follows：
So as to which coolant flow speed control parameter can be by vectorRepresent.
NLP problem solver modules are asked including SQP (Sequential Quadratic Programming, SQP)
Solution, simultaneous differential equations solve two submodules.Simultaneous differential equations includes equation group (10)
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With equation group (11)
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Simultaneous differential equations (10), (11) are solved using Runge-Kutta algorithm, mathematical modeling (6) can be obtained
Target function value and object function are to the vectorial First-order Gradient information of control parameter：
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Self Adaptive Control node distribution module provides a kind of strategy of self-adjusted block control node, specific as follows：
Assuming that by the l times iteration, obtained object function optimal value is J*l, optimal control parameter is
It is corresponding to control the grid to beBy by ΔlIn each control work zone between halved, controlled
GridAnd initial control parameter
ForIn current value beParametersObject function J is declined in order to assess it
The influence of amount, definitionIt is relative to J sensitivity：
Wherein,Represent the maximum integer no more than (j+1)/2.
Assuming that in control intervalIt is interior,The respectively the l-1 times and the l times optimum control ginseng obtained
Numerical value.If following condition meets：
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</msubsup>
<mo>|</mo>
<mo><</mo>
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<mi>&epsiv;</mi>
<mrow>
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</mrow>
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<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>16</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, εuv＞ 0 is given threshold value, then makes
s2k-1=0 and s2k=0 (17)
For Δl'In control nodeIf in next iteration retained, need to meet：
Or
Wherein, rsu＞ 0 is given coefficient,For average sensitivity, it is defined as follows：
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If formula (18) is unsatisfactory for, the control node is eliminated.
Work as control nodeWithWhen being all eliminated, if following condition meets
And
Wherein, coefficient r is givensl∈(0,rsu]、εuh＞ 0, then control nodeAlso should be eliminated.
The steps such as control grid is halved more than and control node eliminates, control grid Δl'Next iteration can be used as
Control grid Δl+1。
Time scale modular converter is that current mathematical modeling (6) is transformed into a new time scale, in order to adaptive
The control grid for answering control node distribute module to obtain optimizes, specific as follows：
For a control node newly insertedIf meet
Or
Wherein, coefficient r is givenss≥rsu, then the control node is considered as important control node.So, between control work zoneWithIt is considered as that its length can be used as variable and optimize between important control work zone, to find control
NodeOptimal location.
Assuming that after the adjustment of Self Adaptive Control node distribution module, it is whole to control time domain [t between P control work zone to be presentk-1,tk)
(k=1,2 ..., P), the length θ between each control work zonekRepresent, and its initial value is
<mrow>
<msubsup>
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For between insignificant control work zone therein, its length is definite value, it is not necessary to is optimized.For important control work zone therein
Between, according to its continuous situation, it is assumed that R (R >=1) part can be divided into, r (1≤r≤R) partly includes nr(nr>=2) individual continuous control
System section, and the length between its all control work zone meets：
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It is as follows to introduce time scale transfer function：
Wherein, τ is new time variable,Represent the maximum integer no more than τ.So, mathematical modeling (6) is in the new time
It can be converted on yardstick：
Wherein,
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The process that the DCS produces flow control valve opening degree instruction is as follows：
Step B1：The production process duration and coolant flow speed control that information acquisition module acquisition engineer specifies will
Ask；
Step B2：Initialization module is run, and sets initial control lattice number p, the initial guess of coolant flow speed control strategy
ValueSet constant value ρ >=0, δ ＞ 0, εuv＞ 0, εuh＞ 0, rsu＞ 0, rsl∈(0,rsu]、rss≥rsu, greatest iteration time is set
Number lmax>=1 and termination error tolJ＞ 0, and make iteration count l=0；
Step B3：Restriction condition treat module carries out the production process mathematical modeling of target product in plug flow tubular reactor
Conversion；
Step B4：Dominant vector parameterized module represents coolant flow speed controlling curve using piece-wise constant strategy, if l=
0, then control time domain is divided into p sections and obtains currently controlling grid, and make all control parameter values beOtherwise, using Δl
As current control grid, the parameter value in each control work zone is in corresponding control time domainValue；
Step B5：SQP in NLP problem solver modules solves module operation, and solves module by simultaneous differential equations
The First-order Gradient information of target function value and object function to control parameter vector is obtained, is finally given under current control grid
Object function optimal value J*lAnd corresponding optimal control parameter
Step B6：End condition judge module is run, for l ＞ 0, if l=lmaxOr
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<mn>27</mn>
<mo>)</mo>
</mrow>
</mrow>
Step B10 is then performed, otherwise, performs step B7；
Step B7：Self Adaptive Control node distribution module is run, and obtains new control grid Δl+1；
Step B8：Iteration count l=l+1 is made, if l=lmax, then step B9 is performed, otherwise, goes to step B4；
Step B9：Mathematical modeling is transformed into new time scale by time scale modular converter, goes to step B4；
Step B10：Control instruction output module exports the optimal coolant flow speed control strategy of acquisition.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108710299A (en) * | 2018-05-30 | 2018-10-26 | 重庆邮电大学 | A kind of continuous stirred tank reactor catalyst feed supplement optimal control system and method |
| CN111308893A (en) * | 2020-03-05 | 2020-06-19 | 江南大学 | Non-uniform grid optimization method for bioreactor fed-batch regulation |
| CN115106019A (en) * | 2022-06-23 | 2022-09-27 | 天津大学 | Plug flow reactor and plug flow reactor experimental system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101464664A (en) * | 2009-01-09 | 2009-06-24 | 浙江工业大学 | Batch reactor optimal control method based on single population and pre-crossed differential evolution algorithm |
| CN101887239A (en) * | 2010-06-30 | 2010-11-17 | 浙江大学 | Adaptive industrial process optimal control system and method |
| CN101887260A (en) * | 2010-06-30 | 2010-11-17 | 浙江大学 | Industrial process optimal control system and method for adaptive synchronization policy |
| CN103309371A (en) * | 2013-06-08 | 2013-09-18 | 浙江大学 | Intermittent reactor control system based on control variable parameterization method |
| CN103309234A (en) * | 2013-06-08 | 2013-09-18 | 浙江大学 | Orthogonal collocation optimization based batch reactor control system |
| CN103557511A (en) * | 2013-11-18 | 2014-02-05 | 华北电力大学（保定） | All-process control method for main steam temperature of utility boiler |
| CN103853127A (en) * | 2012-12-06 | 2014-06-11 | 天津市电力公司 | Method for searching for optimum control variable value in DCS |
| CN103926835A (en) * | 2014-04-04 | 2014-07-16 | 北京航空航天大学 | Lander power descending branch optimal control method based on disturbance observer |
-
2017
- 2017-11-13 CN CN201711115246.3A patent/CN107807528A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101464664A (en) * | 2009-01-09 | 2009-06-24 | 浙江工业大学 | Batch reactor optimal control method based on single population and pre-crossed differential evolution algorithm |
| CN101887239A (en) * | 2010-06-30 | 2010-11-17 | 浙江大学 | Adaptive industrial process optimal control system and method |
| CN101887260A (en) * | 2010-06-30 | 2010-11-17 | 浙江大学 | Industrial process optimal control system and method for adaptive synchronization policy |
| CN103853127A (en) * | 2012-12-06 | 2014-06-11 | 天津市电力公司 | Method for searching for optimum control variable value in DCS |
| CN103309371A (en) * | 2013-06-08 | 2013-09-18 | 浙江大学 | Intermittent reactor control system based on control variable parameterization method |
| CN103309234A (en) * | 2013-06-08 | 2013-09-18 | 浙江大学 | Orthogonal collocation optimization based batch reactor control system |
| CN103557511A (en) * | 2013-11-18 | 2014-02-05 | 华北电力大学（保定） | All-process control method for main steam temperature of utility boiler |
| CN103926835A (en) * | 2014-04-04 | 2014-07-16 | 北京航空航天大学 | Lander power descending branch optimal control method based on disturbance observer |
Non-Patent Citations (1)
| Title |
|---|
| 王立伟: "自适应控制向量参数化动态优化研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108710299A (en) * | 2018-05-30 | 2018-10-26 | 重庆邮电大学 | A kind of continuous stirred tank reactor catalyst feed supplement optimal control system and method |
| CN111308893A (en) * | 2020-03-05 | 2020-06-19 | 江南大学 | Non-uniform grid optimization method for bioreactor fed-batch regulation |
| CN115106019A (en) * | 2022-06-23 | 2022-09-27 | 天津大学 | Plug flow reactor and plug flow reactor experimental system |
| CN115106019B (en) * | 2022-06-23 | 2023-07-14 | 天津大学 | Plug flow reactor and plug flow reactor experimental system |
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