CN107908110A - The tubular reactor dynamic optimization system to be become more meticulous based on control grid - Google Patents
The tubular reactor dynamic optimization system to be become more meticulous based on control grid Download PDFInfo
- Publication number
- CN107908110A CN107908110A CN201711116200.3A CN201711116200A CN107908110A CN 107908110 A CN107908110 A CN 107908110A CN 201711116200 A CN201711116200 A CN 201711116200A CN 107908110 A CN107908110 A CN 107908110A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- flow rate
- mfrac
- mtd
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 claims abstract description 50
- 238000011217 control strategy Methods 0.000 claims abstract description 37
- 230000000875 corresponding effect Effects 0.000 claims abstract description 19
- 238000006243 chemical reaction Methods 0.000 claims abstract description 18
- 238000004519 manufacturing process Methods 0.000 claims abstract description 17
- 230000001276 controlling effect Effects 0.000 claims abstract description 10
- 230000010354 integration Effects 0.000 claims description 21
- 230000008859 change Effects 0.000 claims description 16
- 239000000463 material Substances 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 11
- 230000001568 sexual effect Effects 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000011017 operating method Methods 0.000 claims description 3
- 230000002708 enhancing effect Effects 0.000 abstract description 2
- 238000010079 rubber tapping Methods 0.000 abstract description 2
- 238000010276 construction Methods 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000006116 polymerization reaction Methods 0.000 description 1
- QQONPFPTGQHPMA-UHFFFAOYSA-N propylene Natural products CC=C QQONPFPTGQHPMA-UHFFFAOYSA-N 0.000 description 1
- 125000004805 propylene group Chemical group [H]C([H])([H])C([H])([*:1])C([H])([H])[*:2] 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- 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
- 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
-
- 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
- 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] or computer integrated manufacturing [CIM]
- G05B19/4185—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] or computer integrated manufacturing [CIM] characterised by the network communication
- G05B19/41855—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] or computer integrated manufacturing [CIM] characterised by the network communication by local area network [LAN], network structure
-
- 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
- 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] or computer integrated manufacturing [CIM]
- G05B19/41865—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] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
-
- 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
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25232—DCS, distributed control system, decentralised control unit
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Manufacturing & Machinery (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Feedback Control In General (AREA)
- Organic Low-Molecular-Weight Compounds And Preparation Thereof (AREA)
Abstract
The invention discloses the tubular reactor dynamic optimization system to be become more meticulous based on control grid, shown by tubular reactor, flow rate sensor, analog-digital converter, fieldbus networks, DCS, master control room flow rate and product design, the digital analog converter at flow rate control valve end, flow rate control valve are formed.Control room engineer specifies the concentration of each reaction mass in tubular reactor charging, DCS is by controlling the grid optimization method that becomes more meticulous to send as an envoy to flow rate control strategy of the target product in tubular reactor ends concentration maximum, and be converted to the opening degree instruction of flow rate control valve, the digital analog converter at flow rate control valve end is sent to by fieldbus networks, flow rate control valve is set to perform corresponding actions, flow rate sensor gathers tubular reactor flow rate and is passed back to DCS in real time, control room engineer is grasped production process at any time.The present invention can maximize the concentration of tubular reactor end target product, realize enhancing efficiency by relying on tapping internal latent power.
Description
Technical field
The present invention relates to reactor control field, is mainly based upon the tubular reactor dynamic optimization that control grid becomes more meticulous
System.The flow rate of tubular reactor can be automatically controlled, to maximize the concentration of target product, so as to improve reactor
Production efficiency.
Background technology
Tubular reactor belongs to piston flow reactor, is widely used in the industrial productions such as petrochemical industry, fine chemistry industry, such as
Propylene polymerization produces.
The tubular reactor fixed for pipe range, after the parameter of materials such as raw material quota ratio, concentration determine, influences product
The key factor of yield is reagent flow speed, i.e. flow rate.Since the manufacturing technique requirent of different product is different, so by life
Production. art requirement carries out tubular reactor automatic flow rate control and is of great significance.
Currently, seldom using dynamic optimization theory and corresponding method, controller in the control method of domestic tubular reactor
In parameter often with having there is experience setting, production efficiency needs to be further improved.It is anti-using the tubular type after dynamic optimization method
Answering the product design of device can further improve, and realize enhancing efficiency by relying on tapping internal latent power.
The content of the invention
In order to improve the concentration of tubular reactor target product, the present invention provides the tubular type to be become more meticulous based on control grid
Reactor dynamic optimization system.
The purpose of the present invention is what is be achieved through the following technical solutions：The tubular reactor to be become more meticulous based on control grid
Dynamic optimization system, can automatically control tubular reactor flow rate, to maximize tubular reactor end target product
Concentration.It is characterized in that：By tubular reactor 11, flow rate sensor 12, analog-digital converter 13, fieldbus networks 14,
DCS15, master control room flow rate and product design show the 16, digital analog converter 17 at flow rate control valve end, flow rate control valve 18
Form.Control room engineer specifies the concentration of each reaction mass in tubular reactor charging, and DCS is excellent by controlling grid to become more meticulous
Change method must send as an envoy to flow rate control strategy of the target product in tubular reactor ends concentration maximum, and be converted to flow rate control valve
The opening degree instruction of door, the digital analog converter at flow rate control valve end is sent to by fieldbus networks, makes flow rate control valve
Corresponding actions are performed, flow rate sensor gathers tubular reactor flow rate and is passed back to DCS in real time, control room engineer is slapped at any time
Hold production process.The operational process of the system includes：
Step A1：Control room engineer specify tubular reactor feed in each reaction mass concentration, need to maximize it is dense
Target product and the flow rate control of degree require；
Step A2：DCS performs internal control grid and becomes more meticulous optimization method, and acquisition produces tubular reactor end target
The flow rate control strategy of product concentration maximum；
Step A3：The flow rate control strategy for calculating acquisition is converted to the opening degree instruction of flow rate control valve by DCS, by existing
Field bus network is sent to the digital analog converter at flow rate control valve end, flow rate control valve is held according to received control instruction
Row corresponding actions；
Step A4：The flow rate sensor of tubular reactor gathers flow rate in real time, and fieldbus is used after analog-digital converter
Network is passed back to DCS, and is shown in master control room, control room engineer is grasped production process at any time.
The DCS, including information acquisition module, initialization module, control grid become more meticulous module, ODE solve module,
Gradient computing module, Non-Linear Programming (Non-linear Programming, abbreviation NLP) problem solver module, become more meticulous receipts
Holding back property judgment module, control instruction output module.Wherein information acquisition module includes input concentration state acquisition, target product is adopted
Collection, flow rate control require collection three submodules, NLP problem solver modules include search direction calculating, optimizing step size computation,
NLP convergences judge three submodules.
The production process of tubular reactor can be described as：
Wherein, t represents the change along pipe range direction；U (t) represents flow rate；X (t) is represented in tubular reactor along pipe range side
To the material concentration of change；F () is the differential equation group according to foundation such as material balance, energy balance.Can be with from the description
Find out, the production process of target product can be represented with one group of differential equation group mathematically in tubular reactor.
Maximize the concentration of target product in tubular reactor, with x1(t) represent what target product changed along pipe range
Concentration, then the final expression formula of the problem be：
Wherein, t0At the feed inlet for representing tubular reactor, tfRepresent the end of tubular reactor, J represents to need to maximize
Object function.It is optimization problems in the question essence.But conventional method for such issues that solution, have effect
The defects of rate is low, low precision, it is difficult to meet efficient requirement during actual production.
The technical solution adopted by the present invention to solve the technical problems is：Be integrated with dcs control grid become more meticulous it is excellent
Change method, and a set of optimal control in dynamic system is constructed based on this.It is anti-that the complete structure of the system includes tubular type
Device 21, flow rate sensor 22, analog-digital converter 23, fieldbus networks 24, DCS25, master control room flow rate and product design is answered to show
Show the 26, digital analog converter 27 at flow rate control valve end, flow rate control valve 28.
The operational process of the system includes：
Step A1：Control room engineer specify tubular reactor feed in each reaction mass concentration, need to maximize it is dense
Target product and the flow rate control of degree require；
Step A2：DCS performs internal control grid and becomes more meticulous optimization method, and acquisition produces tubular reactor end target
The flow rate control strategy of product concentration maximum；
Step A3：The flow rate control strategy for calculating acquisition is converted to the opening degree instruction of flow rate control valve by DCS, by existing
Field bus network is sent to the digital analog converter at flow rate control valve end, flow rate control valve is held according to received control instruction
Row corresponding actions；
Step A4：The flow rate sensor of tubular reactor gathers flow rate in real time, and fieldbus is used after analog-digital converter
Network is passed back to DCS, and is shown in master control room, control room engineer is grasped production process at any time.
The DCS, including information acquisition module, initialization module, control grid become more meticulous module, ODE solve module,
Gradient computing module, Non-Linear Programming (Non-linear Programming, abbreviation NLP) problem solver module, become more meticulous receipts
Holding back property judgment module, control instruction output module.Wherein information acquisition module includes input concentration state acquisition, target product is adopted
Collection, flow rate control require collection three submodules, NLP problem solver modules include search direction calculating, optimizing step size computation,
NLP convergences judge three submodules.
To obtain the flow rate control strategy for making tubular reactor end target product concentration maximum, what the DCS was performed
Control grid becomes more meticulous optimization method, and operating procedure is as follows：
Step B1：Information acquisition module 31 obtain the initial concentration of the reaction mass specified of engineer, need to maximize it is dense
Target product and the flow rate control of degree require；
Step B2：Initialization module 32 brings into operation, and is parameterized using piece-wise constant, sets segments N, the correspondence of pipe range
The grid that controls beThe initial guess of the parametrization vector of flow rate control strategySet the calculating essence of NLP problems
Spend tol1The convergence precision tol to become more meticulous with grid2, by iterations k1With the number k that becomes more meticulous2Zero setting；
Step B3：Work as k2When=0, step B4 is performed；Otherwise, by controlling grid to become more meticulous module 33 to controlling gridProcess of refinement is carried out, obtains new control gridAnd its corresponding parametrization vector
Step B4：The material concentration of the acquisition current iteration of module 34 is solved by ODEAnd target function value
Step B5：The gradient information of current iteration is obtained by gradient computing module 35Work as k1Step is skipped when=0
B6 directly performs step B7；
Step B6：NLP problem solver modules 36 are run, and convergence judgement is carried out by NLP convergences judgment module, ifWith the target function value of last iterationThe absolute value of difference be less than precision tol1, then judge convergence sexual satisfaction, hold
Row step B9；If convergence is unsatisfactory for, step B7 is continued to execute；
Step B7：WithValue coveringValue, and by iterations k1Increase by 1；
Step B8：NLP problem solver modules 36 utilize the target function value and gradient information obtained in step B4 and B5,
By calculating search direction and optimizing step-length, ratio is obtainedMore preferably new flow rate control strategyThe step has performed
Jump to step B4 again after；
Step B9：The convergence that becomes more meticulous judgment module 37 is run, noteWork as k2When=0, step B10 is performed, it is no
Then, judgeThe target function value to become more meticulous with the last timeThe absolute value of difference whether be less than precision tol2, if so,
Then judge convergence sexual satisfaction, and the opening degree instruction of the flow rate control strategy conversion flow rate control valve of current iteration is exported, it is no
Then convergence is unsatisfactory for, and puts the number k that becomes more meticulous2:=k2+ 1, step B3 is continued to execute, until the convergence judgment module that becomes more meticulous is expired
Untill foot.
The control grid becomes more meticulous module, is realized using following steps：
Step C1：The left slope of grid node is calculated by the following formulaWith right slope(k=1 ..., N-1)：
Wherein, ukRepresent k-th of component of the parametrization vector u of flow rate control strategy, tkRepresent ukAnd uk+1Between net
Lattice node.
Step C2：If grid node tkThe left and right slope at place meets following requirement, then the node is rejected from grid：
Wherein, εeIt is a less arithmetic number.Grid node tkAfter rejecting, ukAnd uk+1Corresponding mesh update is one
A new grid, parameter thereon are updated to (uk+uk+1)/2。
Step C3：If grid node tkThe left slope at place meets：
Wherein, εiIt is one and is more than εeArithmetic number, then in [tk-1,tk] on interpenetration network node；If grid node tkPlace
Right slope meet：
Then in [tk-1,tk] on interpenetration network node., can be according to the order of magnitude freedom of left and right slope during practical application
Setting adds the number of node.
Step C4：According to the node rejected and be inserted into step C2 and C3, new control grid and corresponding parameter are generated
Change vector.
The ODE solves module, and using four step Runge-Kutta methods, calculation formula is：
Wherein, t represents the change along pipe range direction, tiRepresent the integration moment of Runge-Kutta method choices, ti+1Table
Show and be located at moment tiThe integration moment afterwards, integration step h be any two it is adjacent integration the moment differences, x (ti) represent away from pipe reaction
Device entrance tiThe material concentration at place, F () are the functions for describing state differential equation, and K1, K2, K3, K4 represent Runge- respectively
The functional value of 4 nodes in Kutta method integral processes.
The gradient computing module, using adjoint method:
Step D1：λ (t) is made to be determined for association's state vector, its value by adjoint equation：
Wherein, tfRepresent the end of tubular reactor, H represents hamilton's function, and H=L+ λ (t)TF, L are target letter
Several integral terms, Φ [x (tf)] be object function stable state item.
Step D2：For adjoint equation, association state vector λ (t) is obtained in each integration using four step Runge-Kutta methods
The value at quarter, calculation formula are：
Wherein, t represents the change along pipe range direction, tiThe integration moment selected in module, t are solved for ODEi+1Represent position
In moment tiIntegration moment afterwards, and ti+1=ti+ h, h are integration step, and Q1, Q2, Q3, Q4 represent Runge-Kutta respectively
The functional value of 4 nodes in method integral process.
Step D3：Based on the value of obtained association state vector λ (t), gradient information is obtained by the following formula
Wherein,WithRepresentFirst and second component, and so on.
The NLP problem solver modules, are realized using following steps：
Step E1：IfWith the target function value of last iterationThe difference of absolute value be less than precision tol1,
Then judge convergence sexual satisfaction, return to the flow rate control strategy that current iteration obtains；If convergence is unsatisfactory for, step is continued to execute
Rapid E2；
Step E2：WithValue coveringValue, and by iterations k1Increase by 1；
Step E3：By flow rate control strategyAs some point in vector space, P is denoted as1, P1Corresponding target letter
Numerical value is exactly
Step E4：From point P1Set out, according to the NLP algorithms of selection and point P1The gradient information at placeConstruction vector is empty
Between in a search directionAnd step-length
Step E5:Pass through formulaConstruct corresponding in vector spaceAnother point
P2So that P2Corresponding target function valueThanIt is more excellent, wherein, I be withWith the vector of dimension.
Beneficial effects of the present invention are mainly manifested in：The tubular reactor dynamic optimization system to be become more meticulous based on control grid
System, can calculate the optimal flow rate control strategy of tubular reactor, be adapted to the optimum control curve of problem, particularly look for
To the discontinuity point of problem, higher precision can be obtained；After adaptive strategy, at the beginning of next sub-optimal control curve
Beginning estimate is the optimal curve of current iteration, it is possible thereby to obtain faster convergence rate, reduction obtains optimal control policy
The required calculating time.The present invention can maximize the concentration of target product in tubular reactor, realize and excavate synergy.
Brief description of the drawings
Fig. 1 is the functional schematic of the present invention；
Fig. 2 is the structure diagram of the present invention；
Fig. 3 is DCS internal modules structure chart of the present invention；
Embodiment
As shown in Figure 1, the production process of tubular reactor can be described as：
Wherein, t represents the change along pipe range direction；U (t) represents flow rate；X (t) is represented in tubular reactor along pipe range side
To the material concentration of change；F () is the differential equation group according to foundation such as material balance, energy balance.Can be with from the description
Find out, the production process of target product can be represented with one group of differential equation group mathematically in tubular reactor.
Maximize the concentration of target product in tubular reactor, with x1(t) represent what target product changed along pipe range
Concentration, then the final expression formula of the problem be：
Wherein, t0At the feed inlet for representing tubular reactor, tfRepresent the end of tubular reactor, J represents to need to maximize
Object function.It is optimization problems in the question essence.But conventional method for such issues that solution, have effect
The defects of rate is low, low precision, it is difficult to meet efficient requirement during actual production.
The technical solution adopted by the present invention to solve the technical problems is：Be integrated with dcs control grid become more meticulous it is excellent
Change method, and a set of optimal control in dynamic system is constructed based on this.The complete structure of the system as shown in Fig. 2,
Including tubular reactor 21, flow rate sensor 22, analog-digital converter 23, fieldbus networks 24, DCS25, master control room flow rate and
Product design shows the 26, digital analog converter 27 at flow rate control valve end, flow rate control valve 28.
The operational process of the system includes：
Step A1：Control room engineer specify tubular reactor feed in each reaction mass concentration, need to maximize it is dense
Target product and the flow rate control of degree require；
Step A2：DCS performs internal control grid and becomes more meticulous optimization method, and acquisition produces tubular reactor end target
The flow rate control strategy of product concentration maximum；
Step A3：The flow rate control strategy for calculating acquisition is converted to the opening degree instruction of flow rate control valve by DCS, by existing
Field bus network is sent to the digital analog converter at flow rate control valve end, flow rate control valve is held according to received control instruction
Row corresponding actions；
Step A4：The flow rate sensor of tubular reactor gathers flow rate in real time, and fieldbus is used after analog-digital converter
Network is passed back to DCS, and is shown in master control room, control room engineer is grasped production process at any time.
The DCS, including information acquisition module, initialization module, control grid become more meticulous module, ODE solve module,
Gradient computing module, Non-Linear Programming (Non-linear Programming, abbreviation NLP) problem solver module, become more meticulous receipts
Holding back property judgment module, control instruction output module.Wherein information acquisition module includes input concentration state acquisition, target product is adopted
Collection, flow rate control require collection three submodules, NLP problem solver modules include search direction calculating, optimizing step size computation,
NLP convergences judge three submodules.
To obtain the flow rate control strategy for making tubular reactor end target product concentration maximum, what the DCS was performed
Control grid becomes more meticulous optimization method, and operating procedure is as follows：
Step B1：Information acquisition module 31 obtain the initial concentration of the reaction mass specified of engineer, need to maximize it is dense
Target product and the flow rate control of degree require；
Step B2：Initialization module 32 brings into operation, and is parameterized using piece-wise constant, sets segments N, the correspondence of pipe range
The grid that controls beThe initial guess of the parametrization vector of flow rate control strategySet the calculating essence of NLP problems
Spend tol1The convergence precision tol to become more meticulous with grid2, by iterations k1With the number k that becomes more meticulous2Zero setting；
Step B3：Work as k2When=0, step B4 is performed；Otherwise, by controlling grid to become more meticulous module 33 to controlling gridProcess of refinement is carried out, obtains new control gridAnd its corresponding parametrization vector
Step B4：The material concentration of the acquisition current iteration of module 34 is solved by ODEAnd target function value
Step B5：The gradient information of current iteration is obtained by gradient computing module 35Work as k1Step is skipped when=0
B6 directly performs step B7；
Step B6：NLP problem solver modules 36 are run, and convergence judgement is carried out by NLP convergences judgment module, ifWith the target function value of last iterationThe absolute value of difference be less than precision tol1, then judge convergence sexual satisfaction, hold
Row step B9；If convergence is unsatisfactory for, step B7 is continued to execute；
Step B7：WithValue coveringValue, and by iterations k1Increase by 1；
Step B8：NLP problem solver modules 36 utilize the target function value and gradient information obtained in step B4 and B5,
By calculating search direction and optimizing step-length, ratio is obtainedMore preferably new flow rate control strategyThe step has performed
Jump to step B4 again after；
Step B9：The convergence that becomes more meticulous judgment module 37 is run, noteWork as k2When=0, step B10 is performed, it is no
Then, judgeThe target function value to become more meticulous with the last timeThe absolute value of difference whether be less than precision tol2, if so,
Then judge convergence sexual satisfaction, and the opening degree instruction of the flow rate control strategy conversion flow rate control valve of current iteration is exported, it is no
Then convergence is unsatisfactory for, and puts the number k that becomes more meticulous2:=k2+ 1, step B3 is continued to execute, until the convergence judgment module that becomes more meticulous is expired
Untill foot.
The control grid becomes more meticulous module, is realized using following steps：
Step C1：The left slope of grid node is calculated by the following formulaWith right slope(k=1 ..., N-1)：
Wherein, ukRepresent k-th of component of the parametrization vector u of flow rate control strategy, tkRepresent ukAnd uk+1Between net
Lattice node.
Step C2：If grid node tkThe left and right slope at place meets following requirement, then the node is rejected from grid：
Wherein, εeIt is a less arithmetic number.Grid node tkAfter rejecting, ukAnd uk+1Corresponding mesh update is one
A new grid, parameter thereon are updated to (uk+uk+1)/2。
Step C3：If grid node tkThe left slope at place meets：
Wherein, εiIt is one and is more than εeArithmetic number, then in [tk-1,tk] on interpenetration network node；If grid node tkPlace
Right slope meet：
Then in [tk-1,tk] on interpenetration network node., can be according to the order of magnitude freedom of left and right slope during practical application
Setting adds the number of node.
Step C4：According to the node rejected and be inserted into step C2 and C3, new control grid and corresponding parameter are generated
Change vector.
The ODE solves module, and using four step Runge-Kutta methods, calculation formula is：
Wherein, t represents the change along pipe range direction, tiRepresent the integration moment of Runge-Kutta method choices, ti+1Table
Show and be located at moment tiThe integration moment afterwards, integration step h be any two it is adjacent integration the moment differences, x (ti) represent away from pipe reaction
Device entrance tiThe material concentration at place, F () are the functions for describing state differential equation, and K1, K2, K3, K4 represent Runge- respectively
The functional value of 4 nodes in Kutta method integral processes.
The gradient computing module, using adjoint method:
Step D1：λ (t) is made to be determined for association's state vector, its value by adjoint equation：
Wherein, tfRepresent the end of tubular reactor, H represents hamilton's function, and H=L+ λ (t)TF, L are target letter
Several integral terms, Φ [x (tf)] be object function stable state item.
Step D2：For adjoint equation, association state vector λ (t) is obtained in each integration using four step Runge-Kutta methods
The value at quarter, calculation formula are：
Wherein, t represents the change along pipe range direction, tiThe integration moment selected in module, t are solved for ODEi+1Represent position
In moment tiIntegration moment afterwards, and ti+1=ti+ h, h are integration step, and Q1, Q2, Q3, Q4 represent Runge-Kutta respectively
The functional value of 4 nodes in method integral process.
Step D3：Based on the value of obtained association state vector λ (t), gradient information is obtained by the following formula
Wherein,WithRepresentFirst and second component, and so on.
The NLP problem solver modules, are realized using following steps：
Step E1：IfWith the target function value of last iterationThe difference of absolute value be less than precision tol1,
Then judge convergence sexual satisfaction, return to the flow rate control strategy that current iteration obtains；If convergence is unsatisfactory for, step is continued to execute
Rapid E2；
Step E2：WithValue coveringValue, and by iterations k1Increase by 1；
Step E3：By flow rate control strategyAs some point in vector space, P is denoted as1, P1Corresponding target letter
Numerical value is exactly
Step E4：From point P1Set out, according to the NLP algorithms of selection and point P1The gradient information at placeConstruction vector is empty
Between in a search directionAnd step-length
Step E5:Pass through formulaConstruct corresponding in vector spaceAnother point
P2So that P2Corresponding target function valueThanIt is more excellent, wherein, I be withWith the vector of dimension.
Embodiment 1
Parallel reaction occurs in tubular reactor：A → B and A → C, reaction rate constant are respectively k1And k2, B is target
Product, C are accessory substances, and target is that control obstruction flow rate make it that concentration of the product B in reactor end is maximum.Use xA(t) and xB
(t) concentration of material A and B along pipe range, u (t)=k are represented respectively1L/v (t) is the physical quantity directly related with obstruction flow rate, its
Middle L is reactor length, and v (t) is obstruction flow rate.Finally, which can be expressed as with abbreviation：
Wherein, J represents to want the concentration of maximized material B, tfRepresent the end of tubular reactor, t is represented along pipe range side
To length change, the initial concentration of x (0) material As and B in tubular reactor inlet.In order to obtain so that target product B is in pipe
The flow rate control strategy of formula reactor ends concentration maximum, DCS operation control grids become more meticulous optimization method, its operational process is such as
Shown in Fig. 3, performing step is：
Step F1：Information acquisition module 31 obtains initial concentration x (0)=[1 0] for the reaction mass that engineer specifiesT、
The target product B and flow rate control for needing maximization concentration require 0≤u (t)≤5；
Step F2：Initialization module 32 brings into operation, and is parameterized using piece-wise constant, sets the segments of pipe range for 8, is right
The control grid answeredTo be evenly dividing, flow rate control strategy parametrization vector initial guess u(k)For 0.5, setting
The computational accuracy tol of NLP problems1The convergence precision tol to become more meticulous with grid2Respectively 10-7With 10-6, by iterations k1With
Become more meticulous number k2Zero setting；
Step F3：Work as k2When=0, step F4 is performed；Otherwise, by controlling grid to become more meticulous module 33 to controlling gridProcess of refinement is carried out, obtains new control gridAnd its corresponding parametrization vector
Step F4：The material concentration of the acquisition current iteration of module 34 is solved by ODEAnd target function value
Step F5：The gradient information of current iteration is obtained by gradient computing module 35Work as k1Step is skipped when=0
F6 directly performs step F7；
Step F6：NLP problem solver modules 36 are run, and convergence judgement is carried out by NLP convergences judgment module, ifWith the target function value of last iterationThe absolute value of difference be less than precision tol1, then judge convergence sexual satisfaction, hold
Row step F9；If convergence is unsatisfactory for, step F7 is continued to execute；
Step F7：WithValue coveringValue, and by iterations k1Increase by 1；
Step F8：NLP problem solver modules 36 utilize the target function value and gradient information obtained in step F4 and F5,
By calculating search direction and optimizing step-length, ratio is obtainedMore preferably new flow rate control strategyThe step has performed
Jump to step F4 again after；
Step F9：The convergence that becomes more meticulous judgment module 37 is run, noteWork as k2When=0, step F10 is performed, it is no
Then, judgeThe target function value to become more meticulous with the last timeThe absolute value of difference whether be less than precision tol2, if so,
Then judge convergence sexual satisfaction, and the opening degree instruction of the flow rate control conversion mixing flow rate valve of current iteration is exported, otherwise receive
Holding back property is unsatisfactory for, and puts the number k that becomes more meticulous2:=k2+ 1, step F3 is continued to execute, until the convergence judgment module satisfaction that becomes more meticulous is
Only.
Finally, DCS by by control grid become more meticulous optimization method obtain flow rate control strategy be converted to flow rate control
The opening degree instruction of valve, the digital analog converter at flow rate control valve end is sent to by fieldbus networks, makes flow rate control valve
Door performs corresponding actions according to received control instruction, while gathers tubular reactor in real time with flow rate sensor and be distributed along pipe range
Flow rate, be passed back to DCS with fieldbus networks after analog-digital converter, and shown in master control room.
Above content is that a further detailed description of the present invention in conjunction with specific preferred embodiments, it is impossible to is assert
The specific implementation of the present invention is only limited to these explanations.For general technical staff of the technical field of the invention, not
On the premise of departing from inventive concept, some simple deduction or replace can also be made, should all be considered as belonging to the protection of the present invention
Scope.
Claims (1)
1. the tubular reactor dynamic optimization system to be become more meticulous based on control grid, can carry out tubular reactor flow rate automatic
Control, to maximize the concentration of tubular reactor end target product.It is characterized in that：Sensed by tubular reactor 11, flow rate
Device 12, analog-digital converter 13, fieldbus networks 14, DCS15, master control room flow rate and product design show 16, flow rate control valve
The digital analog converter 17 at door end, flow rate control valve 18 are formed.Control room engineer specifies tubular reactor respectively to be reacted in feeding
The concentration of material, DCS by control grid become more meticulous optimization method must send as an envoy to target product in tubular reactor ends concentration most
Big flow rate control strategy, and the opening degree instruction of flow rate control valve is converted to, it is sent to flow rate control by fieldbus networks
The digital analog converter of valve end processed, makes flow rate control valve perform corresponding actions, and flow rate sensor gathers tubular reactor in real time
Flow rate is simultaneously passed back to DCS, control room engineer is grasped production process at any time.The operational process of the system includes：
Step A1：Control room engineer specifies the concentration of each reaction mass in tubular reactor charging, needs to maximize concentration
Target product and flow rate control require；
Step A2：DCS performs internal control grid and becomes more meticulous optimization method, and obtaining makes tubular reactor end target product dense
Spend maximum flow rate control strategy；
Step A3：The flow rate control strategy for calculating acquisition is converted to the opening degree instruction of flow rate control valve by DCS, by live total
Gauze network is sent to the digital analog converter at flow rate control valve end, flow rate control valve is performed phase according to received control instruction
It should act；
Step A4：The flow rate sensor of tubular reactor gathers flow rate in real time, and fieldbus networks are used after analog-digital converter
DCS is passed back to, and is shown in master control room, control room engineer is grasped production process at any time.
The DCS, including information acquisition module, initialization module, control grid become more meticulous module, ODE solve module, gradient
Computing module, Non-Linear Programming (Non-linear Programming, abbreviation NLP) problem solver module, become more meticulous convergence
Judgment module, control instruction output module.Wherein information acquisition module includes input concentration state acquisition, target product gathers,
Flow rate control requires three submodules of collection, and NLP problem solver modules include search direction calculating, optimizing step size computation, NLP are received
Holding back property judges three submodules.
To obtain the flow rate control strategy for making tubular reactor end target product concentration maximum, the control that the DCS is performed
Grid becomes more meticulous optimization method, and operating procedure is as follows：
Step B1：Information acquisition module 31 obtains the initial concentration for the reaction mass that engineer specifies, needs to maximize concentration
Target product and flow rate control require；
Step B2：Initialization module 32 brings into operation, and is parameterized using piece-wise constant, sets the segments N of pipe range, corresponding control
Grid processed isThe initial guess of the parametrization vector of flow rate control strategySet the computational accuracy tol of NLP problems1
The convergence precision tol to become more meticulous with grid2, by iterations k1With the number k that becomes more meticulous2Zero setting；
Step B3：Work as k2When=0, step B4 is performed；Otherwise, by controlling grid to become more meticulous module 33 to controlling gridInto
Row process of refinement, obtains new control gridAnd its corresponding parametrization vector
Step B4：The material concentration of the acquisition current iteration of module 34 is solved by ODEAnd target function value
Step B5：The gradient information of current iteration is obtained by gradient computing module 35Work as k1It is straight that step B6 is skipped when=0
Connect and perform step B7；
Step B6：NLP problem solver modules 36 are run, and convergence judgement is carried out by NLP convergences judgment module, if
With the target function value of last iterationThe absolute value of difference be less than precision tol1, then judge convergence sexual satisfaction, perform step
Rapid B9；If convergence is unsatisfactory for, step B7 is continued to execute；
Step B7：WithValue coveringValue, and by iterations k1Increase by 1；
Step B8：NLP problem solver modules 36 are passed through using the target function value and gradient information that are obtained in step B4 and B5
Search direction and optimizing step-length are calculated, obtains ratioMore preferably new flow rate control strategyAfter the completion of the step performs
Step B4 is jumped to again；
Step B9：The convergence that becomes more meticulous judgment module 37 is run, noteWork as k2When=0, step B10 is performed, otherwise,
JudgeThe target function value to become more meticulous with the last timeThe absolute value of difference whether be less than precision tol2, if it is,
Judge convergence sexual satisfaction, and the opening degree instruction of the flow rate control strategy conversion flow rate control valve of current iteration is exported, otherwise
Convergence is unsatisfactory for, and puts the number k that becomes more meticulous2:=k2+ 1, step B3 is continued to execute, until the convergence judgment module satisfaction that becomes more meticulous
Untill.
The control grid becomes more meticulous module, is realized using following steps：
Step C1：The left slope of grid node is calculated by the following formulaWith right slope
<mrow>
<msubsup>
<mi>s</mi>
<mi>k</mi>
<mo>-</mo>
</msubsup>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>u</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>u</mi>
<mi>k</mi>
</msub>
</mrow>
<mrow>
<msub>
<mi>t</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>s</mi>
<mi>k</mi>
<mo>+</mo>
</msubsup>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>u</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>u</mi>
<mi>k</mi>
</msub>
</mrow>
<mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>t</mi>
<mi>k</mi>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, ukRepresent k-th of component of the parametrization vector u of flow rate control strategy, tkRepresent ukAnd uk+1Between grid section
Point.
Step C2：If grid node tkThe left and right slope at place meets following requirement, then the node is rejected from grid：
<mrow>
<mo>|</mo>
<msubsup>
<mi>s</mi>
<mi>k</mi>
<mo>-</mo>
</msubsup>
<mo>|</mo>
<mo>+</mo>
<mo>|</mo>
<msubsup>
<mi>s</mi>
<mi>k</mi>
<mo>+</mo>
</msubsup>
<mo>|</mo>
<mo>&le;</mo>
<msub>
<mi>&epsiv;</mi>
<mi>e</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, εeIt is a less arithmetic number.Grid node tkAfter rejecting, ukAnd uk+1Corresponding mesh update is new for one
Grid, parameter thereon is updated to (uk+uk+1)/2。
Step C3：If grid node tkThe left slope at place meets：
<mrow>
<mo>|</mo>
<msubsup>
<mi>s</mi>
<mi>k</mi>
<mo>-</mo>
</msubsup>
<mo>|</mo>
<mo>&GreaterEqual;</mo>
<msub>
<mi>&epsiv;</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, εiIt is one and is more than εeArithmetic number, then in [tk-1,tk] on interpenetration network node；If grid node tkThe right side at place is tiltedly
Rate meets：
<mrow>
<mo>|</mo>
<msubsup>
<mi>s</mi>
<mi>k</mi>
<mo>+</mo>
</msubsup>
<mo>|</mo>
<mo>&GreaterEqual;</mo>
<msub>
<mi>&epsiv;</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Then in [tk-1,tk] on interpenetration network node.During practical application, it can freely be set and added according to the order of magnitude of left and right slope
The number of ingress.
Step C4：According to the node rejected and be inserted into step C2 and C3, generate new control grid and it is corresponding parameterize to
Amount.
The ODE solves module, and using four step Runge-Kutta methods, calculation formula is：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mfrac>
<mi>h</mi>
<mn>6</mn>
</mfrac>
<mrow>
<mo>&lsqb;</mo>
<mrow>
<mi>K</mi>
<mn>1</mn>
<mo>+</mo>
<mn>2</mn>
<mi>K</mi>
<mn>2</mn>
<mo>+</mo>
<mn>2</mn>
<mi>K</mi>
<mn>3</mn>
<mo>+</mo>
<mi>K</mi>
<mn>4</mn>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>K</mi>
<mn>1</mn>
<mo>=</mo>
<mi>F</mi>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>K</mi>
<mn>2</mn>
<mo>=</mo>
<mi>F</mi>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<mfrac>
<mi>h</mi>
<mn>2</mn>
</mfrac>
<mo>,</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mfrac>
<mi>h</mi>
<mn>2</mn>
</mfrac>
<mi>K</mi>
<mn>1</mn>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>K</mi>
<mn>3</mn>
<mo>=</mo>
<mi>F</mi>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<mfrac>
<mi>h</mi>
<mn>2</mn>
</mfrac>
<mo>,</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mfrac>
<mi>h</mi>
<mn>2</mn>
</mfrac>
<mi>K</mi>
<mn>2</mn>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>K</mi>
<mn>4</mn>
<mo>=</mo>
<mi>F</mi>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<mi>h</mi>
<mo>,</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>h</mi>
<mi>K</mi>
<mn>3</mn>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, t represents the change along pipe range direction, tiRepresent the integration moment of Runge-Kutta method choices, ti+1Expression is located at
Moment tiThe integration moment afterwards, integration step h be any two it is adjacent integration the moment differences, x (ti) represent away from tubular reactor entrance
tiThe material concentration at place, F () are the functions for describing state differential equation, and K1, K2, K3, K4 represent Runge-Kutta methods respectively
The functional value of 4 nodes in integral process.
The gradient computing module, using adjoint method:
Step D1：λ (t) is made to be determined for association's state vector, its value by adjoint equation：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mover>
<mi>&lambda;</mi>
<mo>&CenterDot;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>-</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<mi>H</mi>
</mrow>
<mrow>
<mo>&part;</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>f</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<mi>&Phi;</mi>
<mo>&lsqb;</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>f</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mrow>
<mo>&part;</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, tfRepresent the end of tubular reactor, H represents hamilton's function, and H=L+ λ (t)TF, L are object function
Integral term, Φ [x (tf)] be object function stable state item.
Step D2：For adjoint equation, association state vector λ (t) is obtained at each integration moment using four step Runge-Kutta methods
Value, calculation formula are：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mfrac>
<mi>h</mi>
<mn>6</mn>
</mfrac>
<mrow>
<mo>&lsqb;</mo>
<mrow>
<mi>Q</mi>
<mn>1</mn>
<mo>+</mo>
<mn>2</mn>
<mi>Q</mi>
<mn>2</mn>
<mo>+</mo>
<mn>2</mn>
<mi>Q</mi>
<mn>3</mn>
<mo>+</mo>
<mi>Q</mi>
<mn>4</mn>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>Q</mi>
<mn>1</mn>
<mo>=</mo>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>,</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>Q</mi>
<mn>2</mn>
<mo>=</mo>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<mfrac>
<mi>h</mi>
<mn>2</mn>
</mfrac>
<mo>,</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mfrac>
<mi>h</mi>
<mn>2</mn>
</mfrac>
<mi>Q</mi>
<mn>1</mn>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>Q</mi>
<mn>3</mn>
<mo>=</mo>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<mfrac>
<mi>h</mi>
<mn>2</mn>
</mfrac>
<mo>,</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mfrac>
<mi>h</mi>
<mn>2</mn>
</mfrac>
<mi>Q</mi>
<mn>2</mn>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>Q</mi>
<mn>4</mn>
<mo>=</mo>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<mi>h</mi>
<mo>,</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>h</mi>
<mi>Q</mi>
<mn>3</mn>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, t represents the change along pipe range direction, tiThe integration moment selected in module, t are solved for ODEi+1Expression is located at the moment
tiIntegration moment afterwards, and ti+1=ti+ h, h are integration step, and Q1, Q2, Q3, Q4 represent that Runge-Kutta methods integrate respectively
During 4 nodes functional value.
Step D3：Based on the value of obtained association state vector λ (t), gradient information is obtained by the following formula
<mrow>
<msup>
<mi>g</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>k</mi>
<mn>1</mn>
</msub>
<mo>)</mo>
</mrow>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mo>&Integral;</mo>
<msub>
<mi>t</mi>
<mn>0</mn>
</msub>
<msub>
<mi>t</mi>
<mi>f</mi>
</msub>
</msubsup>
<mfrac>
<mrow>
<mo>&part;</mo>
<mi>H</mi>
</mrow>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>u</mi>
<mn>1</mn>
<mrow>
<mo>(</mo>
<msub>
<mi>k</mi>
<mn>1</mn>
</msub>
<mo>)</mo>
</mrow>
</msubsup>
</mrow>
</mfrac>
<mi>d</mi>
<mi>t</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<msubsup>
<mo>&Integral;</mo>
<msub>
<mi>t</mi>
<mn>0</mn>
</msub>
<msub>
<mi>t</mi>
<mi>f</mi>
</msub>
</msubsup>
<mfrac>
<mrow>
<mo>&part;</mo>
<mi>H</mi>
</mrow>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>u</mi>
<mn>2</mn>
<mrow>
<mo>(</mo>
<msub>
<mi>k</mi>
<mn>1</mn>
</msub>
<mo>)</mo>
</mrow>
</msubsup>
</mrow>
</mfrac>
<mi>d</mi>
<mi>t</mi>
</mrow>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,WithRepresentFirst and second component, and so on.
The NLP problem solver modules, are realized using following steps：
Step E1：IfWith the target function value of last iterationThe difference of absolute value be less than precision tol1, then judge
Sexual satisfaction is restrained, returns to the flow rate control strategy that current iteration obtains；If convergence is unsatisfactory for, step E2 is continued to execute；
Step E2：WithValue coveringValue, and by iterations k1Increase by 1；
Step E3：By flow rate control strategyAs some point in vector space, P is denoted as1, P1Corresponding target function value
It is exactly
Step E4：From point P1Set out, according to the NLP algorithms of selection and point P1The gradient information at placeConstruct in vector space
One search directionAnd step-length
Step E5:Pass through formulaConstruct corresponding in vector spaceAnother point P2, make
Obtain P2Corresponding target function valueThanIt is more excellent, wherein, I be withWith the vector of dimension.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711116200.3A CN107908110B (en) | 2017-11-13 | 2017-11-13 | Tubular reactor dynamic optimization system based on control grid refinement |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711116200.3A CN107908110B (en) | 2017-11-13 | 2017-11-13 | Tubular reactor dynamic optimization system based on control grid refinement |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107908110A true CN107908110A (en) | 2018-04-13 |
| CN107908110B CN107908110B (en) | 2020-03-03 |
Family
ID=61845175
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711116200.3A Expired - Fee Related CN107908110B (en) | 2017-11-13 | 2017-11-13 | Tubular reactor dynamic optimization system based on control grid refinement |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107908110B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110119085A (en) * | 2019-04-03 | 2019-08-13 | 杭州电子科技大学 | A kind of Manutec R3 type industrial robot dynamic optimization system |
| CN111308893A (en) * | 2020-03-05 | 2020-06-19 | 江南大学 | Non-uniform grid optimization method for bioreactor fed-batch regulation |
| CN116382209A (en) * | 2023-04-19 | 2023-07-04 | 扬州市管件厂有限公司 | Process optimization method and system for seamless elbow machining |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070038335A1 (en) * | 2005-07-22 | 2007-02-15 | Mcintyre Timothy J | Real Power Regulation for the Utility Power Grid Via Responsive Loads |
| 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 |
| CN101763086A (en) * | 2009-12-29 | 2010-06-30 | 浙江大学 | High-efficiency system and method for dynamic optimization of industrial process |
| CN101763083A (en) * | 2009-12-29 | 2010-06-30 | 浙江大学 | An effective industrial process dynamic optimization system and method capable of controlling variable parameterization |
| CN103309234A (en) * | 2013-06-08 | 2013-09-18 | 浙江大学 | Orthogonal collocation optimization based batch reactor control system |
| CN103324086A (en) * | 2013-06-08 | 2013-09-25 | 浙江大学 | Batch reactor control system based on accurate punishment optimization |
-
2017
- 2017-11-13 CN CN201711116200.3A patent/CN107908110B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070038335A1 (en) * | 2005-07-22 | 2007-02-15 | Mcintyre Timothy J | Real Power Regulation for the Utility Power Grid Via Responsive Loads |
| 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 |
| CN101763086A (en) * | 2009-12-29 | 2010-06-30 | 浙江大学 | High-efficiency system and method for dynamic optimization of industrial process |
| CN101763083A (en) * | 2009-12-29 | 2010-06-30 | 浙江大学 | An effective industrial process dynamic optimization system and method capable of controlling variable parameterization |
| CN103309234A (en) * | 2013-06-08 | 2013-09-18 | 浙江大学 | Orthogonal collocation optimization based batch reactor control system |
| CN103324086A (en) * | 2013-06-08 | 2013-09-25 | 浙江大学 | Batch reactor control system based on accurate punishment optimization |
Non-Patent Citations (1)
| Title |
|---|
| 胡云卿: "基于控制变量参数化的带约束最优控制问题计算方法", 《中国博士学位论文全文数据库信息科技辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110119085A (en) * | 2019-04-03 | 2019-08-13 | 杭州电子科技大学 | A kind of Manutec R3 type industrial robot dynamic optimization system |
| CN110119085B (en) * | 2019-04-03 | 2022-03-15 | 杭州电子科技大学 | Manutec R3 type industrial robot dynamic optimization system |
| CN111308893A (en) * | 2020-03-05 | 2020-06-19 | 江南大学 | Non-uniform grid optimization method for bioreactor fed-batch regulation |
| CN116382209A (en) * | 2023-04-19 | 2023-07-04 | 扬州市管件厂有限公司 | Process optimization method and system for seamless elbow machining |
| CN116382209B (en) * | 2023-04-19 | 2023-10-03 | 扬州市管件厂有限公司 | Process optimization method and system for seamless elbow machining |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107908110B (en) | 2020-03-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Double-mode energy management for multi-energy system via distributed dynamic event-triggered Newton-Raphson algorithm | |
| CN103280821B (en) | Multi-period dynamic reactive power optimization method of intelligent power distribution system | |
| CN103591637B (en) | A kind of central heating secondary network runing adjustment method | |
| CN107908110A (en) | The tubular reactor dynamic optimization system to be become more meticulous based on control grid | |
| CN103309234B (en) | A kind of batch reactor control system optimized based on orthogonal configuration | |
| CN103036245B (en) | A kind of interconnected network Novel AC-DC works in coordination with the method and system reducing network loss | |
| CN106532710B (en) | The micro-capacitance sensor tide optimization method of meter and Voltage Stability Constraints | |
| CN105511263B (en) | A kind of distributed model predictive control method based on hierachical decomposition | |
| CN112636331B (en) | Dynamic economic dispatching distributed optimization method and system for smart power grid | |
| CN101976837A (en) | Method for adjusting unsolvable power flow of electric power system | |
| CN107749628A (en) | The multiple target voltage optimization method that meter and Gas Generator Set Reactive-power control and thermoelectricity are coordinated | |
| CN108462184A (en) | A kind of circuit on power system string benefit Optimal Configuration Method | |
| CN103324086B (en) | A kind of batch reactor control system based on accurately punishment optimization | |
| CN107943119A (en) | The catalyst mixing dynamic optimization system to be become more meticulous based on control grid | |
| CN107656439A (en) | A kind of moon detector in flexible landing optimal control system based on Self Adaptive Control grid | |
| CN104901318A (en) | Multi-objective reactive power optimization method for solving Pareto optimal solution set | |
| CN107703899B (en) | A kind of catalyst hybrid reaction control device based on empirical mode decomposition dynamic optimization | |
| CN106253288A (en) | A kind of optimal load flow algorithm containing THE UPFC based on automatic differential | |
| CN107807528A (en) | A kind of plug flow tubular reactor optimal control system based on adaptive congestion control algorithm node | |
| CN108493939A (en) | A kind of air conditioner load Optimization Scheduling based on optimal load flow | |
| CN109494750B (en) | Hierarchical distributed voltage optimization control method for high and medium voltage distribution network | |
| CN107844056A (en) | A kind of plug flow tubular reactor optimal control system based on adaptive congestion control algorithm grid | |
| CN107093904A (en) | Distribution network var compensation operating point choosing method based on random matrix analysis of spectrum | |
| CN107918277A (en) | A kind of plug flow tubular reactor optimal control system based on self adaptive control grid | |
| CN109586313A (en) | The efficient interior point method for solving of idle work optimization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200303 Termination date: 20211113 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |