CN106773646B - A kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control - Google Patents
A kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control Download PDFInfo
- Publication number
- CN106773646B CN106773646B CN201611113422.5A CN201611113422A CN106773646B CN 106773646 B CN106773646 B CN 106773646B CN 201611113422 A CN201611113422 A CN 201611113422A CN 106773646 B CN106773646 B CN 106773646B
- Authority
- CN
- China
- Prior art keywords
- crude oil
- value
- catalytic cracking
- preheating temperature
- individual
- 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.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 57
- 239000010779 crude oil Substances 0.000 title claims abstract description 43
- 238000004523 catalytic cracking Methods 0.000 title claims abstract description 31
- 238000011835 investigation Methods 0.000 title claims abstract description 11
- 239000011159 matrix material Substances 0.000 claims abstract description 26
- 239000013598 vector Substances 0.000 claims description 7
- 230000001105 regulatory effect Effects 0.000 claims description 5
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000009795 derivation Methods 0.000 claims description 3
- 230000035772 mutation Effects 0.000 claims description 2
- 230000006641 stabilisation Effects 0.000 claims description 2
- 238000011105 stabilization Methods 0.000 claims description 2
- 238000006467 substitution reaction Methods 0.000 claims description 2
- 238000005457 optimization Methods 0.000 abstract description 9
- 229920012306 M5 Rigid-Rod Polymer Fiber Polymers 0.000 abstract description 4
- 230000009514 concussion Effects 0.000 description 3
- 238000011112 process operation Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005530 etching Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 238000005504 petroleum refining Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 230000017105 transposition Effects 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
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/1919—Control of temperature characterised by the use of electric means characterised by the type of controller
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The present invention relates to a kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Controls, it specifically includes: first by bonding state variable, output tracking error and setting value variable, establish the Extended state space model of catalytic cracking process Crude Oil preheating, it is shaken preferably to handle hyperharmonic caused by the unknown disturbance being likely to occur in crude oil warm, carry out the weighting matrix in optimization object function followed by improved MOEA/D-DE algorithm, finally designs a kind of PI-PD (PFC-PIPD) controller with Predictive function control performance.The method increase the accuracy and speeds of temperature tracking, have good control performance.
Description
Technical field
The present invention relates to technical field of automation in industry, more particularly to a kind of catalytic cracking process Crude Oil preheating temperature
Control method.
Background technique
Catalytic cracking process is the key link of petroleum refining, and in catalytic cracking process, the preheating temperature of crude oil is
One very important variable directly affects the thermal balance and reaction depth of reaction process.Therefore good preheating temperature is realized
Control can directly improve the productivity effect of crude oil.Traditional PID is only applicable to Small Time Lag or the control system without time lag, and former
The dynamic process of oil preheating is the process of a large dead time, when input step signal, can often generate biggish hyperharmonic concussion,
It influences to control the preheating temperature of crude oil.Therefore the control performance for proposing that new control method improves Crude Oil Pre-heating System of Atmospheric is urgent
It needs.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control,
It is able to suppress the hyperharmonic concussion of warm appearance, improves control performance.
The technical solution adopted by the present invention to solve the technical problems is: providing a kind of catalytic cracking process Crude Oil preheating
Temprature control method, comprising the following steps:
(1) by bonding state variable, output tracking error and setting value variable, it is pre- to establish catalytic cracking process Crude Oil
The Extended state space model of heat；
(2) good point set theory is introduced into as the weighting matrix determined in objective function using improved MOEA/D-DE algorithm；
(3) design controls crude oil preheating temperature with the PI-PD controller of Predictive function control performance.
The step (1) includes following sub-step:
(11) local increment is established using the method for Real-time data drive；
(12) difference equation model of catalytic cracking Crude Oil preheating temperature process is established according to local increment；
(13) state-space model of catalytic cracking Crude Oil preheating temperature process is established according to difference equation model.
It further include that the state-space model that will be obtained is converted to comprising state variable, output tracking mistake after the step (13)
The step of Extended state space model of difference and setting value variable.
The step (2) specifically includes following sub-step:
(21) objective function of catalytic cracking crude oil preheating temperature process is chosen；
(22) method that good point set is taken in n-dimensional space to good point set theory is described briefly；
(23) initial population that good point set theory initialization size is N is introduced, and is initialized for storing the non-branch of Pareto
External population with solution is empty set；
(24) target function value of each individual in initial population is calculated according to regulating time and overshoot respectively, wherein
According to the calculated target function value of regulating time as first object value, according to the calculated target function value conduct of overshoot
Second target value；
(25) ideal point is initialized, the ideal point is the minimum in population in first object value corresponding to each individual
Minimum value in value and the second target value；
(26) multi-objective problem N number of son is resolved into N number of weight vector equally distributed in Chebyshev's decomposition method to ask
Topic, and determine the objective function of each subproblem；
(27) for the weight vector of each subproblem determined, in calculating other weight vectors and determining that the Europe of subproblem is several
Obtain the T neighbours subproblem that distance obtains each subproblem；
(28) individual corresponding to the objective function of each subproblem carries out differential evolution operator and obtains temporary individual, and
Multinomial mutation operation is carried out to temporary individual, obtains variation temporary individual；
(29) the first object value and the second target value for calculating variation temporary individual, if the optimal value of each target
Value less than the corresponding objective function of individual of each subproblem then updates ideal point；
(210) all T neighbours subproblems point of j-th of subproblem are updated by variation temporary individual and its target value
Not corresponding individual and the corresponding target function value of each individual, and update the non-domination solution stored in external population；
(211) reaching maximum number of iterations then terminates, and provides one group of Pareto optimal solution of weighting matrix.
The step (3) specifically: choose control time domain, obtained weighting matrix is applied into the preheating of catalytic cracking crude oil
In the objective function of temperature course, and to the objective function derivation and make its 0, obtain optimal control law, define one most
Big allowable error, when the error of system is less than or equal to the limits of error, the system of being considered as has reached stabilization, obtains PI-
The parameter of PD control device controls crude oil preheating temperature parameter substitution PI-PD controller.
Beneficial effect
Due to the adoption of the above technical solution, compared with prior art, the present invention having the following advantages that and actively imitating
Fruit: the present invention establishes one kind and changes by establishing real-time data base, model foundation, PREDICTIVE CONTROL, algorithm optimization
Into MOEA/D-DE algorithm optimization catalytic cracking process in crude oil Investigation On The Preheating Temperature Control, can effectively be mentioned using this method
The control precision and stability of high system.
Detailed description of the invention
Fig. 1 is the crude oil preheating temperature control system architecture figure of the RFC-PIPD of improved MOEA/D-DE algorithm optimization；
Fig. 2 is the weighting matrices Q flow chart determined in objective function using improved MOEA/D-DE algorithm.
Specific embodiment
Present invention will be further explained below with reference to specific examples.It should be understood that these embodiments are merely to illustrate the present invention
Rather than it limits the scope of the invention.In addition, it should also be understood that, after reading the content taught by the present invention, those skilled in the art
Member can make various changes or modifications the present invention, and such equivalent forms equally fall within the application the appended claims and limited
Range.
Embodiments of the present invention are related to a kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control, for inhibiting pre-
The hyperharmonic concussion that thermal process occurs, improves control performance.The present invention pass through first bonding state variable, output tracking error and
Setting value variable establishes the Extended state space model of catalytic cracking process Crude Oil preheating, preferably to handle crude oil preheating
Hyperharmonic is shaken caused by the unknown disturbance being likely to occur in the process, is optimized followed by improved MOEA/D-DE algorithm
Weighting matrix in objective function finally designs a kind of PI-PD (PFC-PIPD) controller with Predictive function control performance.
The method increase the accuracy and speeds of temperature tracking, have good control performance.The control system established according to the present invention
As shown in Figure 1.
The step of the method for the present invention includes:
Step 1 establishes the Extended state space model of controlled device, specifically:
1.1 establish local increment using the method for Real-time data drive: establishing the real time execution of crude oil warm
Database acquires real-time process operation data by data acquisition device, using the real-time process operation data of acquisition as data
The sample set of drivingWherein,Indicate the input value of i-th group of technological parameter, y (i) indicates i-th group of technique ginseng
Several output valves, N indicate sampling sum；It is established based on the real-time process operation data set of the object and is based on least square
The controlled local autoregressive moving-average model of the discrete differential equation form of algorithm:
Wherein, yL(k) output valve of the technological parameter of k moment local increment is indicated,It indicates to pass through what identification obtained
The set of model parameter, F and H are the parameter obtained by debating knowledge,Indicate the last time of the technological parameter of local increment
The set for outputting and inputting data, u (k-d-1) indicates that the corresponding control variable of k-d-1 etching processes parameter, d+1 are practical
The time lag of process, Τ are the transposition symbol of matrix；
The identification means of use are as follows:
Wherein,It is two matrixes in parameter identification with P,γ ∈ (0,1) indicate forget because
Son, I indicate unit matrix；
1.2, using coefficient obtained in step 1.1, establish the difference equation mould of catalytic cracking Crude Oil preheating temperature process
Type, form are as follows:
Δ y (k)+H Δ y (k-1)=F Δ u (k-d-1)
Wherein, Δ is difference operator, and F, H are by debating the parameter known and obtained in 1.1 steps, and d is time lag item；Δ y (k) table
Show that the output increment at k moment, Δ u (k-d-1) indicate that the control at k-d-1 moment inputs increment；
1.3, according to the difference equation in step 1.2, establish the state space mould of catalytic cracking Crude Oil preheating temperature process
Type, form are as follows:
Wherein,
Cm=(100 ... 0)
Wherein, Δ x (k) indicates the state variable at k moment, AmFor (d+1) × (d+1) rank matrix, BmFor (d+1) × 1 rank square
Battle array, CmFor 1 × (d+1) rank matrix；
1.4 are converted to state-space model obtained in step 1.3 comprising state variable, output tracking error and setting
It is worth the Extended state space model of variable, form is as follows:
Z (k+1)=Az (k)+B Δ u (k)+C Δ r (k+1)
Setting value be set as 1 it is constant when, Δ r (k+1)=0, Extended state space model simplify are as follows:
Z (k+1)=Az (k)+B Δ u (k)
In formula,
E (k)=r (k)-y (k)
Wherein, z (k) indicates that the quantity of state at k moment, r (k) are the idea output at k moment, and e (k) is that k moment ideal is defeated
Difference between value and real output value out.
Step 2 determines the weighting matrices Q in objective function using improved MOEA/D-DE algorithm, as shown in Fig. 2, tool
Body is:
2.1 choose the objective function J of catalytic cracking crude oil preheating temperature process, and form is as follows:
Q=Qf=diag { qjy1,qjy2,…,qjyn,qju1,…,qjum-1,qje, Q > 0, R > 0, Qf> 0 respectively indicates state mistake
Weighting matrix, weighted input matrix and the terminal weighting matrix of journey, [k0,kf] it is optimization time domain, qj1,qj2,…qjum-1It indicated
The weight coefficient of journey state, qjeFor the weight coefficient of output tracking error, q is taken in present embodimentje=1；
2.2 take real coding mode to take good point set in n-dimensional space H method it is as follows: 1. set initial population scale as
N, chromosome2. make the good point set containing N number of point in n-dimensional space,
Pn(i)={ { r1×i},{r2×i},…,{rn× i }, i=1,2 ..., n }, wherein takingP is the least prime for meeting (p-n)/2 >=n；3. whenWhen for real coding, if value
Range isIt takes
?；
2.3 introduce the initial population X={ x that good point set theory initialization size is N1,x2,…,xN, each xnAll represent
One by all elements { q in Qjy1,qjy2,…,qjyn,qju1,…,qjum-1,qjeComposition population at individual；External kind of initialization
Group EP is empty set, and storage Pareto non-domination solution is used for during searching for optimal solution.
2.4 respectively according to regulating time tsThe target function value F of each individual is calculated with overshoot σn(x), according to index
tsThe f obtainedts(x) it is used as target function value FnFirst aim value, the f obtained according to index σos(x) it is used as target letter
Numerical value FnSecond target value:
Fn(x)=[fts(x), fos(x)]
2.5 initialization ideal point Z*；Wherein It is first aim function fts(x) it up to the present looks for
The minimum value arrived,It is second target function fos(x) minimum value up to the present found；
2.6 by multi-objective problem F (x)=min (fts(x), fos(x)) N number of son is resolved into Chebyshev's decomposition method to ask
The objective function of topic, each specific subproblem is as follows:
In above formula,Current reference point, i.e., each target it is current optimal value composition vector,
The value of m is 2 in present embodiment；gte(x|λj,z*) indicate j-th of subproblem objective function；It is j-th of son
The weight of problem,X indicates a population at individual, fi(x) j-th of subproblem is indicated
Corresponding i-th of the objective function of individual value；
2.7 according to each subproblem gte(x|λj,z*) weight λj, calculate the T neighbours subproblem of each subproblem
B (j)=(Bj1,Bj2,…,BjT), use BjiIt indicates i-th of neighbours subproblem of j-th of subproblem, takes T=in present embodiment
20, i=1,2 ..., T；
2.8 couples of each subproblem gte(x|λj,z*) corresponding individual carries out differential evolution (DE) operation and obtain interim
Body y；
2.9 couples of temporary individual y carry out multinomial variation (PLM) operation, obtain individual y '；
2.10 calculate two target function value F of new temporary individual y 'j', if for each j=1,2 ..., m hasThen update ideal point z*；
2.11 pass through new temporary individual Pj(t+1) and its target value Fj' updates all T neighbours of j-th of subproblem
The corresponding individual of subproblem B (j) and the corresponding target function value of each individual, and update and stored in external population EP
Non-domination solution；
2.12 reach maximum number of iterations G, and then algorithm terminates, and provides one group of Pareto optimal solution { X of weighting matrices Qp,1
≤ p≤N }, G=200 is given in this embodiment；
2.13 according to the actual needs of production scene, and the Pareto optimal solution that selecting step 2.11 obtains is as optimal
Optimal weighting matrices Q can be obtained in satisfactory solution；
Step 3 design function is in the PFC- of the improved MOEA/D-DE algorithm optimization crude oil warm of controlled device
PIPD controller, specific method are:
3.1 take control time domain M=1, and the state variable z (k+P) after step of current k moment P is represented by
Z (k+P)=APz(k)+αΔu(k)+θΔR
Wherein
P is prediction time domain, and M < P, A indicate that (d+1) × (d+1) rank state matrix, B indicate (d+1) × 1 rank input matrix, C
Indicate that 1 × (d+1) rank output matrix, α are indicated by AP-1Be multiplied constituted matrix with B, AP-1Indicate that P-1 matrix A is multiplied, θ
Indicate the matrix relationship formula that state matrix A is multiplied constituted with output matrix C, Δ R is indicated by Δ r (k+j) (j=1 ..., P) institute
The matrix of composition, r (k+j) indicate the setting value of controlled device, and β is the softening coefficient of reference locus, and c (k) is according to controlled pair
As the setting value that practical operation needs to set, Δ r (k+j) indicates the setting value knots modification at k+j moment, in this embodiment,
Enable c (k)=1；
3.2 choose the objective function J of catalytic cracking crude oil preheating temperature process, and form is as follows:
Q=Qf=diag { qjy1,qjy2,…,qjyn,qju1,…,qjum-1,qje, Q is improved MOEA/D-DE algorithm optimization
Obtained weighting matrix；
PI-PD controller in the method can be expressed as an incremental form:
U (k)=u (k-1)+Kp(k)(es(k)-es(k-1))+Ki(k)es(k)
-Kf(k)(y(k)-y(k-1))-Kd(y(k)-2y(k-1)+y(k-2))
=u (k-1)+Kp(k)(es(k)-es(k-1))+Ki(k)es(k)-Kf(k)(y(k)-y(k-1))
-Kd(y(k)-y(k-1))+Kd(y(k-1)-y(k-2))
es(k)=c (k)-y (k)
C (k) is the setting value of controlled device, and y (k) is real output value, es(k) indicate that k moment setting value and reality are defeated
Difference between value out, Kp(k),Ki(k),Kf(k),Kd(k) ratio of the forward path of k moment PI-PD controller is respectively indicated
Coefficient, the integral coefficient of forward path, the proportionality coefficient of feedback loop, feedback loop differential coefficient；
U (k), which is expressed as matrix form abbreviation, to be obtained: u (k)=u (k-1)+wT(k)E(k)
In above formula
To objective function J derivation and make its 0, byOptimal control law can be obtained.
A limits of error δ is defined, e is worked ass(k) it when being less than or equal to limits of error δ, is considered as system and has reached
To stable and Kp(k),Ki(k),Kf(k),Kd(k) no longer change, specific explanations are as follows:
When | es(k) | when≤δ
When | es(k) | when > δ
In the parameter K for obtaining PI-PD controllerp(k),Ki(k),Kf(k),Kd(k) it after, can be calculated by step 3.2
To control amount u (k) act on controlled device to controlling crude oil preheating temperature, and recycle according to this.
It is not difficult to find that the present invention is by establishing real-time data base, model foundation, PREDICTIVE CONTROL, algorithm optimization, into
And the crude oil Investigation On The Preheating Temperature Control in a kind of improved MOEA/D-DE algorithm optimization catalytic cracking process is established, it utilizes
This method can effectively improve the control precision and stability of system.
Claims (4)
1. a kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control, which comprises the following steps:
(1) by bonding state variable, output tracking error and setting value variable, the preheating of catalytic cracking process Crude Oil is established
Extended state space model；
(2) good point set theory is introduced into as the weighting matrix determined in objective function using improved MOEA/D-DE algorithm；Specifically
Include:
(21) objective function of catalytic cracking crude oil preheating temperature process is chosen；
(22) method that good point set is taken in n-dimensional space to good point set theory is described briefly；
(23) initial population that good point set theory initialization size is N is introduced, and is initialized for storing Pareto non-domination solution
External population be empty set；
(24) target function value of each individual in initial population is calculated according to regulating time and overshoot respectively, wherein according to
The calculated target function value of regulating time is as first object value, according to the calculated target function value of overshoot as second
Target value；
(25) initialize ideal point, the ideal point be population in it is each individual corresponding to first object value in minimum value and
Minimum value in second target value；
(26) multi-objective problem is resolved into N number of subproblem with N number of weight vector equally distributed in Chebyshev's decomposition method, and
Determine the objective function of each subproblem；
(27) for each determine subproblem weight vector, calculate other weight vectors and determine subproblem Euclid away from
From T neighbours subproblem for obtaining each subproblem；
(28) individual corresponding to the objective function of each subproblem carries out differential evolution operator and obtains temporary individual, and to facing
When individual carry out multinomial mutation operation, obtain variation temporary individual；
(29) the first object value and the second target value for calculating variation temporary individual, if the optimal value of each target is both less than
The value of the corresponding objective function of individual of each subproblem then updates ideal point；
(210) right respectively come all T neighbours subproblems for updating j-th of subproblem by variation temporary individual and its target value
The corresponding target function value of individual and each individual answered, and update the non-domination solution stored in external population；
(211) reaching maximum number of iterations then terminates, and provides one group of Pareto optimal solution of weighting matrix；
(3) design controls crude oil preheating temperature with the PI-PD controller of Predictive function control performance.
2. catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control according to claim 1, which is characterized in that the step
Suddenly (1) includes following sub-step:
(11) local increment is established using the method for Real-time data drive；
(12) difference equation model of catalytic cracking Crude Oil preheating temperature process is established according to local increment；
(13) state-space model of catalytic cracking Crude Oil preheating temperature process is established according to difference equation model.
3. catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control according to claim 2, which is characterized in that the step
It suddenly further include that the state-space model that will be obtained is converted to comprising state variable, output tracking error and setting value variable after (13)
Extended state space model the step of.
4. catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control according to claim 1, which is characterized in that the step
Suddenly (3) specifically: choose control time domain, obtained weighting matrix is applied to the target of catalytic cracking crude oil preheating temperature process
In function, and to the objective function derivation and make its 0, obtain optimal control law, define a limits of error, when being
When the error of system is less than or equal to the limits of error, the system of being considered as has reached stabilization, obtains the parameter of PI-PD controller,
Parameter substitution PI-PD controller controls crude oil preheating temperature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611113422.5A CN106773646B (en) | 2016-12-06 | 2016-12-06 | A kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611113422.5A CN106773646B (en) | 2016-12-06 | 2016-12-06 | A kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106773646A CN106773646A (en) | 2017-05-31 |
| CN106773646B true CN106773646B (en) | 2019-07-23 |
Family
ID=58879228
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611113422.5A Expired - Fee Related CN106773646B (en) | 2016-12-06 | 2016-12-06 | A kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106773646B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113325896B (en) * | 2021-05-31 | 2022-03-01 | 浙江工业大学 | Multi-target temperature optimization control method of intelligent retail machine |
| CN114237035A (en) * | 2021-12-17 | 2022-03-25 | 杭州司南智能技术有限公司 | Prediction function control method based on simplified extended state space model |
| CN116165891B (en) * | 2023-02-10 | 2023-11-14 | 江苏吉泰科电气有限责任公司 | Method for restraining oil pressure overshoot by motor control and computer readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009116515A (en) * | 2007-11-05 | 2009-05-28 | Fuji Electric Systems Co Ltd | Pid parameter adjustment support device |
| CN102156496A (en) * | 2011-04-07 | 2011-08-17 | 杭州电子科技大学 | Blending control method for temperature of reactive kettle |
| CN104317194A (en) * | 2014-09-23 | 2015-01-28 | 杭州电子科技大学 | Temperature control method for non-minimal state space model predictive control optimization |
-
2016
- 2016-12-06 CN CN201611113422.5A patent/CN106773646B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009116515A (en) * | 2007-11-05 | 2009-05-28 | Fuji Electric Systems Co Ltd | Pid parameter adjustment support device |
| CN102156496A (en) * | 2011-04-07 | 2011-08-17 | 杭州电子科技大学 | Blending control method for temperature of reactive kettle |
| CN104317194A (en) * | 2014-09-23 | 2015-01-28 | 杭州电子科技大学 | Temperature control method for non-minimal state space model predictive control optimization |
Non-Patent Citations (1)
| Title |
|---|
| An Enhanced MOEA/D-DE and Its Application to Multiobjective Analog Cell Sizing;Bo Liu，等;《Evolutionary Computation (CEC), 2010 IEEE Congress》;20101231;第1-7页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106773646A (en) | 2017-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109581870B (en) | Dynamic matrix control method for temperature in energetic material reaction kettle | |
| CN106773646B (en) | A kind of catalytic cracking process Crude Oil Investigation On The Preheating Temperature Control | |
| CN106933105B (en) | Track under confined condition updates integrated forecasting Iterative Learning Control Algorithm | |
| US10346736B2 (en) | Systems and methods for adaptive non-linear control of process systems | |
| Slavov et al. | Application of genetic algorithm to tuning a PID controller for glucose concentration control | |
| CN102540879A (en) | Multi-target evaluation optimization method based on group decision making retrieval strategy | |
| CN105892296B (en) | A kind of fractional order dynamic matrix control method of industry heating furnace system | |
| CN111310348A (en) | Material constitutive model prediction method based on PSO-LSSVM | |
| CN106483853A (en) | The fractional order distributed dynamic matrix majorization method of Heat Loss in Oil Refining Heating Furnace furnace pressure | |
| CN110097929A (en) | A kind of blast furnace molten iron silicon content on-line prediction method | |
| CN109143853A (en) | Fractionating column liquid level self-adaptation control method during a kind of oil refining | |
| CN105652666B (en) | Large-scale drop press upper beam prediction of speed control method based on BP neural network | |
| EP1606672A2 (en) | Methods and articles for detecting, verifying, and repairing collinearity | |
| CN101506743B (en) | Method for model gain matrix modification | |
| CN113268925A (en) | Dynamic soft measurement method based on differential evolution algorithm time delay estimation | |
| CN110597055B (en) | Uncertainty-resistant 2D piecewise affine intermittent process minimum-maximum optimization prediction control method | |
| Anuradha et al. | Direct inverse neural network control of a continuous stirred tank reactor (CSTR) | |
| CN109884884A (en) | A kind of method of adjustment and relevant apparatus of system Control platform | |
| JP7360162B2 (en) | Control system design method and control device | |
| Mayr et al. | Engine control unit PID controller calibration by means of local model networks | |
| CN104281076B (en) | Controlling method for concentration of protein | |
| Duran-Villalobos et al. | Iterative learning modelling and control of batch fermentation processes | |
| Hong et al. | Model Predictive Control Based on Kalman Filter for Constrained Hammerstein‐Wiener Systems | |
| Coulson | Data-enabled predictive control: Theory and practice | |
| CN113568309B (en) | On-line space-time control method for temperature field |
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: 20190723 Termination date: 20211206 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |