CN105974953A - Reaction vessel negative pressure rectification fuzzy control method - Google Patents
Reaction vessel negative pressure rectification fuzzy control method Download PDFInfo
- Publication number
- CN105974953A CN105974953A CN201610525615.5A CN201610525615A CN105974953A CN 105974953 A CN105974953 A CN 105974953A CN 201610525615 A CN201610525615 A CN 201610525615A CN 105974953 A CN105974953 A CN 105974953A
- Authority
- CN
- China
- Prior art keywords
- pressure
- fuzzy
- change
- pressure divergence
- factor
- 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
- 238000006243 chemical reaction Methods 0.000 title abstract description 22
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 30
- 238000011002 quantification Methods 0.000 claims description 30
- 238000005070 sampling Methods 0.000 claims description 21
- 230000000875 corresponding Effects 0.000 claims description 18
- 238000001514 detection method Methods 0.000 claims description 5
- 230000001105 regulatory Effects 0.000 abstract description 3
- 238000005086 pumping Methods 0.000 abstract 2
- 238000000034 method Methods 0.000 description 18
- 230000001276 controlling effect Effects 0.000 description 11
- 230000000694 effects Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 238000005039 chemical industry Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000004821 distillation Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- 241000208340 Araliaceae Species 0.000 description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 235000005035 ginseng Nutrition 0.000 description 2
- 235000008434 ginseng Nutrition 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000001702 transmitter Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000009835 boiling Methods 0.000 description 1
- 238000009833 condensation Methods 0.000 description 1
- 230000005494 condensation Effects 0.000 description 1
- 230000003111 delayed Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005183 dynamical system Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000004301 light adaptation Effects 0.000 description 1
- 230000001095 motoneuron Effects 0.000 description 1
- 230000001473 noxious Effects 0.000 description 1
- 238000011017 operating method Methods 0.000 description 1
- 239000012264 purified product Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 231100000004 severe toxicity Toxicity 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000001429 stepping Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D16/00—Control of fluid pressure
- G05D16/20—Control of fluid pressure characterised by the use of electric means
- G05D16/2006—Control of fluid pressure characterised by the use of electric means with direct action of electric energy on controlling means
- G05D16/2013—Control of fluid pressure characterised by the use of electric means with direct action of electric energy on controlling means using throttling means as controlling means
- G05D16/202—Control of fluid pressure characterised by the use of electric means with direct action of electric energy on controlling means using throttling means as controlling means actuated by an electric motor
Abstract
The invention discloses a reaction vessel negative pressure rectification fuzzy control method which is characterized in that a reaction still, a rectification tower, and a water tank are involved; a negative pressure pumping apparatus, a first control valve and a second control valve are controlled through a controller; a rotation speed of the negative pressure pumping apparatus is real-time and dynamicly regulated in accordance with pressure deviation in the reaction still and pressure deviation variation rate through a fuzzy-PID controller, which improves reaction still negative pressure rectification in terms of speed, precision and stability; and when an actual pressure value in the reaction still is overstruck, the actual pressure value is rapidly stabilized with an expected pressure value error range through pressure compensation regulation.
Description
Technical field
The present invention relates to chemical production field, particularly to a kind of reactor negative pressure rectification fuzzy control method.
Background technology
In nowadays chemical industry specific chemical reaction occasion, negative pressure rectification occupies very important status in chemical industry,
Its degree of accuracy controlled will directly affect the quality of product.Negative pressure distillation technology is a hot subject of recent domestic research,
Stress control to reactor is one of key factor determining rectification product purity.Traditional negative pressure extracting technology has a variety of,
Majority is to use negative-pressure vacuum pump to carry out negative pressure extracting, although this method is cheap, using method simple, but for precision
Requirement the lowest, under the industrial background of Modern Fine Chemical Industry, be difficult to meet the requirement of technique.
Thus Some Enterprises is driven by recirculated water high speed undershoot and detaches air and realize negative pressure extracting, controls chemical reaction kettle
In course of reaction.Its principle is as follows, and the water in water pot is at full speed detached under the drive of motor by water pump, will by pipeline
Water sends water pot back to, forms circulation, and swiftly flowing current can diminish the air taking away in reactor because of pressure, makes air pressure
Reducing, recirculated water flow velocity is the fastest, and air pressure reduces the fastest, such that it is able to reach to control in reactor by controlling the flow velocity of recirculated water
The purpose of air pressure.A lot of factories use the method taking out negative pressure also to rest on and the most slowly regulate mechanical valve by operator certain
Complete the level of reactor negative pressure extracting in time, use this technical operating procedure not only very complicated, to operator's operation
Proficiency level requires the highest, and degree of accuracy is the lowest.Development and the raising of automatization level, Industry Control along with control theory
Thought is introduced in recirculated water and takes out in the middle of vacuum cavitations.
Wherein, the pressure on reactor still top is a typical non-linear variable with large time delay characteristic, therefore will be to it
It is the most difficult for carrying out mathematical modeling, and in control theory because of PID control need not concrete model can be achieved with control purpose and
It is used widely.Since the nineties in last century, a lot of companies develop oneself for the negative pressure rectification process of chemical industry
Control system, its principle be by gather negative pressure signal, the converter in motor is carried out PID control.But, this control
Method hysteresis quality processed is extremely serious, and arithmetic speed is the slowest.For solving this defect, the most suitably reduce in PID control
Integral parameter, increase the differential coefficient during PID controls simultaneously, but thus sacrifice the stability of system.Therefore, design
Reasonably control strategy, proposes conservative control algorithm and becomes as a technical problem urgently to be resolved hurrily in negative pressure rectification process.
Summary of the invention
The deficiency existed for prior art, it is an object of the invention to provide a kind of reactor negative pressure rectification fuzzy control side
Method, the method improving tradition negative pressure extracting, take out the accuracy of negative pressure process, stability and rapidity improving reactor.
The above-mentioned technical purpose of the present invention has the technical scheme that
A kind of reactor negative pressure rectification fuzzy control method, is characterized in that, comprise the steps:
Step 1: according to the actual pressure value in sampling period detection rectifying column, actual pressure value is compared with desired pressure value,
And calculate the two pressure divergence e and pressure divergence rate of change ec as input；
Step 2: carry out fuzzy reasoning according to input pressure deviation e and pressure divergence rate of change ec and output variable Kp, Ki, Kd,
Particularly as follows:
Step 2-1: first arrange the basic domain of pressure divergence e, the basic domain of pressure divergence rate of change ec, output variable Kp,
The basic domain of Ki, Kd；
Next arranges pressure divergence e, the quantification gradation of pressure divergence rate of change ec and output variable Kp, the quantification gradation of Ki, Kd；
Step 2-2: according to the basic domain of pressure divergence e and quantification gradation and the basic domain of pressure divergence rate of change ec and amount
Change grade to respectively obtain the quantizing factor K of pressure divergence eeQuantizing factor K with pressure divergence rate of change ecec；According to defeated
Go out the basic domain of variable Kp, Ki, Kd and quantification gradation to respectively obtain the quantizing factor K of output variable Kp3, output become
The quantizing factor K of amount Ki4, the quantizing factor K of output variable Kd5；
Step 2-3: fuzzy subset corresponding for pressure divergence e, fuzzy subset corresponding for pressure divergence rate of change ec, output change are set
The fuzzy subset that amount Kp, Ki, Kd are the most corresponding, expression formula is:
{ NB, NS, ZE, PS, PB}
In formula, NB represents negative big, and NS represents negative little, and ZE represents moderate, and PS represents the least, and PB represents honest；
Step 2-4: set up pressure divergence e, pressure divergence rate of change ec, output variable Kp, the membership function table of Ki, Kd,
Reflect pressure divergence e, pressure divergence rate of change ec, output variable Kp, Ki, Kd quantification gradation in fuzzy subset
Map；
Step 2-5: set up output variable Kp, Ki, Kd mould according to the fuzzy subset of pressure divergence e and pressure divergence rate of change ec
Stick with paste the fuzzy control rule table of subset；
Step 3: by the pressure divergence e detected in the sampling period first and pressure divergence rate of change ec according to step 2-2 with respectively
Obtain pressure divergence e and the quantification gradation of pressure divergence ec, obtain pressure divergence e and pressure divergence ec further according to step 2-4
Fuzzy subset；
By fuzzy control rule table in step 2-5 to obtain the fuzzy control rule of output variable Kp, Ki, Kd respectively；
Respectively the fuzzy subset of output variable Kp, Ki, Kd is carried out Anti-fuzzy by centroid method, respectively obtain output variable Kp, Ki,
The quantification gradation of Kd, thus according to the quantizing factor K in step 2-23, quantizing factor K4, quantizing factor K5, by Kp, Ki,
The quantification gradation of Kd is converted to the row value in basic domain, is denoted as K respectivelyp0、Ki0、Kd0；
Step 4: the pressure divergence e detected in the sampling period next time and pressure divergence rate of change ec is obtained respectively according to step 3
To three exporting change amounts△Kp、△Ki、△Kd；
Step 5: by three output variables Kp, Ki, Kd according to three exporting change amounts△Kp、△Ki、△Kd carries out on-line tuning,
Formula is as follows；
In formula, Kp0For the proportionality factor in the sampling period first, Ki0For the integrating factor in the sampling period first, Kd0For adopting first
Differential factor in the sample cycle；
△Kp be the proportionality factor in the sampling period next time,△Ki be the integrating factor in the sampling period next time,△Kd is next time
Differential factor in sampling period；
Kp, Ki, Kd are three output variables, respectively proportionality factor, integrating factor, differential factor；
Step 6: Kp, Ki, Kd of obtaining in step 5 are calculated control signal to be transferred to converter, after converter
Output frequency variation signal is to taking out negative pressure device, and then realizes the rotating speed control taking out negative pressure device.
Preferably, the most also include, when actual pressure value is beyond desired pressure value, by changing circulating water pipe
The flow velocity in road carries out pressure compensation regulation to reactor.
In sum, the present invention in contrast to the having the beneficial effect that by fuzzy controller and PID controller of prior art
In conjunction with regulating the rotating speed taking out negative pressure device according to the pressure divergence in reactor and pressure divergence rate of change Real-time and Dynamic, improve anti-
Answer the rapidity of still negative pressure rectification, accuracy and stability；And after actual pressure value overshoot in a kettle., mended by pressure
Repay regulation so that actual pressure value is faster stable in desired pressure value range of error.
Accompanying drawing explanation
Fig. 1 is the system block diagram of reactor negative pressure distillation system；
Fig. 2 is the system block diagram of fuzzy-PID control device；
Fig. 3 is the membership function figure in embodiment；
Fig. 4 is that Kp, Ki, Kd export response curve；
Fig. 5 is centroid method reasoning process schematic diagram；
Fig. 6 is that pressure compensation regulates flow chart.
Reference: 1, take out negative pressure device；2, water tank；3, the first control valve；4, pressure transducer；5, anti-down
Stream device；6, the second control valve；7, reactor；8, rectifying column；9, controller；10, pipeline；11, pipeline.
Detailed description of the invention
Below in conjunction with accompanying drawing, the present invention is described in further detail.
As it is shown in figure 1, the connected mode of reactor negative pressure rectification includes taking out negative pressure device 1 and water tank 2 and for accommodating
Reaction mass, so that reaction mass to carry out the reactor 7 of heating evaporation, is wherein taken out and is passed through pipeline between negative pressure device 1 and water tank 2
10 are attached, and reactor 7 is communicated in, by pipeline 11, the pipeline 10 taken out between negative pressure device 1 and water tank 2；Take out negative pressure
Device 1 is the water pump that can run under driven by motor, and the water in water tank 2 is at full speed detached under the drive of motor by water pump,
By pipeline 10, water being sent water pot back to, thus form circulating water pipeline, swiftly flowing current can pass through pipeline because pressure diminishes
11 take away the air in reactor 7, make air pressure reduce, and the flow velocity in circulating water pipeline is the fastest, and air pressure reduces the fastest.Wherein,
Being provided with the rectifying column 8 being interconnected with it on reactor 7, rectifying column 8 is for entering the gas phase evaporated in reaction mass
The condensation of row counter current contacting is to realize the rectification of reaction mass, and the tower top of rectifying column 8 is followed with the water in rectifying column 8 by pipeline 10
Endless tube road is connected, and is provided with rectification liquid secondary back anti-preventing reaction mass in the junction of pipeline 10 with water circulating pipe
Counterflow unit 5.
Take out and flow to pipeline 10 exit of water tank 2 on negative pressure device 1 and be provided with the first control valve 3, at reactor 7 even
Lead to and be provided with the second control valve 6, the first control valve 3 and the second control valve on the pipeline 11 taking out negative pressure device 1 and water tank 2
6 are normal-closed electromagnetic valve door.Being additionally provided with pressure transducer 5 in rectifying column 8, pressure transducer 5 is used for detection in real time
Force value in rectifying column 8, pressure transducer the 5, first control valve the 3, second control valve 6 and take out negative pressure device 1 and be all connected with
There is controller 9.
Controller 9 includes fuzzy-PID control device and controls terminal, controls terminal and includes the portable terminal and fixed of controlling
Controlling terminal, the portable terminal that controls includes mobile phone, notebook computer etc.；Fixed control terminal includes desktop computer etc., this
It is fixed control terminal that embodiment preferably controls terminal；Fuzzy-PID control device includes fuzzy controller and PID controller,
Wherein, fuzzy-PID control device is connected to the rotating speed taking out negative pressure device 1 for controlling to take out negative pressure device 1, controls terminal and connects
It is used for controlling the first control valve 3 and aperture of the second control valve 6 in the first control valve 3 and the second control valve 6.
Native system detects the actual pressure value in reactor 7, the actual pressure that will detect in real time by pressure transducer 4
Value is sent in fuzzy-PID control device, fuzzy-PID control device by actual pressure value is analyzed with desired pressure value,
Output control signals to take out negative pressure device 1, thus control converter to change its output frequency, take out negative pressure by Frequency Converter Control
In device 1, the exploitation speed of motor changes the flow velocity of recirculated water, thus reaches to control the purpose of pressure in reactor 7 so that
Pressure in reactor 7 faster reaches the desired pressure value needed；It is provided with stable near desired pressure value in controller 9
First error value and the second error value, when the pressure in controller 9 monitors reactor 7 will reach desired pressure value,
Control terminal and will control the aperture of the first control valve 3, change water yield and the flow velocity of pipeline 10, with in reactor 7
Pressure be finely adjusted so that the pressure stability in reactor 7 in the range of the first error value of desired pressure value, thus with
Faster reach desired pressure value so that system stability is more preferable；Actual pressure in reactor 7 is stable at the first error value
Time interior, control terminal has closed the regulation process of desired pressure value in reactor 7 by controlling the second control valve 6 so that anti-
Answer the actual pressure value stabilization in still 7 in the range of the second error value of desired pressure value, thus ensure in reactor 7 anti-
Material is answered to carry out rectification under desired pressure value, to obtain highly purified product.
Fuzzy-PID control device in native system includes fuzzy controller and two parts of PID controller, in fuzzy controller
Fuzzy reasoning be a kind of computer numerical control (CNC) skill based on fuzzy set theory, Fuzzy Linguistic Variable and fuzzy logic inference
Art.Relative to Traditional control, fuzzy control can avoid the mathematical model of object, and it directly uses language type to control rule, is setting
Meter need not set up the mathematical models of controlled device, so that control mechanism and strategy are prone to accept and understand, design
Simply, it is simple to application；And trigger from the qualitative understanding of industrial process, be easier to set up Linguistic control law, thus fuzzy
Control that those mathematical modeies are difficult to acquisition, dynamic characteristic is difficult to grasp or the object of change highly significant is the most applicable；Based on mould
The control algolithm of type and design method, due to starting point and the difference of performance indications, be easily caused larger difference, but one
The Linguistic control law of system but has relative independence, utilizes the fuzzy connection between these control laws, is easily found compromise
Selection, make control effect be better than conventional controller；FUZZY ALGORITHMS FOR CONTROL is to set based on suggestive knowledge and language decision rules
Meter, thus be conducive to process and the method that simulation workman controls, strengthen the adaptation ability of control system, be allowed to have certain
Level of intelligence；And the impact controlling effect is significantly reduced by the strong robustness of Fuzzy control system, interference and Parameters variation,
It is particularly suitable for the control of non-linear, time-varying and dead-time system.
On reactor 7, the pressure parameter of rectifying column 8 is as object of study, the PID controller more ripe by technology and mould
Paste control theory combines, and designs pressure parameter automatic measuring and controlling system in a set of reactor 7, it is achieved the pressure ginseng in reactor 7
Number detection in real time, supervision and stable control, improve the accuracy of negative pressure extracting in reactor 7.Wherein reaction mass is accurately
Under negative pressure state controls, have the effect that one, negative pressure rectification can reduce the boiling point of mixture, thus reduces the temperature of separation,
Therefore can reduce for the steam consumption of heating and use adding vapours thus saving the consumption of power of lower pressure, reaching energy-conservation
Effect；Two, improve the separating power of reaction mass, under negative pressure state, by the relative volatility between separating mixture
The biggest, more can be easily separated.Three, for the separation of noxious substance, negative pressure rectification is used can to prevent the leakage of severe toxicity material, thus
Reduce the pollution to environment, in terms of protection health, there is certain meaning.
Go out fuzzy-PID controller by the fuzzy control Technology design combined with PID, controlled volume change and change are become
Gesture has certain " predictability ", effectively solves conventional means downforce parameter and regulates non-linear, the problem of large time delay.Due to often
Rule PID controller has that algorithm is simple, high reliability, for deterministic controlled device by the tune to three parameters
Whole it is obtained with satisfied control effect.But for time-varying, have delayed, for nonlinear system, PID
Control just to be difficult to reach good effect.Fuzzy control has an outstanding advantages of the mathematical model being independent of controlled device, but surely
The precision of state is poor.So FUZZY ALGORITHMS FOR CONTROL being combined with pid control algorithm, constitute fuzzy-PID control device.Mould
Paste-PID controller has fuzzy control and the advantage of PID control simultaneously, it is not necessary to considers system accurate model, overcomes tradition
The problem of the parameter adjustment difficulty that PID controller causes because of system hysteresis quality and control nonlinearity in parameters, solves real life
During because of load change, interference increases the problem that the architectural characteristic of controlled device caused changes, it is achieved thereby that controller ginseng
The dynamic adjustment of number.
Based on control object, in conjunction with practical situation, we have researched and proposed employing fuzzy-PID control to realize reactor 7
Negative pressure rectification.Fuzzy-PID control device uses two-dimensional fuzzy controller, and two-dimensional fuzzy controller overshoot is little, adjust the time
Short, system parameter variations and external interference are had stronger robustness, it is possible to strict reflects output variable in controlled system
Dynamic characteristic, is to use to compare fuzzy controller widely, and its structure is as in figure 2 it is shown, pressure transducer 4 is according to actual pressure
Value, system calculates pressure divergence e and pressure divergence according to desired pressure value and actual pressure value, and to obtain pressure through differential inclined
Difference rate of change ec is controlled to PID controller, PID as the value of input, Kp, Ki, Kd output made new advances through fuzzy reasoning
Device processed calculates final control signal output valve further according to pressure divergence e, pressure divergence rate of change ec and new Kp, Ki, Kd
U, output valve u controls the electric current of converter through V/F conversion, changes the rotating speed of pump motor thus change reactor 7
In pressure, and pressure transducer 4 monitors the actual pressure value in rectifying column 8 in real time, constantly to change the electric current of converter,
Reach dynamically to control the purpose of pressure in reactor 7.Wherein, pressure transmitter as shown in Figure 2, pressure in general sense
Power transmitter is mainly made up of pressure transducer 4, measuring circuit and process connector three part.It can be by load cell
The physical pressure parameters such as the gas experienced, liquid are transformed into the signal of telecommunication (such as 4～20mADC etc.) of standard, refer to supply
Show that the secondary meters such as alarm, monitor, actuator measure, indicate and procedure regulation, above-mentioned in pressure transducer 4
It is arranged in rectifying column 8 force value in rectifying column 8 carries out detection feedback.
The V/F control of converter is a kind of control mode of converter, it is simply that below reference frequency, converter output
Voltage and the proportional relation of output frequency, thus export a kind of control mode of permanent torque, for the control mode that converter is most basic.
Recording the actual pressure value in reactor 7 by pressure transducer 4 is T, it is desirable to force value is To, then pressure is inclined
Difference is e (t)=T-To, t-Δ t is ec (t)=e (t)-e (t-Δ t) relative to the pressure divergence rate of change of t.
Wherein, from pid parameter feature, Kp is proportional control factor, is used for accelerating the response speed of system, with
The degree of regulation of raising system, wherein Kp increases, and cycle of oscillation reduces, and overshoot becomes big, and governing speed increases；Ki is integration
Adjustment factor, is used for eliminating residual error, and wherein Ki increases, and overshoot becomes big, and stability declines；Kd is differential adjustment factor, uses
Improving the dynamic property of system, wherein Kd increases, and suppresses change of error, and stability improves.
The obfuscation of input and output amount in fuzzy reasoning
In negative pressure distillation control system, play a major role is the force value in reactor 7, and the control to force value is to pass through motor
Rotating speed determines, and motor speed is output valve Kp by fuzzy-PID control device, Ki, Kd decision, thus passes through e
Parameter with ec adjusts fuzzy-PID control device input quantity, after fuzzy reasoning computing, obtains output and turns to adjust motor
Speed.But realize the accurate control to system, then must convert them into fuzzy variable.
Input quantity: e-pressure divergence, ec-pressure divergence rate of change；
Output: Kp-proportional control factor, Ki-integral adjustment coefficient, Kd-differential adjustment factor.
The determination of fuzzy subset
In the present system set pressure divergence e, pressure divergence rate of change ec Fuzzy Linguistic Variable after Fuzzy processing respectively with E,
EC represents, proportional control factor Kp, integral adjustment COEFFICIENT K i, differential adjustment factor Kd Fuzzy Linguistic Variable respectively with KP,
KI, KD represent.As a example by pressure divergence e, in the industrial production, if normal pressure divergence is 0.1Mpa, when inspection in real time
When the pressure divergence e measured is less than this value, deviation is " bearing "；When the pressure divergence e detected in real time is higher than this value, partially
Difference is " just "；And be simultaneously introduced " greatly ", " in ", " little " etc. compare language table and show the degree of deviation setting value.Root
The lucky speech accumulated according to long-term industrial site, existing is 5 grades by the Fuzzy Linguistic Variable of E, EC, KP, KI, KD, respectively
For NB, NS, ZE, PS, PB}, and negative big, negative little, moderate, the least, honest.The i.e. fuzzy subset of input/output variable is
{ NB, NS, ZE, PS, PB}, the stepping number m of linguistic variable is 5.
Quantizing factor and the determination of quantization domain
Assume the basic domain of pressure divergence for [-ex, ex], the basic domain of pressure divergence rate of change is [-xec, xec], by pressure divergence institute
The fuzzy subset taken be converted to integer domain for-n ,-n+1 ...-1,0,1 ... n-1, n}, pressure divergence rate of change is taken
Fuzzy subset be converted to integer domain for-m ,-m+1 ...-1,0,1 ... m-1, m}, the basic domain of controlled quentity controlled variable be [-
Yu, yu], thus the fuzzy subset that taken of controlled quentity controlled variable be converted to integer domain for-u ,-u+1 ...-1,0,1 ... u-1, u}.
Practical Project situation according to negative pressure rectification, the scope that project planner gathers data by pressure transducer 4 obtains
To the basic domain [-0.3,0.3] of basic domain [-0.2, the 0.2] Mpa, pressure divergence rate of change ec of corresponding pressure divergence e, defeated
Go out the basic domain [-6,6] of Kp, the basic domain [-0.2,0.6] of Ki, the basic domain [-0.1,0.3] of Kd, thus for meeting
The requirement of excellent control, passing ratio quantization method sets and quantifies domain, i.e. theory of integers field element number 2n+1 and fuzzy subset
When element number exists 2n+1=km relation, fuzzy subset is the most reasonable to fuzzy domain and the statement of physics domain of system,
Wherein, k value requires between 1 and 2, and m is 5, thus meets the requirement of optimum control when trying to achieve n=3, thus obtains this and be
The quantization domain of system is that {-3 ,-2 ,-1,0,1,2,3}, above-mentioned quantification gradation is 7 grades.
In fuzzy control, the amount in basic domain is precise volume, in order to carry out Fuzzy processing, it is necessary to by input variable
It is transformed into corresponding fuzzy subset's domain from basic domain, thus the concept quantifying factor K will be introduced.Such as there is physical quantity,
Its domain is X=[-x, x], this basic domain be converted into integer domain for-x ,-x+1 ...-1,0,1 ... x-1, x}.In order to
Ensureing the accuracy of quantizing process, rapidity, use linear quantization process herein, quantitative model is direct proportion function model:
K=n/x, wherein n is for quantifying domain width, and x is the width of basic domain, and K is quantizing factor.
Through calculating quantizing factor, it is possible to the exact value x of any time is converted into and quantifies value a corresponding in domain,
I.e.
A=K*x
If a is an integer, then it is exactly to quantify an element in domain.If not an integer, then need to carry out four
House five enters to process, and is become the element quantified in domain.
Thus according to above-mentioned computing formula, can respectively obtain:
Ke=quantifies the quantizing factor value that width=6/0.4=15, Ke are pressure divergence e of the width/basic domain of domain；
Kec=quantifies the quantizing factor value that width=6/0.6=10, Kec are pressure divergence rate of change ec of the width/basic domain of domain.
The quantizing factor exported then should be contrary with input, because output is the actual value by calculating quantization domain is one
Item inverse operation, thus,
K3Width=the 12/6=2, K of the width/quantization domain of=basic domain3Quantizing factor value for proportion adjustment Kp；
K4The quantizing factor value that width=0.8/6=0.133, K4 are integral adjustment COEFFICIENT K i of the width/quantization domain of=basic domain；
K5Width=the 0.4/6=0.067, K of the width/quantization domain of=basic domain5Quantizing factor value for differential adjustment factor Kd.
The determination of membership function
By above-mentioned analysis, it is determined that the quantization domain of native system and fuzzy subset, also two input quantities, quantizations of three outputs
Factor values.But, the obfuscation operational analysis of system to be realized, above several conversion are inadequate, be also predefined two defeated
Enter amount and the membership function of three outputs, will two input quantities actual value by quantify factor values be transformed into quantization domain
In the middle of, thus be mapped in the middle of fuzzy subset by membership function.If to the either element x in domain U, having a number
A (x) is 0, and 1 is the most corresponding, then A is called the fuzzy set on U, and A (x) is referred to as the x degree of membership to A.When x is at U
During middle variation, A (x) is exactly a function, the membership function of referred to as A.Degree of membership A (x), closer to 1, represents that x belongs to
The highest in the degree of A, A (x) is closer to 0 expression x, and to belong to the degree of A the lowest.With value in interval 0,1 be subordinate to letter
Number A (x) characterizes the degree height that x belongs to A.Conventional membership function has Gaussian, triangle or trapezoidal.Triangle
The membership function mathematic(al) representation of shape is simple, susceptiveness is high, therefore uses Triangleshape grade of membership function:
Calculate the degree of membership of each controlled quentity controlled variable.Wherein a, b, c ∈ [-3,3], value is the actual value of PB, PS, ZE, NS, NB domain
Minima, intermediate value and maximum.Wherein, the membership function of e, ec, Kp, Ki, Kd is as shown in Figure 3.
By being calculated, the degree of membership assignment table of E, EC, it is as shown in the table.
The degree of membership assignment table of E, EC
By being calculated, the degree of membership assignment table of KP, KI, KD, it is as shown in the table.
The degree of membership assignment table of KP, KI, KD
Set up fuzzy control rule table
Set up the process of fuzzy control rule, it is simply that utilize language to conclude the process of Non-follow control strategy.In fuzzy control, control plan
Selection slightly is non-the normally off key.Fuzzy algorithmic approach structure embodies the fuzzy relation of fuzzy control rule, and it is equivalent to general control
The transmission function of device, but this algorithm structure not on the basis of controlled device mathematical model comprehensive the most out, but according to control
The mathematics observation of the Input output Relationship of system processed, and use Fuzzy Set Theory to process and obtain.Negative for reactor 7
In pressure rectification fuzzy control method, two inputs, the complex situations of three outputs, it is necessary to use and can reasonably express these
The inference pattern of relation between variable.
Fuzzy control rule by summing up, conclude the Heuristics of expert, and can be processed further, arrange, refine,
The fuzzy control rule produced after keeping its essence and discarding its dross；If the dynamic characteristic of object can describe with language, then
The most just can be inferred by the description of this dynamic process and control rule accordingly, here it is the conventional fuzzy mould according to object
Type draws the method for fuzzy control rule；This method uses the experience of expert and great many of experiments to observe and design fuzzy controller rule
Then, and it is determined the output of each variable by substantial amounts of experiment and controls the power of pump motor effect, to determine reactor
Pressure controlled accuracy, stability in 7.Kp, Ki, Kd as shown in Figure 4 exports response curve, and design rule is necessary
The output of guarantee system reaches optimal stability, accuracy and rapidity, T-T in figure accordingly.For pressure divergence, slope is
Pressure divergence rate of change, i.e. as the pressure divergence e in reactor 7 negative big (NB), is in the first stage of curve, now
No matter why pressure divergence rate of change ec is worth, and proportional control factor Kp should take honest (PB) so that reactor 7 in
Force value at utmost to increase；And integral adjustment COEFFICIENT K i is minimum, improves and control the stability that pump motor runs；
And differential adjustment factor Kd is minimum, suppresses force value change of error.Thus same method, other values can be obtained.
According to fuzzy control rule, if EC is NB, E is NB,
Then KP=PB, KI is NB, KD is NB；
If EC is NB, E is NS,
Then KP=PB, KI is NS, KD is NS；
Thus the relation of all fuzzy control rules can be obtained successively.
The fuzzy reasoning table of KP is as follows:
The fuzzy reasoning table of KP
The fuzzy reasoning table of KI is as follows:
The fuzzy reasoning table of KI
The fuzzy reasoning table of KD is as follows:
The fuzzy reasoning table of KD
Through the fuzzy if-then rules of above-mentioned output, we can be according to pressure divergence e and pressure divergence rate of change at reactor 7
In actual value, value E that two input quantities quantify in domain by quantifying conversion factor to obtain and EC, E and EC are again by being subordinate to
The value quantified in domain is mapped in the middle of fuzzy subset by genus degree function, thus obtains the fuzzy subset of corresponding E and EC.Logical
Cross the mould checking that the fuzzy reasoning table of corresponding KP, KI, KD obtains two outputs of corresponding three outputs KP, KI, KD
Stick with paste subset.
Anti fuzzy method
Anti fuzzy method can also be referred to as ambiguity solution, contrary with obfuscation, and ambiguity solution is exactly by the fuzzy control rule of fuzzy reasoning
Fuzzy set be transformed in quantization domain, thus further according to quantizing factor, obtain controlled volume being carried out directly actuated reality
Border physical quantity.The algorithm carrying out anti fuzzy method conventional has:
1, centroid method ambiguity solution, centroid method is the center by the area asking fuzzy set membership function curve and abscissa to be surrounded
Exact value as controller output；
2, weighted mean method, weighted mean method is output valve accurately the holding as output after being weighted averagely with each element of output
Row amount；
3, the distribution such as area, area equisection method is also referred to as median method, it is simply that the membership function corresponding to the fuzzy set of output
The area that curve is surrounded with abscissa is divided into equal two parts, using the element corresponding to this two parts separation as output
The method of exact value.
This problem uses centroid method ambiguity solution output variable to be carried out precision calculating, shown in centroid method shown in Figure 5
Fuzzy reasoning process.
Assume that the quantified rear corresponding quantification gradation of pressure divergence e and pressure divergence rate of change ec is respectively 1 grade and-3 grades,
By checking that e and ec degree of membership assignment table obtains e: μ ZE (1)=0.5, μ PS (1)=1；EC: μ NB (-3)=1.
Check according to fuzzy reasoning table and obtain rule corresponding to Kp, Ki, Kd:
E=ZE and ec=NB then KP=PB, KI=NS, KD=PS；
E=PS and ec=NB then KP=PB, KI=NS, KD=NS；
Degree of membership assignment table according to KP, KI, KD:
The degree of membership assignment table of KP, KI, KD
It is calculated:
In like manner, according to pressure divergence e and pressure divergence rate of change ec in the change occurred the most in the same time, KP, KI, KD can be obtained
Quantification gradation output table:
The quantification gradation output table of KP
The quantification gradation output table of KI
The quantification gradation output table of KD
The quantification gradation value of KP, KI, KD is obtained by above-mentioned inverse fuzzy arithmetic, thus according to the quantizing factor of Kp, Ki, Kd,
The quantification gradation value of KP, KI, KD is converted to the basic domain row value of output, is denoted as△Kp、△Ki、△Kd, and according to
The new parameter value of lower method acquisition PID:
Wherein,△Kp=Kp*K3,△Ki=Ki*K4,△Kd=Kd*K5.
Final fuzzy-PID control device is output as:
Wherein, T is the sampling period, and must is fulfilled for sampling thheorem；
The computer export value of u (k)-kth time sampling instant, k=0,1,2......；
The deviation value of e (k)-kth time sampling instant input, k=0,1,2......；
Ki-integral coefficient,
Kd-differential coefficient,
From the needs that industry is actual, output valve u of fuzzy-PID control device is floated between [4,20] mA, when pressure divergence e is
During 0.1Mpa, when pressure divergence rate of change ec is 0.2, according to the quantization of pressure divergence e and pressure divergence rate of change ec because of
Son, is calculated E=15*0.1=1.5 by formula, obtains E=2 through rounding up, and the quantification gradation of pressure divergence e is
2；EC=10*0.2=2, the quantification gradation of pressure divergence rate of change ec is 2, is exported by the quantification gradation of KP, KI, KD
Table inquiry learn, the quantification gradation of Kp is-3, and the quantification gradation of Ki is-3, and the quantification gradation of Kd is-3, thus according to Kp,
The quantizing factor of Ki, Kd obtains actual value:
Kp=-3*2=-6, Ki=-3*0.13=-0.39, Kd=-3*0.067=-0.201.
Thus the value of Kp is-6, the value of Ki is-0.39, and the value of Kd is-0.201, according to fuzzy-PID control device output public affairs
Formula is calculated output valve, thus converts according to the V/F value of converter and obtain the exact value of a pump motor electric current.
Thus when pressure transducer 4 detects that actual pressure value changes, pressure divergence e and pressure divergence rate of change
Ec changes the most accordingly, thus obtains through above-mentioned fuzzy reasoning△Kp value,△The value of Ki and△The value of Kd, according to originally
Kp=-6, Ki=-0.39, Kd=-0.201 are as initial value, and each initial value correspondence adds△Kp、△Ki、△Kd and obtain new Kp,
The value of Ki, Kd is as the output of fuzzy reasoning, thus fuzzy-PID control device will export the electric current of respective change to pump motor
On converter, the corresponding rotating speed regulating pump motor of regulation so that the force value in reactor 7 is changed so that reaction
Force value change in still 7 is more accurate, and reaction is more quick, and the stability of whole reactor 7 system is higher.
Pressure compensation regulates
Controller 9 detects the feedback of actual pressure value in rectifying column 8 in real time, and in a dynamical system, overshoot is dynamic
In performance indications one, is linear control system response process curve under step signal inputs i.e. step response curve
Analyze a desired value of dynamic property.Thus during fuzzy-PID control device controls pump motor rotating speed, in reaction
When actual pressure value in still 7 reaches desired pressure value, will also may proceed to decline to a certain degree, and over control occur, thus works as
After fuzzy-PID control device controls overshoot, pressure transducer 4 continues to send the actual pressure value detected to controller 9, from
And the control terminal in controller 9 will determine that and receives actual pressure value whether at first error value or second of desired pressure value
In the range of error value, to control the aperture of the first control valve 3 and the second control valve 6 to carry out later stage pressure compensation regulation.
If in the range of actual pressure value occurs in the first error value of desired pressure value, i.e. P0At [0.8P0, 1.2P0]
Interval in, control terminal and will be considered to the pressure in reactor 7 and will tend towards stability, thus in order to make the pressure in reactor 7 use up
Reach desired pressure value soon, to improve the governing speed of pressure, thus control terminal and carry out pressure benefit by starting the first control valve 3
Repay control, to change the aperture size of the first control valve 3 to change the flow velocity of pipeline 10 with the pressure tune changing in reactor 7
Joint speed.
See known to Fig. 6, control terminal after fuzzy-PID control device overshoot and will determine that whether actual pressure occurs in [0.8P0,
1.2P0] interval in time, if so, control terminal and will enter next step judgement；Otherwise, if it is not, control terminal will control the
The aperture of one control valve 3 reaches maximum, with the force value in prestissimo regulation reactor 7 to reach setting value；
During next step judges, in the range of control terminal will determine that the first error value whether actual pressure value is in desired pressure value, i.e.
P0Whether at [0.8P0, P0] interval in, if so, control terminal and will control the first control valve 3 with original U2=[(-2.5P/
P0)+3 times] and aperture carry out the compensation regulation of pressure in reactor 7；Otherwise, will further determine if it is not, control terminal；
In determining whether, control terminal and will determine that whether actual pressure value is in [P0, 1.2P0] interval in, if so, control terminal
The first control valve 3 will be controlled with original U2=[(-1.5P/P0)+2] and times aperture carry out in reactor 7 pressure compensation regulation；
Otherwise, if it is not, control terminal the aperture that output control signal controls the first control valve 3 is reached maximum, adjust with prestissimo
Force value in joint reactor 7 is to reach desired pressure value.
Thus according to above-mentioned judgement, control the pressure benefit that the aperture controlling the first control valve 3 is carried out in reactor 7 by terminal
Repay regulation, until pressure transducer 4 detects that the actual pressure value in reactor 7 is in the second range of error of desired pressure value
In, i.e. P0At [0.95P0, 1.05P0] interval in, control terminal by closing the first control 3 and check stablizing of whole system
Property, thus when actual pressure value stabilization is at [0.95P0, 1.05P0] time in this interval reaches to set more than time value, control
Terminal processed is zero by controlling the aperture of the second control valve 6, is i.e. closed, and makes reactor 7 keep sealing, with to reaction
Material carries out negative pressure rectification；Otherwise, if pressure transducer 4 detects that the actual pressure value in reactor 7 is not at [0.95P0,
1.05P0] interval in, control terminal and will come back to the first step and judge that step carries out repeating judgement, steady until actual pressure value
It is scheduled on [0.95P0, 1.05P0] interval in set more than time value just closedown the second control valve 6；Wherein, time value is set
Scope is between 10～15 seconds, and it is 10 seconds that the present embodiment preferably sets time value.
Wherein known to complex chart 2 and Fig. 6, P0For desired pressure value, P is actual pressure value, above-mentioned in, U2 represents
The aperture of one control valve 3, U3 represents the aperture of the second control valve 6, and U2=0 represents that the aperture of the first control valve 3 is maximum,
U3=1 represents that the aperture of the second control valve 6 is minimum, is i.e. closed.
From the point of view of specifically, the pressure compensation through the later stage regulates the actual pressure value in reactor 7 at [0.95P0, 1.05P0]
Between will be regarded as stable, make with the most dynamically regulation according to the actual pressure value in reactor 7 through fuzzy-PID control device
Pressure in reactor 7 reaches optimum state, and the regulation of accurate negative pressure extracting controls, thus simplifies monitoring, controlling unit,
Reduce producing cost, improve the production efficiency of reactor 7, thus be effectively increased economic benefit.
The above is only the exemplary embodiment of the present invention, not for limiting the scope of the invention, and the present invention
Protection domain determined by appended claim.
Claims (2)
1. a reactor negative pressure rectification fuzzy control method, is characterized in that, comprises the steps:
Step 1: according to the actual pressure value in sampling period detection rectifying column (8), by actual pressure value and expectation
Force value compares, and calculates the two pressure divergence e and pressure divergence rate of change ec as input；
Step 2: carry out according to input pressure deviation e and pressure divergence rate of change ec and output variable Kp, Ki, Kd
Fuzzy reasoning, particularly as follows:
Step 2-1: first the basic domain of pressure divergence e, the basic domain of pressure divergence rate of change ec, defeated is set
Go out the basic domain of variable Kp, Ki, Kd；
Next arranges pressure divergence e, the quantification gradation of pressure divergence rate of change ec and output variable Kp, Ki, Kd
Quantification gradation；
Step 2-2: basic according to the basic domain of pressure divergence e and quantification gradation and pressure divergence rate of change ec
Domain and quantification gradation are to respectively obtain the quantizing factor K of pressure divergence eeAmount with pressure divergence rate of change ec
Change factor Kec；According to output variable Kp, the basic domain of Ki, Kd and quantification gradation to respectively obtain output
The quantizing factor K of variable Kp3, the quantizing factor K of output variable Ki4, the quantizing factor of output variable Kd
K5；
Step 2-3: arrange fuzzy subset corresponding for pressure divergence e, fuzzy subset corresponding for pressure divergence rate of change ec,
Output variable Kp, the fuzzy subset that Ki, Kd are the most corresponding, expression formula is:
{ NB, NS, ZE, PS, PB}
In formula, NB represents negative big, and NS represents negative little, and ZE represents moderate, and PS represents the least, and PB represents honest；
Step 2-4: set up pressure divergence e, pressure divergence rate of change ec, output variable Kp, being subordinate to of Ki, Kd
Degree function table, reflects pressure divergence e, pressure divergence rate of change ec, output variable Kp, the amount of Ki, Kd
Change grade to the mapping in fuzzy subset；
Step 2-5: according to the fuzzy subset of pressure divergence e and pressure divergence rate of change ec set up to output variable Kp,
The fuzzy control rule table of Ki, Kd fuzzy subset；
Step 3: by the pressure divergence e detected in the sampling period first and pressure divergence rate of change ec according to step
2-2, to obtain pressure divergence e and the quantification gradation of pressure divergence ec respectively, obtains pressure further according to step 2-4
Deviation e and the fuzzy subset of pressure divergence ec；
By fuzzy control rule table in step 2-5 to obtain the fuzzy control rule of output variable Kp, Ki, Kd respectively
Then；
Respectively the fuzzy subset of output variable Kp, Ki, Kd is carried out Anti-fuzzy by centroid method, respectively obtain output
The quantification gradation of variable Kp, Ki, Kd, thus according to the quantizing factor K in step 2-23, quantizing factor
K4, quantizing factor K5, the quantification gradation of Kp, Ki, Kd is converted to the row value in basic domain, respectively
It is denoted as Kp0、Ki0、Kd0；
Step 4: by the pressure divergence e detected in the sampling period next time and pressure divergence rate of change ec according to step
3 respectively obtain three exporting change amount Δ Kp, Δ Ki, Δ Kd；
Step 5: by three output variables Kp, Ki, Kd according to three exporting change amount Δ Kp, Δ Ki, Δ Kd
Carrying out on-line tuning, formula is as follows:
In formula, Kp0For the proportionality factor in the sampling period first, Ki0For the integrating factor in the sampling period first,
Kd0For the differential factor in the sampling period first；
Δ Kp be the proportionality factor in the sampling period next time, Δ Ki be the integrating factor in the sampling period next time,
Δ Kd is the differential factor in the sampling period next time；
Kp, Ki, Kd are three output variables, respectively proportionality factor, integrating factor, differential factor；
Step 6: Kp, Ki, Kd of obtaining in step 5 are calculated control signal to be transferred to converter,
After converter, export frequency variation signal to taking out negative pressure device (1), and then realize turning taking out negative pressure device (1)
Speed controls.
Reactor negative pressure rectification fuzzy control method the most according to claim 1, is characterized in that, in step 6
In also include, actual pressure value beyond desired pressure value time, by change circulating water pipeline flow velocity come instead
Still (7) is answered to carry out pressure compensation regulation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610525615.5A CN105974953B (en) | 2016-07-06 | 2016-07-06 | A kind of reaction kettle negative pressure rectifying fuzzy control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610525615.5A CN105974953B (en) | 2016-07-06 | 2016-07-06 | A kind of reaction kettle negative pressure rectifying fuzzy control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105974953A true CN105974953A (en) | 2016-09-28 |
| CN105974953B CN105974953B (en) | 2019-02-15 |
Family
ID=56954674
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610525615.5A Active CN105974953B (en) | 2016-07-06 | 2016-07-06 | A kind of reaction kettle negative pressure rectifying fuzzy control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105974953B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107670607A (en) * | 2016-09-30 | 2018-02-09 | 青岛大学 | A kind of chemical mechanical system of Fuzzy control of Rotate speed |
| CN107670608A (en) * | 2016-09-30 | 2018-02-09 | 青岛大学 | A kind of chemical mechanical system of Temperature Fuzzy Control |
| CN108279568A (en) * | 2017-12-29 | 2018-07-13 | 浙江中控软件技术有限公司 | Boundary position control method for Variable Composition slot |
| CN108469781A (en) * | 2018-03-16 | 2018-08-31 | 曲阜师范大学 | Organosilicon produces fully integrated Optimal Control System |
| CN108803308A (en) * | 2018-06-28 | 2018-11-13 | 吉林大学 | The mostly logical pond temperature control system of gas based on adaptive section PID control and method |
| CN108814956A (en) * | 2018-07-17 | 2018-11-16 | 广东工业大学 | A kind of mixibustion bed intelligent temperature control system and its method |
| CN109999527A (en) * | 2019-04-25 | 2019-07-12 | 青岛杰瑞工控技术有限公司 | A kind of multi-fluid intelligence ingredient control method |
| CN112578824A (en) * | 2020-11-13 | 2021-03-30 | 云南国钛金属股份有限公司 | Reactor pressure stability control device and method |
| CN114192082A (en) * | 2020-09-18 | 2022-03-18 | 中国石油天然气股份有限公司 | Polymerization kettle pressure control method and device, computer equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102433137A (en) * | 2011-12-27 | 2012-05-02 | 北京佰能电气技术有限公司 | Pressure regulating device for coke oven carbonization chamber and fuzzy control method thereof |
| KR20130126125A (en) * | 2012-05-10 | 2013-11-20 | 수원대학교산학협력단 | Method for controlling constant pressure using fuzzy pid controller |
| CN104267754A (en) * | 2014-09-24 | 2015-01-07 | 中国核动力研究设计院 | Intelligent reactor inlet pressure adjusting system and control method thereof |
-
2016
- 2016-07-06 CN CN201610525615.5A patent/CN105974953B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102433137A (en) * | 2011-12-27 | 2012-05-02 | 北京佰能电气技术有限公司 | Pressure regulating device for coke oven carbonization chamber and fuzzy control method thereof |
| KR20130126125A (en) * | 2012-05-10 | 2013-11-20 | 수원대학교산학협력단 | Method for controlling constant pressure using fuzzy pid controller |
| CN104267754A (en) * | 2014-09-24 | 2015-01-07 | 中国核动力研究设计院 | Intelligent reactor inlet pressure adjusting system and control method thereof |
Non-Patent Citations (3)
| Title |
|---|
| 权悦等: "CFB锅炉蒸汽压力的模糊自校正PID控制", 《济南大学学报（自然科学版）》 * |
| 王书鹏等: "循环流化床锅炉炉膛负压模糊自适应PID控制", 《工业控制计算机》 * |
| 王川川等: "模糊控制器设计中量化因子、比例因子的选择", 《四川兵工学报》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107670607A (en) * | 2016-09-30 | 2018-02-09 | 青岛大学 | A kind of chemical mechanical system of Fuzzy control of Rotate speed |
| CN107670608A (en) * | 2016-09-30 | 2018-02-09 | 青岛大学 | A kind of chemical mechanical system of Temperature Fuzzy Control |
| CN108279568A (en) * | 2017-12-29 | 2018-07-13 | 浙江中控软件技术有限公司 | Boundary position control method for Variable Composition slot |
| CN108279568B (en) * | 2017-12-29 | 2021-06-29 | 浙江中控软件技术有限公司 | Interface control method for variable concentration tank |
| CN108469781A (en) * | 2018-03-16 | 2018-08-31 | 曲阜师范大学 | Organosilicon produces fully integrated Optimal Control System |
| CN108803308A (en) * | 2018-06-28 | 2018-11-13 | 吉林大学 | The mostly logical pond temperature control system of gas based on adaptive section PID control and method |
| CN108814956A (en) * | 2018-07-17 | 2018-11-16 | 广东工业大学 | A kind of mixibustion bed intelligent temperature control system and its method |
| CN109999527A (en) * | 2019-04-25 | 2019-07-12 | 青岛杰瑞工控技术有限公司 | A kind of multi-fluid intelligence ingredient control method |
| CN109999527B (en) * | 2019-04-25 | 2021-10-15 | 青岛杰瑞工控技术有限公司 | Multi-fluid intelligent batching control method |
| CN114192082A (en) * | 2020-09-18 | 2022-03-18 | 中国石油天然气股份有限公司 | Polymerization kettle pressure control method and device, computer equipment and storage medium |
| CN112578824A (en) * | 2020-11-13 | 2021-03-30 | 云南国钛金属股份有限公司 | Reactor pressure stability control device and method |
| CN112578824B (en) * | 2020-11-13 | 2022-11-18 | 云南国钛金属股份有限公司 | Reactor pressure stability control device and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105974953B (en) | 2019-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105974953A (en) | Reaction vessel negative pressure rectification fuzzy control method | |
| Kaneko et al. | Development of a new soft sensor method using independent component analysis and partial least squares | |
| CN102339040B (en) | Optimization and control method of rectification column | |
| CN102411308B (en) | Adaptive control method of dissolved oxygen (DO) based on recurrent neural network (RNN) model | |
| Yuan et al. | Supervised deep belief network for quality prediction in industrial processes | |
| Li et al. | The application of fuzzy control in liquid level system | |
| Kumar et al. | Real-time performance evaluation of a fuzzy PI+ fuzzy PD controller for liquid-level process | |
| Pathmanathan et al. | Development and implementation of fuzzy logic controller for flow control application | |
| US20200024168A1 (en) | Intelligent identification method of sludge bulking based on type-2 fuzzy neural network | |
| CN107132325A (en) | A kind of flexible measurement method of the Anaerobic Waste Treatment System water outlet volatile fatty acid based on particle cluster algorithm and SVMs | |
| CN102004444A (en) | Multi-model predictive control method for component content in process of extracting rare earth | |
| CN110058526A (en) | A kind of control method of the Neutral Differential Equations based on two type T-S model of section | |
| Wu et al. | Integrated soft sensing of coke-oven temperature | |
| Musmade et al. | Feedforward-plus-sliding mode controller design with experimental application of coupled tank system | |
| CN100511042C (en) | Groove type reactor non-linear predication control method based on multi-kernel support vector machine | |
| CN109492265B (en) | Wastewater effluent index prediction method based on dynamic nonlinear PLS soft measurement method | |
| CN110400606A (en) | A method of based on element sulfur solubility in FOA-LSSVM model prediction high sulfur-containing natural gas | |
| CN111142494B (en) | Intelligent control method and system for amine liquid regeneration device | |
| Yang et al. | Domain adaptation network with uncertainty modeling and its application to the online energy consumption prediction of ethylene distillation processes | |
| Feng et al. | A soft sensor modeling approach using support vector machines | |
| CN109507868A (en) | A kind of fuzzy controller and Large-scale pig farm biogas project control system | |
| CN112130542B (en) | Control loop performance evaluation method based on normal operation data and system identification | |
| Mehrpooya et al. | Design and implementation of optimized fuzzy logic controller for a nonlinear dynamic industrial plant using Hysys and Matlab simulation packages | |
| Zhang et al. | Multimodal Control by Variable-Structure Neural Network Modeling for Coagulant Dosing in Water Purification Process | |
| Xing et al. | Dynamic soft sensing of organic pollutants in effluent from UMIC anaerobic reactor for industrial papermaking wastewater |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |