CN105807635A - Predictive fuzzy control optimized control method for waste plastic oil refining cracking furnace chamber pressure - Google Patents

Abstract

The invention discloses a predictive fuzzy control optimized control method for waste plastic oil refining cracking furnace chamber pressure.A chamber pressure object model is set up based on step response data of a chamber pressure object of a cracking furnace, and basic object characteristics are mined; corresponding parameters of a PID controller are set according to characteristics of predictive function control; PID control is conducted on cracking furnace chamber pressure.While good predictive function control and fuzzy control performance is inherited, it is ensured that the form is simple, and requirements of the actual industrial process are met.

Description

The waste plastic oil-refining pyrolysis furnace hearth pressure control method that predicative fuzzy control optimizes

Technical field

The invention belongs to technical field of automation, relate to waste plastic oil-refining pyrolysis furnace furnace pressure PID (PID) control method that a kind of predicative fuzzy control optimizes.

Background technology

Chemical process is the important component part of China's process flow industry process, and its requirement is supplied with qualified industrial products, to meet the needs of China's industry.In current Industry Control, because traditional PID controls technology and has the advantages such as simple in construction, robustness are good, easily operated, so being widely used in the industry.In actual industrial control process, due to interference, the factor such as non-linear, the control performance that coking furnace temperature is controlled by traditional PID is poor, tending not to meet control accuracy and the product quality requirement of the increasingly stringent in actual chemical process, algorithm more advanced, control better effects if still requires study.Oil refining pyrolysis furnace is the important device in petrochemical production process, and wherein cracking process is had critically important impact by furnace pressure, and furnace pressure is excessive may bring danger, and the too low meeting of furnace pressure causes lysis efficiency step-down.Owing to advanced control method and intelligent algorithm are applied limited, the control of current pyrolysis furnace furnace pressure generally adopts PID control.Predictive function control is as the one of advanced control method, the control of pyrolysis furnace furnace pressure is compared PID control to have better control performance, if the performance of Predictive function control can be given to PID control, that will be pushed further into the application of advanced control method, also can obtain better Actual Control Effect of Strong simultaneously.The control that traditional PID control causes because parameter immobilizes is dumb, the shortcomings such as heating accuracy is not high, Fuzzy Adaptive Control Scheme is adopted to achieve controller parameter on-line tuning, the PREDICTIVE CONTROL introduced has reached the purpose that adjusts in advance, it is ensured that the form of control structure is simple and tracking accuracy.

Summary of the invention

It is an object of the invention to the application weak point for existing Dynamic matrix control and intelligent control method, it is provided that a kind of pyrolysis furnace furnace pressure PID control method optimized based on Predictive function control and fuzzy control, to obtain better actual control performance.The method, by controlling in conjunction with Predictive function control, fuzzy control and PID, obtains a kind of PID control method with Predictive function control and fuzzy control performance.The method also ensure that while inheriting Predictive function control and fuzzy control premium properties form is simple and meets the needs of actual industrial process.

The present invention is primarily based on the step response data of pyrolysis furnace furnace pressure object and sets up the model of furnace pressure object, excavates its basic characteristics of objects；Then go to adjust according to the characteristic of Predictive function control the parameter of corresponding PID controller；Finally pyrolysis furnace furnace pressure object is implemented PID to control.

The technical scheme is that and set up by data acquisition, model, predict mechanism, optimization means, establish a kind of PID control method optimized based on anticipation function and fuzzy control, utilize the method can be effectively improved precision and the stability of control.

The step of the inventive method requires:

Step 1, real-time step response data by pyrolysis furnace furnace pressure object set up the model of controlled device, and concrete grammar is:

The proportional plus integral plus derivative controller of process is rested on manual operation state by 1.1, and operation driver plate makes it export Spline smoothing, by the output valve of recording apparatus real process, by real process output valveSpecifically:

$y_p^{*}\left(k\right)=y_p\left(k\right)/y_p\left(\mathrm{\∞}\right)$

Wherein, y_p(∞) it is the output of proportional plus integral plus derivative controller real process output y when having a Spline smoothing_pThe steady-state value of (k).

1.2 choose satisfiedAndTwo calculate some k₁And k₂, the model parameter K of process object is calculated according to following formula_m, T, τ:

K_m=y_p(∞)/q

T=2 (k₁-k₂)

τ=2k₁-k₂

The transmission function of the process object finally obtained is:

$G\left(s\right)=\frac{K_m}{Ts+1}{e}^{-\mathrm{\τ}s}$

Wherein, q is the Spline smoothing amplitude of the proportional plus integral plus derivative controller output of process, and the transmission function that G (s) is process object, s is Laplace transform operator, K_mFor the gain coefficient of model, T is the time constant of model, and τ is parameter lag time of model.

Step 2, design process object PID controller predicted portions, concrete grammar is:

The transmission function that 2.1 pairs obtain is at sampling time T_sUnder add a zero-order holder discretization, obtaining discrete model is:

y_m(k)=a_my_m(k-1)+K_m(1-a_m)u(k-L-1)

Wherein, y_mK process object model prediction output that () is the k moment,The control that u (k-1-L) is the controlled device in k-L-1 moment inputs, T_sFor the sampling time, L is the time lag of discrete transfer function model, L=τ/T_s。

2.2 calculate process object removes the purely retarded step of the P under Predictive function control prediction output later, and form is as follows:

y_mav(k)=a_my_mav(k-1)+K_m(1-a_m)u(k-1)

$y_{mav}(k+P)=a_m^P{y}_{mav}(k-1)+K_m(1-a_m^P)u\left(k\right)$

Wherein, P is prediction step, y_mav(k+P) the P step prediction output under Predictive function control of the process object of purely retarded, y is removed for the k moment_mavK () removes the process model output of purely retarded for the k moment.

The 2.3 actual outputs revising current time obtain comprising the new process real output value of future anticipation information, and form is as follows:

y_pav(k)=y_p(k)+y_mav(k)-y_mav(k-L)

Wherein, y_pavThe new the output of process value that the k k moment that () obtains for correction comprises future anticipation information, y_pK () is the real output value in k moment.

Step 3, design controlled device PID controller self-adaptative adjustment part, comprise the concrete steps that:

3.1 obtain experience pid parameter by PID controllerWherein, experience pid parameter is when without fuzzy controller, first passes through one group of comparatively ideal pid parameter that laboratory method is debugged, and with this parameter empirically pid control parameter.

3.2 build fuzzy controller, and fuzzy controller includes fuzzy device, indistinct logic computer, data base, rule base and defuzzifier.

Fuzzy controller to set up mode as follows:

3.2.1 the determination of Fuzzy Linguistic Variable

Using rate of change Δ e (t) of pressure divergence e (t) and the pressure divergence input language as fuzzy controller, Δ K_p,ΔK_i,ΔK_dFor output language.Its excursion is defined as the basic domain on fuzzy machine: [-6 ,-5 ,-4 ,-3 ,-2 ,-1,0,1,2,3,4,5,6].

Its fuzzy subset is e (t), and Δ e (t)={ NB, NM, NS, ZE, PS, PM, PB}, its subset is expressed as: in negative big, negative, negative little, just little, center, honest.The amount of basic domain carrying out discretization and then carries out Fuzzy Processing, if input language scope is in basic domain, can be converted by linear change formula, formula is as follows:

$y=\frac{10}{b-a}(x-\frac{a+b}{2})$

3.2.2 membership function is determined

In general, the impact controlling effect is not very big by fuzzy membership function, chooses the Triangleshape grade of membership function that computing is relatively simple here, its shape only with slope linear correlation, running memory occupies less, is suitable for on-line tuning.

3.2.3 fuzzy rule base is set up

When system deviation is bigger, in order to accelerate response speed, avoid overshoot and integration saturated simultaneously, at this moment should choose bigger K_p, medium K_dAnd less K_i.When system deviation is less, in order to ensure the stability of system, it should take less K_pAnd K_i, bigger K_d。

According to rule analysis, fuzzy reasoning table can be made.

Above step 3.2 is after control system brings into operation, setting pressure and current pressure are done calculus of differences, according to features such as the size of difference, direction, variation tendencies, made decisions by indistinct logic computer, process through defuzzifier and draw corresponding PID gain parameter Δ K_p,ΔK_i,ΔK_d。

3.3 by experience pid parameterFuzzy Self-Tuning PID Controller is inputted with setting value；

3.4 using the input as fuzzy controller of rate of change Δ e (t) of pressure divergence e (t) and pressure divergence, in fuzzy controller through obfuscation, map fuzzy rule base, ambiguity solution processes, and draws one group of PID gain parameter Δ K_p,ΔK_i,ΔK_d, by this gain parameter and experience pid parameter linear, additive, obtain one group of desirable pid parameter:

$K_p=K_{p_0}+{\mathrm{\ΔK}}_p$

$K_i=K_{i_0}+{\mathrm{\ΔK}}_i$

$K_d=K_{d_0}+{\mathrm{\ΔK}}_d$

Pressure divergence e (t) obtained and desirable pid parameter are done PID arithmetic by 3.5 obtains the controlled quentity controlled variable u (t) of actuator, and its expression is as follows:The controlled quentity controlled variable u (t) of actuator acts in controlled device, thus controlling pressure.

It is predicted bringing in step 3 in Fuzzy Adaptive PID to the output of process by step 2 forecast model, continues real process is predicted according to the step of step 2 to step 3 at subsequent time, circulate successively.

What the present invention proposed is that the performance of Predictive function control and fuzzy control is assigned to PID control by a kind of waste plastic oil-refining pyrolysis furnace furnace pressure PID control method based on Predictive function control and fuzzy control optimization, it is effectively improved the performance of traditional control method, also promotes the application of Dynamic matrix control and fuzzy control method simultaneously.

Accompanying drawing explanation

Fig. 1 is the waste plastic oil-refining pyrolysis furnace furnace pressure Control system architecture figure of the predictive fuzzy PID tune of invention.

Detailed description of the invention

For waste plastic oil-refining pyrolysis furnace furnace pressure process control:

Pyrolysis furnace furnace pressure is the important parameter in pyrolysis furnace cracking process, and regulating measure adopts the aperture of damper, referring to Fig. 1.

Step 1, real-time step response data by pyrolysis furnace furnace pressure object set up the model of controlled device, and concrete grammar is:

The proportional plus integral plus derivative controller of process is rested on manual operation state by 1.1, and operation driver plate makes it export Spline smoothing, by the output valve of recording apparatus real process, by real process output valveSpecifically:

$y_p^{*}\left(k\right)=y_p\left(k\right)/y_p\left(\mathrm{\∞}\right)$

Wherein, y_p(∞) it is the output of proportional plus integral plus derivative controller real process output y when having a Spline smoothing_pThe steady-state value of (k).

1.2 choose satisfiedAndTwo calculate some k₁And k₂, the model parameter K of process object is calculated according to following formula_m, T, τ:

K_m=y_p(∞)/q

T=2 (k₁-k₂)

τ=2k₁-k₂

The transmission function of the process object finally obtained is:

$G\left(s\right)=\frac{K_m}{Ts+1}{e}^{-\mathrm{\τ}s}$

Wherein, q is the Spline smoothing amplitude of the proportional plus integral plus derivative controller output of process, and the transmission function that G (s) is process object, s is Laplace transform operator, K_mFor the gain coefficient of model, T is the time constant of model, and τ is parameter lag time of model.

Step 2, design process object PID controller predicted portions, concrete grammar is:

The transmission function that 2.1 pairs obtain is at sampling time T_sUnder add a zero-order holder discretization, obtaining discrete model is:

y_m(k)=a_my_m(k-1)+K_m(1-a_m)u(k-L-1)

Wherein, y_mK process object model prediction output that () is the k moment,The control that u (k-1-L) is the controlled device in k-L-1 moment inputs, and L is the time lag of discrete transfer function model, L=τ/T_s。

2.2 calculate process object removes the purely retarded step of the P under Predictive function control prediction output later, and form is as follows:

y_mav(k)=a_my_mav(k-1)+K_m(1-a_m)u(k-1)

$y_{mav}(k+P)=a_m^P{y}_{mav}(k-1)+K_m(1-a_m^P)u\left(k\right)$

Wherein, P is prediction step, y_mav(k+P) the P step prediction output under Predictive function control of the process object of purely retarded, y is removed for the k moment_mavK () removes the process model output of purely retarded for the k moment.

The 2.3 actual outputs revising current time obtain comprising the new process real output value of future anticipation information, and form is as follows:

y_pav(k)=y_p(k)+y_mav(k)-y_mav(k-L)

Wherein, y_pavThe new the output of process value that the k k moment that () obtains for correction comprises future anticipation information, y_pK () is the real output value in k moment.

Step 3, design controlled device PID controller self-adaptative adjustment part, comprise the concrete steps that:

3.1 obtain experience pid parameter by PID controllerWherein, experience pid parameter is when without fuzzy controller, first passes through one group of comparatively ideal pid parameter that laboratory method is debugged, and with this parameter empirically pid control parameter.

3.2 build fuzzy controller, and fuzzy controller includes fuzzy device, indistinct logic computer, data base, rule base and defuzzifier；

Fuzzy controller to set up mode as follows:

3.2.1 the determination of Fuzzy Linguistic Variable

Using rate of change Δ e (t) of pressure divergence e (t) and the pressure divergence input language as fuzzy controller, Δ K_p,ΔK_i,ΔK_dFor output language.Its excursion is defined as the basic domain on fuzzy machine: [-6 ,-5 ,-4 ,-3 ,-2 ,-1,0,1,2,3,4,5,6].

Its fuzzy subset is e (t), and Δ e (t)={ NB, NM, NS, ZE, PS, PM, PB}, its subset is expressed as: in negative big, negative, negative little, just little, center, honest.The amount of basic domain carrying out discretization and then carries out Fuzzy Processing, if input language scope is in basic domain, can be converted by linear change formula, formula is as follows:

$y=\frac{10}{b-a}(x-\frac{a+b}{2})$

3.2.2 membership function is determined

In general, the impact controlling effect is not very big by fuzzy membership function, chooses the Triangleshape grade of membership function that computing is relatively simple here, its shape only with slope linear correlation, running memory occupies less, is suitable for on-line tuning.

3.2.3 fuzzy rule base is set up

When system deviation is bigger, in order to accelerate response speed, avoid overshoot and integration saturated simultaneously, at this moment should choose bigger K_p, medium K_dAnd less K_i.When system deviation is less, in order to ensure the stability of system, it should take less K_pAnd K_i, bigger K_d。

According to rule analysis, fuzzy reasoning table can be made.

Above step 3.2 is after control system brings into operation, setting pressure and current pressure are done calculus of differences, according to features such as the size of difference, direction, variation tendencies, made decisions by indistinct logic computer, process through defuzzifier and draw corresponding PID gain parameter Δ K_p,ΔK_i,ΔK_d。

3.3 by experience pid parameterFuzzy Self-Tuning PID Controller is inputted with setting value；

3.4 using the input as fuzzy controller of rate of change Δ e (t) of pressure divergence e (t) and pressure divergence, in fuzzy controller through obfuscation, map fuzzy rule base, ambiguity solution processes, and draws one group of PID gain parameter Δ K_p,ΔK_i,ΔK_d, by this gain parameter and experience pid parameter linear, additive, obtain one group of desirable pid parameter:

$K_p=K_{p_0}+{\mathrm{\ΔK}}_p$

$K_i=K_{i_0}+{\mathrm{\ΔK}}_i$

$K_d=K_{d_0}+{\mathrm{\ΔK}}_d$

Pressure divergence e (t) obtained and desirable pid parameter are done PID arithmetic by 3.5 obtains the controlled quentity controlled variable u (t) of actuator, and its expression is as follows:The controlled quentity controlled variable u (t) of actuator acts in controlled device, thus controlling pressure.

It is predicted bringing in step 3 in Fuzzy Adaptive PID to the output of process by step 2 forecast model, continues real process is predicted according to the step of step 2 to step 3 at subsequent time, circulate successively.

Claims (1)

1. the waste plastic oil-refining pyrolysis furnace hearth pressure control method that predicative fuzzy control optimizes, it is characterised in that the method comprises the following steps；

Step 1, real-time step response data by pyrolysis furnace furnace pressure object set up the model of controlled device, specifically:

The proportional plus integral plus derivative controller of process is rested on manual operation state by 1.1, and operation driver plate makes it export Spline smoothing, and the output valve of recording apparatus real process, by real process output valve

$y_p^{*}\left(k\right)=y_p\left(k\right)/y_p\left(\mathrm{\∞}\right)$

Wherein, y_p(∞) it is the output of proportional plus integral plus derivative controller real process output y when having a Spline smoothing_pThe steady-state value of (k)；

1.2 choose satisfiedAndTwo calculate some k₁And k₂, the model parameter K of process object is calculated according to following formula_m, T, τ:

K_m=y_p(∞)/q

T=2 (k₁-k₂)

τ=2k₁-k₂

The transmission function of the process object finally obtained is:

$G\left(s\right)=\frac{K_m}{Ts+1}{e}^{-\mathrm{\τ}s}$

Wherein, q is the Spline smoothing amplitude of the proportional plus integral plus derivative controller output of process, and the transmission function that G (s) is process object, s is Laplace transform operator, K_mFor the gain coefficient of model, T is the time constant of model, and τ is parameter lag time of model；

Step 2, design process object PID controller predicted portions, specifically:

The transmission function that 2.1 pairs obtain is at sampling time T_sUnder add a zero-order holder discretization, obtaining discrete model is:

y_m(k)=a_my_m(k-1)+K_m(1-a_m)u(k-L-1)

Wherein, y_mK process object model prediction output that () is the k moment,The control that u (k-1-L) is the controlled device in k-L-1 moment inputs, T_sFor the sampling time, L is the time lag of discrete transfer function model, L=τ/T_s；

2.2 calculate process object removes the purely retarded step of the P under Predictive function control prediction output later, and form is as follows:

y_mav(k)=a_my_mav(k-1)+K_m(1-a_m)u(k-1)

$y_{mav}(k+P)=a_m^P{y}_{mav}(k-1)+K_m(1-a_m^P)u\left(k\right)$

Wherein, P is prediction step, y_mav(k+P) the P step prediction output under Predictive function control of the process object of purely retarded, y is removed for the k moment_mavK () removes the process model output of purely retarded for the k moment；

The 2.3 actual outputs revising current time obtain comprising the new process real output value of future anticipation information, and form is as follows:

y_pav(k)=y_p(k)+y_mav(k)-y_mav(k-L)

Wherein, y_pavThe new the output of process value that the k k moment that () obtains for correction comprises future anticipation information, y_pK () is the real output value in k moment；

Step 3, design controlled device PID controller self-adaptative adjustment part, specifically:

3.1 obtain experience pid parameter by PID controller

3.2 build fuzzy controller, and fuzzy controller includes fuzzy device, indistinct logic computer, data base, rule base and defuzzifier；

Fuzzy controller to set up mode as follows:

3.2.1 the determination of Fuzzy Linguistic Variable

Using rate of change △ e (t) of pressure divergence e (t) and the pressure divergence input language as fuzzy controller, △ K_p,△K_i,△K_dFor output language；Its excursion is defined as the basic domain on fuzzy machine: [-6 ,-5 ,-4 ,-3 ,-2 ,-1,0,1,2,3,4,5,6]；

Its fuzzy subset is that e (t), △ e (t)={ NB, NM, NS, ZE, PS, PM, PB}, its subset is expressed as: in negative big, negative, negative little, just little, center, honest；The amount of basic domain carrying out discretization and then carries out Fuzzy Processing, if input language scope is in basic domain, converted by linear change formula, formula is as follows:

$y=\frac{10}{b-a}(x-\frac{a+b}{2})$

3.2.2 determine membership function, choose the Triangleshape grade of membership function that computing is relatively simple；

3.2.3 fuzzy rule base is set up

When system deviation is bigger, in order to accelerate response speed, avoid overshoot and integration saturated simultaneously, at this moment should choose bigger K_p, medium K_dAnd less K_i；When system deviation is less, in order to ensure the stability of system, it should take less K_pAnd K_i, bigger K_d；

According to rule analysis, make fuzzy reasoning table；

Above step 3.2 is after control system brings into operation, and setting pressure and current pressure are done calculus of differences, according to the size of difference, direction, variation tendency, is made decisions by indistinct logic computer, processes through defuzzifier and draws corresponding PID gain parameter △ K_p,△K_i,△K_d；

3.3 by experience pid parameterFuzzy Self-Tuning PID Controller is inputted with setting value；

3.4 using the input as fuzzy controller of rate of change △ e (t) of pressure divergence e (t) and pressure divergence, in fuzzy controller through obfuscation, map fuzzy rule base, ambiguity solution processes, and draws one group of PID gain parameter △ K_p,△K_i,△K_d, by this gain parameter and experience pid parameter linear, additive, obtain one group of desirable pid parameter:

$K_p=K_{p_0}+{\mathrm{\ΔK}}_p$

$K_i=K_{i_0}+{\mathrm{\ΔK}}_i$

$K_d=K_{d_0}+{\mathrm{\ΔK}}_d$

Pressure divergence e (t) obtained and desirable pid parameter are done PID arithmetic by 3.5 obtains the controlled quentity controlled variable u (t) of actuator, and its expression is as follows:The controlled quentity controlled variable u (t) of actuator acts in controlled device, thus controlling pressure；

It is predicted bringing in step 3 in Fuzzy Adaptive PID to the output of process by step 2 forecast model, continues real process is predicted according to the step of step 2 to step 3 at subsequent time, circulate successively.