CN100490930C - Method and system for controlling high-purity rectification of rectifying tower based on generalized prediction control - Google Patents

Abstract

The invention is concerned with a kind of high purity rectify control system of rectify tower based on generalized forecasting control. It relates to locale intelligent instrument connecting with distillation tower, data stored equipment for old data and upper computer. They connect with each other one by one and the upper computer is the generalized forecasting control. The said generalized forecasting control relates to component deduce and control part and generalized forecasting part, and the component deduce and control part relates to check instrument module, I/O component module and component deduce module, while the generalized forecasting part relates to I/O component module and forecasting model module, model self-revise module, rolling optimize module and control sending module. It also affords a kind of control method to generalized forecasting control system of distillation tower. This invention has good control effect to fit the dynamic character, strong nonlinear and the coupling character between loops of the distillation operation.

Description

High-purity rectification of rectifying tower control system and method based on generalized predictive control

(1) technical field

The advanced person who the present invention relates to the chemical industry distillation process controls the field, especially, relates to a kind of high-purity rectification of rectifying tower control system and method based on generalized predictive control.

(2) background technology

Distillation process is very typical very important unit process in the petrochemical production process, and its operational quality directly affects the product quality and the production cost of whole factory.Along with economic globalization and scientific and technological making rapid progress, people require more and more higher to the high efficiency and the accuracy of distillation process especially.Therefore the notion of high-purity distillation also just more and more obtains concern both domestic and external, and its importance is indubitable especially.

The difficult problem of high-purity distillation process control mainly is the coupling between its complicated dynamic characteristic, the non-linear and loop, and traditional is difficult to it is controlled effect preferably as Linear Control schemes such as PID.Current social makes important all the more that research to the high-purity distillation process shows to the requirement of product quality, aspect such as energy-saving and cost-reducing.Numerous domestic and international technology, automatic control experts have done number of research projects to this, proposed many advanced control theories, have also obtained some challenging progress.But, make some important advanced person's control schemes be difficult in the petrochemical production process and implement in view of the restriction of blind controller system hardware.In these years, along with progressively popularizing that computer and DCS use in petrochemical production process, make people application of advanced control scheme improve the control device of rectifying column, thereby the target that reaches raising production economic benefit is become a reality.

(3) summary of the invention

In order to overcome coupling between empty dynamic characteristic, strong nonlinear and the loop of dividing operation of can not adapting to of existing rectifying column control scheme, can not to obtain the deficiency of good control effect, the invention provides a kind of coupling problem that can solve between empty dynamic characteristic, strong nonlinear and the loop of dividing operation, and obtain the high-purity rectification of rectifying tower control system and the method based on generalized predictive control of good control effect.

The technical solution adopted for the present invention to solve the technical problems is:

A kind of high-purity rectification of rectifying tower control system based on generalized predictive control, comprise and the direct-connected field intelligent instrument of rectifying Tata, the data storage device and the host computer that are used for storing history data, intelligence instrument, data storage device and host computer link to each other successively, described host computer is the universal model controller, described universal model controller comprises component deduction control section and universal model control section, described component infers that control section comprises: the instrumentation module, comprise detector unit and pressure detecting element, be used to detect the temperature and pressure of the last tower of rectifying column; The I/O component module is used for the transmission between controller inside and controller and data storage device of the signal of telecommunication and data-signal, and the component inference module is used for inferring component according to detecting the temperature and the pressure data that obtain, and its formula is (1), (2):

$X_D=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P}---\left(1\right)$

$X_B=\frac{\mathrm{P\&alpha;}}{{10}^{(a-\frac{T_n+c}{b})}(\mathrm{\&alpha;}-1)}-\frac{1}{\mathrm{\&alpha;}-1}---\left(2\right)$

Wherein, X _D, X _BBe the product light component at high-purity distillation tower two ends, P is that tower is pressed T ₁, T _nBe respectively cat head, column bottom temperature, α is a relative volatility, and a, b, c are the Peter Antonie constant;

Described generalized predictive control partly comprises: the I/O component module is used for the inside of generalized predictive controller and the signal of telecommunication between controller and the data storage device, the transmission of data-signal; The forecast model module is used for that DCS historical data base or test gained data are carried out least square and fits the system of obtaining and have the discrete differential equation model of step disturbance nonstationary noise at random, and its formula is (3);

A(z ^-1)y(k)＝B(z ^-1)u(k-1)+C(z ^-1)ε(k)/Δ (3)

Wherein $A\left(z^{-1}\right)=1+\underset{i=1}{\overset{n_a}{\mathrm{\&Sigma;}}}{a}_i{z}^{-i},$ $B\left(z^{-1}\right)=\underset{i=0}{\overset{n_b}{\mathrm{\&Sigma;}}}{b}_i{z}^{-i},$ Δ-difference operator, Δ=1-z ^-1

Model self-correcting module is used to adopt the RLS with forgetting factor, on-line correction system model parameter, and concrete steps are as follows:

(1) upgrades vectorial X (k-1) ^T=[Δ y (k-1) ... ,-Δ y (k-n _a), Δ u (k-1) ..., Δ u (k-n _b-1)];

(2) read y (k) and calculate ε (k)=Δ y (k)-X (k-1) ^Tθ (k-1);

(3) use the recursive least squares with forgetting factor to obtain θ (k), its formula is (4):

$\mathrm{\&theta;}\left(k\right)=\mathrm{\&theta;}(k-1)+\frac{P(k-2)X(k-1)\mathrm{\&epsiv;}\left(k\right)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}---\left(4\right)$

Wherein, $P(k-1)=\frac{1}{\mathrm{\&rho;}}[P(k-2)-\frac{P(k-2)X(k-1)X{(k-1)}^{T}P(k-2)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}],$ P (-1) is enough big positive definite matrix, $\mathrm{\&theta;}=[a_1,...,a_{n_a},b_0,...,b_{n_b}];$

(4) from θ (k), extract master mould parameter A (z ^-1), B (z ^-1), and calculate A (z ^-1)=A (z ^-1) Δ, B (z ^-1)=B (z ^-1) Δ;

The rolling optimization module is used for finding the solution controlled quentity controlled variable u (k) based on forecast model module, feedback compensation module and GPC algorithm computing, concrete formula following (5):

Δu(k)＝d ₁ ^T[Y _r(k+1)-F ₀ΔU(k-1)-SY(k)]

u(k)＝u(k-1)+Δu(k) (5)

d ₁ ^TBe (G ^TQG+ λ) ^-1G ^TThe i row vector of Q

Wherein, Y (k)=[y (k) y (k-1) ... y (k-n _a)] ^T,

ΔU(k-1)＝[Δu(k-n _b)Δu(k-n _b+1)…Δu(k-1)] ^T；

The control output module, the data-signal that is used for the u (k) that will calculate outputs to rectifying column.

As preferred a kind of scheme: described general model control system also comprises the DCS system, and described DCS system is made of data-interface, control station and historical data base, and described data storage device is the historical data base of DCS system.

As preferred another kind of scheme: described field intelligent instrument, DCS system, universal model controller connect successively by fieldbus

As preferred another scheme: described generalized predictive controller also comprises human-computer interface module, is used for the value of the control variables u (k) that will calculate, and with it and detect the X that obtains _D, X _BValue on the man-machine interface of controller, show.

The described control method that realizes based on the high-purity rectification of rectifying tower control system of generalized predictive control of a kind of usefulness, described control method may further comprise the steps:

(1) determines the bi-component setting value Y of rectifying column _Lset, X _Nset, and sampling period T;

(2) each sampling instant KT infers component according to detecting the temperature and the pressure data that obtain, and its formula is (1), (2):

$X_D=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P}---\left(1\right)$

$X_B=\frac{\mathrm{P\&alpha;}}{{10}^{(a-\frac{T_n+c}{b})}(\mathrm{\&alpha;}-1)}-\frac{1}{\mathrm{\&alpha;}-1}---\left(2\right)$

Wherein, X _D, X _BBe the product light component at high-purity distillation tower two ends, P is that tower is pressed T ₁, T _nBe respectively cat head, column bottom temperature, α is a relative volatility, and a, b, c are the Peter Antonie constant;

(3) by the DCS historical data base or do the data fitting system that obtains of test and have the discrete differential equation model of step disturbance nonstationary noise at random, its formula is (3):

A(z ^-1)y(k)＝B(z ^-1)u(k-1)+C(z ^-1)ε(k)/Δ (3)

Wherein $A\left(z^{-1}\right)=1+\underset{i=1}{\overset{n_a}{\mathrm{\&Sigma;}}}{a}_i{z}^{-i},$ $B\left(z^{-1}\right)=\underset{i=0}{\overset{n_b}{\mathrm{\&Sigma;}}}{b}_i{z}^{-i},$ Δ-difference operator, Δ=1-z ^-1

(4) put initial value P, M, Q, λ, n _a, n _b, ρ, α, θ (0), length of field, Q were that error weighting matrix, λ are that forgetting factor, α are the softening factor for control weighting matrix, ρ when length of field, M were for control when wherein P was for prediction, $\mathrm{\&theta;}=[a_1,...,a_{n_a},b_0,...,b_{n_b}]$

(5) self-correcting recursion update system model, concrete steps are as follows:

(5.1) upgrade vector

X(k-1) ^T＝[-Δy(k-1)，...，-Δy(k-n _a)，Δu(k-1)，...，Δu(k-n _b-1)]

(5.2) read y (k) and calculate ε (k)=Δ y (k)-X (k-1) ^Tθ (k-1)

(5.3) use the recursive least squares with forgetting factor to obtain θ (k), its calculating formula is

(4)： $\mathrm{\&theta;}\left(k\right)=\mathrm{\&theta;}(k-1)+\frac{P(k-2)X(k-1)\mathrm{\&epsiv;}\left(k\right)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}---\left(4\right)$

Wherein, $P(k-1)=\frac{1}{\mathrm{\&rho;}}[P(k-2)-\frac{P(k-2)X(k-1)X{(k-1)}^{T}P(k-2)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}],$ P (-1) is enough big positive definite matrix;

(5.4) from θ (k), extract master mould parameter A (z ^-1), B (z ^-1), and calculate A (z ^-1)=A (z ^-1) Δ, B (z ^-1)=B (z ^-1) Δ;

(6) find the solution controlled quentity controlled variable u (k), concrete formula following (4):

Δu(k)＝d ₁ ^T[Y _r(k+1)-F ₀ΔU(k-1)-SY(k)]

，u(k)＝u(k-1)+Δu(k) (4)

d ₁ ^TBe (G ^TQG+ λ) ^-1G ^TThe i row vector of Q

Y (k)=[y (k) y (k-1) wherein ... y (k-n _a)] ^T,

ΔU(k-1)＝[Δu(k-n _b)Δu(k-n _b+1)…Δu(k-1)] ^T

(7) data-signal with u (k) returns to rectifying column.

As preferred a kind of scheme: described control method also comprises: (8), fall into a trap in described (6) and to have calculated the value of control variables u (k), and with it and detect the X that obtains _D, X _BValue on the man-machine interface of controller, show.

As preferred another kind of scheme: described data storage device is the historical data base of DCS system, described DCS system is made of data-interface, control station and historical data base, in described (8), data are passed to the DCS system, and at the control station procedure for displaying state of DCS.

Technical conceive of the present invention is: the composition X that adopts the product light component at high-purity distillation tower two ends _D, X _BAs controlled variable, the reflux ratio R/ (R+D) of corresponding overhead product and the reboil ratio V/B of bottom product are as control variables.

Infer control section, be used to solve the difficult problem that the industry spot product component can not directly be measured,, can eliminate greatly and measure hysteresis and have stronger reliability relatively with respect to the chromatographic way of online applicable industry.The generalized predictive control part is used to use GPC algorithm to obtain the value of real-time control variables.

Generalized predictive control and traditional PID control algorithm difference are that algorithm itself is based on matrix operation, the coupled problem of solution multivariable Control that can be essential, algorithm has comprised control image parameter self-correcting module, effectively solved the process nonlinear problem, the fast development of the extensive use of DCS and computer technology in recent years also makes its arithmetic speed increase greatly, and commercial Application becomes possibility.

Beneficial effect of the present invention mainly shows: 1, can adapt to the coupling between dynamic characteristic, strong nonlinear and the loop of high-purity distillation tower operation, realize the quiet run to bi-component control at the bottom of the high-purity distillation process cat head tower; 2, can access good control effect; 3, simple to operate, applicability is strong.

(4) description of drawings

Fig. 1 is the hardware connection layout of rectifying column high-purity distillation control system based on generalized predictive control proposed by the invention.

Fig. 2 is the theory diagram of generalized predictive control of the present invention.

Fig. 3 is the on-the-spot connection layout of rectifying column high-purity distillation control system based on generalized predictive control proposed by the invention.

(5) specific embodiment

Below in conjunction with accompanying drawing the present invention is further described.The embodiment of the invention is used for the present invention that explains, rather than limits the invention, and in the protection domain of spirit of the present invention and claim, any modification and change to the present invention makes all fall into protection scope of the present invention.

Embodiment 1

With reference to Fig. 1, Fig. 2, Fig. 3, a kind of high-purity rectification of rectifying tower control system based on generalized predictive control, comprise and rectifying column 1 direct-connected field intelligent instrument 2, the data storage device and the host computer 6 that are used for storing history data, intelligence instrument 2, data storage device and host computer 6 link to each other successively, described host computer 6 is the universal model controller, described universal model controller comprises component deduction control section and universal model control section, described component infers that control section comprises: instrumentation module 7, comprise detector unit and pressure detecting element, be used to detect the temperature and pressure of the last tower of rectifying column; I/O component module 9 is used for the transmission between controller inside and controller and DCS of the signal of telecommunication and data-signal, and component inference module 10 is used for inferring component according to detecting the temperature and the pressure data that obtain, and its formula is (1), (2):

$X_D=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P}---\left(1\right)$

$X_B=\frac{\mathrm{P\&alpha;}}{{10}^{(a-\frac{T_n+c}{b})}(\mathrm{\&alpha;}-1)}-\frac{1}{\mathrm{\&alpha;}-1}---\left(2\right)$

Wherein, X _D, X _BBe the product light component at high-purity distillation tower two ends, P is that tower is pressed T ₁, T _nBe respectively cat head, column bottom temperature, α is a relative volatility, and a, b, c are the Peter Antonie constant;

Described generalized predictive control part 11 comprises: the I/O component module is used for the inside of universal model controller and the signal of telecommunication between controller and the data storage device, the transmission of data-signal; Forecast model module 11 is used for that DCS historical data base or test gained data are carried out least square and fits the system of obtaining and have the discrete differential equation model of step disturbance nonstationary noise at random, and its formula is (3);

A(z ^-1)y(k)＝B(z ^-1)u(k-1)+C(z ^-1)ε(k)/Δ (3)

Wherein $A\left(z^{-1}\right)=1+\underset{i=1}{\overset{n_a}{\mathrm{\&Sigma;}}}{a}_i{z}^{-i},$ $B\left(z^{-1}\right)=\underset{i=0}{\overset{n_b}{\mathrm{\&Sigma;}}}{b}_i{z}^{-i},$ Δ-difference operator, Δ=1-z ^-1

Model self-correcting module 12 is used to adopt the RLS with forgetting factor, on-line correction system model parameter, and concrete steps are as follows:

(1) upgrades vectorial X (k-1) ^T=[Δ y (k-1) ... ,-Δ y (k-n _a), Δ u (k-1) ..., Δ u (k-n _b-1)];

(2) read y (k) and calculate ε (k)=Δ y (k)-X (k-1) ^Tθ (k-1);

(3) use the recursive least squares with forgetting factor to obtain θ (k), its formula is (4):

$\mathrm{\&theta;}\left(k\right)=\mathrm{\&theta;}(k-1)+\frac{P(k-2)X(k-1)\mathrm{\&epsiv;}\left(k\right)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}---\left(4\right)$

Wherein, $P(k-1)=\frac{1}{\mathrm{\&rho;}}[P(k-2)-\frac{P(k-2)X(k-1)X{(k-1)}^{T}P(k-2)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}],$ P (-1) is enough big positive definite matrix, $\mathrm{\&theta;}=[a_1,...,a_{n_a},b_0,...,b_{n_b}];$

(4) from θ (k), extract master mould parameter A (z ^-1), B (z ^-1), and calculate A (z ^-1)=A (z ^-1) Δ, B (z ^-1)=B (z ^-1) Δ;

Rolling optimization module 13 is used for finding the solution controlled quentity controlled variable u (k) based on forecast model module, feedback compensation module and GPC algorithm computing, concrete formula following (5):

Δu(k)＝d ₁ ^T[Y _r(k+1)-F ₀ΔU(k-1)-SY(k)]

，u(k)＝u(k-1)+Δu(k) (5)

d ₁ ^TBe (G ^TQG+ λ) ^-1G ^TThe i row vector of Q

Wherein, Y (k)=[y (k) y (k-1) ... y (k-n _a)] ^T,

ΔU(k-1)＝[Δu(k-n _b)Δu(k-n _b+1)…Δu(k-1)] ^T

The control output module, the data-signal that is used for the u (k) that will calculate outputs to rectifying column.

Described generalized predictable control system also comprises DCS system 12, and described DCS system 12 is made of data-interface 3, control station 4 and historical data base 5, and described data storage device is the historical data base 5 of DCS system.Described field intelligent instrument 2, DCS system, generalized predictive controller 6 connect successively by fieldbus.

With reference to Fig. 1, the rectifying column high-purity control system based on generalized predictive control of present embodiment, comprise the field intelligent instrument 2, DCS system and the generalized predictive controller 6 that link to each other with high-purity distillation tower 1, described DCS system is made of data-interface 3, control station 4 and historical data base 5; High-purity distillation tower object 1, intelligence instrument 2, DCS system, generalized predictive controller 6 connect successively by fieldbus.

Present embodiment based on the rectifying column high-purity control system hardware structure diagram of generalized predictive control as shown in Figure 1, the core of described rectifying column high-purity control system based on generalized predictive control is a generalized predictive controller 6, comprise in addition: field intelligent instrument 2, DCS system and fieldbus.High-purity distillation tower 1, intelligence instrument 2, DCS system, generalized predictive controller 6 link to each other successively by fieldbus, and uploading of information of realization assigned.Generalized predictable control system in time obtains the value of the control variables of current time by industry spot data detected and that extract from historical data base 5, and returns to first floor system, in time system is dynamically made timely reaction.

The theory diagram of the generalized predictive controller of the rectifying column of present embodiment as shown in Figure 2, the generalized predictive controller of described high-purity distillation tower comprises:

Infer control section, be used to solve the difficult problem that the industry spot product component can not directly be measured,, can eliminate greatly and measure hysteresis and have stronger reliability relatively with respect to the chromatographic way of online applicable industry.

1) the instrumentation module 7: comprise detector unit, can adopt the thermojunction type temperature transmitter, and pressure detecting element, can adopt the pressure resistance type transmitter.

2) the I/O component module 9: be used for the transmission between controller inside and controller and DCS of the signal of telecommunication and data-signal.

3) the component inference module 10: be used for inferring component according to detecting the temperature and the pressure data that obtain.Its formula is (1), (2):

$X_D=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P}---\left(1\right)$

$X_B=\frac{\mathrm{P\&alpha;}}{{10}^{(a-\frac{T_n+c}{b})}(\mathrm{\&alpha;}-1)}-\frac{1}{\mathrm{\&alpha;}-1}---\left(2\right)$

Wherein P is that tower is pressed T ₁, T _nBe respectively cat head, column bottom temperature, α is a relative volatility, and a, b, c are the Peter Antonie constant.

The generalized predictive control part is used to use GPC algorithm to obtain the value of real-time control variables, comprising:

1) I/O element: be used for the inside of generalized predictive control and the signal of telecommunication between controller and the DCS, the transmission of data-signal.

2) the forecast model module 11, are used for that DCS historical data base or test gained data are carried out least square fitting and obtain system and have the discrete differential equation model of step disturbance nonstationary noise at random, and its formula is (3):

A(z ^-1)y(k)＝B(z ^-1)u(k-1)+C(z ^-1)ε(k)/Δ (3)

Wherein $A\left(z^{-1}\right)=1+\underset{i=1}{\overset{n_a}{\mathrm{\&Sigma;}}}{a}_i{z}^{-i},$ $B\left(z^{-1}\right)=\underset{i=0}{\overset{n_b}{\mathrm{\&Sigma;}}}{b}_i{z}^{-i},$ Δ-difference operator, Δ=1-z ^-1

3) model self-correcting module 12 is used to adopt the RLS with forgetting factor, on-line correction system prediction model 11 parameters.Concrete steps are as follows:

(1) upgrades vectorial X (k-1) ^T=[Δ y (k-1) ... ,-Δ y (k-n _a), Δ u (k-1) ..., Δ u (k-n _b-1)]

(2) read y (k) and calculate ε (k)=Δ y (k)-X (k-1) ^Tθ (k-1)

(3) use recursive least squares to obtain θ (k) with forgetting factor

$\mathrm{\&theta;}\left(k\right)=\mathrm{\&theta;}(k-1)+\frac{P(k-2)X(k-1)\mathrm{\&epsiv;}\left(k\right)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}$

$P(k-1)=\frac{1}{\mathrm{\&rho;}}[P(k-2)-\frac{P(k-2)X(k-1)X{(k-1)}^{T}P(k-2)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}],$ P (-1) is enough big positive definite matrix, $\mathrm{\&theta;}=[a_1,...,a_{n_a},b_0,...,b_{n_b}].$

(4) from θ (k), extract master mould parameter A (z ^-1), B (z ^-1), and calculate A (z ^-1)=A (z ^-1) Δ, B (z ^-1)=B (z ^-1) Δ.

4) the rolling optimization module 13, are used for obtaining based on forecast model module, feedback compensation module and GPC algorithm computing the controller output of current time.The concrete operation formula is as follows:

Δu(k)＝d ₁ ^T[Y _r(k+1)-F ₀ΔU(k-1)-SY(k)]

，u(k)＝u(k-1)+Δu(k)

d ₁ ^TBe (G ^TQG+ λ) ^-1G ^TThe i row vector of Q

Y (k)=[y (k) y (k-1) wherein ... y (k-n _a)] ^T,

ΔU(k-1)＝[Δu(k-n _b)Δu(k-n _b+1)…Δu(k-1)] ^T

5) human-computer interface module 8, are used for the selection of controller parameter and the demonstration of historical data and system's current state, and the choosing, set of controller parameter.

Present embodiment based on the on-the-spot connection layout of the rectifying column high-purity control system of generalized predictive control as shown in Figure 3, system adopts light component proportion X in high-purity distillation tower 1 top products _D, light component proportion X in the bottoms _BBe controlled variable, the reboil ratio V/B of reflux ratio R/ of overhead product (R+D) and bottom product is the control corresponding variable.Connect a detector unit TT and pressure detecting element PT at the bottom of the high-purity distillation tower 1 cat head tower respectively and be delivered to upper system, generalized predictive controller by the data computation current time of on-the-spot and historical data base the control variables value and pass to down layer system, the scene changes the value of control variables by the change valve opening by flow controller FC.

The high-purity control method of described rectifying column based on generalized predictive control realizes according to following steps:

1, system initialization

(1) in generalized predictive controller 6, sets the bi-component setting value X of high-purity distillation tower _Dset, X _Bset, and sampling period among the DCS is set.

(2) by the DCS historical data base or do the data that obtain of test and carry out least square fitting and obtain system and have the discrete differential equation initial model of step disturbance nonstationary noise at random, its formula is (3):

A(z ^-1)y(k)＝B(z ^-1)u(k-1)+C(z ^-1)ε(k)/Δ (3)

Wherein $A\left(z^{-1}\right)=1+\underset{i=1}{\overset{n_a}{\mathrm{\&Sigma;}}}{a}_i{z}^{-i},$ $B\left(z^{-1}\right)=\underset{i=0}{\overset{n_b}{\mathrm{\&Sigma;}}}{b}_i{z}^{-i},$ Δ-difference operator, Δ=1-z ^-1, and in forecast model module 11, establish initial value n _a, n _b

(3) put initial value P, M, Q, λ, ρ, α, θ (0) in feedback compensation module 12, the rolling optimization module 13.Wherein P length of field, Q when when prediction, length of field, M were for control is the error weighting matrix, and generally the unit's of getting diagonal matrix, λ are the control weighting matrix, generally gets enough little number, ρ is a forgetting factor, generally gets 0.95～1, α is the softening factor, $\mathrm{\&theta;}=[a_1,...,a_{n_a},b_0,...,b_{n_b}].$

(4) put rolling optimization module 13 initial value R ₁(z ^-1)=1, S ₁(z ^-1)=z[1-A (z ^-1)], G ₁(z ^-1)=B (z ^-1) 2, the putting into operation of system.

1) each DCS sampling instant, intelligence instrument 2 detects temperature, the pressure data of high-purity distillation tower 1 and is sent in the DCS historical data base 5;

2) each controller sampling instant, generalized predictive controller 6 reads the temperature and pressure data from DCS historical data base 5, calculate current time controlled variable XD by component inference module 10, the value of Xn, its formula is (1), (2):

$X_D=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P}---\left(1\right)$

$X_B=\frac{\mathrm{P\&alpha;}}{{10}^{(a-\frac{T_n+c}{b})}(\mathrm{\&alpha;}-1)}-\frac{1}{\mathrm{\&alpha;}-1}---\left(2\right)$

Wherein P is that tower is pressed T ₁～T _nBe respectively cat head, column bottom temperature, α is a relative volatility, and a, b, c are the Peter Antonie constant

3) X by obtaining from component inference module 10 _D, X _BValue, obtain the system model parameter of current time by the computing of model self-correcting module 12, its concrete steps are as follows:

(1) upgrades vectorial X (k-1) ^T=[Δ y (k-1) ... ,-Δ y (k-n _a), Δ u (k-1) ..., Δ u (k-n _b-1)]

(2) read y (k) and calculate ε (k)=Δ y (k)-X (k-1) ^Tθ (k-1);

(3) use the recursive least squares with forgetting factor to obtain θ (k), its calculating formula is (4)

$\mathrm{\&theta;}\left(k\right)=\mathrm{\&theta;}(k-1)+\frac{P(k-2)X(k-1)\mathrm{\&epsiv;}\left(k\right)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}---\left(4\right)$

$P(k-1)=\frac{1}{\mathrm{\&rho;}}[P(k-2)-\frac{P(k-2)X(k-1)X{(k-1)}^{T}P(k-2)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}],$ P (-1) is enough big positive definite matrix.

(4) from θ (k), extract master mould parameter A (z ^-1), B (z ^-1), and calculate A (z ^-1)=A (z ^-1) Δ, B (z ^-1)=B (z ^-1) Δ.

4) calculate R _j(z ^-1), S _j(z ^-1), G _j(z ^-1), the concrete operation formula is as follows:

R _j+1(z ^-1)＝R _j(z ^-1)+r _j+1，jz ^-j，S _j+1(z ^-1)＝z[S _j(z ^-1)-r _j+1，jA(z ^-1)]，

G _j+1(z ^-1)＝G _j(z ^-1)+z ^-js _j，oB(z ^-1)

In the formula $R_j\left(z^{-1}\right)=1+\underset{i=1}{\overset{j-1}{\mathrm{\&Sigma;}}}{r}_{j,i}{z}^{-i},$ $S_j\left(z^{-1}\right)=\underset{i=0}{\overset{n_a}{\mathrm{\&Sigma;}}}{s}_{j,i}{z}^{-i},$

${\stackrel{\&OverBar;}{G}}_j\left(z^{-1}\right)=B\left(z^{-1}\right)R_j\left(z^{-1}\right)=g_{j,0}+g_{j,1}{z}^{-1}+...+g_{j,n_b+j-1}{z}^{-(n_b+j-1)}$

r _j+1，j＝s _j，0＝s _j(0)

s _j+1，0＝s _j，1-a ₁s _j，0

s _j+1，1＝s _j，2-a ₂s _j，0

s _j+1，i＝s _j，i+1-a _i+1s _j，0(0≤i<n _a)

$s_{j+1,n_a}=-{\stackrel{\&OverBar;}{a}}_{n_a+1}{s}_{j,0}$

5) compute matrix G, F ₀, S,

$F_0=\left[\begin{array}{ccccc}{\mathrm{<figure-callout\; id="1"\; label="g"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">g</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="nb\; g"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">nb</figure-callout>}}& g_{}{}_{1,\mathrm{nb}-1}& \&CenterDot;\&CenterDot;\&CenterDot;& {\mathrm{<figure-callout\; id="1"\; label="g"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">g</figure-callout>}}_{\mathrm{1,2}}& {\mathrm{<figure-callout\; id="1"\; label="g"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">g</figure-callout>}}_{\mathrm{1,1}}\\ {\mathrm{<figure-callout\; id="2"\; label="g"\; filenames="C200610155484E00211.png"\; state="\{\{state\}\}">g</figure-callout>}}_{2,\mathrm{nb}+1}& {\mathrm{<figure-callout\; id="2"\; label="g"\; filenames="C200610155484E00211.png"\; state="\{\{state\}\}">g</figure-callout>}}_{2,\mathrm{nb}}& \&CenterDot;\&CenterDot;\&CenterDot;& {\mathrm{<figure-callout\; id="2"\; label="g"\; filenames="C200610155484E00211.png"\; state="\{\{state\}\}">g</figure-callout>}}_{\mathrm{2,3}}& {\mathrm{<figure-callout\; id="2"\; label="g"\; filenames="C200610155484E00211.png"\; state="\{\{state\}\}">\; <figure-callout\; id="2"\; label="g"\; filenames="C200610155484E00211.png"\; state="\{\{state\}\}">g</figure-callout>\; </figure-callout>}}_{\mathrm{2,2}}\\ \&CenterDot;& \&CenterDot;& & \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& & \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& & \&CenterDot;& \&CenterDot;\\ g_{P,\mathrm{nb}+P-1}& g_{P,\mathrm{nb}+P-2}& \&CenterDot;\&CenterDot;\&CenterDot;& g_{P,P+1}& g_{P,P}\end{array}\right],$ $S=\left[\begin{array}{ccc}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">s</figure-callout>}}_{\mathrm{1,0}}& \&CenterDot;\&CenterDot;\&CenterDot;& {\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,n_a}\\ \&CenterDot;& & \&CenterDot;\\ \&CenterDot;& & \&CenterDot;\\ \&CenterDot;& & \&CenterDot;\\ s_{P,0}& \&CenterDot;\&CenterDot;\&CenterDot;& S_{P,n_a}\end{array}\right]$

6) according to the output valve of setting value, current system, determine the interior output reference locus of future anticipation time domain that current time rises, choose and adopt following single order exponential form:

$y_r(k+i)={{\mathrm{\&alpha;}}_r}^i\left(k\right)+(1-{{\mathrm{\&alpha;}}_r}^i)y_{\mathrm{set}}$ (i＝1，2，...)

y _r(k)＝y(k)

Y wherein _SetBe the setting value of y, α is the flexible factor, should choose suitable value in the practical operation, and the value of α is big more, and systematically flexibility is good more, and robustness is strong more, but control ground rapidity is poor.

7) find the solution controlled quentity controlled variable u (k), concrete formula following (5):

Δu(k)＝d ₁ ^T[Y _r(k+1)-F ₀ΔU(k-1)-SY(k)]

，u(k)＝u(k-1)+Δu(k) (5)

d ₁ ^TBe (G ^TQG+ λ) ^-1G ^TThe i row vector of Q

Y (k)=[y (k) y (k-1) wherein ... y (k-n _a)] ^T,

ΔU(k-1)＝[Δu(k-n _b)Δu(k-n _b+1)…Δu(k-1)] ^T

8) result is delivered on the display module of each level system and show, make things convenient for the engineer in time process dynamically to be reacted and operate, comprise human-computer interface module 8, DCS system active station 4 and the operator station of generalized predictive controller.

Embodiment 2

With reference to Fig. 1, Fig. 2 and Fig. 3, the control method that the generalized predictable control system of the described rectifying column of a kind of usefulness is realized, described control method may further comprise the steps:

(1) determines the bi-component setting value Y of rectifying column _Lset, X _Nuset, and sampling period T;

(2) each sampling instant KT infers component according to detecting the temperature and the pressure data that obtain,

Its formula is (1), (2):

$X_D=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="C200610155484E00211.png,C200610155484E00221.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P}---\left(1\right)$

$X_B=\frac{\mathrm{P\&alpha;}}{{10}^{(a-\frac{T_n+c}{b})}(\mathrm{\&alpha;}-1)}-\frac{1}{\mathrm{\&alpha;}-1}---\left(2\right)$

Wherein, X _D, X _BBe the product light component at high-purity distillation tower two ends, P is that tower is pressed T ₁～T _nBe respectively cat head, column bottom temperature, α is a relative volatility, and a, b, c are the Peter Antonie constant;

(3) by the DCS historical data base or do the data fitting system that obtains of test and have the discrete differential equation model of step disturbance nonstationary noise at random, its formula is (3):

A(z ^-1)y(k)＝B(z ^-1)u(k-1)+C(z ^-1)ε(k)/Δ (3)

Wherein $A\left(z^{-1}\right)=1+\underset{i=1}{\overset{n_a}{\mathrm{\&Sigma;}}}{a}_i{z}^{-i},$ $B\left(z^{-1}\right)=\underset{i=0}{\overset{n_b}{\mathrm{\&Sigma;}}}{b}_i{z}^{-i},$ Δ-difference operator, Δ=1-z ^-1

(4) put initial value P, M, Q, λ, n _a, n _b, ρ, α, θ (0), length of field, Q were that error weighting matrix, λ are that forgetting factor, α are the softening factor for control weighting matrix, ρ when length of field, M were for control when wherein P was for prediction, $\mathrm{\&theta;}=[a_1,...,a_{n_a},b_0,...,b_{n_b}]$

(5) self-correcting recursion update system model, concrete steps are as follows:

(5.1) upgrade vector

X(k-1) ^T＝[-Δy(k-1)，...，-Δy(k-n _a)，Δu(k-1)，...，Δu(k-n _b-1)]

(5.2) read y (k) and calculate ε (k)=Δ y (k)-X (k-1) ^Tθ (k-1)

(5.3) use the recursive least squares with forgetting factor to obtain θ (k), its calculating formula is (4):

$\mathrm{\&theta;}\left(k\right)=\mathrm{\&theta;}(k-1)+\frac{P(k-2)X(k-1)\mathrm{\&epsiv;}\left(k\right)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}---\left(4\right)$

Wherein, $P(k-1)=\frac{1}{\mathrm{\&rho;}}[P(k-2)-\frac{P(k-2)X(k-1)X{(k-1)}^{T}P(k-2)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}],$ P (-1) is enough big positive definite matrix;

(5.4) from θ (k), extract master mould parameter A (z ^-1), B (z ^-1), and calculate A (z ^-1)=A (z ^-1) Δ, B (z ^-1)=B (z ^-1) Δ;

(6) find the solution controlled quentity controlled variable u (k), concrete formula following (4):

Δu(k)＝d ₁ ^T[Y _r(k+1)-F ₀ΔU(k-1)-SY(k)]

，u(k)＝u(k-1)+Δu(k) (4)

d ₁ ^TBe (G ^TQG+ λ) ^-1G ^TThe i row vector of Q

Y (k)=[y (k) y (k-1) wherein ... y (k-n _a)] ^T,

ΔU(k-1)＝[Δu(k-n _b)Δu(k-n _b+1)…Δu(k-1)] ^T

(7) data-signal with u (k) returns to rectifying column.

Control method also comprises: (8), fall into a trap in described (6) and to have calculated the value of control variables u (k), and with it and detect the X that obtains _D, X _BValue on the man-machine interface of controller, show.Described data storage device is the historical data base of DCS system, and described DCS system is made of data-interface, control station and historical data base, in described (8), data is passed to the DCS system, and at the control station procedure for displaying state of DCS.

Claims (7)

1, a kind of high-purity rectification of rectifying tower control system based on generalized predictive control, comprise and the direct-connected field intelligent instrument of rectifying column, the data storage device that is used for storing history data and host computer, intelligence instrument, data storage device and host computer link to each other successively, it is characterized in that: described host computer is a generalized predictive controller, described generalized predictive controller comprises component deduction control section and generalized predictive control part

Described component infers that control section comprises:

The instrumentation module comprises detector unit and pressure detecting element, is used to detect the temperature and pressure of the last tower of rectifying column;

The I/O component module is used for the transmission between controller inside and controller and data storage device of the signal of telecommunication and data-signal,

The component inference module is used for inferring component according to detecting the temperature and the pressure data that obtain, and its formula is (1), (2):

$X_D=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{b}{T_1+c})}}{(\mathrm{\&alpha;}-1)P}---\left(1\right)$

$X_B=\frac{\mathrm{P\&alpha;}}{{10}^{(a-\frac{T_n+c}{b})}(\mathrm{\&alpha;}-1)}-\frac{1}{\mathrm{\&alpha;}-1}---\left(2\right)$

Wherein, X _D, X _BBe the product light component at high-purity distillation tower two ends, P is that tower is pressed T ₁, T _nBe respectively cat head, column bottom temperature, α is a relative volatility, and a, b, c are the Peter Antonie constant;

Described generalized predictive control partly comprises:

The I/O component module is used for the inside of generalized predictive controller and the signal of telecommunication between controller and the data storage device, the transmission of data-signal;

The forecast model module is used for that DCS historical data base or test gained data are carried out least square and fits the system of obtaining and have the discrete differential equation model of step disturbance nonstationary noise at random, and its formula is (3);

A(z ^-1)y(k)＝B(z ^-1)u(k-1)+C(z ^-1)ε(k)/Δ (3)

Wherein $A\left(z^{-1}\right)=1+\underset{i=1}{\overset{n_a}{\mathrm{\&Sigma;}}}{a}_i{z}^{-i},$ $B\left(z^{-1}\right)=\underset{i=0}{\overset{n_b}{\mathrm{\&Sigma;}}}{b}_i{z}^{-i},$ ε (k) is the sequence of random variables that is defined on the probability space, Δ-difference operator, Δ=1-z ^-1

Model self-correcting module is used to adopt the RLS with forgetting factor, on-line correction system model parameter, and concrete steps are as follows:

(1) upgrades vectorial X (k-1) ^T=[Δ y (k-1) ... ,-Δ y (k-n _a), Δ u (k-1) ..., Δ u (k-n _b-1)], y (k) is a current time, i.e. k output constantly;

(2) read y (k) and calculate ε (k)=Δ y (k)-X (k-1) ^Tθ (k-1); (3) use the recursive least squares with forgetting factor to obtain θ (k), its formula is (4):

$\mathrm{\&theta;}\left(k\right)=\mathrm{\&theta;}(k-1)+\frac{P(k-2)X(k-1)\mathrm{\&epsiv;}\left(k\right)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}---\left(4\right)$

Wherein, $P(k-1)=\frac{1}{\mathrm{\&rho;}}[P(k-2)-\frac{P(k-2)X(k-1)X{(k-1)}^{T}P(k-2)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}],$ P (-1) is enough big positive definite matrix, $\mathrm{\&theta;}=[a_1,...,a_{n_a},b_0,...,b_{n_b}]$ ；

(4) from θ (k), extract master mould parameter A (z ^-1), B (z ^-1), and calculate A (z ^-1)=A (z ^-1) Δ,

B(z ^-1)＝B(z ^-1)Δ；

The rolling optimization module is used for finding the solution controlled quentity controlled variable u (k) based on forecast model module, feedback compensation module and GPC algorithm computing, concrete formula following (5):

Δu(k)＝d ₁ ^T[Y _r(k+1)-F ₀ΔU(k-1)-SY(k)]

，u(k)＝u(k-1)+Δu(k) (5)

d ₁ ^TBe (G ^TQG+ λ) ^-1G ^TThe i row vector of Q

Wherein, Y (k)=[y (k) y (k-1) ... y (k-n _a)] ^T,

ΔU(k-1)＝[Δu(k-n _b)Δu(k-n _b+1)…Δu(k-1)] ^T

The control output module, the data-signal that is used for the u (k) that will calculate outputs to rectifying column.

2, the high-purity rectification of rectifying tower control system based on generalized predictive control as claimed in claim 1, it is characterized in that: described generalized predictable control system also comprises the DCS system, described DCS system is made of data-interface, control station and historical data base, and described data storage device is the historical data base of DCS system.

3, the high-purity rectification of rectifying tower control system based on generalized predictive control as claimed in claim 2, it is characterized in that: described field intelligent instrument, DCS system, generalized predictive controller connect successively by fieldbus.

4, as the described high-purity rectification of rectifying tower control system of one of claim 1～3 based on generalized predictive control, it is characterized in that: described generalized predictive controller also comprises human-computer interface module, the value that is used for the control variables u (k) that will calculate, and with it and detect the X that obtains _D, X _BValue on the man-machine interface of controller, show.

5, a kind of usefulness control method that realizes based on the high-purity rectification of rectifying tower control system of generalized predictive control as claimed in claim 1, it is characterized in that: described control method may further comprise the steps:

(1) determines the bi-component setting value Y of rectifying column _Lset, X _Nset, and sampling period T;

(2) each sampling instant KT infers component according to detecting the temperature and the pressure data that obtain, and its formula is (1), (2):

$X_D=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{b}{T_1+c})}}{(\mathrm{\&alpha;}-1)P}---\left(1\right)$

$X_B=\frac{\mathrm{P\&alpha;}}{{10}^{(a-\frac{T_n+c}{b})}(\mathrm{\&alpha;}-1)}-\frac{1}{\mathrm{\&alpha;}-1}---\left(2\right)$

Wherein, X _D, X _BBe the product light component at high-purity distillation tower two ends, P is that tower is pressed T ₁, T _nBe respectively cat head, column bottom temperature, α is a relative volatility, and a, b, c are the Peter Antonie constant;

(3) by the DCS historical data base or do the data fitting system that obtains of test and have the discrete differential equation model of step disturbance nonstationary noise at random, its formula is (3):

A(z ^-1)y(k)＝B(z ^-1)u(k-1)+C(z ^-1)ε(k)/Δ (3)

Wherein $A\left(z^{-1}\right)=1+\underset{i=1}{\overset{n_a}{\mathrm{\&Sigma;}}}{a}_i{z}^{-i},$ $B\left(z^{-1}\right)=\underset{i=0}{\overset{n_b}{\mathrm{\&Sigma;}}}{b}_i{z}^{-i},$ ε (k) is the sequence of random variables that is defined on the probability space, Δ-difference operator, Δ=1-z ^-1

(4) put initial value P, M, Q, λ, n _a, n _b, ρ, α, θ (0), length of field, Q were that error weighting matrix, λ are that forgetting factor, α are the softening factor for control weighting matrix, ρ when length of field, M were for control when wherein P was for prediction, $\mathrm{\&theta;}=[a_1,...,a_{n_a},b_0,...,b_{n_b}]$

(5) self-correcting recursion update system model, concrete steps are as follows:

(5.1) upgrade vector

X(k-1) ^T＝[-Δy(k-1)，...，-Δy(k-n _a)，Δu(k-1)，...，Δu(k-n _b-1)]

(5.2) read y (k) and calculate ε (k)=Δ y (k)-X (k-1) ^Tθ (k-1), y (k) is a current time, i.e. k output constantly;

(5.3) use the recursive least squares with forgetting factor to obtain θ (k), its calculating formula is

(4) $\mathrm{\&theta;}\left(k\right)=\mathrm{\&theta;}(k-1)+\frac{P(k-2)X(k-1)\mathrm{\&epsiv;}\left(k\right)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}---\left(4\right)$

Wherein, $P(k-1)=\frac{1}{\mathrm{\&rho;}}[P(k-2)-\frac{P(k-2)X(k-1)X{(k-1)}^{T}P(k-2)}{\mathrm{\&rho;}+X{(k-1)}^{T}P(k-2)X(k-1)}],$ P (-1) is enough big positive definite matrix;

(5.4) from θ (k), extract master mould parameter A (z ^-1), B (z ^-1), and calculate A (z ^-1)=A (z ^-1) Δ, B (z ^-1)=B (z ^-1) Δ;

(6) find the solution controlled quentity controlled variable u (k), concrete formula following (4):

Δu(k)＝d ₁ ^T[Y _r(k+1)-F ₀ΔU(k-1)-SY(k)]

，u(k)＝u(k-1)+Δu(k) (4)

d ₁ ^TBe (G ^TQG+ λ) ^-1G ^TThe i row vector of Q

The data-signal of u (k) is returned to rectifying column.

6, the high-purity rectification of rectifying tower control method based on generalized predictive control as claimed in claim 5, it is characterized in that: described control method also comprises:

(8), fall into a trap in described (6) and to have calculated the value of control variables u (k), and with it and detect the X that obtains _D, X _BValue on the man-machine interface of controller, show.

7, as claim 5 or 6 described high-purity rectification of rectifying tower control methods based on generalized predictive control, it is characterized in that: described data storage device is the historical data base of DCS system, described DCS system is made of data-interface, control station and historical data base, in described (8), data are passed to the DCS system, and at the control station procedure for displaying state of DCS.

Images