CN101881963A - Non-liner control system of space-division king-tower and method - Google Patents

Abstract

The invention discloses a non-liner control system of a space division main tower and a method, comprising an on-site intelligent instrument, a database and an upper computer, wherein the on-site intelligent instrument is directly connected with the space-division king-tower, the database is used for storing historical data, and the on-site intelligent instrument, the database and the upper computer are successively connected; the upper computer comprises a non-liner controller which utilizes non-liner dynamic optimized on-line operation to obtain an output value of a control variable at present; the non-liner controller comprises a component deduction module and a non-liner model prediction control module. In addition, the invention provides a non-liner control method of the space-division king-tower. Compared with the control system, such as traditional PID and the like, the invention not only realizes the stable operation of two-end components and has better dynamic control effect.

Description

The nonlinear control system of main air distillation column and method

Technical field

The present invention relates to the control system and the method design field of main air distillation column, especially, relate to a kind of nonlinear control system and method thereof of main air distillation column.

Background technology

Air separation unit is to be used for airborne each component gas is separated, and obtains the device of high-purity industrial gasses such as oxygen, nitrogen, argon.It is the supportive unit operations of numerous industries that concern the life-blood of the national economy, as chemical industry, metallurgy, electronics, the energy, Aero-Space, food and drink etc., belong to national substantial equipment, its development scale and technology status are to weigh the industry of a country and an important symbol of development in science and technology level.Empty branch operation is one and relates to low temperature, many equipment, long flow process, complicated operation, the exigent complex process of safety in production.In the production, the purity of oxygen, nitrogen, argon product often requires up to more than 99%, belong to high-purity distillation control problem, stationarity to the main air distillation column operation requires very high, and the high-purity distillation process is because the coupling between dynamic perfromance, strong nonlinear and the loop of the complexity that it showed, and traditional is difficult to it is controlled effect preferably as Linear Control schemes such as PID.

Now, air separation technology more and more develops towards specialization, scale, standardized direction, and under the prerequisite that guarantees maximum return, making great efforts to cut down the consumption of energy is the groundwork that this technology faces.This also requires to improve the flow process controlling schemes in real work, thereby further improves the automated control technology level of air separation.

Summary of the invention

In order to overcome the dynamic perfromance of the complexity that existing main air distillation column possesses, strong coupling between strong nonlinear and the loop, traditional be difficult to be controlled preferably the deficiency of effect, the invention provides the coupling between a kind of dynamic perfromance, strong nonlinear and loop that can adapt to main air distillation column and the nonlinear control system and the method for the main air distillation column of good control effect are provided as PID and other Linear Control scheme.

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

A kind of nonlinear control system of main air distillation column, comprise and the direct-connected field intelligent instrument of main air distillation column, the database that is used for storing history data and host computer, described field intelligent instrument, database and host computer link to each other successively, described host computer comprises the gamma controller that obtains the output valve of current time control variable in order to the on-line operation of utilization nonlinear dynamic optimization, and described gamma controller comprises:

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):

${\mathrm{<figure-callout\; id="1"\; label="Y"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">Y</figure-callout>}}_1=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P_1}---\left(1\right)$

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

Wherein, Y ₁Be the nitrogen component in the last column overhead nitrogen product, X _nBe the nitrogen component in the liquid oxygen product at the bottom of the last Tata, P ₁Be last column overhead pressure, P _nFor last Tata base pressure is strong, T ₁, T _nBe respectively column overhead, go up temperature at the bottom of the Tata, α is the relative volatility of nitrogen component, and a, b, c are the Peter Antonie constant of nitrogen component;

The Nonlinear Model Predictive Control module is used to write and store the main air distillation column mechanism model of using differential equation, and supports the critical process parameter is carried out online adjustment, finds the solution following nonlinear dynamic optimization proposition:

Objective function: $\underset{u(\&CenterDot;)}{\min }J={\&Integral;}_t^{t+T_p}({\mathrm{\&mu;}}_1{J}_1\left(e\right)+{\mathrm{\&mu;}}_2{J}_2(z,x,u)+{\mathrm{\&mu;}}_3{J}_3\left({\mathrm{Vu}}_{\mathrm{MV}}\right))\mathrm{d\&tau;}---\left(5\right)$

Constraint condition: X﹠amp; (t)=f (x (t), z (t), u (t), p) (3)

0＝g(x(t)，z(t)，u(t)，p) (4)

$u^{\mathrm{LB}}\&le;u\left(\mathrm{\&tau;}\right)\&le;u^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_c]---\left(6\right)$

$u\left(\mathrm{\&tau;}\right)=u(t+T_c),\&ForAll;\mathrm{\&tau;}\&Element;[t+T_c,t+T_p]---\left(7\right)$

$x^{\mathrm{LB}}\&le;x\left(\mathrm{\&tau;}\right)\&le;x^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_p]---\left(8\right)$

Wherein, T _pAnd T _cBe respectively prediction time domain and control time domain, desired value J is made up of three parts, the J of first ₁Characterize the deviate of predicting output and target trajectory future, e predicts the deviation of exporting with target trajectory, J in the future ₂Characterize in the relevant economic target of process object input and output, x is a differential variable, and z is the algebraically variable, and u is a control variable, J ₃The variable quantity Vu of characterization control variable _MV, μ ₁, μ ₂, μ ₃Be respectively weight coefficient, t is the time, and τ is the transient state time variable, x﹠amp; (t) be the first order derivative of differential variable x, p is a procedure parameter, and f represents differential equation group, g representation algebra system of equations;

Formula (3) is the non-linear process model, formula (4) is the original state of process, formula 6) is the bound constraint of control variable, the value that formula (7) is supposed control variable is more than or equal to the control time domain, remain unchanged in the zone smaller or equal to the prediction time domain, formula (8) is the bound of process differential variable.In each sampling instant, find the solution above-mentioned nonlinear dynamic optimization proposition, obtain the value of optimum control variable, realize the online rolling optimization of system;

Online rolling optimization based on traditional control variable parametric method, carries out discretize to control variable u (t), and the infinite dimension problem is turned to the finite dimension problem.Find the solution ordinary differential equation by calling the explicit quadravalence Runge-Kutta algorithm of fixed step size then, and utilize the SQP algorithm to realize optimizing at last, and first value of getting among the optimization result is the output valve of current time controller.

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

Further, described gamma controller also comprises human-computer interface module, is used for the demonstration of process historic state and predicted state, and the choosing, set of controller parameter.

The described control method of a kind of nonlinear control method of main air distillation column may further comprise the steps:

1) the bi-component setting value Y of tower product on the setting main air distillation column _1set, X _Nset, and the systematic sampling cycle; Determine prediction time domain T _nAnd control time domain T _c

2) the process status number of plates n of setting Nonlinear Model Predictive Control module, feed tray is counted f, the Peter Antonie constant a of nitrogen component, b, c, feed flow rates F, feed component z _f, column plate liquid holdup etc.; Write the nonlinear differential equation model by utilization mechanism equation, obtain following approximation model description:

x&(t)＝f(x(t)，y(t)，u(t)，p) (3)

0＝g(x(t)，y(t)，u(t)，p) (4)

Wherein, x﹠amp; (t) be the first order derivative of differential variable, x (t) is a differential variable, and y (t) is an output variable, and u (t) is a control variable, and p is a procedure parameter, and f represents differential equation group, g representation algebra system of equations;

3) determine in the prediction time domain of current time the ideal trajectory of system's output:

Y _r(k+1)＝[Y _r(k+1) Y _r(k+2)L Y _r(k+P)] ^T

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

${\mathrm{<figure-callout\; id="1"\; label="Y"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">Y</figure-callout>}}_1=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P_1}---\left(1\right)$

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

Wherein, Y ₁Be the nitrogen component in the last column overhead nitrogen product, X _nBe the nitrogen component in the liquid oxygen product at the bottom of the last Tata, P ₁Be last column overhead pressure, P _nFor last Tata base pressure is strong, T ₁, T _nBe respectively column overhead, go up temperature at the bottom of the Tata, α is the relative volatility of nitrogen component, and a, b, c are the Peter Antonie constant of nitrogen component;

5) controller reads X from database ₁And X _nValue as input, adopt improved control variable parametric method to carry out nonlinear dynamic optimization and find the solution the control variable that obtains current time, the prediction of the liquid oxygen of the supreme tower of following tower and liquid air capacity of returns, liquid oxygen product flow and process output;

The data signal transmission of the control variable value that 6) computing is obtained is to the main air distillation column object.

Further, described nonlinear control method also comprises: 7) the prediction output of the process that obtains in real composition historical data that the control variable that calculates in the described step 5) and detection are obtained and the computing shows on the man-machine interface of controller.

Further again, described database 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 step 7), data is passed to the DCS system, and at the control station procedure for displaying state of DCS.

Technical conceive of the present invention is the component Y of nitrogen in the cat head liquid nitrogen product of employing main air distillation column ₁Component X with nitrogen in the liquid oxygen product at the bottom of the tower _nBe controlled variable, the liquid oxygen of the supreme tower of following tower and liquid air capacity of returns, liquid oxygen product flow are the control corresponding variable.

Beneficial effect of the present invention shows: nonlinear control system has effectively solved the strong nonlinearity of main air distillation column, the process of strong coupling and complexity is dynamic, realized steady control to bi-component at the bottom of the main air distillation column cat head tower, adopt nonlinear control system also to optimize the operating conditions of process significantly in addition, more traditional PID control system and other control system based on model have had very big improvement on dynamic property, so very large application prospect is arranged.

Description of drawings

Fig. 1 is the hardware connection layout of the nonlinear control system of main air distillation column proposed by the invention.

Fig. 2 is the theory diagram of the nonlinear control system of main air distillation column proposed by the invention.

Fig. 3 is the on-the-spot connection layout of the nonlinear control system of main air distillation column proposed by the invention.

Fig. 4 is based on the dynamic optimization schematic diagram of improved control variable parametric method.

Embodiment

Below in conjunction with accompanying drawing the present invention is further described.

Embodiment 1

With reference to Fig. 1～Fig. 3, a kind of nonlinear control system of main air distillation column comprises and main air distillation column 1 direct-connected field intelligent instrument 2, the database that is used for storing history data and host computer 6, and intelligence instrument 2, database and host computer 6 link to each other successively.Described host computer 6 is a gamma controller, and described gamma controller comprises:

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):

${\mathrm{<figure-callout\; id="1"\; label="Y"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">Y</figure-callout>}}_1=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P_1}---\left(1\right)$

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

Wherein, Y ₁Be the nitrogen component in the last column overhead nitrogen product, X _nBe the nitrogen component in the liquid oxygen product at the bottom of the last Tata, P ₁Be last column overhead pressure, P _nFor last Tata base pressure is strong, T ₁, T _nBe respectively column overhead, go up temperature at the bottom of the Tata, α is the relative volatility of nitrogen component, and a, b, c are the Peter Antonie constant of nitrogen component;

Nonlinear Model Predictive Control module 11 is used to write and store the main air distillation column mechanism model of using differential equation, and supports the critical process parameter is carried out online adjustment, as feed component, and feed flow rates etc.This mechanism model can reduce:

x&(t)＝f(x(t)，z(t)，u(t)，p) (3)

0＝g(x(t)，z(t)，u(t)，p) (4)

Comprise two kinds of equations, differential equation group f and Algebraic Equation set g, parameter vector is defined as p, wherein x﹠amp; (t) be the first order derivative of differential variable, x is a differential variable, and z is the algebraically variable, and u is a control variable.

And find the solution following nonlinear dynamic optimization and assign a topic:

Objective function $\underset{u(\&CenterDot;)}{\min }J={\&Integral;}_t^{t+T_p}({\mathrm{\&mu;}}_1{J}_1\left(e\right)+{\mathrm{\&mu;}}_2{J}_2(z,x,u)+{\mathrm{\&mu;}}_3{J}_3\left({\mathrm{Vu}}_{\mathrm{MV}}\right))\mathrm{d\&tau;}---\left(5\right)$

Constraint condition x﹠amp; (t)=f (x (t), z (t), u (t), p) (3)

0＝g(x(t)，z(t)，u(t)，p) (4)

$u^{\mathrm{LB}}\&le;u\left(\mathrm{\&tau;}\right)\&le;u^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_c]---\left(6\right)$

$u\left(\mathrm{\&tau;}\right)=u(t+T_c),\&ForAll;\mathrm{\&tau;}\&Element;[t+T_c,t+T_p]---\left(7\right)$

$x^{\mathrm{LB}}\&le;x\left(\mathrm{\&tau;}\right)\&le;x^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_p]---\left(8\right)$

Wherein, T _pAnd T _cBe respectively prediction time domain and control time domain, desired value J is made up of three parts, the J of first ₁Characterize the deviate of predicting output and target trajectory future, e predicts the deviation of exporting with target trajectory, J in the future ₂Characterize in the relevant economic target of process object input and output, x is a differential variable, and z is the algebraically variable, and u is a control variable, J ₃The variable quantity Vu of characterization control variable _MV, μ ₁, μ ₂, μ ₃Be respectively weight coefficient, t is the time, and τ is the transient state time variable, x﹠amp; (t) be the first order derivative of differential variable x, p is a procedure parameter, and f represents differential equation group, g representation algebra system of equations;

Formula (3) is the non-linear process model, formula (4) is the original state of process, formula 6) is the bound constraint of control variable, formula (7) supposes that the value of control variable more than or equal to the control time domain, remains unchanged in the zone smaller or equal to the prediction time domain, and formula (8) is the bound of process differential variable, in each sampling instant, find the solution above-mentioned nonlinear dynamic optimization proposition, obtain the value of optimum control variable, realize the online rolling optimization of system;

Online rolling optimization, the on-line operation of utilization nonlinear dynamic optimization obtains the control variable of current time.

The concrete grammar of finding the solution above-mentioned nonlinear dynamic optimization is improved control variable parametric method.With reference to Fig. 4, improved control variable parametric method carries out discretize based on traditional control variable parametric method to control variable u (t), and the infinite dimension problem is turned to the finite dimension problem.Find the solution ordinary differential equation by calling the explicit quadravalence Runge-Kutta algorithm of fixed step size then, and utilize the SQP algorithm to realize optimizing at last, and first value of getting among the optimization result is the output valve of current time control variable.

Described gamma controller also comprises: detection module 7, comprise detector unit and pressure detecting element, and be used to detect the temperature and pressure at tower two ends on the main air distillation column; I/O module 9 is used for the transmission between controller inside and controller and DCS of electric signal and data-signal;

Described Nonlinear Model Predictive Control module 11 also comprises: the I/O module is used for the inside of gamma controller and the electric signal between controller and the DCS, the transmission of data-signal.

Described nonlinear control system also comprises DCS system 12, and described DCS system 12 is by data-interface 3, and control station 4 and historical data base 5 constitute, and described database is the historical data base 5 of DCS system.Described field intelligent instrument 2, DCS system 12, gamma controller 6 connect successively by fieldbus.

The nonlinear control system hardware configuration of the main air distillation column of present embodiment as shown in Figure 1, the core of described nonlinear control system is a gamma controller 6, comprises in addition: field intelligent instrument 2, DCS system and fieldbus.On-the-spot main air distillation column 1, gamma controller 6, field intelligent instrument 2, DCS link to each other successively by fieldbus, and uploading of information of realization assigned.Nonlinear control system is real-time must to obtain the value of current time control variable by the industry spot data operation that detects or extract from historical data base 5, and returns to the bottom control system, in time process is dynamically made a response.

The theory diagram of the gamma controller of the main air distillation column of present embodiment as shown in Figure 2, described gamma controller 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 higher reliability with respect to the chromatographic way of online applicable industry.-

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

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

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):

${\mathrm{<figure-callout\; id="1"\; label="Y"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">Y</figure-callout>}}_1=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P_1}---\left(1\right)$

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

Wherein, Y ₁Be the nitrogen component in the last column overhead nitrogen product, X _nBe the nitrogen component in the liquid oxygen product at the bottom of the last Tata, P ₁Be last column overhead pressure, P _nFor last Tata base pressure is strong, T ₁, T _nBe respectively column overhead, go up temperature at the bottom of the Tata, α is the relative volatility of nitrogen component, and a, b, c are the Peter Antonie constant of nitrogen component;

Nonlinear Model Predictive Control module 11 is used to use the Nonlinear Model Predictive Control algorithm to obtain the value of real-time control variable.

I/O module 9 is used for the inside of gamma controller and the electric signal between controller and the DCS, the transmission of data-signal; Read the current state value as controller from the historical data base of DCS, controller passes to the bottom control loop with the control variable value that computing obtains.

Nonlinear Model Predictive Control module 11 is used to write and store the main air distillation column mechanism model of using differential equation, and supports the critical process parameter is carried out online adjustment, as feed component, and feed flow rates etc.This mechanism model can reduce:

x&(t)＝f(x(t)，z(t)，u(t)，p) (3)

0＝g(x(t)，z(t)，u(t)，p) (4)

Comprise two kinds of equations, differential equation group f and Algebraic Equation set g, parameter vector is defined as p, wherein x﹠amp; (t) be the first order derivative of differential variable, x is a differential variable, and z is the algebraically variable, and u is a control variable.

And find the solution following nonlinear dynamic optimization and assign a topic:

Objective function $\underset{u(\&CenterDot;)}{\min }J={\&Integral;}_t^{t+T_p}({\mathrm{\&mu;}}_1{J}_1\left(e\right)+{\mathrm{\&mu;}}_2{J}_2(z,x,u)+{\mathrm{\&mu;}}_3{J}_3\left({\mathrm{Vu}}_{\mathrm{MV}}\right))\mathrm{d\&tau;}---\left(5\right)$

Constraint condition x﹠amp; (t)=f (x (t), z (t), u (t), p) (3)

0＝g(x(t)，z(t)，u(t)，p) (4)

$u^{\mathrm{LB}}\&le;u\left(\mathrm{\&tau;}\right)\&le;u^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_c]---\left(6\right)$

$u\left(\mathrm{\&tau;}\right)=u(t+T_c),\&ForAll;\mathrm{\&tau;}\&Element;[t+T_c,t+T_p]---\left(7\right)$

$x^{\mathrm{LB}}\&le;x\left(\mathrm{\&tau;}\right)\&le;x^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_p]---\left(8\right)$

Wherein, T _pAnd T _cBe respectively prediction time domain and control time domain, desired value J is made up of three parts, the J of first ₁Characterize the deviate of predicting output and target trajectory future, e predicts the deviation of exporting with target trajectory, J in the future ₂Characterize in the relevant economic target of process object input and output, x is a differential variable, and z is the algebraically variable, and u is a control variable, J ₃The variable quantity Vu of characterization control variable _MV, μ ₁, μ ₂, μ ₃Be respectively weight coefficient, t is the time, and τ is the transient state time variable, x﹠amp; (t) be the first order derivative of differential variable x, p is a procedure parameter, and f represents differential equation group, g representation algebra system of equations;

Formula (3) is the non-linear process model, formula (4) is the original state of process, formula 6) is the bound constraint of control variable, formula (7) supposes that the value of control variable more than or equal to the control time domain, remains unchanged in the zone smaller or equal to the prediction time domain, and formula (8) is the bound of process differential variable, in each sampling instant, find the solution above-mentioned nonlinear dynamic optimization proposition, obtain the value of optimum control variable, realize the online rolling optimization of system;

Online rolling optimization, the on-line operation of utilization nonlinear dynamic optimization obtains the control variable of current time.

The concrete grammar of finding the solution above-mentioned nonlinear dynamic optimization is improved control variable parametric method.With reference to Fig. 4, improved control variable parametric method carries out discretize based on traditional control variable parametric method to control variable u (t), and the infinite dimension problem is turned to the finite dimension problem.Find the solution ordinary differential equation by calling the explicit quadravalence Runge-Kutta algorithm of fixed step size then, and utilize the SQP algorithm to realize optimizing at last, and first value of getting among the optimization result is the output valve of current time control variable.

The gamma controller of described main air distillation column also comprises human-computer interface module 8, is used for the demonstration of historical data and system's current state, and the operation of control system parameter selection etc.

The on-the-spot connection layout of the nonlinear control system of the main air distillation column of present embodiment as shown in Figure 3, system adopts the component Y that goes up nitrogen in the tower 14 top nitrogen products ₁, go up the component X of nitrogen in the tower 14 bottom liquid oxygen products _nBe controlled variable, the flow of the liquid oxygen of following tower 15 supreme towers 14 and the capacity of returns of liquid air, liquid oxygen product is the control corresponding variable.Connect a detector unit TT and pressure detecting element PT respectively and be delivered to upper system at the bottom of the last tower 14 cat head towers, gamma controller by the data computation current time of on-the-spot and historical data base the control variable value and pass to down layer system, the scene changes the value of control variable by the change valve opening by flow controller FC.

Embodiment 2

With reference to Fig. 1～Fig. 3, a kind of nonlinear control method of main air distillation column, realize according to following steps:

First, system initialization

1) in gamma controller 6, sets the bi-component setting value Y of tower product on the main air distillation column _1set, X _Nset, and the systematic sampling cycle.

2) the process status parameter of setting Nonlinear Model Predictive Control module; Write the nonlinear differential equation model by utilization mechanism equation, can obtain following approximation model description:

x&(t)＝f(x(t)，z(t)，u(t)，p) (3)

0＝g(x(t)，z(t)，u(t)，p) (4)

Comprise two kinds of equations, differential equation group f and Algebraic Equation set g, parameter vector is defined as p, wherein x﹠amp; (t) be the first order derivative of differential variable, x is a differential variable, and z is the algebraically variable, and u is a control variable.

3) determine prediction time domain T _pAnd control time domain T _c

4) the optimization precision of nonlinear dynamic optimization module is set, and the segments of improved variable parameter method.

Second portion: system puts into operation

1) each DCS sampling instant, field intelligent instrument 2 detects temperature, the pressure data of main air distillation column 1 and is sent in the database 5;

2) determine in the prediction time domain of current time the ideal trajectory of system's output:

Y _r(k+1)＝[Y _r(k+1) Y _r(k+2)L Y _r(k+P)] ^T

3) each controller sampling instant, gamma controller 6 reads the temperature and pressure data from database 5, calculate current time controlled variable Y by component inference module 10 ₁, X _nValue, its formula is (1), (2):

${\mathrm{<figure-callout\; id="1"\; label="Y"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">Y</figure-callout>}}_1=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{\mathrm{<figure-callout\; id="1"\; label="b\; T"\; filenames="HDA0000022891990000011.png,HDA0000022891990000032.png"\; state="\{\{state\}\}">b</figure-callout>}}{T_{}})}}{(\mathrm{\&alpha;}-1)P_1}---\left(1\right)$

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

Wherein, Y ₁Be the nitrogen component in the last column overhead nitrogen product, X _nBe the nitrogen component in the liquid oxygen product at the bottom of the last Tata, P ₁Be last column overhead pressure, P _nFor last Tata base pressure is strong, T ₁, T _nBe respectively column overhead, go up temperature at the bottom of the Tata, α is the relative volatility of nitrogen component, and a, b, c are the Peter Antonie constant of nitrogen component;

4) based on the Y that obtains from component inference module 10 ₁, the value of Xn obtains the output valve of the control variable of current time by the computing of Nonlinear Model Predictive Control module 11.The concrete operation method is the value that the on-line operation of utilization nonlinear dynamic optimization obtains the control variable of current time.Find the solution following nonlinear dynamic optimization proposition:

Objective function $\underset{u(\&CenterDot;)}{\min }J={\&Integral;}_t^{t+T_p}({\mathrm{\&mu;}}_1{J}_1\left(e\right)+{\mathrm{\&mu;}}_2{J}_2(z,x,u)+{\mathrm{\&mu;}}_3{J}_3\left({\mathrm{Vu}}_{\mathrm{MV}}\right))\mathrm{d\&tau;}---\left(5\right)$

Constraint condition x﹠amp; (t)=f (x (t), z (t), u (t), p) (3)

0＝g(x(t)，z(t)，u(t)，p) (4)

$u^{\mathrm{LB}}\&le;u\left(\mathrm{\&tau;}\right)\&le;u^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_c]---\left(6\right)$

$u\left(\mathrm{\&tau;}\right)=u(t+T_c),\&ForAll;\mathrm{\&tau;}\&Element;[t+T_c,t+T_p]---\left(7\right)$

$x^{\mathrm{LB}}\&le;x\left(\mathrm{\&tau;}\right)\&le;x^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_p]---\left(8\right)$

Wherein, T _pAnd T _cBe respectively prediction time domain and control time domain, desired value J is made up of three parts, the J of first ₁Characterize the deviate of predicting output and target trajectory future, e predicts the deviation of exporting with target trajectory, J in the future ₂Characterize in the relevant economic target of process object input and output, x is a differential variable, and z is the algebraically variable, and u is a control variable, J ₃The variable quantity Vu of characterization control variable _MV, μ ₁, μ ₂, μ ₃Be respectively weight coefficient, t is the time, and τ is the transient state time variable, x﹠amp; (t) be the first order derivative of differential variable x, p is a procedure parameter, and f represents differential equation group, g representation algebra system of equations;

Formula (3) is the non-linear process model, formula (4) is the original state of process, formula 6) is the bound constraint of control variable, the value that formula (7) is supposed control variable is more than or equal to the control time domain, remain unchanged in the zone smaller or equal to the prediction time domain, formula (8) is the bound of process differential variable.In each sampling instant, find the solution above-mentioned nonlinear dynamic optimization proposition, obtain the value of optimum control variable, realize the online rolling optimization of system;

The concrete grammar of finding the solution above-mentioned nonlinear dynamic optimization is improved control variable parametric method.With reference to Fig. 4, improved control variable parametric method carries out discretize based on traditional control variable parametric method to control variable u (t), and the infinite dimension problem is turned to the finite dimension problem.Find the solution ordinary differential equation by calling the explicit quadravalence Runge-Kutta algorithm of fixed step size then, and utilize the SQP algorithm to realize optimizing at last, and first value of getting among the optimization result is the output valve of current time controller.

5) data-signal with the current time controller returns to the DCS system, and acts on on-the-spot main air distillation column.

6) result is delivered on the explicit module of each level system and show, make things convenient for the slip-stick artist in time process dynamically to be made a response and operated, comprise the human-computer interface module 8 of main air distillation column, the control station 4 of DCS system and work on the spot station.

Claims (6)

1. the nonlinear control system of a main air distillation column, comprise and the direct-connected field intelligent instrument of main air distillation column, the database that is used for storing history data and host computer, described field intelligent instrument, database and host computer link to each other successively, it is characterized in that: described host computer comprises the gamma controller that obtains the output valve of current time control variable in order to the on-line operation of utilization nonlinear dynamic optimization, and described gamma controller comprises:

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):

$Y_1=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{b}{T_1+c})}}{(\mathrm{\&alpha;}-1)P_1}---\left(1\right)$

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

Wherein, Y ₁Be the nitrogen component in the last column overhead nitrogen product, X _nBe the nitrogen component in the liquid oxygen product at the bottom of the last Tata, P ₁Be last column overhead pressure, P _nFor last Tata base pressure is strong, T ₁, T _nBe respectively column overhead, go up temperature at the bottom of the Tata, α is the relative volatility of nitrogen component, and a, b, c are the Peter Antonie constant of nitrogen component;

The Nonlinear Model Predictive Control module is used to write and store the main air distillation column mechanism model of using differential equation, and supports the critical process parameter is carried out online adjustment, finds the solution following nonlinear dynamic optimization proposition:

Objective function: $\underset{u(\&CenterDot;)}{\min }J={\&Integral;}_t^{t+T_p}({\mathrm{\&mu;}}_1{J}_1\left(e\right)+{\mathrm{\&mu;}}_2{J}_2(z,x,u)+{\mathrm{\&mu;}}_3{J}_3\left({\mathrm{Vu}}_{\mathrm{MV}}\right))\mathrm{d\&tau;}---\left(5\right)$

Constraint condition: x﹠amp; (t)=f (x (t), z (t), u (t), p) (3)

0＝g(x(t)，z(t)，u(t)，p) (4)

$u^{\mathrm{LB}}\&le;u\left(\mathrm{\&tau;}\right)\&le;u^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_c]---\left(6\right)$

$u\left(\mathrm{\&tau;}\right)=u(t+T_c),\&ForAll;\mathrm{\&tau;}\&Element;[t+T_c,t+T_p]---\left(7\right)$

$x^{\mathrm{LB}}\&le;x\left(\mathrm{\&tau;}\right)\&le;x^{\mathrm{UB}},\&ForAll;\mathrm{\&tau;}\&Element;[t,t+T_p]---\left(8\right)$

Wherein, T _pAnd T _cBe respectively prediction time domain and control time domain, desired value J is made up of three parts, the J of first ₁Characterize the deviate of predicting output and target trajectory future, e predicts the deviation of exporting with target trajectory, J in the future ₂Characterize in the relevant economic target of process object input and output, x is a differential variable, and z is the algebraically variable, and u is a control variable, J ₃The variable quantity Vu of characterization control variable _MV, μ ₁, μ ₂, μ ₃Be respectively weight coefficient, t is the time, and τ is the transient state time variable, x﹠amp; (t) be the first order derivative of differential variable x, p is a procedure parameter, and f represents differential equation group, g representation algebra system of equations;

Formula (3) is the non-linear process model, formula (4) is the original state of process, formula 6) is the bound constraint of control variable, formula (7) supposes that the value of control variable more than or equal to the control time domain, remains unchanged in the zone smaller or equal to the prediction time domain, and formula (8) is the bound of process differential variable, in each sampling instant, find the solution above-mentioned nonlinear dynamic optimization proposition, obtain the value of optimum control variable, realize the online rolling optimization of system;

Online rolling optimization, based on traditional control variable parametric method, control variable u (t) is carried out discretize, the infinite dimension problem is turned to the finite dimension problem, find the solution ordinary differential equation by calling the explicit quadravalence Runge-Kutta algorithm of fixed step size then, and utilize the SQP algorithm to realize optimizing at last, and first value of getting among the optimization result is the output valve of current time controller.

2. the nonlinear control system of main air distillation column as claimed in claim 1, it is characterized in that: described nonlinear control system also comprises the DCS system, described DCS system is made of data-interface, control station and historical data base, and described database is the historical data base of DCS system.

3. the nonlinear control system of main air distillation column as claimed in claim 1 or 2, it is characterized in that: described gamma controller also comprises human-computer interface module, is used for the demonstration of process historic state and predicted state, and the choosing, set of controller parameter.

4. control method that realizes with the nonlinear control system of main air distillation column as claimed in claim 1, it is characterized in that: described control method may further comprise the steps:

1) the bi-component setting value Y of tower product on the setting main air distillation column _1set, X _Nset, and the systematic sampling cycle; Determine prediction time domain T _pAnd control time domain T _c

2) the process status number of plates n of setting Nonlinear Model Predictive Control module, feed tray is counted f, the Peter Antonie constant a of nitrogen component, b, c, feed flow rates F, feed component z _f, column plate liquid holdup etc.; Write the nonlinear differential equation model by utilization mechanism equation, obtain following approximation model description:

x&(t)＝f(x(t)，y(t)，u(t)，p) (3)

0＝g(x(t)，y(t)，u(t)，p) (4)

Wherein, x﹠amp; (t) be the first order derivative of differential variable, x (t) is a differential variable, and y (t) is an output variable, and u (t) is a control variable, and p is a procedure parameter, and f represents differential equation group, g representation algebra system of equations;

3) determine in the prediction time domain of current time the ideal trajectory of system's output:

Y _r(k+1)＝[Y _r(k+1) Y _r(k+2)L Y _r(k+P)] ^T

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

$Y_1=\frac{\mathrm{\&alpha;}}{\mathrm{\&alpha;}-1}-\frac{{10}^{(a-\frac{b}{T_1+c})}}{(\mathrm{\&alpha;}-1)P_1}---\left(1\right)$

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

Wherein, Y ₁Be the nitrogen component in the last column overhead nitrogen product, X _nBe the nitrogen component in the liquid oxygen product at the bottom of the last Tata, P ₁Be last column overhead pressure, P _nFor last Tata base pressure is strong, T ₁, T _nBe respectively column overhead, go up temperature at the bottom of the Tata, α is the relative volatility of nitrogen component, and a, b, c are the Peter Antonie constant of nitrogen component;

5) controller reads Y from database ₁And X _nValue as input, adopt improved control variable parametric method to carry out nonlinear dynamic optimization and find the solution the control variable that obtains current time, the prediction of the liquid oxygen of the supreme tower of following tower and liquid air capacity of returns, liquid oxygen product flow and process output;

The data signal transmission of the control variable value that 6) computing is obtained is to the main air distillation column object.

5. nonlinear control method as claimed in claim 4 is characterized in that: described nonlinear control method also comprises:

7) the prediction output of the process that obtains in real composition historical data that the control variable that calculates in the described step 5) and detection are obtained and the computing shows on the man-machine interface of controller.

6. nonlinear control method as claimed in claim 5, it is characterized in that: described database is the historical data base of DCS system, described DCS system is made of data-interface, control station and historical data base, in described step 7), data are passed to the DCS system, and at the control station procedure for displaying state of DCS.

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