Disclosure of Invention
The invention aims to provide a dynamic matrix control system for high-purity rectification, aiming at overcoming the defects of the prior art, and effectively realizing the stable operation of the high-purity rectification process and the high-purity control of double-end components. Unlike conventional rectification control systems.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
a dynamic matrix control system for high purity distillation, comprising: the system comprises a rectifying tower, an intelligent detection instrument, a DCS (distributed control system), an upper computer and a field bus; the rectifying tower, the intelligent detection instrument, the DCS and the upper computer are sequentially connected through a field bus, and uploading and issuing of information flow are achieved.
Furthermore, the upper computer comprises an inference controller, a dynamic matrix controller and a human-computer interface.
The control system takes the light component purity XD of the top product and the light component purity Xn of the bottom product of the rectification process as controlled variables, and takes the reflux ratio and the reboiling ratio as control variables.
With the development of analytical instruments, in particular, on-line application of industrial chromatographs, solutions have gradually appeared which are directly controlled according to the purity of the product. However, besides the high investment and operation cost, the scheme is mainly limited by two aspects at present: firstly, the measurement process lags greatly, the reaction is slow, secondly, the reliability of the long-term online operation of the industrial chromatograph is poor, and the long-period operation of the control process is difficult to guarantee. Therefore, the invention introduces component inference control based on mechanism derivation, accurately infers the product components by real-time detection of the temperature and the pressure at the bottom of the rectifying tower and a mechanism function relation, carries out high-precision long-period real-time control and can effectively overcome the defects of direct control of product composition.
The inference algorithm for inference control of product components is as follows, taking the relation between the overhead temperature and the light component fraction of the overhead product as an example:
according to raoult's law and the law of dalton's partial pressure:
wherein P is 1 Is the total pressure of the gas phase at the top of the column, y 1 Is the gas phase fraction of the light components at the top of the column, x 1 The liquid phase fraction of the light components at the top of the column. P b1 The degree is the saturated vapor pressure of the light components at the top of the column.
According to the Antoine equation:
T 1 =[b/(a-1gP b1 °)]-c (2)
wherein a, b and c are called antoin constants. T is a unit of 1 In units of ℃, (. Sup.P) b1 The unit of degree is millimeter mercury.
y 1 And x 1 The functional relation between the two is obtained according to the phase equilibrium relation:
wherein α is the relative volatility.
Thus, the following y is obtained 1 About T 1 、P 1 The relation is as follows:
thus, the composition inference control function relation between the purity of the tower top product and the temperature and pressure is obtained:
if the condenser is a complete condenser, the condenser is a full condenser,
if the condenser is a partial condenser,
wherein k is D Is the gas-liquid equilibrium constant of the top partial condenser.
And similarly, obtaining a component inference control equation of the tower bottom product composition, the tower bottom temperature and the tower bottom pressure:
the system controller adopts a model predictive control algorithm of dynamic matrix control, takes an actually measured output value y as the input quantity of the dynamic matrix controller, obtains predicted values of two control variables of a reflux ratio and a reboiling ratio at the next moment after the optimal control law treatment of the dynamic matrix control algorithm, and transmits the predicted values as the output of the controller to a DCS (distributed control system) to realize the high-purity control of double-end composition.
The invention has the advantages that the high-purity rectification dynamic matrix control system can overcome the difficulty that the traditional PID control system has poor control effect even can not be controlled under the high-purity condition, and realizes better control effect and dynamic quality, and the control method is based on DCS and a computer upper computer platform, so the control method is easy to implement.
Detailed Description
The present invention will be described in detail with reference to fig. 1, 2 and 3, and the object and effect of the present invention will be more apparent.
The control system comprises: the device comprises a rectifying tower (a control object), an intelligent detection instrument (sensing transmission), a DCS (distributed control system), an upper computer and a field bus. The rectifying tower, the intelligent detection instrument, the DCS and the upper computer are sequentially connected through a field bus, and uploading and issuing of information flow are achieved. The upper computer comprises an inference controller, a dynamic matrix controller and a human-computer interface.
The difference between the system connection mode and the traditional industrial process is as follows: the system makes full use of the development of the existing DCS and computer technology, the detecting instruments at the top and the bottom of the tower are connected to the DCS, detected signals are uploaded to a real-time database, the upper computer is connected with the DCS and obtains historical data from the upper computer, the signals from the upper computer reach the DCS, and the DCS is controlled through the DCS instruction regulating valve.
The control method is implemented according to the following steps:
1. establishing inferred control module from temperature, pressure to product component concentration
This part of the work is done by the inference controller on the upper computer 10. The purity values of the product components are rapidly and accurately deduced according to the temperature and pressure data from the DCS real-time database 9 by forming an inference control equation as follows:
purity of the product at the top of the tower: if the condenser is a complete condenser, the condenser is a full condenser,
if the condenser is a partial condenser,
for the purity of the product at the bottom of the column:
compared with the conventional component inference control method based on data regression analysis, the component inference control method based on mechanism model derivation has the advantages of accurate inference prediction, strong extrapolation capability, no influence of feed composition disturbance and the like.
2. Design and parameter selection for dynamic matrix control algorithms
This work is done on a dynamic matrix controller on the upper computer 10, and is implemented by the following steps:
1) Prediction model
The method is implemented by adopting the following steps:
(1) carrying out open loop step response on the system to obtain a step response curve shown in figure three;
(2) and determining the model time domain length N. The principle is as follows: the larger the N is taken, the higher the precision is, but the quantity is calculated simultaneously
(3) The larger the size is, the higher the requirement on the computer performance is, and the N is generally 20-60;
(4) and T is selected. The principle is as follows: bringing the process response value at time NT close to its steady state value;
(5) at each sampling time T = T, 2T, 3, NT, a sequence of sampling values is obtained from a dynamic database in the DCS, and a dynamic coefficient a is used 1 ,a 2 ,...,a N To represent;
(6) normalizing to obtain a finite set of unit step responses and sampled data { a } 1 ,a 2 ,...,a N The parameters are used as prediction model parameters in the DMC algorithm;
(7) calculating the output predicted value of the future P time system according to the following formula:
Y m (k+1)=Y 0 (k+1)+A·ΔU(k) (6)
and (3) solving the output of a prediction model of the system at the future P moment, wherein P is called a prediction length, and the formula is as follows:
model output initial value Y 0 (k + 1) satisfies:
Y 0 (k+1)=A 0 U(k-1) (7)
in the formula
Thus, a prediction model output is obtained:
Y m (k+1)=AΔu(k)+A 0 U(k-1) (8)
2) Feedback correction
The output prediction value of the system is corrected based on the output of the prediction model and the actual output error by the following formula
Y p (k+1)=Y m (k+1)+h[y(k)-y m (k)]
=AΔU(k)+A 0 U(k-1)+he(k) (9)
In the formula
Y p (k+1)=[y p (k+1),y p (k+2),…,y p (k+P)] T
Wherein e (k) is the measured output value y (k) and the model prediction value y m (k) The difference between the two; h is a feedback correction coefficient.
3) Roll optimization
And (3) performing rolling optimization by adopting the following optimal control law optimization formula:
ΔU(k)=(A T QA+λ) -1 A T Q[Y r (k+1)-A 0 U(k-1)-he(k)] (10-1)
the following sequential open-loop control increments from time k to time k + M-1 are obtained,
in the formula
Thus, the controller output value at the present time is obtained.
Through the three steps of the prediction model, the feedback correction and the rolling optimization, the design of the controller is completed, and the output value of the controller at the current moment, namely the values of the reflux ratio and the reboiling ratio, can be obtained through deducing the output predicted value of the controller at the previous moment, namely the double-end purity.
4) Controller parameter initialization
The method comprises a model time domain length N, a prediction time domain length P, a feedback correction coefficient h, an error weight matrix Q and the like. Selecting a rule: n is 20-60, P is a number slightly less than N, h is a unit vector, and Q is a unit diagonal matrix.
5) Controller reference trajectory selection
In the form of a first order index as follows:
y i (k+1)=α r i y(k)+(1-α r i )y est (12)
y r (k)=y(k)
it is advantageous to reduce the excessive control action and to make the output of the system smoothly reach the set value.
3. The system starts to be put into operation:
1) Setting the time interval of each control action by using a timer, wherein the time interval is consistent with the sampling time of the prediction model in principle;
2) The on-site intelligent instrument 6 detects the data of the top and bottom temperature, pressure intensity and the like of the rectifying tower and transmits the data to the real-time database 9 of the DCS;
3) The upper computer 10 is used for obtaining the temperature, the pressure and other data of the tower top and the tower bottom at the previous moment from the real-time database 9 of the DCS in each timing period and taking the data as the input of the inference controller module in the upper computer 10;
4) The inference controller in the upper computer 10 obtains an inferred prediction value of the tower top and tower bottom components at the previous moment through inference control, and the inferred prediction value is used as input of a dynamic matrix controller module in the upper computer 10;
5) The dynamic matrix controller in the upper computer 10 obtains the control variables at the current moment through calculation: the reflux ratio and the reboiling ratio are sent to a DCS;
6) The DCS system implements control action on the on-site high-purity rectification process through an on-site adjusting device, and ensures the quality of high purity formed by double-end products.
Compared with the traditional rectification control system and method, the technology of the invention obviously improves the stable control capability of the double-end product in the rectification process under high purity, and improves the dynamic quality of the high-purity rectification process; meanwhile, the system also has the advantages of strong robustness, easy detection and implementation of parameters and the like.
While the high purity distillation dynamic matrix control system of the present invention has been described with reference to specific embodiments thereof, it will be apparent to those skilled in the art that the present technology may be practiced with modification, or with appropriate modification and combination, of the apparatus and method of operation described herein without departing from the spirit, scope, and spirit of the invention. It is expressly intended that all such similar substitutes and modifications which are obvious to those skilled in the art are deemed to be within the spirit, scope and content of the invention.