Method of generating control action for industrial control object

IPC classes for russian patent Method of generating control action for industrial control object (RU 2450303):

G05B13/04 - involving the use of models or simulators

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FIELD: physics.

SUBSTANCE: method is realised by generating control action consisting of two components, one of which is a forecasting component, which is proportional to the trend of change of the technological parameter. The time constant of industrial objects is used to determine elementary sampling rate; technological changes of the parameter are measured with a selected sampling rate until conditions are met; the range of the forecasting time is determined; standard deviation of technological changes of the parameter and the variation factor are calculated, and polynomial characteristics are used to find the forecast time value and the weight coefficient of the forecasting component, and the trend and forecast value of the technological parameter are then determined; the difference between the forecast and given values of the technological parameter is determined; the forecasting component and the component for the control action from a controller are calculated and control action on the actuating mechanism is generated via algebraic summation of two components from the controller, and the process of generating the control action is resumed every time the forecast time is reached.

EFFECT: broader functional capabilities of controllers.

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The invention relates to the field of management of industrial control objects with changing process parameters and is intended to generate a control action taking into account the forecast component determined by trends in technological parameter. Preemptive scope is a large industrial facilities and installations in the oil and gas industry.

There is a method of managing dynamic objects with attached external perturbations on the set parameters of quality by forming a control action on the basis of the comparison result of the predetermined exposure amount measured values of the state variables of the object, supplemented by values of variables that are measured directly at the points of application of the perturbation [patent No. 2261466, CL G05B 11/01, 2005].

The main disadvantage of this method is that the dimension of the state variables of the object only at the points of application of the perturbation does not take into account the forecast change in the state variables of the object, and thus has limited functionality.

There is a method of control of the technological object, which form the job and measure adjustable parameter of the technological object, determine the deviation of the controlled parameter on the job and the speed of this deviation, and then form periodically with a period equal to the sum of the time delay and time constant of the object, the control action [patent No. 2017196, CL G05B 11/00, 1994].

The disadvantage of this method is limited functionality, since the control action is generated by the deviation of the controlled parameter from the job and the speed deviation, without taking into account the forecast component characterizing further the change of technological parameters.

The closest technical solution is the method of identification of current interest in management systems, including preliminary assessment of the statistical errors of prediction and control, application of test impact test, recording the trajectory of change of the output variables over time to estimate the dynamic characteristics of channel regulation [patent No. 2277259, CL G05B 13/04 was investigated, G05B 23/00, 2006].

The disadvantages of this method is the prediction of the trajectories of the working control only model, as well as the need to apply a trial testing the impact that severely limits the functionality of the method of identification of existing objects in the control systems.

The technical result of the invention is to expand the functionality of the pin is of Oller, by introducing a predictive component in the formation control for industrial control object.

The technical result is achieved by the use of the controller, the input of which every time serves the error signal equal to the difference between the measured value of the process value and set value, and the output controller to receive the control action, which is served on the Executive mechanism of COI, characterized in that find the elementary increments of one hundredth from the time constant SOC, taking into account the elementary discrete measured value of a process parameter, after each measurement to calculate the mathematical expectation, determine the difference between the expectation and the specified value of the process parameter to the conditions under which the difference becomes less than the adopted threshold value, the moment this condition finish time interval measurements and by the difference between the time constant of the COI and time interval measurements are time range forecasting, calculate the standard deviation of the values of the process parameter measured at the time interval measurement and find the coefficient of variation as the ratio of standard deviation to the floor is built mathematical expectation, find the amount of time forecasting, does not extend beyond the time range forecasting, as well as the weight of the forecast component polynomial dependencies, and then determine the trend by relations of mathematical expectation to the time interval measurement, calculate the predicted value of the process parameter by multiplying the trends on the time value of forecasting technological parameter, determine the forecast deviation as the difference between predicted and specified values of the process parameter, calculate the forecast component, as the product was found weighting factor predictive component to the predicted deviation, then calculate the component to control from the controller, as the product of the weighting factor for the control from the controller to the current the value of the output signal from the controller and generate the control action to the actuator by algebraic summation of the forecast component and a component to control from the controller, and after reaching the time of forecasting the process of forming the control each time the resume.

The invention is illustrated by drawings, where figure 1 shows the function CX is mA automatic control system (ACS) technology parameter y(t)taking into account the forecast component; the figure 2 shows the functional diagram of the method of production control; figure 3 shows a graph of the changes in the technological parameter as the temperature in the reactor at the stage of regeneration; figure 4 shows the structural model of SAU temperature in the reactor tail gas treatment unit, implemented in an integrated environment VisSim; figure 5 shows a fragment of the simulation work SAU temperature in the reactor tail gas treatment unit, comprising a controller to the PID control law, and applying to the input random signal with a normal distribution in normal control treatment (figure 1), and control the impact of taking into account the forecast component implemented under the proposed method (figure 2).

In figures 1, 2 and 4 is represented by the following blocks:

1 - specifies the element (ZÉ);

2 - element comparison (ES);

3 controller ();

4 is a block for determining the weighting factor predictive component (BVCPS). Designed for the determination of the weighting factor predictive component α_PSthat is determined by the polynomial dependencies:

5 is a block for determining the weighting factor for the control from the controller (BUCK). Designed for the determination of the weighting factor to control all the key α _kthat is found by the expression:

When calculating the weights, I always consider the condition of the equality unit sum of weight coefficients:

6 - adder ();

7 - industrial control object (COI);

8 - sensor (D);

9 is a block for determining the discreteness (OD) measurement of technological parameter Δ. The unit is designed to find elementary discreteness in the form of one-hundredth from the time constant SOC T (Δ=0,01*T), as a rather small value and at the same time, providing presentationhost sample measurement process parameter;

10 - the unit of measurement of values of the process parameter (STP), in which the measured process parameter with the received elementary discreteness;

11 - unit definition of mathematical expectation (HMO), which is calculated after each measurement process parameter. Calculate the mathematical expectation of technological parameter in the expression:

where x_i(t) is the measured value of a process parameter;

n - number of measurements;

12 - unit test conditions (PU) in comparison to the difference between the expectation and the specified value of the process parameter with the adopted threshold value ε. Once again the guard reaches a threshold, then define the time interval measurement ΔT_and. Find the time range forecasting using the expression:

14 is a block for determining statistical parameters (BSA). The unit is designed to calculate the standard deviation, taking into account the measured values of the process parameter, the expression:

wherethe normalized value of the process parameter,

the normalized value of the mathematical expectation.

The coefficient of variation of process parameter determined by the expression:

15 - unit-time prediction of t_CR(AFP)

technological parameter, which is found in polynomial dependencies:

13 is a block for determining trends (FROM) using the expression:

16 is a unit for computing predicted values (O.S.) process parameter using the expression:

17 is a block determine the magnitude of the forecast variance (brigade) between predicted and specified values of the process parameter

18 - shaping unit forecast component (FAC), as works found the weight ratio PR is groznoi component α _PSthe predicted deviations Δx_CR(t). The signal equivalent to the forecast component, determined by the expression:

19 is a block forming part of the control action from the controller (FGC), works as the weighting factor for the control from the controller α_kthe current value of the output signal from the controller that corresponds to the current value of the process parameter. Signal equivalent component of the industrial controller to the PID control law is determined by the expression:

20 - shaping unit control (qualification development Department) to the actuator as the algebraic summation of the forecast component and a component to control from the controller. The signal equivalent to the control effect determined by the expression:

21 enforcement mechanism (IM);

22 - random signal (GSS);

23 is an integral index of quality (IPC).

The figures use the following notation signals:

x_ass- set the process value;

y(t) is the actual value of the process parameter;

Δ(t) is the difference between the actual and set value process is the parameter;

u(t) is the control action from the controller;

x_OS(t) is the feedback signal;

x*{t) - signal equivalent component to control from the controller;

Δ_CR(t) is the difference between the forecast and the specified value of the process parameter;

x_CR(t) - signal equivalent predictive component;

x(t) is the signal equivalent to the managing impacts, taking into account two components.

The way to develop control for industrial control object is implemented as follows.

With the help of block 1 set the set value process value x_assthat is compared in block 2 with the actual value of the process variable y(t), measured by unit 8, and converted into a feedback signal x_oc(t). The value of the difference between the actual and preset values of the process parameter Δx(t) fed to the input unit 3.

Using known dynamic response of block 7 in the form of a time constant T, find basic measurement resolution, in block 9, which discretizing further the process of measuring a process variable. Measure with the selected discrete value of a process parameter x₁(t), x₂(t), x₃(t)...x_n(t) in block 10, which calculates m thematic waiting in block 11. Measurements continue until a condition is met in block 12, which is determined by comparing the difference between the expectation and the specified value of the process parameter with the adopted threshold value ε, for example, from 1 to 5%. Find the moment at which the difference becomes equal to or less than the adopted threshold value ε, and at this point, determine the magnitude of the time interval measurement δ t_And. Using the difference between the time constant of the COI and time interval measurements are time range forecasting δ t_CR=T-δ t_And. On the measured values of the process parameters determine the standard deviation in the block 14. For values of σ_xand m_xalso in block 14, determine the coefficient of variation of process parameter ν. Next, use a polynomial dependence (8) to determine the time of forecasting t_CRin block 15, within the time range forecasting δ t_CR. On the calculated coefficient of variation determine the amount of time forecasting technological parameter t_CRand the weight ratio of the forecast component α_PSfor the polynomial dependence (1) in block 4, and the weighting factor for the control from the controller unit 5. Then determine the trend in the block 13 and the predictive value of t is geologicheskogo parameter in the block 16. Next, determine the magnitude of the forecast variance in block 17 Δ_CRas the difference between predicted value and a set value process value Δx_CR{t)=(x_CR(t)-x_assand form control on the block 21 by algebraic summation using block 6, two components in the block 20 x(t)=x*(t)+x_CR(t), one of which is found as the product found the weight ratio of the forecast component of the predicted variance in block 18 x_CR(t)=α_PS·Δ_CR(t), and the other in block 19 as well as the product of the weighting factor for the control from the controller to the current value of the output signal of the controller that corresponds to the current value of the process parameter. After reaching time forecasting t_CRthe process of forming the control each time the resume.

A specific example of the method

The method is implemented for COI in the form of reactor tail gas treatment unit in the plant for the production of sulfur LLC "Gazprom dobycha Orenburg".

The reactor has two stages:

The first step is adsorption, which is heated in the tube bundle regeneration gases at a temperature of 120-140°C and a flow rate of not less than 25,000 m³/h pass into the reactor.

The second stage is PE is inertia. Regeneration of the catalyst involves two steps:

- heating of the catalyst to a temperature of 260°C and desorption of sulfur from its surface at a temperature of 200-250°C;

- cooling of the catalyst.

The time constant T of the reactor tail gas treatment unit is known from the normative-technical documentation installation and equal to 45 minutes.

Define basic measurement resolution of Δ=0,01*45 minutes rounded to the nearest larger integer equal to 1 minute. With the obtained elementary discrete measured values of the temperature in the reactor tail gas treatment unit to Θ(t_i),°C (Fig.6, table 1). As temperature measurements in the reactor tail gas treatment unit calculate the mathematical expectation of the expression (4).

Table 1
n_i	Date and time	Θ(t_i), °C		m_x(t_i), °C
1	13.01.2011 11:12	237,5	0,75	237,50	0,95	5,6%
2	13.01.2011 11:13	234,0	0,68	235,75	0,94	4,8%
...	...	...	...	...	...	...
14	13.01.2011 11:25	227,0	0,54	229,63	0,92	2,1%
15	13.01.2011 11:26	221,0	0,42	229,05	0,92	1,8%
16	13.01.2011 11:27	218,8	0,38	228,41	0,91	1,5%
17	13.01.2011 11:28	202,0	0,04	226,85	0,91	0,8%

The temperature in the reactor to measure until

will not be less than the accepted threshold value of ε=1%.

Temperatures in the reactor normalized in accordance with the expression:

wherethe normalized value of the temperature in the reactor;

Θ(t_l) - the initial value of the temperature in the reactor, which is equal to 20°C;

Θ(t_K) - the final value of the temperature in the reactor, which is equal to 250°C.

The value of the mathematical expectation is also normalized in accordance with the expression:

where m_HB- the base value of the temperature in the reactor, which is equal to 227°C.

Define the time interval measurement ΔT_Andis 17 minutes. Calculate the time range forecasting by expression (5):

Δ_CR=45-17=28 min (figure 3).

Determine the standard deviation in accordance with the expression,

and then calculate the coefficient of variation of process parameter according to the expression (7):.

For the polynomial dependence of the form t_CR=8,27+0,46·ν-0,504·ν²+0,025·ν³with regard to the calculated coefficient of variation ν=0,46, find the value of time for predicting the s t _CRthat is 8.4 minutes, within a time range forecasting δ t_CR=28 minutes.

For the polynomial dependence of the form α_PS=0,4-0,073·ν-0,006·ν²+0,0001·ν³with regard to the calculated coefficient of variation ν=0,46, find the weight of the forecast component α_PS=0,37.

Determine a weighting factor for the control from the controller by expression (2) α_To=0,63.

Calculate the trend in expression (9)and then determine the predictive value of a process variable by expression (10):

x_CR(t)=k^*·t_CR=13,35·25.4mm=339,09°C.

Then determine the forecast variance expression (11)

Δ_CR=x_CR(t)-x_ass=339,09-225=114,09°C.

Form control action x(t) to the actuator COI in accordance with the expression (14)as the algebraic sum of two components:

x^*(0=301C; x_CR(t)=43°C; x(t)=344°C.

Using the model of automatic control system (ACS) the temperature in the reactor when cleaning the tail gas from the sulfur compounds in the feed to the input random signal generator with a normal distribution (figure 4, block 22) with the given values of mathematical expectation of 0.91 and a standard deviation equal 0,43 implemented in an integrated environment is izmalkovo simulation (VisSim) obtained timelines, shown in figure 5. Used for the control of the controller with PID control law, and for assessing the quality of management - normalized integral quadratic criterion J, is also implemented in an integrated environment VisSim (figure 4, block 23).

The resulting modeling two time graph of temperature change in the reactor (figure 5): 1 - change temperature when using ACS with output control action PID controller without the forecast component. The value of the normalized integral quadratic criterion was J₁=0,84; 2 - change temperature when using ACS with development control in two pillars, one of which is from the PID controller with the corresponding weight coefficient α_To=0,63, and the second predictive component also has its weight α_PS=0,37. The value of the normalized integral quadratic criterion was J₁=0,57. The deviation of the steady-state value of the temperature in the reactor tail gas treatment unit from the set x_ass=225°C for curve 1 is 23°C, and curve 2 is 12°C (table 2).

Table 2
Indicators	Controlling influence is a journey	Improved on, %
normal	with the forecast component
Quality control (J²) on integral quadratic criterion	0,84	0,57	32
The maximum deviation from the target (225°C)	23°C	12°C	47,7

Consequently, the use of the controller with the forecast component for SAU temperature improves quality control by 32% and to reduce the deviation of the set value of the temperature in the reactor tail gas treatment unit from the set by 47.7%.

In addition, the decreased the flow of process gas for heating on average by 12.6%, while the energy saving for the whole installation was 13.1%. The result achieved by improving the efficiency of functioning of the automated installation of 11.3%.

Thus, implementation of the proposed control method COI can improve quality control, reduce the maximum deviation of process parameters from the specified values, as well as significantly reduce resource costs, which leads to significant improvements in EF is aktivnosti operation of industrial facilities management in the oil and gas industry.

The way to develop control for industrial control object (COI) by use of the controller, the input of which every time serves the error signal equal to the difference between the measured value of the process value and set value, and the output controller to receive the control action, which is served on the Executive mechanism of COI, characterized in that find the elementary increments of one hundredth from the time constant SOC, taking into account the elementary discrete measured value of a process parameter, after each measurement to calculate the mathematical expectation, determine the difference between the expectation and the specified value of the process parameter to the conditions under which the difference will be less than the adopted threshold value, when this condition finish time interval measurements and by the difference between the time constant of the COI and time interval measurements are time range forecasting, calculate the standard deviation of the values of the process parameter measured at the time interval of the measurements, and find the coefficient of variation as the ratio of standard deviation to the obtained mathematical expectation, find the amount of time forecasting, the comfort is second for the time range forecasting, as well as the weight of the forecast component polynomial dependencies, and then determine the trend by relations of mathematical expectation to the time interval measurement, calculate the predicted value of the process parameter by multiplying the trends on the time value of forecasting technological parameter, determine the forecast deviation as the difference between predicted and specified values of the process parameter, calculate the forecast component as the product found the weighting factor predictive component to the predicted deviation, then calculate the component to control from the controller as the product of the weighting factor for the control from the controller to the current value of the output signal from the controller and generate the control action to the actuator by algebraic summation the forecast component and a component to control from the controller, and after reaching the time of forecasting the process of formation control every time resume.