CC BY
18
Section 7. Technical sciences
https://doi.org/10.29013/ESR-20-1.2-78-83
Sidikov Isamidin Hakimovich, Atajonov Muhiddin Odiljonovich, Sayora Yunusova Toshkenboyevna, Nashvandova Gulruxsor Murod Qizi, Tashkent State Technical University, Department of Information Processing and Control Systems Tashkent, Uzbekistan E-mail: cool.atajonov@mail.ru; atajonovmuhiddin80@gmail.com
ADAPTIVE ANALYTICAL CONTROL OF TECHNOLOGICAL PARAMETERS BASED ON THE PROBABILITY METHOD OF OIL REFINING INSTALLATIONS
Abstract. A systematic analysis of the existing system of analytical control of the technological parameters of the equipment of oil refineries was carried out. It has been studied as an adaptive control object, which quickly changes its parameters, depending on the real state of the controlled technological process. A technique is proposed for determining the frequency of removal of technological parameters, based on the methods of mathematical statistics and based on the analysis of experimental data of a particular process. One of the ways to increase production efficiency is to obtain reliable, accurate, and timely data for management purposes. In this regard, an important role is played by the analytical control of technological processes in production, covering its entire life cycle, in the production station, i.e. from raw materials to final products.
The character of the functioning of this system in many respects depends on the completeness of reflection of the state of the technological process and the effectiveness of decisions made by the managing staff. In this regard, the use of the considered adaptive control algorithm of the analytical control system is essential for improving the production economy and increasing the level of technological discipline.
Keywords: Adaptive control, artificial intelligence, analytical control, optimization of the technological process, safety, self-adjusting regulator, stochastic control, probability.
1. Introduction product quality control at all stages of technological
In modern conditions, the effectiveness of pro- processes [1]. The tasks of monitoring and control-
duction management is largely determined by the ling the quality of industrial products, optimization
methods and technical means of monitoring and of technological processes are solved on the basis of
integrated production automation, widespread implementation of automation systems and tools [2]. One of the main conditions for successfully solving the problem of automation of production is the provision of automatic control systems by means of operational automatic control of parameters -characteristics of technological processes: physical, chemical and other quantities, information about which is necessary to ensure optimal control [3].
The stochastic nature of the course of most real technological processes during the processing of petroleum products creates the prerequisites for deviations from the planned quality indicators of materials, substances and industrial products [4]. In this regard, attempts are currently being made to improve the quality of theoretical models used in the management of technological processes in industrial production. The creation of such models will allow the transition to adaptive control problem solving methods based on the theory of stochastic control, vector optimization methods, and artificial intelligence theory [5].
2. Methodology
Deviations of technological parameters from regulated set norms, due to various destabilizing factors of real production, require adjustment of the composition of the feedstock, intermediate reagents. In practice, such an adjustment occurs with a certain delay, when compared with the moment the managerial decision is made about its necessity [6].
It should also be taken into account that the technological processes occurring at various stages of production are of both fleeting and slow-flowing nature, which essentially fulfills the control process [7].
In addition, information on technological parameters can be either redundant or insufficient, and these factors affect the quality of the management process, and this ultimately affects the quality of products and economic indicators ofproduction [8].
These and other factors determine the creation of an effective control system that takes into account the features of the current state of the pro-
cess. In this regard, there is a need to search for solutions that allows to reduce the cost of analytical control of the process parameters while maintaining the necessary reliability, reliability and accuracy of the information received [9]. To this end, it is proposed to determine the frequency of data collection on the state of the technological process based on the most effective experimental studies of the variability of a real technological process as a result of which an adaptive control algorithm for this system can be developed [10].
It is known that changes in the composition parameters and the properties of technological flows that need to be controlled in the real case are probabilistic in nature. Existing methods for reflecting the characteristics of objects under the influence of interference require not only information about their static and dynamic properties, but also full information about their static characteristics. Therefore, in our case, it is necessary to create a control system that provides high quality control processes for the control of the technological process under conditions of incomplete information on the static characteristics of signals and interference.
The technological parameters for solving this problem will be investigated by the system of analytical control of oil refining production as a control object. it should be taken into account that the quality of control of material flows of a technological process depends on the operating modes of technological equipment. Moreover, for all monitored parameters, the following states are characteristic: normal and emergency state. The emergency state has two varieties:
- "leaving" from a normal state;
- "return" from emergency to the zone of normal condition.
3. Experimental part
The nature of the changes in the controlled parameter of the technological parameter is presented in (Figure 1).
Figure 1. The nature of the changes
Marked states of controlled parameters are characterized by the following features (see Figure 1) normal state (observed in time intervals (t <tpt2 <t <t3,t6 <t <t5,t >t8) theparameter values are located in the central part of the zone of its regulated (permissible) changes (1st zone):
Pmin +APU. < Pmax -Ai>, (1)
where is P - the current value of the controlled parameter;
Pmax - APJ. = Puand Pmin + AP;., = Pun - the values of the upper and lower warning limits of regulation (representing a reduced and accepted value AP,, margin of regulation upper and lower limits allowed values of the parameter).
The emergency state of "departure" of the parameter values (observed in time intervals t1 <t <t2,t3 <t <t4 and t5 <t <t6,t7 < t < t8 the parameter values are inside the zone of regulated limits of its permissible changes, but near one of its borders (2nd zone)):
Pmin < P < Pmin + AP,. or PmaX - Ap., < P < Pmax (2)
emergency state of "return" of indicator values (in the figure is observed in the time interval t4 < t < t5 ) -the parameter values are outside the zone of regulated limits of its permissible changes (3rd zone):
P > Pmax or P > Pmin (3)
The determination of the optimal control frequency for each of the conditions considered above is possible on the basis of the information obtained
in the controlled process parameter
on the characteristics of the variability of the controlled parameters of the technological process in a form that allows developing a reasoned approach to determining the reasonable time for their subsequent control. To obtain such information, it is necessary to conduct several series of focused experimental studies of the characteristics of the real variability of the technological process and its parameters.
Determination of the required control frequency (control interval) of the process parameters consists of the following steps:
1. Conduct several series (experiments) of the survey of the current production at the above-mentioned characteristic modes of its operation, during which it is necessary to determine the value of the parameter for each parameter at regular intervals X.
Xl = (4)
where i - point number in this experiment (i = 1,2,...,k); z - experiment number (z = 1,2,...,m);
Xl - current parameter value in i- the point z-is experiment.
According to the results of each of the z-th experiments, it is necessary to determine the estimate of the mathematical expectation at the z-th implementation of the random process
tx*
M (X ) =
K
(5)
where K - total number of points obtained in this experiment.
t=i
Figure 2. An example of an experimental study of the variability characteristics of a controlled process parameter
2. For each of the experiments, it is necessary to construct the corresponding graphs of the parameter changes (see the example in Fig. 2), plotting on them also the lines corresponding to:
- assessment of mathematical expectation M(Xz );
- the boundaries of the zone of regulated parameter values Xregx and Xrefn (they correspond to any of j(+) and j(_) levels respectively);
- actual boundaries of the zones of increased risk of parameter control in this experiment Xz max and X . .
z ,mm
In each of the experiments, it is necessary to determine the magnitude of the levels of variation in the intensity of control of the parameter:
AX
X+ = AXvar+
jz z
X- = AXvar- • jzz
r\ ar+
AX,
at the AXz>maX = Xz>maX - M(Xz ) > 0(6)
AX™r -=-
AX,
at the AX,
= X.
-M(Xz ) < 0 (7)
where is AXzvar +, AXzvar - - steps to vary the levels of control intensity, respectively, for cases when the current value of the parameter is greater or less M(Xz) the mathematical expectation of a variable in this experiment;
v - the number of selected levels of variation of the parameter control intensity (selected depending on the requirements for the accuracy of the experimental data processing).
3. Next, calculate the magnitude of the levels of intensity control parameter is calculated by the equations:
■ j(+), (8)
■ j(-), (9) where j - parameter control intensity level number
(j = 0,l,2,„.,v).
For each of the levels of control parameter intensity, relative coefficients of control intensity are determined. For this, the current values Xfz (see Fig. 2) in each experiment are compared with the corresponding values X+z h X_z and the number of points in the experiment is calculated n+z and n_z by inequalities
n+z at the X* > X+z, (10)
n_ at the Xz < X-z, (11)
The relative coefficients of the intensity control parameter determine the equations:
(12)
n+
K =
jz K
- j-z jz K
(10)
where Aj+z - relative coefficient of intensity of parameter control at AXr > M(X ) ; A ■ - relative coefficient of intensity of parameter control at
AXl > M (Xz ).
For each of z experiments, data obtained in the form Aj+z = f'(X;
r+ )
'■jz )
Ajz = F X )
v
v
represent two generalized variational series of the form
A +=^X+), (13)
A "= F (X*), (14)
The values of the arguments of the variation series (13) and (14) are divided into equal intervals, the values of which are taken equal to the maximum values of the steps of variation of the relative coefficients of the intensity of control and. To determine the value of the functions A + and A- at each interval, one should be guided by the recommendations of the "theory of pessimism". According to this theory, when assessing the case of stochastic uncertainty of conditions when the probability distribution for the parameters either does not exist or cannot be obtained, it is always necessary to focus on the worst conditions. Therefore, from several values of the functions that appear inside the interval, the value corresponding to the smallest relative coefficient of control intensity is selected and is taken as the value of the function in this interval. After performing these calculations in all intervals, two curves are constructed that characterize the value of the relative coefficient of control intensity depending on the deviation of the parameter from its mathematical expectation of the form,
= a(X+), (15)
Amin =a(X -), (16)
4. We accept, based on practical experience, the time interval between measurements:
- with parameter values located in the area of its mathematical expectation (1st zone in Fig. 1), At = Atm (x );
- at parameter values close to the boundary values of the regulated zone (2nd zone in Fig.1) AT = AT"? h AT = Azreg ;
min max
- for parameter values equal to the boundary in high-risk areas (2nd zone of Fig. 1), the time interval between measurements can be determined, for example, on the basis of Kotelnikov's theorem according to which:
AT<n,
(17)
where is At - time between measurements;
coc - maximum frequency of the spectrum of the
studied variable.
Therefore, in our case ATmax = At = n
5. Based on the dependences obtained above, we can construct the desired resulting dependences of the time intervals between measurements on the deviation of the measured parameter from its mathematical expectation in the form at the X+
AT+=ATMaX]+- A^B +{X +- M[X ]}, (18) at the X-
AT-=ATMa!Xr-AminB-{X--M[X]}. (19)
The use of the considered adaptive control algorithm of the analytical control system (which allows you to quickly determine the occurrence ofviolations and quantify changes in the parameters of the process) is essential for improving the production economy and increasing the level of technological discipline.
5. Conclusion
Thus, we performed an analysis of the analytical control system of oil refinery equipment as a control object, determining the requirement for experimental studies of the variability of the parameters of analytical control in the development of an adaptive control algorithm for an analytical control system, an image of the prerequisite for creating an adaptive analytical control system for the process of production of oil refineries. The character of the functioning of this system in many respects depends on the completeness of reflection of the state of the technological process and the effectiveness of decisions made by the managing staff. In this regard, the use of the considered adaptive control algorithm of the analytical control system (which allows you to quickly determine the occurrence ofviolations and quantify changes in the process parameters) is essential for improving the production economy and increasing the level of technological discipline.
6. Acknowledgement
Huge thanks to my supervisor Dr I. H. Sidikov for providing me great insight and knowledge, for his continuous encouragement and for providing me with many of his valuable time.
I thank you so much for giving me such an opportunity to work under you. I thank my parents who continued to support me, morally and financially.
I would also thank my friends who gave me moral support and stayed at university with me for many days. Without their support, I would not be able to put so much time and effort into my article.
Finally I thank goes to the Tashkent State Technical University, Information Processing and Control Systems department staffs for their support and taking me to a next level in my studies.
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