Integrated application of synergetic approach for enhancing intelligent steam generator control systems

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 2, April 2024, pp. 1508~1518
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i2.pp1508-1518  1508
Journal homepage: http://ijece.iaescore.com
Integrated application of synergetic approach for enhancing
intelligent steam generator control systems
Siddikov Isamiddin1
, Umurzakova Dilnoza2
1
Department of Information Processing and Management System, Faculty of Electronics and Automation, Tashkent State Technical
University named after Islam Karimov, Tashkent, Uzbekistan
2
Department of Information Technology, Faculty of Computer Engineering, Fergana branch of Tashkent University of Information
Technologies named after Muhammad Al-Khwarizmi, Fergana, Uzbekistan
Article Info ABSTRACT
Article history:
Received Oct 27, 2023
Revised Dec 5, 2023
Accepted Dec 13, 2023
This article focuses on the integrated application of the synergetic approach
to enhance the quality of intelligent steam generator control systems.
By combining various techniques such as model-based control, adaptive
control, and artificial intelligence, an efficient and flexible control system
can be developed. Model-based control utilizes mathematical models of
steam generators to formulate control algorithms and predict system
behavior. Adaptive control enables the system to adapt to changing
conditions by adjusting control parameters based on real-time
measurements. Artificial intelligence techniques, including neural networks
and genetic algorithms, facilitate learning, optimization, and data-driven
decision-making processes. The objectives of this research are to investigate
the benefits of the synergetic approach in steam generator control, including
improved steam generation efficiency, optimized energy consumption,
enhanced system stability and reliability, and adaptability to varying
operating conditions and disturbances. The findings and conclusions of this
study are expected to provide valuable insights for engineers, researchers,
and professionals involved in the design and implementation of intelligent
steam generator control systems. By integrating the synergetic approach,
substantial enhancements in control quality can be achieved, leading to
optimal operation and maximum efficiency of power plants.
Keywords:
Control laws
Control system
Excitation system
Robust control
Steam generator
Synergetic control theory
Thermal power systems
This is an open access article under the CC BY-SA license.
Corresponding Author:
Umurzakova Dilnoza
Department of Information Technology, Faculty of Computer Engineering, Fergana branch of Tashkent
University of Information Technologies named after Muhammad Al-Khwarizmi
Mustakillik street 185, Fergana, 150100, Uzbekistan
Email: umurzakovadilnoz@gmail.com
1. INTRODUCTION
Intelligent control systems play an increasingly important role in modern power plants, including
steam generators. They provide the ability to automate and optimize control processes, resulting in improved
efficiency, reliability and safety of system operation. However, the dynamic and non-linear nature of steam
generators presents challenges for traditional control methods [1], [2]. In recent years, the synergetic
approach has become increasingly recognized in the control of complex systems. Synergetics studies the
interaction of components in a system and the creation of emergent properties that are manifested when
components act together. The integrated application of the synergetic approach allows the combination of
different methods and techniques to achieve a synergistic effect, where the interaction of components results
in higher performance than when individual methods are used.

Int J Elec & Comp Eng ISSN: 2088-8708 
Integrated application of synergetic approach for enhancing … (Siddikov Isamiddin)
1509
This paper discusses the integrated application of synergetic approach to improve the quality of
intelligent steam generator control system. This approach combines various aspects and techniques including
model-based control, adaptive control and artificial intelligence to create an efficient and flexible steam
generator control system [3]–[5]. Model-based control provides a mathematical model of the steam
generator, which is used to develop control algorithms and predict its behavior. Adaptive control allows the
system to adapt to changing conditions and requirements by adjusting control parameters based on current
measurements. Artificial intelligence includes techniques and algorithms such as neural networks and genetic
algorithms that allow the system to learn, optimize its decisions and make decisions based on data analysis
[6], [7].
The aim of this paper is to investigate and analyze the benefits of an integrated application of the
synergistic approach in the context of steam generator control. Various aspects will be considered, including
increasing steam generation efficiency, optimizing energy consumption, improving system stability and
reliability, adapting to changing operating conditions and disturbances. The results and conclusions of this
paper will be useful for engineers, researchers and professionals involved in the design and implementation
of intelligent steam generator control systems. The integrated application of the synergetic approach can lead
to a significant improvement in the quality of steam generator control, ensuring optimal operation and
maximum efficiency of power plants [8]–[10].
The authors of the paper [11] investigate the application of synergetic synthesis method to achieve
optimal control quality of steam generators. Synergetics is a science that studies complex systems and their
interactions, and synergetic synthesis offers an approach based on combining different elements of a system
to jointly achieve higher efficiency and synergy. The article [12] also draws attention to the importance of
modelling and simulation of the steam generator control system. By creating a mathematical model of a
steam generator and conducting simulation experiments, the effectiveness of proposed control methods and
strategies can be evaluated. This allows scientists and engineers to conduct an iterative optimization process
and improve the control system based on the results obtained.
Currently, in most cases, the steam generator control systems are a set of local subsystems for
regulating boiler power, superheated steam temperature, and fuel consumption. At the same time, each of the
subsystems is calculated as autonomous, and their mutual influences are considered as disturbing effects. The
main disadvantages of this approach are the following:
a. Ensuring autonomy is an artificial technique. And although Kolesnikov [12], who owns the idea of the
synthesis of autonomous control systems, considered autonomy as a way to improve the quality of
regulation, it is obvious that this, at least, is not always the case. For example, in the conditions of actual
restrictions on control actions, an autonomous system has degraded dynamic characteristics compared to
a system of connected regulation.
b. The synthesis of the control system is based on linear boiler models obtained either by linearization of a
nonlinear model, or, more often, by approximation of the model through separate channels by transfer
functions. The parameters of the linear model, which adequately describe the processes occurring in the
boiler in a narrow area near the selected mode, change significantly when the mode is changed. This, in
particular, leads to the fact that the adjustment coefficients of local regulators, optimal in accordance with
some quality criterion for one mode, for another mode will not only be not optimal, but may not even
provide stable regulation. Recording the model in the form of transfer functions on separate channels, in
addition to the above, has the following drawback: the structure of these transfer functions, as a rule, is
such that it provides a fairly rough approximation. For example, the transfer function of a drum boiler
through the channel “feed water consumption - water level in the boiler drum”.
𝑊(𝑝) =
𝑘
𝑝
𝑒−𝜏𝑝
Describes the dynamics of the level in the drum, starting from point 𝐴 in Figure 1. The phenomenon of
“swelling” of the level that occurs at 𝑡 ∈ [0, 𝜏] is excluded from consideration in this case. At the same
time, it is known that the main share of protection triggers excessive increase or decrease in the level is
caused by this phenomenon.
The noted disadvantages are primarily due to the fact that the natural dynamics of a thermal power
facility-the nonlinearity and interconnectedness of the processes occurring in it, is not taken into account
when solving the problem of synthesis of the regulator. The desire to maximize the range of possible modes
of operation of the boiler, to ensure its more effective participation in post-accident regulation, to increase its
maneuverability makes it necessary to base the synthesis of the control system on a nonlinear,
multidimensional and multi-connected model, using the methods of nonlinear control theory.

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 2, April 2024: 1508-1518
1510
Figure 1. Changing the level
2. METHOD
To synthesize the control system, we will use a nonlinear mathematical model of a drum boiler
described in [13]–[15]. Let‘s introduce the notation: 𝑥1 is deviation of the water level in the boiler drum, 𝑥2 is
steam pressure in the drum, 𝑥3 is mass vapor content at the outlet of the lifting pipe system, 𝑥4 is steam
pressure at the steam generator outlet, 𝑢1 is feed water consumption, 𝑢2 is heat flow to the heating screen
surfaces; considering the fact that the steam flow rate from the circulation circuit (CC) 𝐷′′
̃ and the steam flow
rate entering the superheater (ES) differ by the amount of steam discharged into the condenser 𝐷𝑐𝑜𝑛.
𝐷′′
= 𝐷′′
̃ + 𝐷𝑐𝑜𝑛,
Let‘s write it in the form:
𝑑𝑥1
𝑑𝑡
=
1
𝐹𝑒.𝑚.
(
1
∆𝑒
((𝑒22 − 𝑒1
̃ 𝑒21)𝑙1 − (𝑒12 − 𝑒1
̃ 𝑒11)𝑙2) +
1
𝑒33
𝜕𝜑𝑙𝑖𝑓
̅̅̅̅̅̅(𝑥2,𝑥3)
𝜕𝑥3
𝑉𝑙𝑖𝑓𝑙3) ;
𝑑𝑥2
𝑑𝑡
=
1
∆𝑒
(𝑒11𝑙2 − 𝑒21𝑙1);
𝑑𝑥3
𝑑𝑡
=
𝑒32
∆𝑒𝑒33
(𝑒21𝑙1 − 𝑒11𝑙2) +
1
𝑒33
𝑙3;
𝑑𝑥4
𝑑𝑡
=
1
𝑒44
(𝐷′′
̃ (𝑥2, 𝑥4) + 𝐷𝑐𝑜𝑜𝑙 − 𝐷𝑏); (1)
where the functions of the state variables included in the right-hand sides of (1) are defined by (2):
𝑒11 = 𝜌′
− 𝜌′′
; 𝑒12 = 𝑉
𝑠.𝑜.
𝜕𝜌′
𝜕𝑥2
+ (𝑉
𝑜 − 𝑉
𝑠.𝑜.)
𝜕𝜌′
𝜕𝑥2
; 𝑒21 = 𝜌′
𝑖′
− 𝜌′′
𝑖′′
;
𝑒22 = 𝑉
𝑠.𝑜. (𝑖′ 𝜕𝜌′
𝜕𝑥2
− 𝑝′ 𝜕𝜌′
𝜕𝑥2
) +
𝜕𝜌′
𝜕𝑥2
(𝑉
𝑜 − 𝑉
𝑠.𝑜.) (𝑖′′ 𝜕𝜌′′
𝜕𝑥2
+ 𝑝′′ 𝜕𝜌′′
𝜕𝑥2
) + 𝐺𝑜𝑐𝑝
𝜕𝜗′′
𝜕𝑥2
;
𝑒32 = (𝑝′ 𝜕𝜌′
𝜕𝑥2
− 𝑥3𝑟
𝜕𝜌′
𝜕𝑥2
) (1 − 𝜑
̅𝑙𝑖𝑓)𝑉𝑙𝑖𝑓 + ((1 − 𝑥3)𝑟
𝜕𝜌′′
𝜕𝑥2
+ 𝑝′′ 𝜕𝜌′′
𝜕𝑥2
) ∙ 𝜑
̅𝑙𝑖𝑓𝑉𝑙𝑖𝑓 + (𝜌′′
+
(𝜌′
− 𝜌′′)𝑥3)𝑟𝑉𝑙𝑖𝑓
𝜕𝜑
̅𝑙𝑖𝑓
𝜕𝑥2
+ 𝐺𝑙𝑖𝑓𝑐𝑝
𝜕𝜗′′
𝜕𝑥2
;
𝑒33 = ((1 − 𝑥3)𝜌′′
+ 𝑥3𝜌′
)𝑟𝑉𝑙𝑖𝑓
𝜕𝜑
̅𝑙𝑖𝑓
𝜕𝑥3
; 𝑒44 = 𝑉
𝑝
𝜕𝜌4(𝑥4,𝜗𝜌=𝜗𝜌
0)
𝜕𝑥4
;
∆𝑒 = 𝑒11𝑒22 − 𝑒12𝑒21; 𝑒̃1 = (
𝜕𝜑
̅𝑙𝑖𝑓
𝜕𝑥2
−
𝑒32
𝑒33
𝜕𝜑
̅𝑙𝑖𝑓
𝜕𝑥3
) 𝑉𝑙𝑖𝑓; 𝑟 = 𝑖′′
− 𝑖′
;
𝑉
𝑠.𝑜. = 𝑉
𝑠.𝑜.
0
+ 𝑉𝑙𝑜𝑤 + (1 − 𝜑
̅𝑙𝑖𝑓)𝑉𝑙𝑖𝑓 + 𝐹𝑒.𝑚.𝑥1; 𝑙1 = 𝑢1𝐷
̃′′(𝑥2, 𝑥4) − 𝐷𝑐𝑜𝑛 − 𝐷𝑝𝑢𝑟;
𝑙2 = 𝑢2 + 𝑢1𝑖𝑓.𝑤. − (𝐷
̃′′(𝑥2, 𝑥4) + 𝐷𝑐𝑜𝑛)𝑖′′(𝑥2) − 𝐷𝑝𝑢𝑟𝑖′(𝑥2);

1511
𝑙3 = 𝑢2 − 𝑥3𝑟(𝑥2)𝐷𝑙𝑜𝑤(𝑥2, 𝑥3); 𝐷
̃′′(𝑥2, 𝑥4) = 𝐷
̃′′
√
𝜌
𝜌(𝑥4,𝜗𝑏
0)(𝑥2−𝑥4)
𝜌
𝜌(𝑥4,𝜗𝜌
0)(𝑥2
0−𝑥4
0)
. (2)
where 𝜌 is density; 𝑖 is enthalpy; 𝑉 is volume; 𝐺 is metal mass of heating surfaces; 𝜗 is temperature;
functions 𝑖′
, 𝑖′′
and 𝜌′
, 𝜌′′
describe the state of water and steam on the saturation line (these are functions of
the steam pressure in the boiler drum 𝑥2); the indices “f.w.”, “low”, “lif”, “o”, “p” indicate that the
parameter belongs to the feed water, the system of lowering or lifting pipes, the entire CC or the output
package of ES; 𝐷𝑝𝑢𝑟, 𝐷𝑐𝑜𝑜𝑙, 𝐷𝑏 - accordingly, the water consumption for purging and cooling, as well as
steam from the boiler; 𝐹𝑒.𝑚. - evaporation mirror area; the index “0” indicates the value of the parameter in
the nominal mode; function 𝜑𝑙𝑖𝑓
̅̅̅̅̅ is a well - known function describing the average volume vapor content in
lifting pipes.
If we neglect the inertia of the steam lines connecting the boiler to the turbine or to the main steam
main (MSM), then the steam flow 𝐷𝑏 is equal to the steam flow through the valves regulating its flow to
turbine 𝐷𝑏 = 𝐷𝑡 or MSM 𝐷𝑏 = 𝐷𝑚. The task assigned to these control valves is to maintain the required
steam flow rate. For example, in the case of a steam generator operating as part of a power unit, this
consumption depends on the number and power of consumers connected to the unit; when several steam
generators are operating on an MSM, it is set by an upper-level control system that coordinates the control
systems of individual steam generators. In any case, from the point of view of the control system of the steam
generator, 𝐷𝑏 is a piecewise constant disturbance, because in comparison with the time of regulation of steam
pressure and water level in the boiler, flow control is carried out very quickly [16], [17].
The flow rates of steam taken into the condenser and water injected into the desuperheater and
removed from the CC during continuous blowdown are regulated. At the same time, the time of their
regulation is considered to be negligible compared to the time of the processes of interest to us. Thus, it is
considered that at any moment of time these costs take on fixed values, and when constructing the control
system for the steam-water path of the steam generator, they are considered as measurable piecewise constant
perturbations [18].
In view of the foregoing, we formulate the control problem as follows: for a steam generator
described by the system of (1), assuming that the measured disturbances 𝐷𝑏, 𝐷𝑝𝑢𝑟, 𝐷𝑐𝑜𝑛 and 𝐷𝑐𝑜𝑜𝑙, act on the
steam generator, find the control law 𝑢 that ensures the translation of the representative point (RP) of the
closed control system from an arbitrary initial state belonging to the area of permissible change of coordinates
to a given state (𝑥4 = 𝑥4
0
and 𝑥1 = 0), in which stabilization of the steam pressure at the outlet of the steam
generator ES (𝑥4 = 𝑥4
0
) and the water level in the boiler drum (𝑥1 = 0) is ensured. In accordance with the
ADAR method [19]–[22], we introduce into consideration the following parallel set of macro-variables:
𝜓1 = 𝛽11𝑥1 + 𝛽12 (𝐷′′
̃ (𝑥2, 𝑥4) + 𝐷𝑐𝑜𝑜𝑙 − 𝐷𝑏 + 𝑒44𝛼3(𝑥4 − 𝑥4
0
)) ;
𝜓2 = 𝛽21𝑥1 + 𝛽22 (𝐷′′
̃ (𝑥2, 𝑥4) + 𝐷𝑐𝑜𝑜𝑙 − 𝐷𝑏 + 𝑒44𝛼3(𝑥4 − 𝑥4
0
)) ; (3)
such that the relations 𝜓𝑖 = 0 are the solution of the system of functional equations:
𝜓1(𝑡) + 𝛼1𝜓1 = 0,
𝜓2(𝑡) + 𝛼2𝜓2 = 0, (4)
where 𝛼1, 𝛼2 - coefficients. Under the conditions 𝛼1 > 0, 𝛼2 > 0 the trivial solution of (4) 𝜓1 = 0, 𝜓2 = 0
will be asymptotically stable. By jointly solving (1), (3), (4), we find expressions for external controls 𝑢1 and
𝑢2:
𝑢1 =
1
∆𝐹
(
𝑓3(𝑥2,𝑥4)+𝐷𝑐𝑜𝑜𝑙−𝐷𝑏
𝜕𝐷′′
̃
𝜕𝑥2
𝑒44
(
𝜕𝐷′′
̃ (𝑥2,𝑥4)
𝜕𝑥4
+ 𝑒44𝛼3) − 𝑓1𝑓6 + 𝑓3𝑓4 +
1
∆𝛽
𝜕𝐷′′
̃ (𝑥2,𝑥4)
𝜕𝑥2
(𝑘1𝑓3 +
𝑘2𝑓6
𝜕𝐷′′
̃ (𝑥2,𝑥4)
𝜕𝑥2
)) ; (5)

 ISSN: 2088-8708
1512
𝑢2 =
1
∆𝐹
(
𝑓2(𝐷′′
̃ (𝑥2,𝑥4)+𝐷𝑐𝑜𝑜𝑙−𝐷𝑏)
𝜕𝐷′′
̃ (𝑥2,𝑥4)
𝜕𝑥2
𝑒44
(
𝜕𝐷′′
̃ (𝑥2,𝑥4)
𝜕𝑥4
+ 𝑒44𝛼3) − 𝑓1𝑓5 + 𝑓3𝑓4 +
1
∆𝛽
𝜕𝐷′′
̃ (𝑥2,𝑥4)
𝜕𝑥2
(𝑘1𝑓2 +
𝑘2𝑓5
𝜕𝐷′′
̃ (𝑥2,𝑥4)
𝜕𝑥2
)) ; (6)
where
𝑓1 = −
1
𝐹𝑒.𝑚.
(
𝑒22−𝑒12𝑖′′(𝑥2)−𝑒1
̃(𝑒21−𝑒11𝑖′′(𝑥2))
∆𝑒
𝐷′′
̃ (𝑥2, 𝑥4) + 𝐷𝑐𝑜𝑛) +
𝑒22−𝑒12𝑖′′(𝑥2)−𝑒1
̃(𝑒21−𝑒11𝑖′′(𝑥2))
∆𝑒
𝐷𝑝𝑢𝑟 +
𝑥3𝑟(𝑥2)
𝑒33
̅̅̅̅̅̅(𝑥2,𝑥3)
𝜕𝑥3
𝑉𝑙𝑖𝑓𝐷𝑙𝑜𝑤(𝑥2, 𝑥3);
𝑓2 =
1
𝐹𝑒.𝑚.
𝑒22−𝑒12𝑖𝑓.𝑤.(𝑥2)−𝑒1
̃(𝑒21−𝑒11𝑖𝑓.𝑤.)
∆𝑒
;
𝑓3 =
1
𝐹𝑒.𝑚.
(
𝑒12−𝑒1
̃𝑒11
∆𝑒
−
1
𝑒33
̅̅̅̅̅̅(𝑥2,𝑥3)
𝜕𝑥3
𝑉𝑙𝑖𝑓);
𝑓4 =
𝑒12−𝑒11𝑖′′(𝑥2)
∆𝑒
(𝐷′′
̃ (𝑥2, 𝑥4) + 𝐷𝑐𝑜𝑛) +
𝑒21−𝑒11𝑖′′(𝑥2)
∆𝑒
𝐷𝑝𝑢𝑟;
𝑓5 =
𝑒11𝑖𝑓.𝑤.−𝑒21
∆𝑒
; 𝑓6 =
𝑒11
∆𝑒
. (7)
𝑘1 = 𝛽11𝛼2𝜓2 − 𝛽21𝛼1𝜓1; 𝑘2 = 𝛽12𝛼2𝜓2 − 𝛽22𝛼1𝜓1;
∆𝛽 = 𝛽11𝛽22 − 𝛽12𝛽21; ∆𝐹 = 𝑓2𝑓6 − 𝑓3𝑓5; (8)
At the end of transient processes in system (4), the duration of which is determined by the coefficients
𝛼𝑖 , 𝑖 = 1.2 the equalities 𝜓𝑖 = 0 will be fulfilled. If the matrix 𝐵 = [
𝛽11 𝛽12
𝛽21 𝛽22
] is non-degenerate, this will
mean that the following conditions are met [23]:
𝑥1 = 0, 𝐷′′
̃ (𝑥2, 𝑥4)+𝐷𝑐𝑜𝑜𝑙 − 𝐷𝑏 = 𝑒44𝛼3(𝑥4 − 𝑥4
0), (9)
The first of which corresponds to the stabilization of the water level in the boiler drum. When the RP system
hits the intersection of the manifolds 𝜓𝑖 = 0, 𝑖 = 1.2 the dynamic decomposition of the phase space of the
system (1) occurs. The further behavior of the closed system will be described by the first order equation:
𝑥̇(𝑡) = −𝛼3(𝑥4, 𝑥4
0
), (10)
According to which, if the condition 𝛼3 > 0 is met, the variable 𝑥4 after a time of the order of 3 𝛼3
⁄ will take
on a well-defined value 𝑥4
0
(the pressure at the outlet of the ES is equal to the specified one).
Controls (5), (6) ensure that the representative point of the closed control system is brought to the
intersection of manifolds 𝜓1 = 0 ∩ 𝜓2 = 0, where the condition for stabilization of the water level in the
boiler drum (𝑥1 = 0) is ensured and its subsequent movement to the intersection with the manifold 𝜓3 = 0:
𝜓3 = 𝑥4 − 𝑥4
0
, where the condition 𝑥4 = 𝑥4
0
- the steam pressure at the outlet of the steam generator is equal
to the set value.
Controls (5), (6) guarantee for 𝛼𝑖 > 0, 𝑖 = 1,2,3 the asymptotic stability of the motion of a closed
control system to the intersection of manifolds 𝜓𝑖 > 0, 𝑖 = 1,2,3. The motion of a closed system along the
intersection of manifolds is described by a first-order equation. In order to write the decomposed model, it is
necessary to substitute in the equation describing the change in the mass vapor content 𝑥3 of the closed
control system (1), (5), (6), instead of the variables 𝑥1, 𝑥2, 𝑥4 their values 𝑥1
0
, 𝑥2
0
, 𝑥4
0
, which they take at the
intersection of the manifolds [24]. In this case, the value of 𝑥2
0
is found by solving the equation.
𝐷′′
̃ (𝑥2, 𝑥4)+𝐷𝑐𝑜𝑜𝑙 − 𝐷𝑏 = 0.
The equation of the decomposed system will have the form:

1513
𝑑𝑥3𝜓
𝑑𝜏
=
1
𝑒33(𝑥2
0,𝑥3𝜓)
(
1
∆𝐹
(𝑓1𝑓5 − 𝑓2𝑓4) − 𝑥3𝜓𝑟(𝑥2
0
)𝐷𝑙𝑜𝑤(𝑥2
0
, 𝑥3𝜓)); (11)
where the functions ∆𝐹 and 𝑓𝑖 are calculated by formulas (8), (7) provided that 𝑥1 = 0; 𝑥4 = 𝑥4
0
; 𝑥2 = 𝑥2
0
=
𝑓(𝑥4
0
, 𝐷𝑏 − 𝐷𝑐𝑜𝑜𝑙). Thus, the right-hand side of (11) is a function of 𝑥3𝜓. The form of this function at
different load values 𝛾: 𝛾 = (𝐷𝑏 − 𝐷𝑐𝑜𝑜𝑙) 𝐷0
′′
̃
⁄ is shown in Figure 2.
Figure 2. Graph of the behavior of the right side of (11)
As can be seen from this graph, the function on the right in (11), under any loads, is monotonically
decreasing in almost the entire physically permissible range of the 𝑥3 coordinate change and intersects the
abscissa axis at a single point. The area of monotony lies in the interval: 0.01 ÷ 1 at loads 𝛾 = 0.5; 0.02 ÷ 1
at loads 𝛾 = 0.8; 0.045 ÷ 1 at loads 𝛾 = 1.1. It should be noted that the lower limit of the range is
approximately an order of magnitude less than the steady-state value of the vapor content corresponding to
the same load value and is almost never reached in real operating conditions. Thus, there is a unique
asymptotically stable equilibrium position, and the motion of a closed system along the intersection of
manifolds is asymptotically stable.
On Figures 3 to 6 show the results of modeling the system (1) with control laws (5), (6) at various
loads: I - 𝐷𝑏 = 0.7𝐷𝑏
0
, II - 𝐷𝑏 = 0.8𝐷𝑏
0
, III -𝐷𝑏 = 0.95𝐷𝑏
0
. The simulation was carried out for a steam
generator with a steam capacity of 670 t/h, all the necessary parameters of which are given in the technical
guidelines [25]–[28]. In this case, the regulator had the following adjustment factors:
𝛽11 = 1, 𝛽12 = 1, 𝛽21 = 40, 𝛽22 = 1, 𝛼1 = 1 30
⁄ , 𝛼2 = 1 32
⁄ , 𝛼3 = 3 2
⁄ .
The task for the steam pressure at the outlet of the steam generator, respectively, is equal to 𝑥4
0
=
13.73 MPa. As can be seen from the graphs, the control laws (5), (6) ensure the stabilization of the steam
pressure at the outlet of the steam generator in Figure 6 and the water level in the drum as shown in Figure 3
over a wide range of varying loads.
Control with an incompletely measurable state vector. Let us now discuss the following question. If
the level deviation in the boiler drum 𝑥1 and the steam pressure at the outlet of the steam generator 𝑥4 are
directly measurable, then it is difficult to directly measure the mass vapor content at the outlet of the lifting
pipes. Although such techniques exist, their use requires additional funds. Below we will consider exactly
how the control law (5), (6) should be modified so that it does not contain an immeasurable variable 𝑥3.
In [25], it is noted that the linear model of the CC of the steam generator, written on the basis of the
first three equations of the system (1), has the following poles:
𝑝1 = 0;
𝑝2 = − 𝛽1 𝐴;
⁄
𝑝3 = −
𝑟(𝑥2
0
)
𝑒33(𝑥2
0,𝑥3
0)
(𝐷𝑙𝑜𝑤(𝑥2
0
, 𝑥3
0
) + 𝑥3
0 𝜕𝐷𝑙𝑜𝑤(𝑥2,𝑥3)
𝜕𝑥3
|𝑥2 = 𝑥2
0
, 𝑥3 = 𝑥3
0
),
where 𝛽1 − 𝐷0
′′ 𝜕𝑖′′
𝜕𝑥2
|𝑥2 = 𝑥2
0
+ 𝐷𝑝𝑢𝑟
0 𝜕𝑖′
𝜕𝑥2
| 𝑥2 = 𝑥2
0
;

 ISSN: 2088-8708
1514
𝐴 = 𝑉𝑏.𝑜.
0
(𝜌′ 𝜕𝑖′
𝜕𝑥2
+
𝜌′′
𝜌′−𝜌′′ |𝑥2 = 𝑥2
0
+ (𝑉 − 𝑉𝑏.𝑜.
0
) (𝜌′′ 𝜕𝑖′′
𝜕𝑥2
+
𝜌′′
𝜌′−𝜌′′ 𝑟) |𝑥2 = 𝑥2
0
+ 𝐺0𝑐𝑝
𝜕𝜗′′
𝜕𝑥2
|𝑥2 = 𝑥2
0
).
Figure 3. Changing the level Figure 4. Changing the steam pressure in the drum
Figure 5. Change in vapor content Figure 6. Change in steam pressure at the steam
generator outlet
The value of parameter 𝐴 is always positive, and parameter 𝛽1 can be either positive or negative. If
the purge value 𝑝 = 𝐷𝑝𝑢𝑟
0
𝐷0
′′
⁄ satisfies the condition
𝑝 < −
𝜕𝑖′′
𝜕𝑥2
𝜕𝑖′
𝜕𝑥2
⁄ ,, (12)
pole 𝑝2 > 0. In the absence of purging, condition (12) is fulfilled at pressures greater than 3 MPa. The value
of the third pole is always negative and for the steam generator modeled above at the nominal operating
mode is equal to 𝑝3 = −0.1205. It characterizes the time of the transition process in the variable 𝑥3. This
time is small compared to the time of regulating the pressure and level in the boiler [26]. Therefore, we can
use the function in the control laws (5), (6) instead of the value of 𝑥3.
𝑥3 = 𝐹(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟), (13)
Found on the basis of the steady state equations of the steam generator. Unfortunately, it is not possible to
obtain an expression for 𝑥3 explicitly, because the steady-state equations are nonlinear with respect to 𝑥3,
therefore it is also not possible to obtain an expression for the function 𝐹(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟) analytically. In
this paper, the function 𝐹(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟) was approximated by a polynomial.
𝑥3(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟) = 𝛽0 + 𝛽1 (
𝑥2
106) + 𝛽2 (
𝑥2
106)
2
+ 𝛽3 (
𝑥2
106)
3
, (14)
whose coefficients 𝛽𝑖 = 𝛽𝑖(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟).
Let’s write down the steady-state equations for the steam generator described by the model (1).
𝑢1 − 𝐷
̃′′(𝑥2, 𝑥4) − 𝐷𝑐𝑜𝑛 − 𝐷𝑝𝑢𝑟 = 0;
𝑢2 + 𝑢1𝑖𝑓.𝑤. − (𝐷
̃′′(𝑥2, 𝑥4) − 𝐷𝑐𝑜𝑛 − 𝐷𝑝𝑢𝑟)𝑖′′(𝑥2) − 𝐷𝑝𝑢𝑟𝑖′(𝑥2) = 0;

1515
𝑢2 − 𝑥3𝑟(𝑥2)𝐷𝑙𝑜𝑤(𝑥2, 𝑥3) = 0. (15)
The third of (15) can be rewritten as (16).
𝑥3𝑟(𝑥2)𝐷𝑙𝑜𝑤(𝑥2, 𝑥3) = (𝐷
̃′′(𝑥2, 𝑥3) + 𝐷𝑐𝑜𝑛)(𝑖′′(𝑥2) − 𝑖𝑓.𝑤.) + 𝐷𝑝𝑢𝑟(𝑖′(𝑥2) − 𝑖𝑓.𝑤.). (16)
Substituting into (16) the expressions for the steam flow rate in the downcomers 𝐷𝑙𝑜𝑤 and the average
volumetric steam content 𝜑
̅𝑙𝑖𝑓.
𝐷𝑙𝑜𝑤 = √
2𝜌′(𝑥2)𝑓𝑙𝑖𝑓(𝜌′(𝑥2) − 𝜌′′(𝑥2))𝑔𝜑
̅𝑙𝑖𝑓𝑉𝑙𝑖𝑓
𝑘
;
𝜑
̅𝑙𝑖𝑓 =
𝜌′(𝑥2)
𝜌′(𝑥2) − 𝜌′′(𝑥2)
[1 −
𝜌′
′(𝑥2)
(𝜌′(𝑥2) − 𝜌′′(𝑥2))𝑥3
𝑙𝑛 (1 +
𝜌′(𝑥2) + 𝜌′′(𝑥2)
𝜌′′(𝑥2)
𝑥3)] ;
and introducing the notation
𝐷(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟) = 𝐷
̃′′(𝑥2, 𝑥4) + 𝐷𝑐𝑜𝑛 +
𝑖′(𝑥2)−𝑖𝑓.𝑤.
𝑖′′(𝑥2)−𝑖𝑓.𝑤.
𝐷𝑝𝑢𝑟,
We will get
𝑥3
2
−
𝜌′′𝑥3
𝜌′−𝜌′′ 𝑙𝑛 (1 +
𝜌′−𝜌′′
𝜌′′ 𝑥3) =
𝑘
2𝑔𝑓𝑙𝑖𝑓𝑉𝑙𝑖𝑓
(
𝑖′′−𝑖𝑓.𝑤.
𝜌′𝑟
𝐷(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟)). (17)
The last equation can be rewritten as (18).
𝑥3
2
[1 −
1
𝐴𝑥3
𝑙𝑛(1 + 𝐴𝑥3)] = 𝐵
𝑘
𝐷2
(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟), (18)
where
𝐴 =
𝜌′−𝜌′′
𝜌′′ , 𝐵 = (
𝑖′′−𝑖𝑓.𝑤.
𝜌′𝑟
)
2
.
To approximate 𝑥3 by the functional dependence
𝑥3 = ∑ 𝛽𝑖(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟) (
𝑥2
106
)
𝑖
3
𝑖=0
The (18) was solved with respect to 𝑥3 at a fixed value 𝐷 from the range (0.4 ÷ 1.4)𝐷(𝑥2
0
, 𝑥4
0
, 𝐷𝑐𝑜𝑛
0
, 𝐷𝑝𝑢𝑟
0
)
and changing 𝑥2 from 0.3𝑥2
0
to 1.3𝑥2
0
. Obtained in a table, the 𝑥3(𝑥2) function was then approximated by the
least squares method. Repeated several times for different - 𝐷(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟), this procedure gives tables
of functions 𝛽𝑖 (𝐷(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟)). Based on these tables, the functions 𝛽𝑖 (𝐷(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟)) were
then approximated by a 2nd order polynomial
𝛽𝑖 = 𝛽𝑖0 + 𝛽𝑖1𝐶 + 𝛽𝑖2𝐶2
,
where 𝐶 =
𝑘
𝐷2
(𝑥2, 𝑥4, 𝐷𝑐𝑜𝑛, 𝐷𝑝𝑢𝑟).
The computational experiment diagram is shown in Figure 7. During the computational experiment,
new control laws for drum steam boilers were developed and tested on the basis of their nonlinear
mathematical model, adequately describing the processes in a wide range of operating modes. The obtained
results allow us to conclude about the increase of efficiency and stability of steam boiler control when
applying the developed control laws.

 ISSN: 2088-8708
1516
Figure 7. Block diagram of the experiment in MATLAB Simulink
3. RESULTS AND DISCUSSION
Figures 8 to 11 show the results of comparative modeling of the system (1) with the control laws (5),
(6), for two cases: i) when a variable appears in the laws 𝑥3 and ii) when it is replaced by an estimate
obtained based on the calculation of the function (14). The simulation was carried out for a load of 80% of
the nominal and the same parameters of the steam generator and regulator as in the previous case. As can be
seen from the figures, the largest discrepancy is the graph of the level change. At the same time, the steady-
state deviation of the level is different from zero, which is explained by inaccuracies in the approximation of
the function (14). Figures 12 and 13 show graphs of changes in steam pressure at the outlet of the steam
generator and the water level in its drum with varying load: I - 𝐷𝑏 = 0.7𝐷𝑏
0
, II - 𝐷𝑏 = 0.8𝐷𝑏
0
, III - 𝐷𝑏 =
0.95𝐷𝑏
0
. For a regulator with an 𝑥3 rating.
Figure 8. Changing the level Figure 9. Changing the steam
pressure in the drum
Figure 10. Change in vapor content
Figure 11. Change in steam
pressure at the steam generator
outlet
Figure 12. Level change Figure 13. Steam pressure change at
the steam generator outlet

1517
4. CONCLUSION
In conclusion, this article highlights the significance of the integrated application of the synergetic
approach in enhancing the quality of intelligent steam generator control systems. By combining model-based
control, adaptive control, and artificial intelligence techniques, a more efficient and flexible control system
can be developed. The synergetic approach, which focuses on the interaction and collaboration of system
components, offers the potential for achieving higher performance compared to individual control methods.
The use of mathematical models, adaptive control mechanisms, and artificial intelligence algorithms enables
improved steam generation efficiency, optimized energy consumption, enhanced system stability and
reliability, and adaptability to varying operating conditions and disturbances.
The findings of this research provide valuable insights for engineers, researchers, and professionals
involved in the design and implementation of intelligent steam generator control systems. By embracing the
synergetic approach, significant enhancements in control quality can be achieved, leading to optimal
operation and maximum efficiency of power plants. Further research in this area could explore the
application of synergetic synthesis methods for optimal control quality of steam generators. Additionally, the
importance of modeling and simulation in evaluating control methods and strategies should be emphasized,
allowing for iterative optimization and continuous improvement of the control system based on simulation
experiments.
REFERENCES
[1] M. A. Awadallah and H. M. Soliman, “A neuro-fuzzy adaptive power system stabilizer using genetic algorithms,” Electric Power
Components and Systems, vol. 37, no. 2, pp. 158–173, Jan. 2009, doi: 10.1080/15325000802388740.
[2] D. M. Umurzakova, “Mathematical modeling of transient processes of a three-pulse system of automatic control of water supply
to the steam generator when the load changes,” in 2020 Dynamics of Systems, Mechanisms and Machines (Dynamics), Nov. 2020,
pp. 1–4, doi: 10.1109/Dynamics50954.2020.9306117.
[3] D. Umurzakova, “System of automatic control of the level of steam power generators on the basis of the regulation circuit with
smoothing of the signal,” IIUM Engineering Journal, vol. 22, no. 1, pp. 287–297, Jan. 2021, doi: 10.31436/iiumej.v22i1.1415.
[4] J. Devooght, “Model uncertainty and model inaccuracy,” Reliability Engineering & System Safety, vol. 59, no. 2, pp. 171–185,
Feb. 1998, doi: 10.1016/S0951-8320(97)00137-3.
[5] M. F. Farhan, N. S. A. Shukor, M. A. Ahmad, M. H. Suid, M. R. Ghazali, and M. F. Mat Jusof, “A simplify fuzzy logic controller
design based safe experimentation dynamics for pantograph-catenary system,” Indonesian Journal of Electrical Engineering and
Computer Science (IJEECS), vol. 14, no. 2, pp. 903–911, May 2019, doi: 10.11591/ijeecs.v14.i2.pp903-911.
[6] R. Fullér, Introduction to neuro-fuzzy systems. Heidelberg: Physica-Verlag HD, 2000.
[7] S. I. Xakimovich and U. D. Maxamadjonovna, “Mathematical modeling of transient processes of the automatic control system of
water level in the steam generator,” Universal Journal of Mechanical Engineering, vol. 7, no. 4, pp. 139–146, Jul. 2019, doi:
10.13189/ujme.2019.070401.
[8] I. X. Siddikov and D. M. Umurzakova, “Synthesis algorithm for fuzzy-logic controllers,” in 2020 Dynamics of Systems,
Mechanisms and Machines (Dynamics), Nov. 2020, pp. 1–5, doi: 10.1109/Dynamics50954.2020.9306165.
[9] S. I. Xakimovich and U. D. Maxamadjonovna, “Synthesis of adaptive control systems of a multidimensional discrete dynamic
object with a forecasting models,” in 2019 International Conference on Information Science and Communications Technologies
(ICISCT), Nov. 2019, pp. 1–5, doi: 10.1109/ICISCT47635.2019.9012033.
[10] H. Z. Igamberdiev and T. V. Botirov, “Algorithms for the synthesis of a neural network regulator for control of dynamic
objects,” in World Conference Intelligent System for Industrial Automation, 2021, pp. 460–465, doi: 10.1007/978-3-030-
68004-6_60.
[11] H. Z. Igamberdiev and U. F. Mamirov, “Regular algorithms for the parametric estimation of the uncertain object control,” in
World Conference Intelligent System for Industrial Automation, 2021, pp. 322–328.
[12] A. A. Kolesnikov, “Introduction of synergetic control,” in 2014 American Control Conference, Jun. 2014, pp. 3013–3016, doi:
10.1109/ACC.2014.6859397.
[13] J. Legault, R. S. Langley, and J. Woodhouse, “Physical consequences of a nonparametric uncertainty model in structural
dynamics,” Journal of Sound and Vibration, vol. 331, no. 25, pp. 5469–5487, Dec. 2012, doi: 10.1016/j.jsv.2012.07.017.
[14] M. Moutchou, A. Jbari, and Y. Abouelmahjoub, “Implementation of reduced induction machine fuzzy logic control based on
dSPACE-1104 R&D controller board,” International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 12, no. 2,
pp. 1015–1023, Jun. 2021, doi: 10.11591/ijpeds.v12.i2.pp1015-1023.
[15] M. Sh. Aziz and A. G. Abdullah, “Hybrid control strategies of SVC for reactive power compensation,” Indonesian Journal of
Electrical Engineering and Computer Science (IJEECS), vol. 19, no. 2, pp. 563–571, Aug. 2020, doi: 10.11591/ijeecs.v19.i2.pp563-
571.
[16] M. I. Berbek and A. A. Oglah, “Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management
in internet congestion,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 26, no. 1, pp. 229–
242, Apr. 2022, doi: 10.11591/ijeecs.v26.i1.pp229-242.
[17] B. R. Mace, K. Worden, and G. Manson, “Uncertainty in structural dynamics,” Journal of Sound and Vibration, vol. 288, no. 3,
pp. 423–429, Dec. 2005, doi: 10.1016/j.jsv.2005.07.014.
[18] T. Nilsen and T. Aven, “Models and model uncertainty in the context of risk analysis,” Reliability Engineering & System Safety,
vol. 79, no. 3, pp. 309–317, Mar. 2003, doi: 10.1016/S0951-8320(02)00239-9.
[19] N. A. Niyozmatova, N. S. Mamatov, B. I. Otaxonova, A. N. Samijonov, and K. K. Erejepov, “Classification based on decision
trees and neural networks,” in 2021 International Conference on Information Science and Communications Technologies
(ICISCT), Nov. 2021, pp. 01–04, doi: 10.1109/ICISCT52966.2021.9670345.
[20] H. Peng, X. Zhu, L. Yang, and G. Zhang, “Robust controller design for marine electric propulsion system over controller area
network,” Control Engineering Practice, vol. 101, Aug. 2020, doi: 10.1016/j.conengprac.2020.104512.
[21] S. Kaur and K. K. Chahal, “Prediction of Chikungunya disease using PSO-based adaptive neuro-fuzzy inference system model,”
International Journal of Computers and Applications, vol. 44, no. 7, pp. 641–649, 2022, doi: 10.1080/1206212X.2020.1870196.

 ISSN: 2088-8708
1518
[22] C. Soize, “A comprehensive overview of a non-parametric probabilistic approach of model uncertainties for predictive models in
structural dynamics,” Journal of Sound and Vibration, vol. 288, no. 3, pp. 623–652, Dec. 2005, doi: 10.1016/j.jsv.2005.07.009.
[23] C. Soize, “A nonparametric model of random uncertainties for reduced matrix models in structural dynamics,” Probabilistic
Engineering Mechanics, vol. 15, no. 3, pp. 277–294, Jul. 2000, doi: 10.1016/S0266-8920(99)00028-4.
[24] C. Soize, “Random matrix theory and non-parametric model of random uncertainties in vibration analysis,” Journal of Sound and
Vibration, vol. 263, no. 4, pp. 893–916, Jun. 2003, doi: 10.1016/S0022-460X(02)01170-7.
[25] V. V. Klimenko, V. S. Luferov, and A. G. Stefantsov, “Neuro-fuzzy models for operational forecasting of electric energy
consumption of the urban system,” AIP Conference Proceedings, 2021, doi: 10.1063/5.0067549.
[26] N. Yusupbekov, H. Igamberdiev, and U. Mamirov, “Algorithms for robust stabilization of a linear uncertain dynamic object based
on an iterative algorithm,” in Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation:
Proceedings of the INFUS 2021 Conference, 2022, pp. 225–232, doi: 10.1007/978-3-030-85626-7_28.
[27] N. R. Yusupbekov, H. Z. Igamberdiev, O. O. Zaripov, and U. F. Mamirov, “Stable iterative neural network training algorithms
based on the extreme method,” in International Conference on Theory and Applications of Fuzzy Systems and Soft Computing,
2021, pp. 246–253, doi: 10.1007/978-3-030-64058-3_30.
[28] X. Zhu and W. Li, “Takagi–Sugeno fuzzy model based shaft torque estimation for integrated motor–transmission system,” ISA
Transactions, vol. 93, pp. 14–22, Oct. 2019, doi: 10.1016/j.isatra.2019.03.002.
BIOGRAPHIES OF AUTHORS
Siddikov Isamiddin He received his degree in electrical engineering with a
degree in automation and telemechanics in 1976 from the Tashkent Polytechnic Institute,
Tashkent, Uzbekistan. In 1989 he defended his Ph.D. thesis in the specialty of control in
technical systems. In 2016 he defended his doctoral thesis in the specialty "Intellectualization
of control processes for dynamic objects and technological processes." He is currently a
professor at the Tashkent State Technical University named after Islam Karimov. Under his
leadership, 17 PhDs were trained. His research interests include the intellectualization of
control processes for non-linear continuous-discrete dynamic objects, and the developed
methods, and models used in the field of automation of electric power facilities, oil and gas,
chemical-technological industries, and the light industry. In addition, he is a reviewer of
leading scientific journals such as “Chemical Technology. Control and management” and
“Technical science and innovation”. He is the author or co-author of more than 150 refereed
journals and conference articles, 7 monographs and 4 textbooks, 28 scientific articles indexed
in the Scopus database (Elsevier). He can be contacted at email: isamiddin54@gmail.com.
Umurzakova Dilnoza holds a PhD in technical sciences from the National
Research University “Tashkent Institute of Irrigation and Agricultural Mechanization
Engineers”. She is currently an assistant professor at the Fergana branch of the Tashkent
University of Information Technologies named after Muhammad al-Khwarizmi. She can be
contacted at umurzakovadilnoz@gmail.com.

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