=Paper=
{{Paper
|id=Vol-2608/paper37
|storemode=property
|title=Decision support system for microclimate control at large industrial enterprises
|pdfUrl=https://ceur-ws.org/Vol-2608/paper37.pdf
|volume=Vol-2608
|authors=Natalia Pankratova,Petro Bidyuk,Igor Golinko
|dblpUrl=https://dblp.org/rec/conf/cmis/PankratovaBG20
}}
==Decision support system for microclimate control at large industrial enterprises==
https://ceur-ws.org/Vol-2608/paper37.pdf
Decision Support System for Microclimate
Control at Large Industrial Enterprises
Nataliya Pankratova1[0000-0002-6372-5813], Petro Bidyuk2[0000-0002-7421-3565],
Igor Golinko3[0000-0002-7640-4760]
1, 2, 3
Igor Sikorsky Kyiv Polytechnic Institute, Peremogy str. 37, 03056 Kyiv, Ukraine
1
natalidmp@gmail.com,
2
pbidyuke_00@ukr.net,
3
conis@ukr.net
Abstract. The structure and mathematical support for the climate control sys-
tem for large enterprises is proposed. The developed air conditioning control
algorithms and multifunctional software are used at the middle level of an en-
terprise management using decision-making system. Criteria are defined and
algorithms for optimization and adaptation of control systems for industrial air
conditioners are proposed. The use of the proposed decision-making system can
improve efficiency of the functioning of the microclimate systems of industrial
premises when changing their operating conditions. The developed models,
methods and control algorithms are recommended for use at the stage of design,
commissioning and operation of industrial air conditioners at the middle level
of operational production management.
Keywords: integrated enterprise management system, manufacturing execution
system, automatic control system, industrial air conditioning, decision-making
system, mathematical model.
1 Introduction
Modern production processes place high demands on industrial air conditioning
systems. The quality of the product and the reduction in its cost [1] depend to a large
extent on the microclimate of industrial premises. The maximum effect of the indus-
trial air conditioners automation is achieved by integrating the air conditioning control
system into the enterprise management system.
The world practice of introducing integrated enterprise management systems
shows a significant increase in the efficiency of their work by reducing: energy costs,
production downtime, optimal distribution of material and energy flows, the use of
hidden reserves. Modern automation systems are integrated and include control sub-
systems interconnected by functions and levels. The architecture of the modern inte-
grated enterprise management system is shown in Fig. 1. The functions of the first
three levels are implemented by the automatic control system (ACS) of technological
processes, which ensures optimal flow of the technological process in the workshop
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
or on a separate production area. At the fourth level of enterprise management, a
manufacturing execution system (MES) is used, which plays the role of an informa-
tion bridge between the supervisory control and data acquisition (SCADA) and the
enterprise resource planning (ERP) system in a single information space of the enter-
prise.
5. ERP
4. MES
3. SCADA
2. PLC
ACS
level
1. Sensor
Plant
Fig. 1. Integrated enterprise management system hierarchical architecture
At most enterprises, SCADA and ERP are implemented and operated. The next
step in improving the quality of products while reducing their cost is the introduction
of the MES for operational management of production. MES were first proposed by
the Manufacturing Enterprise Solutions Association (MESA) International in 1994.
MESA International has established a basic set of functions for MES, which has sub-
sequently been repeatedly adjusted. These results are published in a large number of
articles and manuals [2–4]. The most advanced ideas of integrated production were
approved by International Society of Automation (ISA) in the international standards
ISA – 88, ISA – 95, ISA – 106 [5–7]. The listed standards are the most significant for
the development of integrated enterprise management systems.
The developed international standards describe the basic rules for the operation of
MES, but do not disclose the mathematical support for the operation of specific sys-
tems, in particular for the climate control systems of industrial premises, which are
discussed here. In publication [8], a new concept for the automation of industrial air
conditioners was proposed. In [9], the principles of automation of industrial air condi-
tioners are considered, which provide an increase in the efficiency of the operation of
HVAC equipment through the use of control algorithms for air conditioners and mul-
tifunctional mathematical models of climatic equipment [8–12]. A literature review of
industrial air conditioning automation systems confirms that all the prerequisites for
integrating industrial air conditioning control systems into the enterprise management
system have been created.
2 Problem Statement
At all levels of integrated enterprise management, operator jobs are used in which
dispatching decision support system (DSS) are used [13]. The purpose of this research
is to develop the structure, software and functioning algorithms of the DSS to support
the microclimate in large industrial enterprises. The developed system will improve
the overall production efficiency when changing the operating conditions of industrial
rooms.
3 Decision Support System for Control the Microclimate
According to Fig. 1, DSS for the industrial air conditioners operational manage-
ment is realize at the MES level of enterprise management. The block diagram of the
developed DSS is shown in Fig. 2.
The proposed DSS is designed and implemented using the following system analysis
principles [14]: the hierarchy principle; optimal solutions principles; mathematical
models’ adaptability; identification and minimization of the uncertainties influence
that appear in the control system; the functional orthogonality principle and others.
So, the integrated enterprise management system architecture is built according to the
hierarchical principle, which assumes the management of several levels plant, united
by information links. The operational control system assumes the optimization control
actions, which ensures the air conditioning system optimization necessary to deter-
mine the acceptable temperature and humidity limits in an industrial room and of the
recirculation coefficient, which affects the economical use of energy resources by the
microclimate system. DSS provides the functions implementation providing for the
structural and parametric adaptation of mathematical models, thereby achieving their
high degree of adequacy to the object. Stochastic uncertainties in the form of random
external disturbances and measurement errors are minimized by using optimal filter-
ing methods for measurements at the stage of preparing data for modeling. The exist-
ing parametric uncertainty of the models is minimized by using alternative methods
for estimating the mathematical models’ parameters of the studied objects. The func-
tional orthogonality principle ensures the rational implementation of all the control
system basic functions and the elimination of their duplication. Thus, the use the sys-
tem analysis principles in the design of DSS makes it possible to improve quality of
the intermediate computation results, and the management quality in general.
ERP
Decision support system (MES level)
DB Simulation subsystem
Conditionally
permanent
Model of steam
chamber model
Spray-type
information
humidifier
humidifier
Air heater
Mixing
Cooler
model
model
model
model
Room
External
conditions
DB
Internal
perturbations Industrial air conditioner model
DB
Allowable
microclimate Dispatcher
Task solution subsystem
plant DB
Parameters Decision-making unit
and variables
plant DB Optimizing Adaptation
industrial air industrial air
Online informa- conditioning conditioning
task task Dialog
tion subsystem
Air conditioner evaluation
Information- efficiency unit
processing subsys-
tem
SCADA
Sensors PLC 1 Sensors PLC N Plant sensors
Air conditioning 1 … Air conditioning N
Plant
Fig. 2. DSS block diagram for microclimate management
At the lower level, local air conditioning control systems are implemented on pro-
gram logic controllers (PLC). The SCADA performs cyclical measurements of all
current process variables, including the microclimate of industrial premises. The in-
formation-processing subsystem DSS performs analysis of input information, replen-
ishes the database (DB) with new information and provides operational information
about the condition of industrial air conditioners: the disturbing effects magnitude;
operating modes of mixing chambers, water heaters, coolers, steam humidifiers, and
industrial rooms.
Using models of climatic equipment, the simulation subsystem implements a mod-
el of an industrial air conditioner. The task of control solving subsystem offers the
operator a set of possible scenarios for optimization and adaptive control of industrial
air conditioners that are used in production. The effectiveness of the proposed solu-
tions is evaluated according to the criteria chosen by the operator. The decisions made
by the operator in the form of PLC settings are transferred to the lower hierarchical
system control level for execution.
3.1 Mathematical model of industrial air conditioning
The methodology for implementing the air conditioner model was considered in
[10, 11]. In general, it is proposed to implement a model of an industrial air condi-
tioner in a state space
X AX BU W,
(1)
Y C X V.
The vectors of the variable statistical perturbations model W and V are determined in
the DSS using the Kalman filtering methods considered in [14, 15]. The matrices
content of deterministic component model (A, B, and C) depends on the technological
scheme for processing air by an industrial air conditioner. For example, the state ma-
trix A is determined by for:
a direct-flow conditioning system
A P1 0 0 0 0 0
CP1 0
A 0 A Pi 0 0 ,
0 CPi A PN 1 0
0 0 0 0 CPN 1 A PN
Pi
where A is the state matrix of the i-th equipment, N is the number of devices in
the technological chain;
a system with recirculation or heat recovery
A P1 0 0 0 0 C PN
CP1 A
P2 0
0 CP2 A Pi 0 0 0
A ;
0 0 CPi A Pi 1 0 0
CPi 1 A PN 1 0
0 0 0 0 CPN 1 A PN
multi-zone conditioning systems, the filling of matrix A must be analyzed addition-
ally, since it depends on the number of rooms with a microclimate system.
The control matrix B is formed on the basis of the existing climate equipment that is
involved in controlling the air conditioning. The observation matrix C is formed
based on the number of state variables that take part in controlling the air condition-
ing. Among the state variables that are necessarily involved in the management of
mandatory are temperature and humidity at the outlet of the air conditioner (or in-
doors).
A more detailed acquaintance with the methodology for the synthesis of industrial
air conditioners complex dynamic models can be found in [10, 11]. For control sys-
tems for industrial air conditioners, a synthesis procedure has been developed for a
multidimensional linear-quadratic digital controller (LQDC) [10], which takes into
account the logical switching of the climate equipment and allows the ACS of indus-
trial air conditioner to adapt to changing of dynamic properties plant. At the ASC-
level, direct digital control algorithms are used [9–11]. On a PLC, the control system
can be implemented using one-dimensional digital controllers (DC), or using multi-
dimensional LQDC. The use of ACS with LQDC improves the integral quality indica-
tors by 1.2 – 2.3 times in comparison with ACS where one-dimensional DC are used
[12].
Consider the tasks of operational management to optimize and adapt the ACS of
industrial air conditioners, which are implemented by the task solution sub-system.
3.2 Operational management for the optimization ACS air conditioning
The task of operational management to optimize the air conditioning system is
necessary to determine the acceptable boundaries of temperature and humidity in an
industrial room, as well as the recirculation coefficient (for air conditioners with re-
circulation), which affects the economical use of microclimate system energy.
As an example, consider an air conditioner with a steam humidifier and recircula-
tion. For this air conditioner, see Fig. 3 shows a h – d diagram of the preparation of
outdoor air with an inlet temperature Amin max min max
0 ... A0 , and moisture content d A0 ... d A0 .
Region V characterizes the permissible microclimate in an industrial room with a
min max min max
range of a given temperature ref ... ref and moisture content, dref ... dref . The
smaller is the area V, the higher are the production requirements for the indoor micro-
climate.
When processing outdoor air (point A or A*) to a predetermined value (point D or
*
D ), the minimum energy consumption of the air conditioner will be at the edges of
the area V boundary. Therefore, the larger is the area V, the less energy is needed to
prepare the outdoor air. Region V, on the other hand, is limited by production re-
quirements for indoor microclimate. To optimize the area of permissible microcli-
mate, the DB of the DSS should contain information on the permissible microclimate
parameters for all plants of production.
, h
III VIV VII
Amax
0 A*
max
mix
max
B*
ref
II C
min D V D*
ref
IV =100 %
I
min
mix B C*
Amin
0
A
min min max max
d Amin
0 d mix d ref d ref d mix d Amax
0 d
Fig. 3. H – d preparation diagram air
Optimization of the permissible microclimate in industrial premises of production
is carried out during the transition of the technological line to the manufacture of new
products and consists of the following steps:
1) the decision-making unit initiates a dialogue with the operator and offers accept-
able microclimate limits in the room for manufacturing of new products;
2) the simulation model of an industrial air conditioner is formed according to the
methodology given in [10];
3) the simulation subsystem performs the ambient air preparation quantitative model-
ing: the transition from point A (A*) to point D (D*);
4) then, the assessment is made of the industrial air conditioner the efficiency of which
is based on the criterion of minimizing energy consumption:
N
Qi min (2)
i 1
where, Qi is the i-th apparatus power consumption; for the water heat-transfer agent
Qi Gi ci i0 i , where Gi , ci are respectively the flow rate and the coolant heat ca-
pacity; i 0 , i are the heat-transfer agent temperatures at the inlet and the apparatus
outlet;
5) the operator analyzes the accumulated statistical information and decides to change
the specified reference microclimate zone;
6) the laboratory section analyzes influence of the microclimate of the industrial prem-
ise on the product quality; if necessary, the range of the boundaries permissible region
V is modify by the operator according to steps 1 – 6; thus, statistical information is
collected that allows one optimize the energy consumption of the air conditioner due to
the permissible range parameters of microclimate in the room.
A significant reduction in energy consumption of the air conditioner is possible
through the use of up-take air recirculation. Utilization of the exhaust air heat makes it
possible to bring the parameters of the microclimate ambient air from point A to point
B for the winter season, and from point A* to point B* for the summer season (see
Fig. 3). At the same time, the energy consumption for air processing is significantly
reduced (transition from point B / B* to point D / D*). At 100% recirculation, the en-
ergy efficiency air conditioner is maximum. However, according to sanitary norms
and rules, there are standards for air exchange with the external environment, which
must be maintained in the production premises if there are workers in the room. To
optimize the recirculation coefficient of an industrial air conditioner, an air condition-
ing system database should contain regulatory information on air exchange for indus-
trial premises with employees.
The air conditioner recirculation coefficient is optimized during the transition of the
artificial microclimate system from winter to summer mode and vice versa, as well as
when the number of workers in the industrial premises changes and consists of the
following steps:
1) decision making unit initiates a dialogue with the operator and offers permissible
recirculation value for the number of employees in the room;
2) the simulation model of an industrial air conditioner is formed according to the
methodology given in the studies [10];
3) the simulation subsystem performs an ambient air preparation quantitative model-
ing: this is the transition from point A (A*) to point D (D*);
4) the assessment of the effectiveness of the operation air conditioner based on mini-
mizing the criterion of energy consumption (1);
5) the operator changes the recirculation coefficient for the air conditioner model based
on the experience and DSS statistics:
6) an analysis is performed of the energy efficiency air conditioner; when changing the
number of employees or the operation mode air conditioner, steps 1 – 6 are repeated to
optimize the recirculation coefficient.
Thus, the DSS helps the operator to optimize the energy efficiency of industrial air
conditioning equipment without loss of product quality in production.
3.3 Operational management for the adaptation ACS air conditioning
The operational management task under adaptation is necessary for the correction
of ACS settings when changing the dynamic properties of an industrial air condi-
tioner. Any adaptive control system has two loops: internal – a direct-digital control
circuit; external – the circuit for evaluating the parameters of plant and calculating the
parameters of the controller. The internal adaptive control loop is implemented on the
PLC, and the external one in the frames of the constructed DSS (see Fig. 2).
The ACS adaptation of an air conditioner is carried out during the transition of the
artificial microclimate system from winter to the summer mode and vice versa. The
operational control algorithm for adaptation includes of the following steps:
1) the decision-making unit initiates a dialogue with the operator and offers to evalu-
ate the parameters of mathematical models of HVAC equipment; for models, heat-
and mass- transfer coefficients are estimated, which depend on many factors; to eval-
uate the parameters, one can use the scanning or gradient search methods; as a refer-
ence model, the accumulated information of measured data of the operated air condi-
tioning systems, which are contained in the DB of the DSS, is used;
2) an industrial air conditioner simulation model is formed according to the method
given in the study [10];
3) the LQDC feedback matrix is computed for the diagram regions h – d [10];
4) the assessment is made of the simulation ACS effectiveness functioning with
LQDC using a quadratic criterion for all areas of the h – d diagram:
I XTQX UTRU dt , (3)
where Q, R, are weighting coefficient matrices for elements of state and control vec-
tors; X is state vector, which includes variable temperatures and moisture content
d; U is the control vector;
5) the operator changes the LQDC parameters if the air conditioner ACS efficiency
with the calculated parameters is higher than the existing one;
6) the steps 1 – 5, aiming to adapt LQDC are repeated when the industrial air condi-
tioner changes its operating mode.
4 Conclusions
The DSS structure for the integrating industrial air conditioners ACS into the inte-
grated enterprise management system is proposed. It was proposed to use mathemati-
cal models and control algorithms developed in the previous developments [8–12] at
the enterprise management (MES) level in the decision support system proposed,
which is implemented to improve the industrial air conditioners operation efficiency
under changing operating conditions. As a result of applying the system analysis
methodology, the multifunctional mathematical and algorithmic software was devel-
oped for integrating an industrial air conditioning control system into computer-
integrated production management system. The developed models, methods and algo-
rithms are recommended to be used at the stage of design, commissioning and opera-
tion at middle level of operational production management. The proposed mathemati-
cal models, methods and algorithms for controlling air conditioners are brought to
practical implementation and theirs are proposed to be implemented in DSS. The
industrial implementations results have confirmed the high efficiency of automatic
control systems developed on the basis of the systemic approach to building concep-
tually integrated industrial control systems discussed above.
References
1. Belova, E.: Central air conditioning systems in buildings. Euroclimate, Moscow (2006).
2. Kletti, J., Berres, B., Brauckmann O.: Manufacturing Execution Systems – MES. Springer,
Heidelberg (2007).
3. Meyer, H., Fuchs, F., Thiel, K.: Manufacturing Execution Systems. Optimal Design, Plan-
ning, and Deployment. McGraw–Hill, USA (2009).
4. Itskovich, E.: Production enterprises automation complex methods in technological indus-
tries. KRASAND, Moscow (2013).
5. Enterprise/Control System Integration. Part 1: Models and Terminology: ANSI/ISA-
95.00.01-2010. International Society of Automation, USA (2010).
6. Enterprise/Control System Integration. Part 2: Object Model Attributes: ANSI/ISA -
95.00.02-2010. International Society of Automation, USA (2010).
7. Enterprise/Control System Integration. Part 3: Activity Models of Manufacturing Opera-
tions Management: ANSI/ISA-95.00.03-2013. International Society of Automation, USA
(2013).
8. Golinko, I.: Industrial air conditioners automatic control systems synthesis principles. Au-
tomation technological and business–processes, 8, 33–42 (2016).
9. Golinko, I., Galytska I.: Microclimate control system synthesis using relative humidity and
humidity content as a stabilization parameter. Information systems, mechanics and control,
13, 88–98 (2015).
10. Golinko, I., Galytska, I.: Optimization of multidimensional control systems for complexes
of artificial microclimate. Precarpathian bulletin of the Shevchenko Scientific Society. Se-
ries Number, 1(33), 61–73 (2016).
11. Golinko, I., Galytska, I.: Mathematical model of heat exchange for non-stationary mode of
water heater. ICCSEEA 2019: Advances in Computer Science for Engineering and Educa-
tion II, pp. 58–67. Springer, Switzerland (2020).
12. Golinko, I., Galytska, I.: An artificial microclimate control system synthesis using relative
humidity and moisture content as a parameter of stabilization. Information systems, me-
chanics and control, 13, 88–98 (2015).
13. Zgurovsky, M., Pankratova, N.: Basics of system analysis. BHV Publishing Group, Kiev
(2007)
14. Zgurovsky, M., Podladchikov, V.: Analytical methods of Kalman filtration for systems
with a priori uncertainty. Naukova Dumka, Kiev (1995).
15. Ponyatsky, V.: Study of ways to implement an adaptive control system with a Kalman fil-
ter. Stochastic optimization in computer science, 1, 196–210, (2008).