Fuzzy Application Library/Technical Applications/Complex Chilling systems
Application of Fuzzy Control for Optimal Operation of Complex Chilling systems
by Prof. Dr.-Ing., Dr.-Ing. h.c. R. Talebi-Daryani, Polytechnics of Cologne, Faculty of mechanical engineering, Department for measurement and control engineering, and Dipl.-Ing. Claudia Luther, JCI - Johnson Controls, Cologne Germany
Citation Reference: This paper has been published in the proceeding of the Second International Conference on Application of Fuzzy Systems and Soft Computing in June 25- 27, 1996 in University of Siegen, Germany.
The optimisation potentials for the operation of chilling systems within the building supervisory control systems are limited to abilities of PLC functions with their binary logic. Little information about thermal behaviour of the building and the chilling system is considered by operation of chilling systems with PLC-solutions. The aim of this project introduced in this paper is to replace inefficient PLC- solutions for the operation of chilling system by a fuzzy control system. The focus of the optimisation strategy realised by fuzzy control is to ensure an optimal operation of a chilling system. Optimal operation means:
Further requirement on the
optimisation strategy is providing a user net chill water supply
temperature with a set point error as little as possible. This
feature of the chilling system is important, in order to ensure
research and working conditions in the building. Analysis of the
on-line thermal behaviour of the building and the chilling system
is necessary, in order to find the current efficient cooling
potentials and methods during the operation. The thermal analysis
also focuses the measurement of important physical values of the
system as input variables for different fuzzy controllers, since
no expert knowledge is existing for optimally operation of the
system. This realised fuzzy control system will open new
application fields for fuzzy technology within the building
automation engineering. The designed fuzzy controllers are
software solutions, in order to use the existing building
supervisory control system with its interface units , connected
to the chilling system. Three different fuzzy controller have
been developed with a total rule number of just 70. Comparison of
the system behaviour before and after the implementation of fuzzy
control system proved the benefits of the fuzzy logic based
operation system realised here.
The chilling system described here supplies chill water to the air conditioning systems (AC-systems) installed in the Max Plank- Institute for Radio astronomy in Bonn. The AC-systems ensure the research conditions by supplying conditioned air to the building. The amount of cooling power for the building is the sum of internal cooling load (produced by occupants, equipment and computers) and the external cooling load, which depends on out door air temperature (T_{out}) and sun radiation through the windows. The cooling machines installed here, uses the compression cooling method . The principle of a compression cooling machine can be described in two thermodynamically processes. In the first step of the cooling process , the heat energy will be transferred from the system to the heat exchanger (evaporator) of the cooling machine, and therefore the liquid gas will evaporate by absorbing the heating energy. After the compression of the heated gas, in the second part of the process , the gas condense again by cooling the gas through the air cooling system. In that step of the process, the heat transfer is from the condensation system to the out door air space. The process is continuous, and based on the second law of the thermodynamics.
Figure 1 presents the chilling system as a schematic diagram. The whole system consists of the following components:
Figure 1: Schematic diagram as a part of the chilling system with simplified instrumentation (large) |
During the operation of the cooling machines, the air cooling systems will be used, in order to transfer the condensation energy of the cooling machine to the out door air space. If the out door air temperature is much lower than user net return temperature on heat exchanger one, the air cooling system should serve as a free cooling system and replace the cooling machine. The additional cooling load storage systems are installed in order to fulfil the following requirements: firstly to load cooling energy during the night time, and therefore reduce the cost of electrical power consumption (by using cheap night tariff for electrical power), and secondly supplying cooling energy during the operation time, if a maximum cooling energy is needed and cannot be provided by existing cooling machines. In both cases the cooling storage system does not reduce energy consumption, but the cost of energy production and consumption.
1.2 State of the control engineering for operation of chilling systemsThe heart of a building energy management system is the Building Supervisory Control System , which consists of a hierarchically organised, function orientated control system having separate intelligent automation units. A clearly defined division of functions by hierarchical levels with extensive communication horizontally and vertically across all levels is an essential aspect of perfect operational efficiency. The building supervisory control System with its "distributed intelligence" is configured into four hierarchical information processing levels, as shown in Figure 2 [ 1 ] .
1.2.1 Supervisory level for implementation of the Fuzzy Control System
The initial function of this Level is to analyse the operating status of the systems. The main function of this level is to Control, monitor and log the processes within the Building as a whole but serves also for configuring of the automation units at the automation level. The supervisory control level has access to all physical data points of the chilling system. The fuzzy control system for optimisation strategy realised here, is a software solution and is implemented into the supervisory control system. The designed software Fuzzy control has to be translated into a system orientated mathematical, and logical programming language ( GPL) [2]. All the operation instruction formulated in the Supervisory level will be transferred to the chilling system through the automation level as shown in Figure 2.
Figure 2: Distributed intelligence building supervisory systems with implemented Fuzzy Controller (large) |
1.2.2 Automation level for the operation of the chilling system
The automation level houses the distributed intelligence for mathematically and physically based operation functions as multi controllers. The purpose of the D³-C = "Distributed Direct Digital Control" systems is to monitor and control the most important status and processes within the building. The D³-C system, which also provides PLC functions, allows a logical link to be set up in the form of time or status elements, in order to guarantee optimum performance. The control strategy for the inner control loops of the chilling system has been realised on this level. Therefore the supervisory level sends the set points and the start/ stop instructions as result of fuzzy controllers for each unit of the system to this level.
2 Optimisation strategies for the operation of the chilling system
2.1 Free Cooling system (FC-system)
At lower out door air temperature(T_{out}), the air cooling system serves as a FC- system, in order to reduce the running time of the cooling machines, and costs for cooling energy production. The cooling power of the FC-system depends on the T_{out, }and user net return temperature (Tr_{-un }). As demonstrated in figure 3, the input temperature of the FC- system T_{in} is 11°C, at a Tr_{-un }of 12°C. The temperature gradient which is produced by FC- system is 4k, if the T_{out }is 1°C. The cooling power produced by the FC-system is 99 kW, by using just 1.6 kW electrical power. The same cooling power produced by a cooling machine, and an air cooling system, needs an electrical power of 44 kW [ 3 ]. The efficiency factor of the FC-system is 27 in comparison to the cooling machine . This factor will decrease, if T_{out} increases. The cooling power of the FC-system is only 33 kW, if the T_{out} is 7°C . Other important physical value for the FC-system is the Tr_{-un}. A temperature different of 1 K must exist on the HE1, and 5k between Tr_{-un} and the T_{out}, in order to use the air cooling system as a FC- system [3].
Figure 3: Temperature distribution within the free cooling system (large) |
The aim of the thermal analysis of the building is to find measurable information for the needed current cooling load. Alternation for internal cooling load of computers and machines could not be exactly registered or measured. Measurement of current cooling power of the building as shown in Figure 4 has proved that there is not a significant correlation between T_{out} and the current cooling power. Analyses have proved that at higher internal load, there is a heat transmission to the out door air space, if T_{out} is lower than 23°C. The current cooling power will increase, if T_{out} gets higher than 23°C. Although the equipment and computers are on service for 24 hours a day, there is a big alternation of cooling power. In the summer time, when the T_{out} increases to about 34°C, the current cooling power will be more influenced by T_{out}. So T_{out} can be used for forecasting the maximum cooling power. An additional information is necessary, in order to analyse the thermal behaviour of the building. We gain this information by measuring the return temperature of the user net (Tr_{-un}). Any change of total cooling load will influence Tr_{-un}, and is an important input for the fuzzy controller.
Figure 4: Alternation of current cooling power and out door air temperature (large) |
3 Fuzzy control system for different optimisation strategies
3.1 Requirements for the design of the fuzzy control system
The aim of the fuzzy control system which has to be developed ,and implemented into the existing building supervisory system as shown in Figure 2 is to run the chilling system in such a way that the following requirements for the operation of the system will be fulfilled: regarding the cooling potential of the out door air, the air cooling system should serve as a cooling power generator as long as possible. The free cooling system (FC system) should run before the cooling load storage system (CLS-system),and cooling machines. This has to be considered by the fuzzy controller for the operation of cooling machines. Analytical system knowledge of the building and FC- system as described in 3.1, must be considered for the formulation of the rules of the fuzzy control system. The CLS- system should run during the day time before any cooling machine, if the cooling load of the building is expected to be low. Optimisation strategy for the discharge of CLS-system will ensure that there will not be a peak in the electrical power consumption, and reduce the cost of electrical power consumption, by keeping of low price tariffs for electrical power. The cooling machines should run at their lowest possible level. The fuzzy control system must ensure supply of the needed cooling power during the operation time of the building by lowest cost and shortest system operation time with a low range of set point error for the supply temperature. A concept of knowledge engineering by measuring and analysing of system behaviour is necessary, since no expert knowledge exists for the formulate of the fuzzy rules. Measuring of two physical values of the system is necessary, in order to consider system behaviour for an on-line optimisation strategy. These process values are: the out door air temperature T_{out}, which partially presents the thermal behaviour of the building, and the user net return temperature (Tr_{-un}) ,which contains the total cooling load alternation of the building. These requirements focus on three different fuzzy controllers for the different components of the chilling system as shown in Figure5, and described in the following.
Figure 5: Combination of three fuzzy controllers for the operation of the chilling system (large) |
3.2 Fuzzy controller 1 for operation of the cooling load storage system
The optimum start point for the
discharge of the cooling load storage system depends on the
maximum cooling power needed, which can differ every day. For
calculation maximum cooling power, T_{out} must be
processed by the fuzzy controller, since the maximum cooling
power in the summertime will be influenced extremely by T_{out}.
A feedback of current cooling power calculated by Fuzzy control
Block 2 is also necessary, in order to estimate the maximum
cooling power. If the peak of a maximum cooling power is
estimated by the fuzzy controller, then this will be compensated
by optimally discharging the cooling load storage system parallel
to the cooling machines. Figure 6 shows the fuzzy controller 1.
Figure 6: Fuzzy Controller 1 for optimally discharging cooling load storage system (large) |
The input variables of the controller 1 are:
- Outdoor air temperature T_{out}
- Differential of T_{out}
- Current cooling power of the cooling machines.
For the fuzzification of the T_{out}, we have following system knowledge. Observation of the system has shown that above T_{out} of 25°C , a second cooling machine is necessary, in order to meet demand for increasing cooling load. Therefore the fuzzyfication will be around T_{out} 25°C with only three fuzzy sets as shown in Figure 5. The second fuzzy variable is calculated by equation 1:
( 1 ) | T_{out} /dt =(T_{out} (k) - T_{out} ( k-1))/ TC | with | T_{out} (K) = Outdoor air temperature |
by | K ^{Th}
cycle T_{out} ( k-1) = Outdoor air temperature by k-1^{ Th }cycle TC: = Scan time of the PLC ( here 10 min ) |
The third input variable is the output value of the Fuzzy controller 2, and represents the current cooling power. The output of the fuzzy controller 1 is the estimated maximum cooling power CP-max. This value will be transformed in to the PLC system, where crisp limits are formulated for the discharge of the cooling load storage system. The membership function used for the fuzzy variables are available as L, P, Z, and S-functions. For the defuzzification, "Centre of maximum" has been supported by the SUCOsoft Fuzzy TECH 4.0 [ 4 ]. The controller consists of 17 rules. Figure 7 shows the P membership function as calculated by equation 2:
( 2 ) | with | m = degree of
membership x = process variable as input variable A,B,C = parameters for the membership function in value of the input variable, e.g. °C |
Figure 7 : Membership function P Type (large) |
3.3 Fuzzy controller 2 for the operation of the cooling machines>
The fuzzy controller 2 ( FC-2) is the important part of the optimisation control system, in order to use the cooling potential of the out door air, before starting any cooling machine. If "e1" is zero, or negative, then the capacity of free cooling system is enough for the required cooling power. The output signal of FC- 2 will be zero. In other cases, FC- 2 is responsible for the operation of the cooling machines. This controller consists of 21 rules with the 3 input variables as following:
- Set point error" e1" at heat exchanger 1,
- Set point error "e2" at heat exchanger 2;
- Difference between user net return temperature ( Tr_{-un}) , and T_{set point}
The input variable 1, is calculated as the difference between user net set point temperature (T _{set point}), and output temperature of the heat exchanger ( T_{HE1} ), according to equation 3:
( 3 ) | e1= T_{set point} - T_{HE1} |
For this variable, only three sets are necessary, in order to define if, e1 is NS, ZR, or PS. The range of e1 is between +1k and -1k. The second input variable is calculated as the between (T _{set point}), and output temperature of heat exchanger 2 (T_{HE2}), according to equation 4:
( 4 ) | e2 = T_{set point} - T_{HE2 } |
The third input variable is determined by equation 5:
( 5 ) | DTr_{-un} = Tr_{-un} - T_{set point} |
Calculation of DTr-un is necessary, because T_{set pint} is variable, and therefore D T_{run} contains the real information about the cooling load of the building. Figure 8 shows the fuzzy controller 2. As soon as the first variable of the controller "e1" reaches the values of PS or ZR, this indicates that the capacity of FC-system is enough to cover the demanded cooling power, and the output signal for cooling machines is zero. In cases, where the capacity of the free cooling system is not enough,"e" will have values of NS, so that other rules will determine the output of the controller. In that case the third input variable DTr_{-un}. is more weighted for the output value of the controller, because DTr_{-un }represents the real alternation of the cooling load of the building. For this controller only 17 rules were necessary in order to run the cooling machines optimally.
Figure 8: Fuzzy Controller 2 for optimal operation of cooling machines (large) |
3.4 Fuzzy controller 3 for operation of the free cooling system
This control block is necessary, in order to use the cooling potential of the out door air, and run the air cooling systems of the cooling machines as Free cooling systems. The cooling potential depends on the different between user net return temperature Tr_{-un,} and the out door air temperature Tout. As described in chapter 3.1, the operation of the free cooling system becomes efficient, if the difference between T_{out }and Tr_{-un} is bigger then 5k. The input variables of the control block 3 are:
- Difference between Tr_{-un} and set point, DTr_{-un}
- Set point error 1 at heat exchanger 1
- Different between T_{out}, and Tr-_{un}, DT_{out}.
Calculation of input variables 1 and 2 has been explained by control block 2. The third input variable of this controller contains the cooling potential of the out door air and is:
( 6 ) | DT_{out }= Tr_{-un} - T_{out} |
An important aspect for the formulation of the rules for this controller is the cooling potential of the system, which is represented by the input variable 3, DT_{out}. The higher the value of this variable is, the fewer FC-system components are necessary in order to supply the demanded cooling power for the building. This controller consists of 29 rules. Figure 9 shows the control block 3.
Figure 9: Fuzzy controller 3 for optimal operation of the free cooling system (large) |
4 Results of system optimisation by Fuzzy Control
Figure 10 shows the course of user net supply temperature, before the optimisation of the system operation by fuzzy control. The alternation of the supply temperature is between 10.5 °C and 4.8°C. The reason for such a big set point error range is in the discontinuous operation of the chilling system. We can also see this in Figure 4, where the alternation of the cooling power as a function of the operation status of the cooling machines is registered. This high alternation of the supply temperature is a reflected image of the alternation of the system status. This unsatisfied system behaviour was realised by PLC functions, which does not have the ability of fine tuning for the system operation as it is realised now by fuzzy control system.
Figure 10: Course of supply temperature by operation of the chilling system by PLC- System (large) |
Figure 11 represents the course of the supply temperature after
commissioning the fuzzy control system within the building
supervisory control system. As to the set point error, we can see
from figure 11 that it is between 6.2°C and 5 °C. This set
point error of the supply temperature is a result of the working
principle of the compression cooling machines. As the feature of
compression cooling machines they have a discontinuous output
range for the cooling power, and therefore it is not possible to
keep the supply temperature within a smaller error range as shown
herein Figure 11. The course of the supply temperature in Figure
11 indicates, a remarkable improvement of the system behaviour.
This relatively constant supply temperature will ensure research
and working conditions in the building, by using air conditioning
systems in combination with the chilling system.
Figure 11: Course of supply temperature by operation of the chilling system with Fuzzy controller (large) |
Conclusions
An optimisation strategy for the operation of a complex chilling system is realised by Fuzzy control system, and implemented into an existing building automation system. The focus of the optimisation strategy by fuzzy control is to ensure an optimal operation of a chilling system. Optimal operation means: reducing operation time and operation costs of the system, reducing cooling energy generation - and consumption costs. Based on the thermal analyses of the building and chilling system, different optimisation strategies have been defined for developing proper fuzzy controllers. Missing expert knowledge, on-line measurement of different physical values and their evaluation are the basis for the fuzzy control system. Few rules for each controller were necessary, in order to have the fine tuning of the fuzzy control system. Three fuzzy controller were necessary in order to reach maximum efficiency by operation of different components of the chilling system. This realised fuzzy control system is able to forecast the maximum cooling power of the building, but also to determine the cooling potential of the out door air. Operation of both systems by fuzzy control enormously reduced the cost of cooling power. This new fuzzy control system has been successfully commissioned, and remarkable improvement of the system behaviour is reached. This project opens new application field in flourishing market of building automation.
The system described here is s a joint project between polytechnic of Cologne, and Johnson Controls International Cologne. The fuzzy software tool used here (SUCO soft fuzzy TECH 4.0), was provided by Klöckner Möller Bonn [4].
References
[1] | Talebi- Daryani, R.: Building automation Text book 1995, Polytechnics of Cologne, faculty of mechanical engineering, department for measurement and control engineering. |
[2] | Johnson Controls International :GPL- METASYS sytem handbook , Essen Germany |
[3] | Claudia Luther: Entwicklung und Test eines Programmes zur optimalen Betriebsweise von Kälteanlagen mit Eisspeicher mittels SPS und Fuzzy- Logic, Master these, polytechnic of Cologne, faculty of mechanical engineering. Non published master these 1995 |
[4] | Moeller: SUCO soft fuzzy TECH 4.0, FT4-400-DX2 , Bonn 1994 |
[5] | Prof. Dr.-Ing., Dr.-Ing. h.c. R. Talebi-Daryani, Dipl.-Ing. Claudia Luther: Application of Fuzzy Control for optimal operation of complex chilling systems. ICAFS’96, June 25- 27, 1996, Second International Conference on Application of Fuzzy Systems and Soft Computing, P- 165- 175, Conference Proceedings, Siegen Germany |