This document describes a two-way ducting system that uses fuzzy logic control. The system uses two ducts - a supply duct to deliver cool air from an air cooler to a room, and a return duct to ventilate room air back to the cooler. Temperature and humidity sensors provide input to a fuzzy logic controller. The controller uses 30 fuzzy rules to determine the speed of the cooler fan, water pump, and exhaust fan. Membership functions were defined for the input and output variables. The system was simulated in MATLAB. The two-way ducting system aims to maintain room temperature more efficiently by circulating air between the room and cooler, requiring lower speeds for the fan and pump compared to traditional single duct systems.

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TWO WAY DUCTING SYSTEM USING FUZZY LOGIC CONTROL SYSTEM
Rajani Kumari1, Sandeep Kumar2, Dr. Vivek Kumar Sharma3
1Jagannath
University, Chaksu, Jaipur, India
University, Chaksu, Jaipur, India
3Asst. Professor, Jagannath University, Chaksu, Jaipur, India
2Jagannath
1
rajanikpoonia@gmail.com,2sandpoonia@gmail.com,3vivek.kumar@jagannathuniversity.org
ABSTRACT: The duct system is an air distribution system. It is basically used to supply cool air
into the required areas. This research paper proposes a new design of two way ducting
systems and simulation of proposed system done using Mat lab. The rule base takes two input
values as temperature and humidity, measured by sensors. Six triangle membership functions
for temperature and five membership functions for humidity based on their crisp values are
used for input. It uses thirty fuzzy rules, with the help of Mamdani type fuzzy inference it
decide single value for each output (cooler fan, pump and exhaust fan). Correctness of this
model tested using simulation.
KEYWORDS: Ducting System, Fuzzy Logic, Mamdani type fuzzy inference, Mat lab.
I.
INTRODUCTION
Fuzzy logic can be defined as a form of probabilistic logic. It deals with approximation not with
determinism. Fuzzy logic establishes a relation between two different form of knowledge,
objective knowledge and subjective knowledge. First type of knowledge exists in mathematical
form and second in linguistic form. Fuzzy logic can work with both type of knowledge
simultaneously. Fuzzy logic derived from fuzzy set theory deals with approximation rather
than exactness taken from classical logic, Classical logic deals with two valued logic either
element belong to set or not; whereas fuzzy set permit to asses an element based on its degree
of membership, that may lie in interval [0,1]. This interesting concept of fuzzy logic was given
by L.A. Zadeh in year 1965 [1]. L.A. Zadeh done great work in field of fuzzy logic control
including Fuzzy sets as a basis for a theory of possibility [2], A fuzzy-set-theoretic
interpretation of linguistic hedges [3], Decision-making in a fuzzy environment [4], Toward a
theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic
[5], Fuzzy logic and approximate reasoning [6]. Rajani Kumari et al. discussed air conditioning
system with fuzzy logic [7].
A number of fuzzy inference system and defuzzification methods are proposed in last decade in
order to improve performance of existing systems. A fuzzy logic controller for determining the
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2. International Journal of Electronics and Communication Engineering & Technology (IJECET),
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air and fuel to be delivered to an engine proposed by [8].A number of engineering application
described by T. J. Ross [9]. An advanced control strategies for adjustment and preservation of
air quality, thermal and visual comfort for buildings' occupants while, simultaneously, energy
consumption reduction is achieved by [10].Now a day a large number of researchers taking
interest in application of fuzzy logic in home appliance like [11] presented a study of the air
conditioning system by using fuzzy logic control. It will take into account the energy savings
and the room temperature remained in range of comfort zone for the resident's satisfaction.
Fuzzy logic control for active bus suspension system was suggested by [12]. An evolutionary
algorithm developed by [13] for an effective tuning of fuzzy logic controllers in heating,
ventilating and air conditioning systems. M. Abbas described the design and implementation of
an autonomous room air cooler using fuzzy rule based control system [14]. Various new
algorithms are developed for air conditioning system including neuro-fuzzy controller
algorithm for air conditioning system [15]. Neuro-fuzzy control mixes the learning capabilities
of neural networks and control capabilities of fuzzy logic control.
This proposed two-way ducting system based on fuzzy logic control with two inputs and one
output. In proposed system duct may be an assortment of tubes that distributes the heated or
cooled air to the various rooms from cooler. A duct system could be a branching network of
spherical or rectangular tubes, usually created of sheet, fiberglass board, or a versatile plastic
and-wire composite situated inside the walls, floors, and ceilings.
II.
TRADITIONAL DUCTING SYSTEM
The traditional ducting system designed with one duct, which was used to supply air from
cooler to room, they do not use the return duct. Supply duct used to deliver the cool air into the
room from the cooler. Cooler receives air from the outer surroundings. Sometimes exhaust fan
are used in order to reduce humidity. The room temperature is incredibly less as compared to
temperature of outer surroundings. So at every time it is needed that cooler fan and water
pump must run at high speed to maintain the low temperature. It results in higher
consumption of power.
III.
PROPOSED MODEL
This model is intended for autonomous ducting system to regulate the entire processing and
attain the most effective results of the desire need to maintain the room environment cool and
non-humid. This system solely used to convert the high temperature into low temperature; it's
ineffectual to convert the low temperature to high temperature. Subsequently this duct system
accustomed maintains the temperature, if temperature is high.
The proposed model work on two ducts, one is supply duct and another is return duct. These
ducts are connected between the air cooler and room. Cooler fan supply cool air from cooler to
room and it utilize exhaust fan to supply air from room to cooler. Starting point of supply duct
connected at the front of cooler wherever the cooler fan located and ending point connected
with grills of any room. This duct employed for supply the cool air into the room from cooler.
Starting point of return duct connected with room wherever the exhaust fan is to be found and
ending point connected at the rear of cooler. This duct employed for ventilation and provides
the room air to the cooler. Air supplied by return duct must be cooler than environment.
In proposed design the cooler receive air from the room, not from the outer surroundings and
the room temperature is extremely less as compare to outer surroundings. By rotation of the
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cooled air from cooler to room and room to cooler this technique maintain the desired room
temperature at terribly low speed of cooler fan and pump, hence low consumption of power.
This work proposed to compute the speed of cooler fan, pump and exhaust fan with the help of
Mamdani type fuzzy inference system. This model uses six triangle membership functions for
temperature that determined over a scale range from 0ºC to 55ºC and five triangle
membership functions for humidity that determined over a scale range from 0% to 100%.
Membership functions for fuzzy sets can be defined in many ways as long as they follow the
rules of the definition of a fuzzy set. Table 1 and 2 shows membership functions and ranges for
input variables Temperature and Humidity respectively.
Membership Function
Range
Too Cold
0-10
Cold
5-20
Normal
10-30
Warm
20-40
Hot
35-50
Very Hot
45-55
Table I: Membership function and range of Input variable “Temperature”
Membership Function
Range
Dry
0-25
Refreshing
10-45
Moist
30-70
Wet
55-90
Too Wet
80-100
Table II: Membership function and range of Input variable “Humidity”
The Shape of the membership function used defines the fuzzy set and so the decision on which
type to use is dependent on the purpose. Choice of membership function depends on aspect of
fuzzy logic; it allows the required values to be interpreted in desired form. Temperature
measured as Too cold, Cold, Normal, Warm, Hot, Very hot. Humidity measured as Dry,
Refreshing, Moist, Wet, Too wet. Fan, pump and exhaust fan measured as Stop, Very slow,
Medium, High, Very high. Fig. 1 and 2 shows plot of membership functions for input variables
Temperature and Humidity respectively.
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Fig.1: Membership function for input variable “Temperature”
Fig. 2: Membership function for input Variable “Humidity”
Membership Function
Range
Stop
0-5
Very Slow
0-35
Slow
20-60
Medium
40-80
High
60-90
Very High
80-100
Table III: Membership function and range of Output variables Fan, Pump and Exhaust Fan
Speed
Table 3 shows membership functions and ranges for output variables speed of fan, speed of
pump and speed of exhaust fan. Fig. 3, 4 and 5 shows plot of membership functions for output
variables speed of fan, speed of pump and speed of exhaust fan respectively.
Fig. 3: Membership function for output Variable “Fan Speed”
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Fig. 4: Membership function for output Variable “Pump Speed”
Fig. 5: Membership function for output Variable “Duct Speed”
In this work use 30 rules fuzzy logic that are based on IF THEN statement. The generalized
form of logical decision is as follow:
If A, then B.
A.
Therefore, B.
This form strictly followed in case of logical decision, B can only be if A. Fuzzy logic loosens this
strictness by saying that B can mostly be if A is mostly or:
If A, then B.
mostly A.
Therefore, mostly B.
Where A and B are now fuzzy numbers. The reasoning above requires a set of rules to be
defined. These rules are linguistic rules to relate different fuzzy sets and numbers. The general
form of these rules is: "if x is P then y is Q," where x and y are fuzzy numbers in the fuzzy sets P
and Q respectively. These fuzzy sets are defined by membership functions. There can be any
number of input and output membership functions for the same input as well, depending on
the number of rules in the system. For example, a system could have membership functions
that represent slow, medium, and fast as inputs. Table 4 contains thirty rules. These rules are
based on IF THEN statement. For example
IF Temperature is Cold and Humidity is Dry THEN Fan Speed is Very Slow, Pump Speed is Very
Slow and Duct Speed is Stop.
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Rules
Temperature
Humidity
Fan Speed
Pump Speed
Duct In Speed
1
Too Cold
Dry
Stop
Stop
Stop
2
Too Cold
Refreshing
Stop
Stop
Stop
3
Too Cold
Moist
Stop
Stop
Stop
4
Too Cold
Wet
Stop
Stop
Stop
5
Too Cold
Too Wet
Very Slow
Stop
Stop
6
Cold
Dry
Very Slow
Very Slow
Stop
7
Cold
Refreshing
Very Slow
Very Slow
Very Slow
8
Cold
Moist
Very Slow
Stop
Very Slow
9
Cold
Wet
Slow
Stop
Slow
10
Cold
Too Wet
Slow
Stop
Slow
11
Normal
Dry
Very Slow
Very Slow
Very Slow
12
Normal
Refreshing
Slow
Very Slow
Very Slow
13
Normal
Moist
Medium
Slow
Slow
14
Normal
Wet
Medium
Very Slow
Slow
15
Normal
Too Wet
Medium
Stop
Medium
16
Warm
Dry
Medium
Medium
Slow
17
Warm
Refreshing
Medium
Medium
Medium
18
Warm
Moist
High
Slow
Medium
19
Warm
Wet
High
Slow
High
20
Warm
Too Wet
High
Stop
High
21
Hot
Dry
High
High
Medium
22
Hot
Refreshing
High
High
Medium
23
Hot
Moist
High
High
High
24
Hot
Wet
Very High
Medium
High
25
Hot
Too Wet
Very High
Slow
Very High
26
Too Hot
Dry
Very High
Very High
Very High
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27
Too Hot
Refreshing
Very High
Very High
Very High
28
Too Hot
Moist
Very High
High
Very High
29
Too Hot
Wet
Very High
Medium
High
30
Too Hot
Too Wet
High
Slow
High
Table IV: Set of proposed rules
IV.
STRUCTURE OF PROPOSED MODEL
The architecture of proposed model of two-way ducting system consists of a cooler with fuzzy
logic control system. The cooler placed on roof of building and it has a fan to circulate air, a
water pump to lift water from tank and spread it on grills of grass. An exhaust fan to pull air
from room and transfer it into cooler. To keep track of room temperature and humidity in
environment it requires sensors. Fuzzifiers of fuzzy logic control system communicate with
sensors. It use defuzzifiers for each output connected via actuators. Fig. 6 shows detailed
architecture of proposed system.
Fig. 6 Architecture of proposed system
V.
WORKING OF PROPOSED MODEL
This model works on the basis of measured temperature and humidity. Thus foremost measure
the room temperature and humidity points’ exploitation temperature and humidity value.
Apply fuzzy base rules on these measured values to find the crisp value of cooler fan speed,
speed of pump and speed of exhaust fan. As per calculation of the crisp value, set the speed of
all the output (speed of cooler fan, speed of pump and speed of exhaust fan).
For example if measured temperature is 27 ºC and humidity point is 50%. The
calculated crisp value of cooler fan speed will be 68.6, crisp value for speed of pump will be 40
and crisp value for speed of exhaust fan will be 53.6. Now according to the crisp values set the
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speed of all output (speed of cooler fan, speed of pump and speed of exhaust fan) with help of
actuators.
Fig. 7: Calculation of Crisp value using Matlab
VI.
CONCLUSION
This model work with two-way duct system, one is supply duct and another is return duct. In
order to rotates constant air inside room. By exploitation supply duct give the desired
temperature within the room and therefore the same air returns back within the cooler
exploitation exhaust fan that is settled in reciprocally duct (return duct). In most of cases the
desired temperature is maintained with minimum speed of fan and pump.
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9. International Journal of Electronics and Communication Engineering & Technology (IJECET),
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