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Two way ducting system using fuzzy logic control system

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Two way ducting system using fuzzy logic control system

  1. 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), INTERNATIONAL JOURNAL OF ELECTRONICS AND ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Special Issue (November, 2013), pp. 309-317 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2013): 5.8896 (Calculated by GISI) www.jifactor.com IJECET ©IAEME 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 International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 309
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME 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 International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 310
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME 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. International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 311
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME 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” International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 312
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME 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. International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 313
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME 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 International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 314
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME 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 International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 315
  8. 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME 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. REFERENCES [1] Zadeh, Lotfi Asker. "Fuzzy sets." Information and control 8.3 (1965): 338-353. [2] Zadeh, Lotfi Asker. "Fuzzy sets as a basis for a theory of possibility." Fuzzy sets and systems 1.1 (1978): 3-28. [3] Zadeh, Lotfi A. "A fuzzy-set-theoretic interpretation of linguistic hedges." (1972): 4-34. [4] Bellman, Richard E., and Lotfi Asker Zadeh. "Decision-making in a fuzzy environment." Management science 17.4 (1970): B-141. [5] Zadeh, Lotfi A. "Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic." Fuzzy sets and systems 90.2 (1997): 111-127. [6] Zadeh, Lotfi A. "Fuzzy logic and approximate reasoning." Synthese 30.3-4 (1975): 407428. Rajani Kumari et al.,“Air Conditioning System with Fuzzy Logic and Neuro Fuzzy [7] Algorithm”, Advances in Intelligent Systems and Computing, Springer, Vol. 236, ISBN 978-81322-1601-8.(2013). International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 316
  9. 9. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME [8] Kong, Hakchul H., and Miyeon Kong. Fuzzy logic air/fuel controller. U.S. Patent No. 5,524,599. 11 Jun. 1996. [9] Ross, Timothy J. Fuzzy logic with engineering applications. Wiley, 2009. [10] Kolokotsa, D., et al. Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal–visual comfort and indoor air quality satisfaction." Energy and buildings 33.6 (2001): 531-543. [11] Plodprong, Chutima, Worarat Patprakorn, and Pornrapeepat Bhasaputra. "A Fuzzy Logic Based on Theoptimal Energy Conservation with Human Satisfaction of the Inverter Air Conditioning System for Tropical Area." Advanced Materials Research 622 (2013): 122-129. [12] Turkkan, Mujde, and Nurkan Yagiz. "Fuzzy logic control for active bus suspension system." Journal of Physics: Conference Series. Vol. 410. No. 1. IOP Publishing, 2013. [13] Gacto, María José, Rafael Alcalá, and Francisco Herrera. "A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems." Applied Intelligence 36.2 (2012): 330-347. [14] Abbas, M., M. Saleem Khan, and Fareeha Zafar. "Autonomous room air cooler using fuzzy logic control system." International Journal of Scientific and Engineering Research 2.5 (2011): 74-81. [15] Kaur, Arshdeep, and Amrit Kaur. "Development of Neuro Fuzzy Controller Algorithm for Air Conditioning System." International Journal of Engineering Science (2012). International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 317

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