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Research Papers

Experimental Analysis on Adsorption Characteristics of Methanol and R134A by Activated Carbon in Adsorption Refrigeration System

[+] Author and Article Information
V. Baiju

Department of Mechanical Engineering,
National Institute of Technology Calicut,
Kozhikode 673601, Kerala, India
e-mail: baij84@yahoo.co.in

C. Muraleedharan

Department of Mechanical Engineering,
National Institute of Technology Calicut,
Kozhikode 673601, Kerala, India

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received March 11, 2014; final manuscript received July 23, 2014; published online October 28, 2014. Assoc. Editor: Zahid Ayub.

J. Thermal Sci. Eng. Appl 7(1), 011004 (Oct 28, 2014) (8 pages) Paper No: TSEA-14-1048; doi: 10.1115/1.4028548 History: Received March 11, 2014; Revised July 23, 2014

This paper presents adsorption and desorption characteristics of two different working pairs—activated carbon–methanol and activated carbon–R134a—determined experimentally. Dubinin–Radushkevich (D–R) equation is used to correlate the adsorption isotherms and to form the pressure, temperature, and concentration diagrams for both the assorted working pairs. The results show that the maximum adsorption capacity of activated carbon–R134a working pair is 1.21 times that of activated carbon–methanol. Temperature and pressure distribution throughout the adsorbent bed and their variation with adsorption time are also predicted. Use of artificial neural network (ANN) is proposed to determine the uptake from measured pressure and temperature. The back propagation algorithm with three different variants, namely, scaled conjugate gradient (SCG), Pola–Ribiere conjugate gradient (CGP), and Levenberg–Marquardt (LM) and logistic sigmoid transfer function are used, so that the best approach could be found out. After training, it is found that LM algorithm with 11 neurons is the most suitable for modeling adsorption refrigeration system. The adsorption and desorption uptake obtained experimentally are compared with the uptake predicted by D–R equation and ANN modeling.

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References

Figures

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Fig. 4

Linear fitting of D–R equation for activated carbon– methanol working pair

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Fig. 3

Artificial neural network

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Fig. 2

Photograph of the system

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Fig. 1

Schematic diagram of experimental system used for adsorption–desorption study

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Fig. 5

Linear fitting of D–R equation for activated carbon–R134a working pair

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Fig. 6

Comparison of variations of uptake during adsorption

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Fig. 7

Effect of heat source temperature on adsorption (activated carbon–methanol)

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Fig. 8

Effect of heat source temperature on adsorption (activated carbon–R134a)

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Fig. 9

Comparison of uptake (activated carbon–methanol)

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Fig. 10

Comparison of uptake (activated carbon–R134a)

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Fig. 11

Error bars for uptake (activated carbon–methanol)

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Fig. 17

Variation of uptake with time for two different working pairs

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Fig. 18

Uptake for two working pairs

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Fig. 19

P–T–x chart for activated carbon–methanol working pair

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Fig. 20

P–T–x chart for activated carbon–R134a working pair

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Fig. 12

Error bars for the uptake (activated carbon–R134a)

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Fig. 13

Temperature transients of the adsorbent bed for activated carbon–methanol working pair

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Fig. 14

Temperature transients of adsorbent bed for activated carbon–R134a working pair

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Fig. 15

Pressure transients of adsorbent bed for activated carbon–methanol working pair

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Fig. 16

Pressure transients of adsorbent bed for activated carbon–R134a working pair

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