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A Statistical Approach to Determine Optimal Models for IUPAC-Classified Adsorption Isotherms

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  • Md. Matiar Rahman

    (Mechanical Engineering Department, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
    International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
    Department of Statistics, University of Dhaka, Dhaka 1000, Bangladesh)

  • Mahbubul Muttakin

    (International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan)

  • Animesh Pal

    (International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
    Department of Nuclear Engineering, University of Dhaka, Dhaka 1000, Bangladesh)

  • Abu Zar Shafiullah

    (Department of Statistics, University of Auckland, Auckland 1010, New Zealand)

  • Bidyut Baran Saha

    (Mechanical Engineering Department, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
    International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan)

Abstract

Adsorption heat transformation (AHT) systems can play a major role in protecting our environment by decreasing the usage of fossil fuels and utilizing natural and alternative working fluids. The adsorption isotherm is the most important feature in characterizing an AHT system. There are eight types of International Union of Pure and Applied Chemistry (IUPAC) classified adsorption isotherms for different “adsorbent-adsorbate” pairs with numerous empirical or semi-empirical mathematical models to fit them. Researchers face difficulties in choosing the best isotherm model to describe their experimental findings as there are several models for a single type of adsorption isotherm. This study presents the optimal models for all eight types of isotherms employing several useful statistical approaches such as average error; confidence interval (CI), information criterion (ICs), and proportion tests using bootstrap sampling. Isotherm data of 13 working pairs (which include all eight types of IUPAC isotherms) for AHT applications are extracted from literature and fitted with appropriate models using two error functions. It was found that modified Brunauer–Emmet–Teller (BET) for Type-I(a) and Type-II; Tóth for Type-I(b); GAB for Type-III; Ng et al. model for Type-IV(a) and Type-IV(b); Sun and Chakraborty model for Type-V; and Yahia et al. model for Type-VI are the most appropriate as they ensure less information loss compared to other models. Moreover; the findings are affirmed using selection probability; overall; and pairwise proportion tests. The present findings are important in the rigorous analysis of isotherm data.

Suggested Citation

  • Md. Matiar Rahman & Mahbubul Muttakin & Animesh Pal & Abu Zar Shafiullah & Bidyut Baran Saha, 2019. "A Statistical Approach to Determine Optimal Models for IUPAC-Classified Adsorption Isotherms," Energies, MDPI, vol. 12(23), pages 1-34, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4565-:d:292513
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    1. Béchir Wanassi & Ichrak Ben Hariz & Camélia Matei Ghimbeu & Cyril Vaulot & Mejdi Jeguirim, 2017. "Green Carbon Composite-Derived Polymer Resin and Waste Cotton Fibers for the Removal of Alizarin Red S Dye," Energies, MDPI, vol. 10(9), pages 1-17, September.
    2. Chakraborty, Anutosh & Saha, Bidyut Baran & Aristov, Yuri I., 2014. "Dynamic behaviors of adsorption chiller: Effects of the silica gel grain size and layers," Energy, Elsevier, vol. 78(C), pages 304-312.
    3. Sultan, Muhammad & Miyazaki, Takahiko & Koyama, Shigeru, 2018. "Optimization of adsorption isotherm types for desiccant air-conditioning applications," Renewable Energy, Elsevier, vol. 121(C), pages 441-450.
    4. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    5. Zhenjian Liu & Zhenyu Zhang & Xiaoqian Liu & Tengfei Wu & Xidong Du, 2019. "Supercritical CO 2 Exposure-Induced Surface Property, Pore Structure, and Adsorption Capacity Alterations in Various Rank Coals," Energies, MDPI, vol. 12(17), pages 1-14, August.
    6. Jie Zou & Reza Rezaee, 2019. "A Prediction Model for Methane Adsorption Capacity in Shale Gas Reservoirs," Energies, MDPI, vol. 12(2), pages 1-13, January.
    7. Palomba, Valeria & Aprile, Marcello & Motta, Mario & Vasta, Salvatore, 2017. "Study of sorption systems for application on low-emission fishing vessels," Energy, Elsevier, vol. 134(C), pages 554-565.
    8. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
    9. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    10. Brancato, V. & Frazzica, A. & Sapienza, A. & Gordeeva, L. & Freni, A., 2015. "Ethanol adsorption onto carbonaceous and composite adsorbents for adsorptive cooling system," Energy, Elsevier, vol. 84(C), pages 177-185.
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    1. Linus Kweku Labik & Bright Kwakye-Awuah & Eric Kwabena Kyeh Abavare & Baah Sefa-Ntiri & Isaac Nkrumah & Craig Williams, 2020. "Adsorption Characteristics of Zeolite A Synthesized from Wassa Kaolin for Thermal Energy Storage," Journal of Materials Science Research, Canadian Center of Science and Education, vol. 9(3), pages 1-21, July.
    2. Md. Matiar Rahman & Abu Zar Shafiullah & Animesh Pal & Md. Amirul Islam & Israt Jahan & Bidyut Baran Saha, 2021. "Study on Optimum IUPAC Adsorption Isotherm Models Employing Sensitivity of Parameters for Rigorous Adsorption System Performance Evaluation," Energies, MDPI, vol. 14(22), pages 1-20, November.
    3. Adrianna Kamińska & Joanna Sreńscek-Nazzal & Karolina Kiełbasa & Jadwiga Grzeszczak & Jarosław Serafin & Agnieszka Wróblewska, 2023. "Carbon-Supported Nickel Catalysts—Comparison in Alpha-Pinene Oxidation Activity," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    4. Li, Chang & Wang, Yishuang & Tang, Zhiyuan & Zhou, Zinan & Qin, Baolong & Chen, Mingqiang, 2023. "The bifunctional active sites on carbon supported Fe-Mo bimetallic catalyst to improve Kraft lignin liquefaction," Renewable Energy, Elsevier, vol. 219(P2).

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