IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i23p4565-d292513.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/23/4565/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/23/4565/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aline Riboli Marasca & Maurício Scopel Hoffmann & Anelise Reis Gaya & Denise Ruschel Bandeira, 2021. "Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(3), pages 1121-1137, June.
    2. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. dos Santos, Fabio Luis Marques & Duboz, Amandine & Grosso, Monica & Raposo, María Alonso & Krause, Jette & Mourtzouchou, Andromachi & Balahur, Alexandra & Ciuffo, Biagio, 2022. "An acceptance divergence? Media, citizens and policy perspectives on autonomous cars in the European Union," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 224-238.
    4. Nicolas Depraetere & Martina Vandebroek, 2014. "Order selection in finite mixtures of linear regressions," Statistical Papers, Springer, vol. 55(3), pages 871-911, August.
    5. repec:jss:jstsof:06:i02 is not listed on IDEAS
    6. Emmanuel Nyarko Ayisi & Karel Fraňa, 2020. "The Design and Test for Degradation of Energy Density of a Silica Gel-Based Energy Storage System Using Low Grade Heat for Desorption Phase," Energies, MDPI, vol. 13(17), pages 1-15, September.
    7. Yang, Chih-Chien, 2006. "Evaluating latent class analysis models in qualitative phenotype identification," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1090-1104, February.
    8. GONZALO, Jesus & PITARAKIS, Jean-Yves, 1994. "Comovements in Large Systems," LIDAM Discussion Papers CORE 1994065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Shabir, Faizan & Sultan, Muhammad & Miyazaki, Takahiko & Saha, Bidyut B. & Askalany, Ahmed & Ali, Imran & Zhou, Yuguang & Ahmad, Riaz & Shamshiri, Redmond R., 2020. "Recent updates on the adsorption capacities of adsorbent-adsorbate pairs for heat transformation applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    10. Tian, Amy Wei & Meyer, John P. & Ilic-Balas, Tatjana & Espinoza, Jose A. & Pepper, Susan, 2023. "In search of the pseudo-transformational leader: A person-centered approach," Journal of Business Research, Elsevier, vol. 158(C).
    11. D. R. Anderson & K. P. Burnham & G. C. White, 1998. "Comparison of Akaike information criterion and consistent Akaike information criterion for model selection and statistical inference from capture-recapture studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(2), pages 263-282.
    12. 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.
    13. R. Scott Hacker & Abdulnasser Hatemi-J, 2021. "Model selection in time series analysis: using information criteria as an alternative to hypothesis testing," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 49(6), pages 1055-1075, September.
    14. Marianna Virtanen & Jussi Vahtera & Jenny Head & Rosemary Dray-Spira & Annaleena Okuloff & Adam G Tabak & Marcel Goldberg & Jenni Ervasti & Markus Jokela & Archana Singh-Manoux & Jaana Pentti & Marie , 2015. "Work Disability among Employees with Diabetes: Latent Class Analysis of Risk Factors in Three Prospective Cohort Studies," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    15. Danks, Nicholas P. & Sharma, Pratyush N. & Sarstedt, Marko, 2020. "Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM)," Journal of Business Research, Elsevier, vol. 113(C), pages 13-24.
    16. Martin Lukac & Nadja Doerflinger & Valeria Pulignano, 2019. "Developing a Cross-National Comparative Framework for Studying Labour Market Segmentation: Measurement Equivalence with Latent Class Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(1), pages 233-255, August.
    17. Po-Hsien Huang, 2017. "Asymptotics of AIC, BIC, and RMSEA for Model Selection in Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 407-426, June.
    18. Morgan, Grant B. & Hodge, Kari J. & Baggett, Aaron R., 2016. "Latent profile analysis with nonnormal mixtures: A Monte Carlo examination of model selection using fit indices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 146-161.
    19. Qi Chen & Wen Luo & Gregory J. Palardy & Ryan Glaman & Amber McEnturff, 2017. "The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored," SAGE Open, , vol. 7(1), pages 21582440177, March.
    20. Lu, Zhenqiu (Laura) & Zhang, Zhiyong, 2014. "Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 220-240.
    21. Roy Levy & Gregory R. Hancock, 2011. "An Extended Model Comparison Framework for Covariance and Mean Structure Models, Accommodating Multiple Groups and Latent Mixtures," Sociological Methods & Research, , vol. 40(2), pages 256-278, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4565-:d:292513. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.