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Pinto FR, Marcellos CFC, Manske C, Gomes Barreto A. Statistical analysis of parameters and adsorption isotherm models. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:53729-53742. [PMID: 38308775 DOI: 10.1007/s11356-023-31820-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 12/27/2023] [Indexed: 02/05/2024]
Abstract
The present work intends to discuss parameter estimation and statistical analysis in adsorption. The Langmuir and Tóth isotherm models are compared for a set of carbon dioxide adsorption data on 13X zeolite from literature at different temperatures: 303, 323, 373, and 423 K. Statistical analyses were performed under frequentist and Bayesian perspectives. Under the frequentist statistical view, parameters were estimated using Maximum Likelihood estimation (MLE). Statistical analyses of parameters were performed by confidence regions in terms of elliptical approximation and likelihood region, while the evaluation of models was performed by chi-square statistics. The results showed that, for these nonlinear models, the elliptical region offers a poor approximation of the parameter estimates' confidence region, especially for the most correlated parameter pairs. Additionally, the four-parameter Tóth's equation yields less correlated parameters than the three-parameter Langmuir model. From a Bayesian perspective, the Markov chain Monte Carlo (MCMC) technique facilitated the reconstruction of the probability density functions of parameters as well as enabled the propagation of parametric uncertainties in the model responses. Finally, the accurate assessment of experimental uncertainty significantly influences the evaluation of models and their respective parameters.
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Affiliation(s)
- Felipe R Pinto
- Programa de Pós-Graduação em Engenharia de Processos Químicos e Bioquímicos (EPQB), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Caio F C Marcellos
- Programa de Pós-Graduação em Engenharia de Processos Químicos e Bioquímicos (EPQB), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Carla Manske
- Programa de Pós-Graduação em Engenharia de Processos Químicos e Bioquímicos (EPQB), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Amaro Gomes Barreto
- Programa de Pós-Graduação em Engenharia de Processos Químicos e Bioquímicos (EPQB), Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil.
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Teniou A, Rhouati A, Madi IAE, Mouhoub R, Catanante G, Mashifana T, Vasseghian Y, Berkani M. Colorimetric Detection of Hemoglobin by Aptamer-Based Biosensor. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Affiliation(s)
- Ahlem Teniou
- Bioengineering Laboratory, Higher School of Biotechnology, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
| | - Amina Rhouati
- Bioengineering Laboratory, Higher School of Biotechnology, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
| | - Ibrahim Alaa eddine Madi
- Bioengineering Laboratory, Higher School of Biotechnology, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
- Biotechnologies Laboratory, Higher School of Biotechnology, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
| | - Riane Mouhoub
- Bioengineering Laboratory, Higher School of Biotechnology, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
- Biotechnologies Laboratory, Higher School of Biotechnology, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
| | - Gaëlle Catanante
- BAE Laboratory, Perpignan University, F-66100 Perpignan, France
- LBBM Laboratoire de Biodiversité et Biotechnologies Microbiennes, Sorbonne Universités, UPMC Univ. Paris 06, CNRS, Observatoire Océanologique, F-66650 Banyuls/Mer, France
| | - Tebogo Mashifana
- The University of Johannesburg, Department of Chemical Engineering, P.O. Box 17011, Doornfontein 2088, South Africa
| | - Yasser Vasseghian
- Department of Chemistry, Soongsil University, Seoul 06978, South Korea
- School of Engineering, Lebanese American University, Byblos, Lebanon
- Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India
| | - Mohammed Berkani
- Biotechnologies Laboratory, Higher School of Biotechnology, Ville Universitaire Ali Mendjeli, BP E66 25100, Constantine, Algeria
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Nogueira IBR, Santana VV, Ribeiro AM, Rodrigues AE. Using Scientific Machine Learning to Develop Universal Differential Equation for Multicomponent Adsorption Separation Systems. CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Idelfonso B. R. Nogueira
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM Department of Chemical Engineering, Faculty of Engineering University of Porto, Rua Dr. Roberto Frias, 4200‐465, Porto Portugal
- ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering University of Porto, Rua Dr. Roberto Frias Porto Portugal
| | - Vinicius V. Santana
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM Department of Chemical Engineering, Faculty of Engineering University of Porto, Rua Dr. Roberto Frias, 4200‐465, Porto Portugal
- ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering University of Porto, Rua Dr. Roberto Frias Porto Portugal
| | - Ana M. Ribeiro
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM Department of Chemical Engineering, Faculty of Engineering University of Porto, Rua Dr. Roberto Frias, 4200‐465, Porto Portugal
- ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering University of Porto, Rua Dr. Roberto Frias Porto Portugal
| | - Alírio E. Rodrigues
- Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM Department of Chemical Engineering, Faculty of Engineering University of Porto, Rua Dr. Roberto Frias, 4200‐465, Porto Portugal
- ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering University of Porto, Rua Dr. Roberto Frias Porto Portugal
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A Complete Heterogeneous Model for the Production of n-Propyl Propionate Using a Simulated Moving Bed Reactor. SEPARATIONS 2022. [DOI: 10.3390/separations9020043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
n-Propyl Propionate (ProPro) is a harmless biodegradable product employed in several fields for the production of drugs, inks, coating, food, and perfume. ProPro is synthesized in an equilibrium reaction for which its yield can be enhanced by constant withdraw of the products as the reaction takes place. Simulated Moving Bed Reactor (SMBR) is a candidate for the production of ProPro with high efficiency as it is a multifunction unit able to simultaneously run reaction and separation, hence shifting the equilibrium reaction toward products. This paper proposes a complete phenomenological model for the ProPro synthesis in a Simulated Moving Bed Reactor (SMBR) packed with the heterogeneous catalyst Amberlyst 46 resin. The operating conditions are defined by the Triangle Theory to design an SMBR unit to produce ProPro efficiently and a comprehensive parameter estimation procedure is employed to obtain more representative parameters. The validated phenomenological model was applied to design an SMBR unit to produce high purity (99.28%) ProPro.
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Harada H, Suzuki K, Sato K, Okada K, Tsuruta M, Yajima T, Kawajiri Y. Process development for advanced simulated moving bed (ASMB) chromatography by parameter refinement using pilot plant experimental data. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2021.119932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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