Separation of water–acetic acid mixture by pervaporation through plasma-treated asymmetric poly(4-methyl-1-pentene) membrane and dip-coated with polyacrylic acid

Wang, Yi-Chieh; Li, Chi-Lan; Chang, Pi-Feng; Fan, Shu-Chin; Lee, Kueir‐Rarn; Lai, Juin‐Yih

doi:10.1016/s0376-7388(01)00673-1

Cited by 36 publications

(5 citation statements)

References 17 publications

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“…Pervaporation (PV) is a popular process for organic dehydration and, more recently, for organic separation, especially in low-concentration aqueous solutions . PV exhibits several advantages over conventional separation processes, including a small footprint, low energy demand, and high selectivity. , In PV, the permeable component adsorbs and diffuses through the semipermeable membrane, driven by the chemical potential difference across the membrane . The selectivity between different components is evaluated by the separation factor β = y B y A / x B x A , expressed by the relative mass fractions of permeate components ( B ) and coexisting component ( A ) in the permeate ( y ) and feed ( x ) streams, where higher separation factor indicates better selectivity toward the permeate component (B) .…”

Section: Introductionmentioning

confidence: 99%

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management

Yang

Zhu

McGaughey

et al. 2023

Environ. Sci. Technol.

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The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO 2 during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers' properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables' importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers' concentration and membranes' effective area contributed to information leakage. These results demonstrate ML models' advances in membrane design and fabrication and the importance of vigorous model validation.

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Section: Introductionmentioning

confidence: 99%

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management

Yang

Zhu

McGaughey

et al. 2023

Environ. Sci. Technol.

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“…The modification of the membrane surface chemistry can be induced by using different techniques as blends, dip-coatings, wet chemical treatments, grafting, plasma, etc. [13][14][15][16]. As changes in surface free energies occur, dispersion as well as dipolar, induction, H-bonding or metallic interactions can direct the adhesion of chemical species on the membrane surface, controlling the separation.…”

Section: Introductionmentioning

confidence: 99%

Study of the surface character as responsible for controlling interfacial forces at membrane–feed interface

Gugliuzza

Reniero

Garavaglia

et al. 2006

Journal of Colloid and Interface Science

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“…The dehydration of aqueous acetic acid mixtures has received relatively little attention because of difficulty in the selection of the membrane material. Several membrane materials have been developed for this purpose, − almost all of them of polymeric nature. However, the membranes that have been employed in such a separation often failed .…”

Section: Introductionmentioning

confidence: 99%

Pervaporative Recovery of Acetic Acid from an Acetylation Industrial Effluent Using Commercial Membranes

Gorri

Urtiaga

Ortiz

2005

Ind. Eng. Chem. Res.

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This paper reports the comparative behavior of commercial pervaporation membranes in the recovery of acetic acid from acetylation industrial effluents. The main components of the industrial feed mixture are acetic acid and water; the initial content C H 2 O = 24−26 wt %. The performances of four commercial membranes were investigated; the results indicate that all of the membranes were highly selective and permeable toward water. Both the selectivity and permeability were strongly feed-composition-dependent. The selectivity decreased and the permeability increased as the water concentration in the feed increased. Calculation of the pervaporation separation index (at 80 °C and 10 wt % water concentration) as the product of flux and the separation factor led to the selection of the Symplex membrane (GKSS) that behaved more successfully. Because the Symplex membrane is not commercially available on and industrial scale, the CMC-CF23 membrane from Celfa was selected in order to make detailed studies. After a careful experimental design, the flux rate of water permeation and the separation selectivity were determined in the range of temperature 50−80 °C and water content 1−25 wt %. Increased temperatures resulted in larger fluxes. For a fixed water concentration value in the feed of 10 wt %, the water flux through the CMC-CF23 membrane increased from 0.38 kg m-2 h-1 (50 °C) to 1.56 kg m-2 h-1 (80 °C). The results were satisfactorily predicted by a semiempirical model based on Fick's law with concentration-dependent diffusivity parameters.

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Separation of water–acetic acid mixture by pervaporation through plasma-treated asymmetric poly(4-methyl-1-pentene) membrane and dip-coated with polyacrylic acid

Cited by 36 publications

References 17 publications

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management

Study of the surface character as responsible for controlling interfacial forces at membrane–feed interface

Pervaporative Recovery of Acetic Acid from an Acetylation Industrial Effluent Using Commercial Membranes

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