Artificial neural network models based on QSAR for predicting rejection of neutral organic compounds by polyamide nanofiltration and reverse osmosis membranes

Yangali-Quintanilla, Victor; Verliefde, Arne; Kim, T.-U.; Sadmani, A.H.M. Anwar; Kennedy, Maria D.; Amy, Gary L.

doi:10.1016/j.memsci.2009.06.048

Cited by 105 publications

(53 citation statements)

References 61 publications

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“…As a result, theoretical estimations of expected TrOC rejection have been suggested based on experimental results and the qualitative prediction framework by Bellona et al [8]. Rigorous mathematic [9,10] and artificial neural network [11] models have also been developed to predict and simulate the rejection of TrOCs by LPRO membranes under a range of operating conditions. However, the availability of these predictive tools does not replace the need to experimentally validate the rejection of TrOCs if they are not routinely monitored in potable water recycling applications.…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Validating the rejection of trace organic chemicals by reverse osmosis membranes using a pilot-scale system

et al. 2015

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Nghiem, L. D. (2015). Validating the rejection of trace organic chemicals by reverse osmosis membranes using a pilot-scale system. Desalination, Validating the rejection of trace organic chemicals by reverse osmosis membranes using a pilot-scale system Abstract A protocol to validate the rejection of organic chemicals of potential health risk by low pressure reverse osmosis (LPRO) membranes was developed for decision making support regarding the monitoring level required for potable water reuse. Ten organic chemicals were selected for evaluation, based on their recorded usage, the scarcity of rejection data, and difficulty in analytical determination at concentrations relevant to their potential impact on human-health. An analytical method was developed for these organic chemicals. The target rejections of 90 and 99% for neutral and charged chemicals respectively were successfully achieved under the standard operating condition with only two exceptions (i.e. bisoprolol and carazolol rejections by the TFC-HR membrane). These lower rejections by the TFC-HR can be attributed to its highestwater permeability amongst the three membranes while both bisoprolol and carazolol are positively charged. Changes in operating conditions including permeate flux, feed temperature and chemical cleaning can exert a considerable impact on conductivity rejection by the three LPRO membranes investigated here. Feed temperature showed an apparent impact on the rejection of the selected organic chemicals. However, their rejections were still higher than the target validation values. The protocol developed here can be expanded for the validation of other organic chemicals.

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Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%

Validating the rejection of trace organic chemicals by reverse osmosis membranes using a pilot-scale system

et al. 2015

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“…Several studies have been considered for the application of ANN in modeling of various processes in membrane technology [1][2][3][4][5][6][7][8][9][10]. A feed-forward ANN was developed by Abbas and Al-Bastaki [1] for the prediction of a reverse osmosis (RO) performance using a FilmTec SW30 membrane for desalination of various salt solutions ranging between brackish water and seawater salinities.…”

Section: Introductionmentioning

confidence: 99%

“…Zhao and co-workers [3] have performed a comparison between a modified solution diffusion model and ANN to predict RO/NF water quality effluent. Yangali-Quintanilla et al [4] also used ANN to predict the rejection of neutral organic compounds by NF and RO using polyamide membranes. Libotean and collaborators [5] proposed an ANN with back-propagation to forecast the performance of an RO plant and for potential use in operational diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network model for desalination by sweeping gas membrane distillation

Khayet

Cojocaru

2013

Desalination

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Sweeping gas membrane distillation process (SGMD) has been used for desalination and its performance index, defined as the product of the distillate flux and the salt rejection factor, has been modeled using artificial neural network (ANN) methodology. A feed-forward ANN has been developed for prediction of the performance index based on a set of 53 different experimental SGMD tests. A feed solution of 30 g/L sodium chloride was used in all experiments and the salt rejection factors were found to be greater than 99.4%. The individual and interaction effects of the input variables, namely the feed inlet temperature, the feed flow rate or the feed circulation velocity, and the air flow rate or the air circulation velocity, on the SGMD performance index have been investigated. The optimum point was determined by means of Monte Carlo simulation. The obtained optimal conditions were a feed inlet temperature of 69°C, an air flow rate of 34.5 L/min (i.e. 2.02 m/s air circulation velocity) and a feed flow rate of 160 L/h (i.e. 0.155 m/s liquid circulation velocity). Under these operating conditions a performance index of 1.493×10−3 kg/m 2 .s has been achieved experimentally being the maximal SGMD performance index obtained inside the region of experimentation.

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“…To screen the penetration enhancers, three penetrations covering a broad range of lipophilicity values (logP values of penetrations amounted to -2.11, -0.92 and 7.7 for urea, propylene glycol and oleic acid, respectively) (Table II) (Yangali-Quintanilla et al, 2009) were chosen to investigate the impact of the partitioning characteristic of penetrations on the permeation. The weight ratio of the penetration enhancers were reported in a previously published paper (Halina;Krzysztof;Stanislaw, 2000;Chen et al, 1992).…”

Section: Effect Of the Penetration Enhancers On The Permeationmentioning

confidence: 99%

Formulation and evaluation of transdermal drug-delivery system of isosorbide dinitrate

Zhan

Mao

Chen

et al. 2015

Braz. J. Pharm. Sci.

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The purpose of this study was to develop a reservoir-type transdermal delivery system for isosorbide dinitrate (ISDN). The developed patch consisted of five layers from bottom to top, namely, a temporary liner, an adhesive layer, a rate-controlling membrane, a reservoir and a backing. The effects of chemical penetration enhancers, reservoir materials and rate-controlling membranes on the release behaviour of ISDN from the transdermal patch were studied, and the in vitro release of ISDN from the developed patch was studied and compared with the commercially available ISDN patch. The results showed that there was no significant difference in permeation rates between the developed reservoir-type patch and the commercially available ISDN patch (p > 0.05). Moreover, the cumulative release ratio of the commercially available ISDN patch in 48 h was up to 89.8%, whereas the developed patch was only 34.9%, which meant the sustained release time of the developed patch was much longer than the commercially available ISDN patch, and would promote the satisfaction of the patient. Uniterms:Isosorbide dinitrate/transdermal drug-delivery system. Transdermal patches/in vitro release. Transdermal patches/sustained release. Controlling membrane/drugs release rate. Skin penetration/ drugs release.O objetivo do presente estudo foi desenvolver um sistema de liberação transdérmico do tipo reservatório para o dinitrato de isossorbida (ISDN, abrevitura em Inglês). A formulação transdérmica desenvolvida constou de cinco camadas de baixo para cima, ou seja, um revestimento temporário, uma camada adesiva, uma membrana controladora da taxa de liberação, um reservatório e um reforço. Estudaram-se os efeitos dos potenciadores de penetração química, materiais do reservatório e membranas de controle da taxa de liberação no comportamento da formulação transdérmica de dinitrato de isossorbida. A liberação in vitro da formulação transdérmica de dinitrato de isossorbida desenvolvida foi estudada em comparação com a formulação de dinitrato de isossorbida disponível comercialmente. Os resultados mostraram que não existem diferenças significativa nas taxas de permeação entre o tipo de reservatório desenvolvido e o de dinitrato de isossorbida desenvolvido comercialmente (p>0,05). Ademais, a taxa de liberação cumulativa da formulação de dinitrato de isossorbida disponível comercialmente em 48 horas foi de até 89,8% e a da formulação desenvolvida, de apenas de 34,9%, o que provou que a liberação sustentada da formulação desenvolvida foi muito maior do que a de dinitrato de isossorbida desenvolvida comercialmente, o que promoveria a satisfação do paciente.

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Artificial neural network models based on QSAR for predicting rejection of neutral organic compounds by polyamide nanofiltration and reverse osmosis membranes

Cited by 105 publications

References 61 publications

Validating the rejection of trace organic chemicals by reverse osmosis membranes using a pilot-scale system

Validating the rejection of trace organic chemicals by reverse osmosis membranes using a pilot-scale system

Artificial neural network model for desalination by sweeping gas membrane distillation

Formulation and evaluation of transdermal drug-delivery system of isosorbide dinitrate

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