Neural network approach for modeling the performance of reverse osmosis membrane desalting

Libotean, Dan; Giralt, Jaume; Giralt, Francesc; Ralló, Robert; Wolfe, Tom; Cohen, Yoram

doi:10.1016/j.memsci.2008.10.028

Cited by 69 publications

(13 citation statements)

References 23 publications

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“…Arulchinnappan, presented a multivariate fuzzy regression model to simulate RO process conditions (Arulchinnappan and Rajendran 2011). Artificial neural network (ANN) presented by (Libotean et al 2009;Abbasi Maedeh et al 2013) used numerous daily performance data as inputs to simulate permeate flux and salt passage. A two-dimensional (2-d) mathematical model (computational fluid dynamics and biofilm models) was used by Radu et al (2010) to describe the negative effects of biofilm growth on the performance of a spiral-wound reverse osmosis which indicates the importance of RO system performance monitoring.…”

Section: Introductionmentioning

confidence: 99%

A mathematical method and artificial neural network modeling to simulate osmosis membrane’s performance

Salami

Ehetshami

Karimi-Jashni

et al. 2016

Model. Earth Syst. Environ.

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Lack of fresh water has been a major obstacle to development and flourishing in human history. Desalination provides a new vision toward fresh water production in the upcoming future. The study has proposed a simple mathematical equation and ANN models to simulate eight types of sea water RO membranes. The Artificial Neural Network (ANN) models have been developed to simulate TDS corresponding to the temperature (T,°C), flow rate (gpm) and recovery percentage. The feed data was generated by ROSA software. The model developed using a simple rational mathematical method. ANN models were trained using feed-forward back propagation algorithm with two hidden layers and various numbers of neurons in each layer. The model verification analysis proved both mathematical and ANN models to be highly accurate, reliable and practical for analyzing, designing, operating and optimizing of RO systems. The correlation coefficients (R) of 0.96 and 0.97, respectively, confirmed that the equation and ANN models resulted in this study are in good agreement with the measured data.

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Section: Introductionmentioning

confidence: 99%

A mathematical method and artificial neural network modeling to simulate osmosis membrane’s performance

Salami

Ehetshami

Karimi-Jashni

et al. 2016

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

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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%

“…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. Al-Abri and Hilal [6] developed an ANN model for simulation of a combined humic substance coagulation and membrane filtration.…”

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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“…A variant of the feed water temperature and trans-membrane pressure created to have a significant effect on permeate TDS and flow rate. Libotean et al [21] developed a model with BP and support vector regression (SVR) algorithms for forecasting RO performance and possible use for operational diagnostics located at Port Hueneme, California. It includes the concept of the short-term memory time interval to capture the time-variability of plant performances.…”

Section: R Mahadeva Et Al / Desalination and Water Treatment 156 (2mentioning

confidence: 99%

A review of the artificial neural network based modelling and simulation approaches applied to optimize reverse osmosis desalination techniques

Mahadeva¹,

Manik²,

Goel³

et al. 2019

DWT

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The current global issue of water scarcity has demanded for over-abstraction of conventional freshwater resources. The states of water scarcity are anticipated to worsen, as by 2050 the population is estimated to reach 9 billion worldwide. Desalination is considered a solution to solve the water scarcity issues, as it is considered a drought-proof water source, which does not depend on climate change, river flows or reservoir levels. Moreover, membrane fouling is still the main "Achilles heel" for the effective operation of desalination systems. This makes the technology chemically, energetically and operationally intensive and requires a considerable infusion of capital. The application of an artificial neural network (ANN), the computing model inspired by the human brain, and its variants, have been developed that can optimize the operation of membrane-based desalination system through analyzing the complex experimental and real-time data. This review paper presents the recent trends and developments focussed primarily on the modelling and simulation of reverse osmosis (RO) plant using ANN to solve the challenging problem in membrane-based desalination systems. The literature review suggested that ANN has a potential application in predicting linear, nonlinear, complicated complex systems with high accuracy and with better control, prediction of membrane fouling, cost analysis. Therefore, ANN considered a strong basis to attract and motivate the researchers to work in this field in the future.

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Neural network approach for modeling the performance of reverse osmosis membrane desalting

Cited by 69 publications

References 23 publications

A mathematical method and artificial neural network modeling to simulate osmosis membrane’s performance

A mathematical method and artificial neural network modeling to simulate osmosis membrane’s performance

Artificial neural network model for desalination by sweeping gas membrane distillation

A review of the artificial neural network based modelling and simulation approaches applied to optimize reverse osmosis desalination techniques

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