Statistical regression and modeliing analysis for reverse osmosis desalination process

Subramani, Sobana; Panda, Rames C.

doi:10.1016/j.desal.2014.07.038

Cited by 15 publications

(12 citation statements)

References 7 publications

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“…Statistical analysis was carried out using single-factor analysis of variance (ANOVA) [30]. A value of p ≤ 0.05 was considered statistically significant.…”

Section: Discussionmentioning

confidence: 99%

In situ investigation of combined organic and colloidal fouling for nanofiltration membrane using ultrasonic time domain reflectometry

Zhang

Hou

et al. 2015

Desalination

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“…Statistical analysis was carried out using single-factor analysis of variance (ANOVA) [30]. A value of p ≤ 0.05 was considered statistically significant.…”

Section: Discussionmentioning

confidence: 99%

In situ investigation of combined organic and colloidal fouling for nanofiltration membrane using ultrasonic time domain reflectometry

Zhang

Hou

et al. 2015

Desalination

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“…Similarly, Kolluri et al (2015) used Multivariate ANOVA to evaluate the performance and efficiencies of the pretreatment processes. Also, Subramani et al (2014) applied a statistical analysis based on stream characteristic data (flow rate, concentration, and pH) over time while the significance of regression was evaluated based on Multivariate ANOVA and found that the permeate characteristics are dependent on feed stream flow rate.…”

Section: Anova and Mnaova Methodsmentioning

confidence: 99%

Productivity Prediction of Sea Water Reverse Osmosis Desalination Plant Using Robust Regression

Sabry,

Youssef,

El-Kilany

2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Multiple Linear Regression (MLR) is utilized to correlate the process parameters to the output permeate flow rate for a Seawater Reverse Osmosis (SWRO) desalination plant. Thorough residual analysis robust regression provided alternative methods to predict the response with higher accuracy compared to basic MLR analysis. Robust regression methods are developed in this study to predict the productivity of a seawater reverse osmosis plant based on key empirical correlations. The robust regression methodology considers important input operating parameters such as feed flow rate, feed pressure, outlet pressure of the multi-media filter, inlet and outlet cartridge filter pressures, outlet pressure of the high-pressure pump, and inlet seawater flow rate to pressure exchanger and correlates them to the permeate flow rate. The robust regression models are capable of accurately predicting response for any input operating parameters for the reverse osmosis plant. The regression models demonstrated strong statistical goodness-of-fit measures using Huber's method in terms of three-way interactions with a high R 2 of approximately 0.99 and a mean absolute percentage error of 2.4%. Furthermore, the robust regression results have been validated experimentally and the results showed very good agreement with measured values, with an error of approximately 0.8%.

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“…Reverse osmosis (RO), pressure-driven membrane process, has been widely used in the field of wastewater reclamation [1] and desalination [2,3] due to the higher salt rejection with the simple operation. RO uses hydraulic pressure as the driving force for water transport through the membrane, thus finally leading to the long-standing problem of fouling [4].…”

Section: Introductionmentioning

confidence: 99%

Relating membrane surface properties and flux recovery during the chemical cleaning of forward osmosis membrane

Ruengruehan

Fagkaew

Ahn

et al. 2016

Desalination and Water Treatment

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In this study, we evaluated the efficiencies of chemical cleaning agents for the removal of foulant and related the changes of membrane surface properties to the flux recovery in forward osmosis (FO) membrane process. Commercially available chemical cleaning agents were added and processed according to the protocols of reverse osmosis (RO) membrane processes into two steps-acid treatment, then base treatment, and the flux recovery and membrane surface properties were tested in FO mode. The flux of the fouled membrane was only 23% of initial water flux tested in FO mode, and the addition of cleaning chemicals could improve the water flux to be 65 and 83% of initial water flux after each step, respectively. The reverse flux selectivity of fouled membrane was increased up to 61 and 86% after each cleaning steps due to the decrease in the concentration polarization inside the layer. The removal of biofouling layer during each cleaning step was demonstrated by scanning electron microscopy (SEM) and FT-IR, while incomplete removal of colloidal matters on membrane surfaces was observed. The total membrane surface free energy was increased after the cleaning steps due to the increase in electron-donating and electron-accepting character. Overall, chemical cleaning agents designed for RO could remove various membrane foulants as well as recover the hydrophilicity of membrane surfaces and consequently be applied for the cleaning of membranes operated in FO process, but additional cleaning protocol should be developed for the removal of colloidal matters from membrane surfaces.

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Statistical regression and modeliing analysis for reverse osmosis desalination process

Cited by 15 publications

References 7 publications

In situ investigation of combined organic and colloidal fouling for nanofiltration membrane using ultrasonic time domain reflectometry

In situ investigation of combined organic and colloidal fouling for nanofiltration membrane using ultrasonic time domain reflectometry

Productivity Prediction of Sea Water Reverse Osmosis Desalination Plant Using Robust Regression

Relating membrane surface properties and flux recovery during the chemical cleaning of forward osmosis membrane

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