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Relevant bibliographies by topics / Silt Density Index (SDI)

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Academic literature on the topic 'Silt Density Index (SDI)'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Silt Density Index (SDI)"

1

Nahrstedt, A., and J. Camargo-Schmale. "New insights into silt density index and modified fouling index measurements." Water Supply 8, no. 4 (October 1, 2008): 401–11. http://dx.doi.org/10.2166/ws.2008.087.

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In order to investigate the accuracy and reproducibility of the parameters SDI (silt density index) and MFI (modified fouling index), tests in diverse conditions were performed: with three different types of filter holder, two microfilter pore sizes, with and without permeate spacer and with two foulants (alginate as organic foulant and silica flour as the particulate). Additionally the effect of pressure on fouling indices was analysed. It was concluded, that there is a need to define more boundary conditions in the ASTM standard (exact type of filter holder, exact type of membrane, start conditions) for the SDI to achieve a parameter with reliable and comparable values. But in comparison to MFI, SDI seems to be a more robust parameter for a use in practice pertaining to the influence of a feed pressure level and the effective membrane area. The MFI gives more insights in acting mechanisms and offers advantages for research. It shows a correlation to a concentration of particulate or organic foulants. Like the SDI, it is necessary to define and standardize exact boundary conditions for the MFI tests (classified by the use of MF, UF or NF filter media).

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2

Wei, Chun-Hai, Stephanie Laborie, Roger Ben Aim, and Gary Amy. "Full utilization of silt density index (SDI) measurements for seawater pre-treatment." Journal of Membrane Science 405-406 (July 2012): 212–18. http://dx.doi.org/10.1016/j.memsci.2012.03.010.

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3

Prihasto, N., and Seung-Hyun Kim. "The sensitivity of SDI and MFI to a change in particle concentration and properties under saline conditions." Journal of Water Reuse and Desalination 2, no. 3 (September 1, 2012): 140–48. http://dx.doi.org/10.2166/wrd.2012.064.

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The sensitivity of existing fouling indices of SDI (silt density index) and MFI (modified fouling index) to a change in particle concentration and properties was evaluated in this study. The MFI formula predicts an increased fouling tendency when the particle concentration increases and the particle size and density decrease. Consequently, concentration, size and density were selected as parameters to examine. For this purpose, silica particles and Aldrich humic acid (HA) were selected as target foulants because of the substantial difference in their size and density. According to this study, both SDI and MFI were sensitive to a change in particle properties of size and density, while MFI was more sensitive to a change in particle concentration than SDI. It is also found that the fouling tendency could be represented better by separate evaluation of T0 and T15 than SDI, at which the ratio of T0 and T15 is evaluated. T15 is found to be as sensitive as the MFI for both silica suspension and HA solution. The saline condition increased the fouling index. MFI and T15 were sensitive to a change in ionic strength. When inorganic and organic foulants were mixed, their relative proportion determined the fouling index values.

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4

Park, C., H. Kim, S. Hong, S. Lee, and S. I. Choi. "Evaluation of organic matter fouling potential by membrane fouling index." Water Supply 7, no. 5-6 (December 1, 2007): 27–33. http://dx.doi.org/10.2166/ws.2007.130.

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The membrane fouling index, widely used in the reverse osmosis (RO) membrane industry, is developed mainly to assess the fouling potential of particulate matter. However, the fouling potential by organic matter should also be accurately evaluated as the productivity loss by organic fouling is often more severe than particle fouling, despite its low concentration in feed water. This study was performed to investigate the influence of organic characteristics and feed water solution chemistry on the membrane fouling index, such as the silt density index (SDI). The results showed that the feed solution chemistry (i.e. pH, ionic strength and hardness) affected the SDI values of organic rich feed water to some degree. In addition, Aldrich humic acid (AHA) exhibited higher fouling potential by SDI than Suwannee river humic acid (SRHA), emphasising the importance of organic properties. Lastly, it is shown that although SDI values measured were the same, the degree of NOM fouling was significantly different for the membranes with different surface properties.

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Takeuchi, K., K. Matsui, Y. Ito, Y. Goda, I. Hamano, and N. Ukawa. "Analysis of the filtration curve and the effect of temperature on silt density index (SDI)." Desalination and Water Treatment 57, no. 45 (December 18, 2015): 21364–76. http://dx.doi.org/10.1080/19443994.2015.1123653.

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6

Rachman, R. M., N. Ghaffour, F. Wali, and G. L. Amy. "Assessment of silt density index (SDI) as fouling propensity parameter in reverse osmosis (RO) desalination systems." Desalination and Water Treatment 51, no. 4-6 (January 2013): 1091–103. http://dx.doi.org/10.1080/19443994.2012.699448.

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7

Habib, Muddasar, Unsia Habib, Abdul Rehman Memon, Usman Amin, Zahid Karim, Amad Ullah Khan, Sayed Naveed, and Sultan Ali. "Predicting colloidal fouling of tap water by silt density index (SDI): Pore blocking in a membrane process." Journal of Environmental Chemical Engineering 1, no. 1-2 (June 2013): 33–37. http://dx.doi.org/10.1016/j.jece.2013.03.005.

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8

Riverol, C., and M. V. Pilipovik. "Prediction of the behaviour of the Silt Density Index (SDI) in the Caribbean Seawater and its impact on RO desalination plants." Desalination 268, no. 1-3 (March 2011): 262–65. http://dx.doi.org/10.1016/j.desal.2010.09.049.

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9

Abushaban, Almotasembellah, Sergio G. Salinas-Rodriguez, Moses Kapala, Delia Pastorelli, Jan C. Schippers, Subhanjan Mondal, Said Goueli, and Maria D. Kennedy. "Monitoring Biofouling Potential Using ATP-Based Bacterial Growth Potential in SWRO Pre-Treatment of a Full-Scale Plant." Membranes 10, no. 11 (November 21, 2020): 360. http://dx.doi.org/10.3390/membranes10110360.

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Several potential growth methods have been developed to monitor biological/organic fouling potential in seawater reverse osmosis (SWRO), but to date the correlation between these methods and biofouling of SWRO has not been demonstrated. In this research, the relation between a new adenosine triphosphate (ATP)-based bacterial growth potential (BGP) test of SWRO feed water and SWRO membrane performance is investigated. For this purpose, the pre-treatment of a full-scale SWRO plant including dissolved air flotation (DAF) and two stage dual media filtration (DMF) was monitored for 5 months using BGP, orthophosphate, organic fractions by liquid chromatography coupled with organic carbon detection (LC-OCD), silt density index (SDI), and modified fouling index (MFI). Results showed that particulate fouling potential was well controlled through the SWRO pre-treatment as the measured SDI and MFI in the SWRO feed water were below the recommended values. DAF in combination with coagulation (1–5 mg-Fe3+/L) consistently achieved 70% removal of orthophosphate, 50% removal of BGP, 25% removal of biopolymers, and 10% removal of humic substances. Higher BGP (100–950 µg-C/L) in the SWRO feed water corresponded to a higher normalized pressure drop in the SWRO, suggesting the applicability of using BGP as a biofouling indicator in SWRO systems. However, to validate this conclusion, more SWRO plants with different pre-treatment systems need to be monitored for longer periods of time.

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Dias, Isabelli N., Ana C. Cerqueira, Geraldo L. Sant'Anna, and Marcia Dezotti. "Oil refinery wastewater treatment in biofilm reactor followed by sand filtration aiming water reuse." Journal of Water Reuse and Desalination 2, no. 2 (June 1, 2012): 84–91. http://dx.doi.org/10.2166/wrd.2012.022.

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Oil refinery wastewater was sequentially treated in a moving-bed biofilm reactor (MBBR) and a slow-rate sand filter (SF) in order to obtain an effluent with adequate characteristics for downstream reverse osmosis (RO) operation. Experiments were conducted in bench scale units and the results showed that the MBBR was able to remove 90% chemical oxygen demand (COD), 75% NH4+, 95% phenols, operating with a hydraulic retention time (HRT) of 9 h. Additional removal of COD (15–40%) and ammonia (30–60%) was achieved in the slow-rate SF that was also effective for removing microorganisms. The silt density index (SDI) of the treated wastewater (4.5) was below the maximum limit recommended for RO operation. The quality of the effluent from the combined treatment system (MBBR+SF) was already adequate for cooling tower make-up. The RO produced an effluent with quality compatible with that required for use in boilers.

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Dissertations / Theses on the topic "Silt Density Index (SDI)"

1

Gal, Yona. "Microbubble interferences with silt density index measurements of ultrafiltration permeate /." [Sede-Boker] : Ben-Gurion University of the Negev, 2007. http://aranne5.lib.ad.bgu.ac.il/others/GalYona.pdf.

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2

Rachman, Rinaldi. "Assessment of Silt Density Index (SDI) as Fouling Propensity Parameter in Reverse Osmosis Desalination." Thesis, 2011. http://hdl.handle.net/10754/209376.

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Reverse osmosis operations are facing persistent fouling phenomenon that has challenged the integrity of these processes. Prediction of fouling potential by measuring a fouling index toward feed water is essential to ensure robust operation. Moreover, employing a reliable fouling index with good reproducibility and precision is necessary. Silt density index (SDI) is considered insufficient in terms of reliability and empirical theory, among other limitations. Nevertheless due its simplicity, SDI measurement is utilized extensively in RO desalination systems. The aim of this research is to assess the reliability of SDI. Methods include the investigation of different SDI membranes and study of the nature of the SDI filtration. Results demonstrate the existence of the membrane properties' variation within manufacturers, which then causes a lack of accuracy in fouling risk estimation. The nature of particles during SDI filtration provides information that particle concentration and size play a significant role on SDI quantification with substantial representation given by particles with size close to membrane nominal pore size. Moreover, turbidity assisted SDI measurements along with determination of UF pretreated and clean water fouling potential, establishes the indication of non-fouling related phenomena involved on SDI measurement such as a natural organic matter adsorption and hydrodynamic condition that alters during filtration. Additionally, it was found that the latter affects the sensitivity of SDI by being represented by some portions of SDI value. Keywords: Reverse Osmosis, Fouling index, Particulate Fouling, Silt Density Index (SDI), and Assessment of SDI.

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Conference papers on the topic "Silt Density Index (SDI)"

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Acosta, Elias R., Bhagwanpersad Nandlal, and Ryan Harripersad. "SATURATION EXPONENT AS A FUNCTION OF RESERVOIR HETEROGENEITY AND WETTABILITY IN THE TAMBAREDJO OIL FIELD, SURINAME." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0118.

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This research proposed an alternative method for determining the saturation exponent (n) by finding the best correlations for the heterogeneity index using available core data and considering wettability changes. The log curves of the variable n were estimated, and the effect on the water saturation (Sw) calculations and the Stock Tank Oil Initially In Place (STOIIP) in the Tambaredjo (TAM) oil field was analyzed. Core data were employed to obtain the relationship between n and heterogeneity using cross-plots against several heterogeneity indices, reservoir properties, and pore throat size. After filtering the data, the clay volume (Vcl), shale volume, silt volume, basic petrophysical property index (BPPI), net reservoir index, pore grain volume ratio, and rock texture were defined as the best matches. Their modified/improved equations were applied to the log data and evaluated. The n related to Vcl was the best selection based on the criteria of depth variations and logical responses to the lithology. The Sw model in this field showed certain log readings (high resistivity [Rt] reading ≥ 500 ohm.m) that infer these intervals to be probable inverse-wet (oil-wet). The cross-plots (Rt vs. Vcl; Rt vs. density [RHOB]; Rt vs. total porosity [PHIT]) were used to discard the lithologies related to a high Rt (e.g., lignites and calcareous rocks) and to correct Sw when these resulted in values below the estimated irreducible water saturation (Swir). The Sw calculations using the Indonesian equation were updated to incorporate n as a variable (log curves), comparing it with Sw from the core data and previous calculations using a fixed average value (n = 1.82) from the core data. An integrated approach was used to determine n, which is related to the reservoir’s heterogeneity and wettability changes. The values of n for high Rt (n > 2) intervals ranged from 2.3 to 8.5, which is not close to the field average n value (1.82). Specific correlations were found by discriminating Swir (Swir < 15%), (Swir 15%–19%), and Swir (> 19%). The results showed that using n as a variable parameter improved Sw from 39.5% to 36.5% average in the T1 and T2 sands, showing a better fit than the core data average and increasing the STOIIP estimations by 6.81%. This represents now a primary oil recovery of 12.1%, closer to the expected value for these reservoirs. Although many studies have been done on n determination and its effect on Sw calculations, using average values over a whole field is still a common practice regardless of heterogeneity and wettability considerations. This study proposed a method to include the formation of heterogeneity and wettability changes in n determination, allowing a more reliable Sw determination as demonstrated in the TAM oil field in Suriname.

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Reports on the topic "Silt Density Index (SDI)"

1

Cain, S. M. Predicting the performance of ceramic filters by the use of silt density index. Office of Scientific and Technical Information (OSTI), January 1989. http://dx.doi.org/10.2172/5379129.

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