Exploring sand and bentonite - enhanced sand as filter media for nitrate removal

Increasing nitrate concentrations in ground water is deleterious to human health as ingestion of such water can cause methemoglobinemia in infants and even cancer in adults (desirable limit for nitrate as NO3 - 45 mg/L, IS code 10500-1991). Excess nitrate concentrations in ground water is contributed by reason being disposal of sewage and excessive use of fertilizers. Though numerous technologies such as reverse osmosis, ion exchange, electro-dialysis, permeable reactive barriers using zerovalent iron etc exists, nitrate removal continues to be one of challenging issue as nitrate ion is highly mobile within the soil strata. The tapping the denitrification potential of soil denitrifiers which are inherently available in the soil matrix is the most sustainable approach to mitigate accumulation of nitrate in ground water. The insitu denitrification of sand and bentonite enhanced sand (bentonite content = 5%) in presence of easily assimilable organic carbon such as ethanol was studied. Batch studies showed that nitrate reduction by sand follows first order kinetics with a rate constant 5.3x10(-2) hr(-1) and rate constant 4.3 x 10(-2) hr(-1) was obtained for bentonite-enhanced sand (BS) at 25 degrees C. Filter columns (height = 5 cm and diameter = 8.2 cm) were constructed using sand and bentonite-enhanced sand as filter media. The filtration rate through both the filter columns was maintained at average value of 2.60 cm/h. The nitrate removal rates through both the filter media was assessed for solution containing 22.6 mg NO3-N/L concentrations while keeping C/N mass ratio as 3. For sand filter column, the nitrate removal efficiency reached the average value of 97.6% after passing 50 pore volumes of the nitrate solution. For bentonite-enhanced sand filter column, the average nitrate removal efficiency was 83.5%. The time required for effective operation for sand filter bed was 100 hours, while bentonite-enhanced sand filter bed did not require any maturation period as that of sand filter bed for effective performance because the presence of micropores in bentonite increases the hydraulic retention time of the solution inside the filter bed.

Generalized Partial Response Equalization and Data-Dependent Noise Predictive Signal Detection Over Media Models for TDMR

Two-dimensional magnetic recording (2-D TDMR) is an emerging technology that aims to achieve areal densities as high as 10 Tb/in(2) using sophisticated 2-D signal-processing algorithms. High areal densities are achieved by reducing the size of a bit to the order of the size of magnetic grains, resulting in severe 2-D intersymbol interference (ISI). Jitter noise due to irregular grain positions on the magnetic medium is more pronounced at these areal densities. Therefore, a viable read-channel architecture for TDMR requires 2-D signal-detection algorithms that can mitigate 2-D ISI and combat noise comprising jitter and electronic components. Partial response maximum likelihood (PRML) detection scheme allows controlled ISI as seen by the detector. With the controlled and reduced span of 2-D ISI, the PRML scheme overcomes practical difficulties such as Nyquist rate signaling required for full response 2-D equalization. As in the case of 1-D magnetic recording, jitter noise can be handled using a data-dependent noise-prediction (DDNP) filter bank within a 2-D signal-detection engine. The contributions of this paper are threefold: 1) we empirically study the jitter noise characteristics in TDMR as a function of grain density using a Voronoi-based granular media model; 2) we develop a 2-D DDNP algorithm to handle the media noise seen in TDMR; and 3) we also develop techniques to design 2-D separable and nonseparable targets for generalized partial response equalization for TDMR. This can be used along with a 2-D signal-detection algorithm. The DDNP algorithm is observed to give a 2.5 dB gain in SNR over uncoded data compared with the noise predictive maximum likelihood detection for the same choice of channel model parameters to achieve a channel bit density of 1.3 Tb/in(2) with media grain center-to-center distance of 10 nm. The DDNP algorithm is observed to give similar to 10% gain in areal density near 5 grains/bit. The proposed signal-processing framework can broadly scale to various TDMR realizations and areal density points.

Modeling the co-transport of viruses and colloids in unsaturated porous media

A mathematical model is developed to simulate the co-transport of viruses and colloids in unsaturated porous media under steady-state flow conditions. The virus attachment to the mobile and immobile colloids is described using a linear reversible kinetic model. Colloid transport is assumed to be decoupled from virus transport; that is, we assume that colloids are not affected by the presence of attached viruses on their surface. The governing equations,are solved numerically using an alternating three-step operator splitting approach. The model is verified by fitting three sets of experimental data published in the literature: (1) Syngouna and Chrysikopoulos (2013) and (2) Walshe et al. (2010), both on the co-transport of viruses and clay colloids under saturated conditions, and (3) Syngouna and Cluysikopoulos (2015) for the co-transport of viruses and clay colloids under unsaturated conditions. We found a good agreement between observed and fitted breakthrough curves (BTCs) under both saturated and unsaturated conditions. Then, the developed model was used to simulate the co-transport of viruses and colloids in porous media under unsaturated conditions, with the aim of understanding the relative importance of various processes on the co-transport of viruses and colloids in unsaturated porous media. The virus retention in porous media in the presence of colloids is greater during unsaturated conditions as compared to the saturated conditions due to: (1) virus attachment to the air-water interface (AWI), and (2) co-deposition of colloids with attached viruses on its surface to the AWL A sensitivity analysis of the model to various parameters showed that the virus attachment to AWI is the most sensitive parameter affecting the BTCs of both free viruses and total mobile viruses and has a significant effect on all parts of the BTC. The free and the total mobile viruses BTCs are mainly influenced by parameters describing virus attachment to the AIM, virus interaction with mobile and immobile colloids, virus attachment to solid-water interface (SWI), and colloid interaction with SWI and AWL The virus BTC is relatively insensitive to parameters describing the maximum adsorption capacity of the AWI for colloids, inlet colloid concentration, virus detachment rate coefficient from the SW!, maximum adsorption capacity of the AWI for viruses and inlet virus concentration. (C) 2015 Elsevier B.V. All rights reserved.

Bearing capacity of a circular foundation on layered sand-clay media

The bearing capacity of a circular footing lying over fully cohesive strata, with an overlaying sand layer, is computed using the axisymmetric lower bound limit analysis with finite elements and linear optimization. The effects of the thickness and the internal friction angle of the sand are examined for different combinations of c(u)/(gamma b) and q, where c(u)=the undrained shear strength of the cohesive strata, gamma=the unit weight of either layer, b=the footing radius, and q=the surcharge pressure. The results are given in the form of a ratio (eta) of the bearing capacity with an overlaying sand layer to that for a footing lying directly over clayey strata. An overlaying medium dense to dense sand layer considerably improves the bearing capacity. The improvement continuously increases with decreases in c(u)/(gamma b) and increases in phi and q/(gamma b). A certain optimum thickness of the sand layer exists beyond which no further improvement occurs. This optimum thickness increases with an increase in 0 and q and with a decrease in c(u)/(gamma b). Failure patterns are also drawn to examine the inclusion of the sand layer. (C) 2015 The Japanese Geotechnical Society. Production and hosting by Elsevier B.V. All rights reserved.

Prototype Selection on Large and Streaming Data

Since streaming data keeps coming continuously as an ordered sequence, massive amounts of data is created. A big challenge in handling data streams is the limitation of time and space. Prototype selection on streaming data requires the prototypes to be updated in an incremental manner as new data comes in. We propose an incremental algorithm for prototype selection. This algorithm can also be used to handle very large datasets. Results have been presented on a number of large datasets and our method is compared to an existing algorithm for streaming data. Our algorithm saves time and the prototypes selected gives good classification accuracy.