Effect of operating conditions in removal of arsenic from water by nanofiltration membrane

The removal of arsenic from synthetic waters and surface water by nanofiltration (NF) membrane was investigated. In synthetic solutions, arsenic rejection experiments included variation of arsenic retentate concentration, transmembrane pressure, crossflow velocity and temperature. Arsenic rejection increased with arsenic retentate concentration. Arsenic was removed 93-99% from synthetic feed waters containing between 100 and 382 μg/L as V, resulting in permeate arsenic concentrations of about 5 μg/L. Under studied conditions, arsenic rejection was independent of transmembrane pressure, crossflow velocity and temperature. In surface water, the mean rejection of As V was 95% while the rejection of sulfate was 97%. The co-occurrence of dissolved inorganics does not significantly influence arsenic rejection. The mean concentration of As in collected permeated was 8 μg/L. The mean rejection of TDS, total hardness and conductivity were 75, 88 and 75% respectively.     

Einfluß von Lösungsmitteln auf den Partikeldurchmesser von Dispersionen anionischer Polyurethanionomerer

Aqueous dispersions of anionic polyurethanes were prepared by reaction of polyethers, 1,6-hexamethylene diisocyanate, 1,2,4-benzenetricarboxylic acid anhydride and triethyl amine. Formation of the dispersion was achieved by phase reversal or by precipitation. Phase reversal occurred when the solution or the melt of the polymer was treated with water, while precipitation took place when the solution was stirred into water. The amount of acetone used has an effect on the mean particle diameter, D?_T, obtained by turbidity measurements. There was an optimal acetone concentration at which D?_T reached a minimum. A similar effect was observed when tetrahydrofuran, 2-butanone or acetonitrile were used as solvents instead of acetone. When the dispersion was formed by precipitation, only adequately diluted acetonic solutions formed colloidal dispersions, while concentrated solutions gave dispersions with a broad particle size distribution.     

Practical validation of the two-fluid model applied to dense gas-solid flows in fluidized beds

This paper discusses the simulation of bubbling gas-solid flows by using the Eulerian two-fluid approach. Predictions of particle motion, bed expansion, bubble size and bubble velocity in bubbling beds containing Geldart B particles are compared with experimental results and correlations found in the literature. In addition, gas mixing in a bed of Geldart A particles is investigated.An in-house code has been developed based on the finite-volume method and the time-splitting approach using a staggered grid arrangement. The velocities in both phases are obtained by solving the 2D Reynolds-averaged Navier/Stokes equations using a partial elimination algorithm (PEA) and a coupled solver. The k-ε turbulence model is used to describe the turbulent quantities in the continuous phase.In general, the model predictions are in good agreement with experimental data found in the literature. Most important observations are: the level of the restitution coefficient was found to be crucial in order to obtain successful results from 2D axisymmetric simulations of a system containing Geldart B particles. Bubble size and bubble rise velocities are not as sensitive to the restitution coefficient. The turbulence model is of outmost importance concerning gas mixing in a fluidized bed of Geldart A particles.From these numerical analyzes an optimized granular flow two-fluid model can be designed for the purpose of simulating reactive systems in fluidized bed reactors.     

A linear algebra approach to OLAP

Inspired by the relational algebra of data processing, this paper addresses the foundations of data analytical processing from a linear algebra perspective. The paper investigates, in particular, how aggregation operations such as cross tabulations and data cubes essential to quantitative analysis of data can be expressed solely in terms of matrix multiplication, transposition and the Khatri–Rao variant of the Kronecker product. The approach offers a basis for deriving an algebraic theory of data consolidation, handling the quantitative as well as qualitative sides of data science in a natural, elegant and typed way. It also shows potential for parallel analytical processing, as the parallelization theory of such matrix operations is well acknowledged.     

Fast online graph clustering via Erd?s-Rényi mixture

In the context of graph clustering, we consider the problem of simultaneously estimating both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size over time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms are an efficient alternative to classical batch algorithms. We present an original online algorithm for graph clustering based on a Erd?s-Rényi graph mixture. The relevance of the algorithm is illustrated, using both simulated and real data sets. The real data set is a network extracted from the French political blogosphere and presents an interesting community organization.     

Experimental measurement of a time-varying optical path difference by the small-aperture beam technique

We discuss the use of time series of the jitter angle of multiple, small-aperture probe beams (the small-aperture beam technique, or SABT) as they emerge from a turbulent, optically active flow-field to quantify the time-varying optical path difference (OPD). The flow field studied is that for the transitionally turbulent region of a two-dimensional heated jet. Techniques to construct a complete time series of instantaneous realizations of the OPD are first applied to a numerically generated flow field and then to an experimental flow field. The SABT sensor's measurement accuracy is assessed, and its application to flow fields that differ from the numerical heated jet is discussed.     

Health learning materials support in South Africa compared with other developing countries

An investigation was carried out in to thyroid hormones (TSH, T3, T4) and lipid parameters (total cholesterol, LDL cholesterol, HDL cholesterol, triglyceride) in 136 adolescents (94 femals, average age 13 years). An iodine deficiency (grade II-II) with respect to the daily urine excretion per 1,73 m2 BSA was found in 75%. With few exceptions the serum levels of TSH and T4 were in the normal range. In 36% of the patients we noticed compensatory elevated T3 levels. Correlations between thyroid hormones TSH, T4, renal iodine excretion and the volume of thyroid glands were not detectable, only T3 showed a dignificant positive correlation to the thyroid gland volume. The average values of lipids in patients were found to be higher than in normals. We consider the changed lipids as a sign of a disturbed efficacy of thyroid hormones. The regional insufficient iodine supply causes goiters and to a high degree the observed hyperchole-sterolemia, too. Our results underline the necessity of a common iodine salt prophylaxis as well as the treatment of "harmless" goiters in puberty.     

Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage

Iris recognition has been widely used in several scenarios with very satisfactory results. As it is one of the earliest stages, the image segmentation is in the basis of the process and plays a crucial role in the success of the recognition task. In this paper we analyze the relationship between the accuracy of the iris segmentation process and the error rates of three typical iris recognition methods. We selected 5000 images of the UBIRIS, CASIA and ICE databases that the used segmentation algorithm can accurately segment and artificially simulated four types of segmentation inaccuracies. The obtained results allowed us to conclude about a strong relationship between translational segmentation inaccuracies – that lead to errors in phase – and the recognition error rates.     

Outliers detection in environmental monitoring databases

Environmental monitoring is nowadays an important task in many industrial operations. In order to comply with strong environmental laws, they have implemented monitoring systems based on a network of air quality and meteorological stations providing real-time measurements of key variables associated to the distribution of pollutants in surrounding areas. These measurements can be contaminated by outliers, which must be discarded in order to have a consistent set of data. This work presents a nonlinear procedure for outliers detection based on residual analysis of regression with Partial Least Squares and Artificial Neural Networks. In order to minimize the negative effect of outliers in the training dataset a learning algorithm with regularization is proposed. This algorithm is based on a Quasi-Newton optimization method and it was tested on a simulated nonlinear process, on real data from environmental monitoring contaminated with synthetic outliers, and finally applied to a real environmental monitoring data obtained from a monitoring station and having natural outliers. The results are encouraging and further developments are foreseen for including information from neighboring stations and emission source operation.