Removal of chromate from wastewater using amine-based-surfactant-modified clinoptilolite

This study presents the results of chromate adsorption upon organically modified clinoptilolite obtained by the treatment of Bigadic clinoptilolite with surfactants in primary amine structure, namely 1-dodecylamine, 1-hexadecylamine and oleylamine. Natural and organo-clinoptilolites were characterized by X-ray diffraction, thermal gravimetry and Fourier transform infrared spectrometry which proved the integration of amine groups followed by the order oleylamine > 1-hexadecylamine > 1-dodecylamine. Organo-clinoptilolites were tested for their efficacy in chromate removal under base case conditions which implied 100 ppm initial concentration, pH 4.0, 10 g l^?1 adsorbent dose and at 298 K. Results showed that amine loading induced chromate removal reaching up to 90 % for oleylamine-modified clinoptilolite. Experimentation was continued to determine the effects of pH, adsorbent dosage and initial concentration on chromate adsorption. Accordingly, maximum removal of 94.0 % was attained with 14 g l^?1 of oleylamine-modified clinoptilolite, from Cr(VI) solution of 100 ppm initially at pH 3.0. Langmuir isotherm described the adsorption of chromate on oleylamine-modified clinoptilolite with 96.4 % consistency. Maximum theoretical uptake capacity was calculated as 6.72 mg g^?1. Kinetic data were consistent with pseudo-second-order model with the controlling steps being film and pore diffusions.     

Comparisons among the five ground-motion models developed using RESORCE for the prediction of response spectral accelerations due to earthquakes in Europe and the Middle East

This article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan.     

Prediction of temperature variation within a snowpack in open areas and under different canopy covers

Snow temperature is a major component of many physical processes in a snowpack. The temperature and the change in temperature across a layer have a dominant effect on physical properties of snow grains as well as its hardness, strength, and failure resistance. In this study, temperature and snow cover thickness were measured during the snow season of 2007–2008 in 11 elevation classes and in three different sampling locations, one in an open area and two under different forest canopy covers for each class along Kartalkaya road, Bolu. Each sampling site was visited 44 times to collect data including snow depth, snow surface temperature, ground temperature, and temperature within snowpack at 20‐cm intervals. Seven different models are developed to determine snowpack temperature variations under forest canopy covers and in an open area with different leaf area index values. All models were performed using a multilayer perceptron (MP) method for the Bolu–Kartalkaya area, Turkey. MP approach constitutes a standard form of neural network modeling and can modify two‐layer linear perceptron methods using three and more layers. The ability of MP is to handle complex nonlinear interactions, which ease the natural process of modeling. This method can overcome complex computations using neuron networks, and they can easily nonlinearly link input and output variables. The predictive errors are determined on the basis of mean absolute error and mean square error criteria. The Nash–Sutcliffe sufficiency score showing compliance between observed and predicted values is also calculated. According to the mean absolute error, the mean square error, and the Nash–Sutcliffe sufficiency score criteria, the predictive errors are within reasonable error intervals, justifying the use of the developed MP models for engineering applications. Copyright © 2012 John Wiley & Sons, Ltd.