Radiological characterization of clay mixed red mud in particular as regards its leaching features

The reuse of industrial by-products such as red mud is of great importance. In the case of the building material industry the reuse of red mud requires a cautious attitude, since the enhanced radionuclide content of red mud can have an effect on human health. The natural radionuclide content of red mud from the Ajka red mud reservoir and the clay sample from a Hungarian brick factory were determined by gamma spectrometry. It was found that maximum 27.8% red mud content can be added to fulfil the conditions of the EU-BSS. The effect of heat treatment was investigated on a red mud-clay mixture and it was found that in the case of radon and thoron exhalation the applied heat reduced remarkably the exhalation capacities. The leaching features of red mud and different mixtures were studied according to the MSZ-21470-50 Hungarian standard, the British CEN/TS 14429 standard and the Tessier sequential extraction method. The Tessier method and the MSZ-21470-50 standard are suitable for the characterization of materials; however, they do not provide enough information for waste deposition purposes. To this end, we propose using the CEN/TS 14429 method, because it is easy to use, and gives detailed information about the material's behaviour under different pH conditions, however, further measurements are necessary.

Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers

In order to predict compressive strength of geopolymers prepared from alumina-silica natural products, based on the effect of Al 2 O 3 /SiO 2, Na 2 O/Al 2 O 3, Na 2 O/H 2 O, and Na/[Na+K], more than 50 pieces of data were gathered from the literature. The data was utilized to train and test a multilayer artificial neural network (ANN). Therefore a multilayer feedforward network was designed with chemical compositions of alumina silicate and alkali activators as inputs and compressive strength as output. In this study, a feedforward network with various numbers of hidden layers and neurons were tested to select the optimum network architecture. The developed three-layer neural network simulator model used the feedforward back propagation architecture, demonstrated its ability in training the given input/output patterns. The cross-validation data was used to show the validity and high prediction accuracy of the network. This leads to the optimum chemical composition and the best paste can be made from activated alumina-silica natural products using alkaline hydroxide, and alkaline silicate. The research results are in agreement with mechanism of geopolymerization.


Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)MT.1943-5533.0000829

Simplified model for prediction of compressive strength of alkali-activated natural pozzolans

The assessment of pozzolanic activity is essential for estimating the reaction of a material as pozzolan. Natural pozzolans can be activated and condensed with sodium silicate in an alkaline environment to synthesize high performance cementitious construction materials with low environmental impact. In this paper, the pozzolanic activities of five natural pozzolans are studied. The correlation between type and chemical composition of natural pozzolan, which affects the formation of the geopolymer gel phase, both for the calcined and untreated natural pozzolans, have been reviewed. The improvement in pozzolanic properties was studied following heat treatment including calcinations and/or elevated curing temperature by using alkali solubility, and compressive strength tests. A model was developed to allow prediction of the alkali-activated pozzolan strength versus their chemical compositions, alkali solubility, and crystallinity.