Effect of sonication on the particle size of montmorillonite clays

This paper reports on the effect of sonication on SAz-1 and SWy-1 montmorillonite suspensions. Changes in the size of the particles of these materials and modifications of their properties have been investigated. The variation of the particle size has been analyzed by DLS (dynamic light scattering). In all cases the clay particles show a bimodal distribution. Sonication resulted in a decrease of the larger modal diameter, as well as a reduction of its volume percentage. Simultaneously, the proportion of the smallest particles increases. After 60 min of sonication, SAz-1 presented a very broad particle size distribution with a modal diameter of 283 nm. On the other hand, the SWy-1 sonicated for 60 min presents a bimodal distribution of particles at 140 and 454 nm. Changes in the properties of the clay suspensions due to sonication were evaluated spectroscopically from dye-clay interactions, using Methylene Blue. The acidic sites present in the interlamellar region, which are responsible for dye protonation, disappeared after sonication of the clay. The changes in the size of the scattering particles and the lack of acidic sites after sonication suggest that sonication induces delamination of the clay particles. (c) 2008 Elsevier Inc. All rights reserved.

Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.