Mechanisms of hydrogen sulfide removal with steel making slag

In the present study, we experimentally investigated the removal of hydrogen sulfide using steel-making slag (SMS) and clarified the mechanism of hydrogen sulfide removal with the SMS. The results proved that SMS is able to remove hydrogen sulfide dissolved in water, and the maximum removal amount of hydrogen sulfide per unit weight of the SMS for 8 days was estimated to be 37.5 mg S/g. The removal processes of hydrogen sulfide were not only adsorption onto the SMS, but oxidation and precipitation as sulfur. The chemical forms of sulfide adsorbed onto the SMS were estimated to be sulfur and manganese sulfide in the ratio of 81% and 19%, respectively. It is demonstrated here that the SMS is a promising material to remediate organically enriched coastal sediments in terms of removal of hydrogen sulfide. Furthermore, using SMS is expected to contribute to development of a recycling-oriented society.     

Data Aggregation for Increase Performance of Wireless Sensor Networks Using Learning Automata Approach

Wireless sensor networks, a new generation of networks, are composed of a large numbers of nodes and the communication between nodes takes place wirelessly. The main purpose of these networks is collecting information about the environment surrounding the network sensors. The sensors collect and send the required information. There are many challenges and research areas concerned in the literature, one of which is power consumption in network nodes. Nodes in these networks have limited energy sources and generally consume more energy in long communication distances and therefore run out of battery very fast. This results in inefficacy in the whole system. One of the proposed solutions is data aggregation in wireless networks which leads to improved performance. Therefore, in this study an approach based on learning automata is proposed to achieve data aggregation which leads to dynamic network at any hypothetical region. This approach specifies a cluster head in the network and nodes send their data to the cluster head and the cluster head sends the information to the main receiver. Also each node can change its sensing rate using learning automata. Simulation results show that the proposed method increases the lifetime of the network and more nodes will be alive.