A field study conducted at Kidston Gold Mine, to evaluate the impact of arsenic and zinc from mine tailing to grazing cattle

The grazing trial at Kidston Gold Mine, North Queensland, was aimed specifically to assess the uptake of metals from the tailing and the potential for unacceptable contamination of saleable meat. Further aims included estimating metal dose rates and identifying potential exposure pathways including plant uptake of heavy metals, mine tailings adhered to plants and direct ingestion of mine tailing. It was found that of the 11 metals analysed (As, Zn, Co, Cd, Cr, Sn, Pb, Sb, Hg, Se and Ni) in the animal's liver, muscle and blood during the 8-month trial period, only accumulation of arsenic and zinc occurred. A risk assessment including these two metals was conducted to determine the potential for chronic metal toxicity and long-term contamination, using the estimates of metal dose rate. It was concluded that no toxicity or long-term contamination in cattle was likely at this site. Management procedures were therefore not required at this site; however, the results highlight percent ground cover and standing dry matter (DM) as important factors in decreasing metal exposure from direct ingestion of tailings and dust adhered to plants. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Residential energy resources management by a SCADA system in case of excess wind generation

The end consumers in a smart grid context are seen as active players. The distributed generation resources applied in smart home system as a micro and small-scale systems can be wind generation, photovoltaic and combine heat and power facility. The paper addresses the management of domestic consumer resources, i.e. wind generation, solar photovoltaic, combined heat and power, electric vehicle with gridable capability and loads, in a SCADA system with intelligent methodology to support the user decision in real time. The main goal is to obtain the better management of excess wind generation that may arise in consumer’s distributed generation resources. The optimization methodology is performed in a SCADA House Intelligent Management context and the results are analyzed to validate the SCADA system.