Synthesis of solidification experience for synthetic wastes.
The NNAPICS project is being conducted to try to predict the effects of impurities in wastes on the final properties of cement/waste products using data mining techniques, such as neural network analysis, applied to data collected from the literature. The data collected by the project includes information for more than 500 solidified products prepared using synthetic wastes. As synthetic wastes are less complex than real wastes, interactions with contaminants should be easier to characterise in products prepared with synthetic wastes. Neural network models were constructed for portland cement systems containing barium, cadmium, chromium, copper, lead, nickel or zinc, as oxides, hydroxides, nitrates, or chlorides, from 8 literature sources. The models were able to predict unconfined compressive strength and pH with excellent correlation between measured and predicted values. The effect of the heavy metals on the Portland cement was more evident for pH, than for unconfined compressive strength. © 2000 Elsevier Ltd. All rights reserved.
|Title:||Synthesis of solidification experience for synthetic wastes|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
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