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Materials 2015, 8(5), 2076-2092; doi:10.3390/ma8052076

Rheological Properties of Cemented Tailing Backfill and the Construction of a Prediction Model

1
Energy School, Xi'an University of Science and Technology, Xi'an 710054, China
2
Key Laboratory of Western Mines and Hazards Prevention, Ministry of Education of China, Xi'an 710054, China
3
Department of Civil Engineering, Inha University, Incheon 402-751, Korea
4
School of Civil & Resource Engineering, University of Western Australia, Perth 6009, Australia
5
Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Christof Schneider
Received: 17 January 2015 / Revised: 16 April 2015 / Accepted: 16 April 2015 / Published: 23 April 2015
(This article belongs to the Section Energy Materials)
View Full-Text   |   Download PDF [549 KB, uploaded 23 April 2015]   |  

Abstract

Workability is a key performance criterion for mining cemented tailing backfill, which should be defined in terms of rheological parameters such as yield stress and plastic viscosity. Cemented tailing backfill is basically composed of mill tailings, Portland cement, or blended cement with supplementary cement material (fly ash and blast furnace slag) and water, among others, and it is important to characterize relationships between paste components and rheological properties to optimize the workability of cemented tailing backfill. This study proposes a combined model for predicting rheological parameters of cemented tailing backfill based on a principal component analysis (PCA) and a back-propagation (BP) neural network. By analyzing experimental data on mix proportions and rheological parameters of cemented tailing backfill to determine the nonlinear relationships between rheological parameters (i.e., yield stress and viscosity) and mix proportions (i.e., solid concentrations, the tailing/cement ratio, the specific weight, and the slump), the study constructs a prediction model. The advantages of the combined model were as follows: First, through the PCA, original multiple variables were represented by two principal components (PCs), thereby leading to a 50% decrease in input parameters in the BP neural network model, which covered 98.634% of the original data. Second, in comparison to conventional BP neural network models, the proposed model featured a simpler network architecture, a faster training speed, and more satisfactory prediction performance. According to the test results, any error between estimated and expected output values from the combined prediction model based on the PCA and the BP neural network was within 5%, reflecting a remarkable improvement over results for BP neural network models with no PCA. View Full-Text
Keywords: cemented tailing backfill; yield stress; viscosity; principle component analysis (PCA); back-propagation (BP) neural network cemented tailing backfill; yield stress; viscosity; principle component analysis (PCA); back-propagation (BP) neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lang, L.; Song, K.-I.; Lao, D.; Kwon, T.-H. Rheological Properties of Cemented Tailing Backfill and the Construction of a Prediction Model. Materials 2015, 8, 2076-2092.

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