Subtopic Deep Dive
Tailings Hydraulic and Rheological Properties
Research Guide
What is Tailings Hydraulic and Rheological Properties?
Tailings Hydraulic and Rheological Properties characterize the permeability, consolidation, yield stress, and flow behavior of mining tailings slurries and thickened pastes under varying density, chemistry, and curing conditions.
This subtopic examines hydraulic conductivity (k) ranging from 10^-8 to 10^-6 m/s in homogenized hard rock tailings (Aubertin et al., 1996, 144 citations). Rheological properties include yield stress evolution in cemented paste backfill (CPB) during curing (Yilmaz et al., 2014, 198 citations). Over 50 papers since 1996 address coupled thermo-hydro-mechanical-chemical (THMC) models for deposition prediction (Ghirian and Fall, 2013, 167 citations; Cui and Fall, 2015, 112 citations).
Why It Matters
Accurate measurement of hydraulic and rheological properties prevents tailings dam failures by predicting beach slope and desaturation zones, as analyzed in case histories (Lyu et al., 2019, 240 citations). These properties guide dewatering strategies to reduce storage volume by 70-80% in thickened tailings deposition. In CPB applications, curing time effects on consolidation inform underground backfill design for mine stability (Yilmaz et al., 2014). Blight (2009, 149 citations) details geotechnical principles for safe waste storage facilities.
Key Research Challenges
Scale-dependent Permeability Measurement
Laboratory k values for homogenized tailings (10^-9 to 10^-7 m/s) diverge from field-scale due to heterogeneity and erosion (Aubertin et al., 1996). Consolidation tests overlook chemical clogging effects. Ghirian and Fall (2013) highlight thermal impacts on physical processes.
Rheology Evolution During Curing
Yield stress and viscosity in CPB increase nonlinearly with cement type and curing time, complicating pumpability predictions (Yilmaz et al., 2014). Coupled THMC models require validation across densities. Cui and Fall (2015) model underground backfill behavior.
Predicting Deposition Morphology
CFD models for thickened tailings deposition fail to capture desaturation and beach angle variability under climate effects (Blight, 2009). Empirical correlations ignore chemistry. Lyu et al. (2019) link properties to dam failure risks.
Essential Papers
Re-Thinking Mining Waste through an Integrative Approach Led by Circular Economy Aspirations
Maedeh Tayebi-Khorami, Mansour Edraki, Glen Corder et al. · 2019 · Minerals · 287 citations
Mining wastes, particularly in the form of waste rocks and tailings, can have major social and environmental impacts. There is a need for comprehensive long-term strategies for transforming the min...
A Comprehensive Review on Reasons for Tailings Dam Failures Based on Case History
Zongjie Lyu, Junrui Chai, Zengguang Xu et al. · 2019 · Advances in Civil Engineering · 240 citations
On a global scale, the demand for mineral products has increased substantially with economic development. Consequently, the mining of mineral resources results in the production and accumulation of...
Utilization of tailings in cement and concrete: A review
Mifeng Gou, Longfei Zhou, Nathalene Wei Ying Then · 2019 · Science and Engineering of Composite Materials · 200 citations
Abstract One of the advantages of cement and the cement concrete industry in sustainability is the ability to utilize large amounts of industrial solid wastes such as fly ash and ground granulated ...
Curing time effect on consolidation behaviour of cemented paste backfill containing different cement types and contents
Erol Yilmaz, Tikou Belem, Bruno Bussière et al. · 2014 · Construction and Building Materials · 198 citations
Reuse of iron ore mineral wastes in civil engineering constructions: A case study
Mohan Yellishetty, Vanda Karpe, E.H. Reddy et al. · 2008 · Resources Conservation and Recycling · 195 citations
Coupled thermo-hydro-mechanical–chemical behaviour of cemented paste backfill in column experiments. Part I: Physical, hydraulic and thermal processes and characteristics
Alireza Ghirian, Mamadou Fall · 2013 · Engineering Geology · 167 citations
Geotechnical Engineering for Mine Waste Storage Facilities
G. E. Blight · 2009 · 149 citations
Chapter 1: Waste Engineering, Characteristics of Mine Wastes and Types of Waste Storage * The nature and magnitude of the mine waste storage activity * Origins and quantities of mine waste * The ef...
Reading Guide
Foundational Papers
Start with Aubertin et al. (1996) for k measurement basics, then Yilmaz et al. (2014) for CPB consolidation, and Blight (2009) for geotechnical storage principles.
Recent Advances
Study Ghirian and Fall (2013) for THMC column data and Cui and Fall (2015) for coupled modeling advances.
Core Methods
Core techniques: constant-head permeameters for k (Aubertin et al., 1996); rheometers for yield stress; oedometers for consolidation; CFD/THMC simulations (Cui and Fall, 2015).
How PapersFlow Helps You Research Tailings Hydraulic and Rheological Properties
Discover & Search
Research Agent uses searchPapers('tailings hydraulic conductivity') to retrieve Aubertin et al. (1996), then citationGraph reveals 144 citing works on permeability scaling, and findSimilarPapers expands to Ghirian and Fall (2013) for THMC extensions.
Analyze & Verify
Analysis Agent applies readPaperContent on Yilmaz et al. (2014) to extract consolidation data tables, runs runPythonAnalysis for yield stress curve fitting with NumPy, and verifyResponse via CoVe with GRADE scoring to confirm k trends against Aubertin et al. (1996). Statistical verification tests rheological model fits.
Synthesize & Write
Synthesis Agent detects gaps in rheology scaling from 50+ papers, flags contradictions in k values; Writing Agent uses latexEditText for equations, latexSyncCitations for Blight (2009), and latexCompile for deposition diagrams via exportMermaid flowcharts.
Use Cases
"Fit yield stress evolution curve from Yilmaz 2014 CPB data"
Analysis Agent → readPaperContent(Yilmaz et al. 2014) → runPythonAnalysis(NumPy curve_fit on table data) → matplotlib plot with R²=0.95 fit equation.
"Draft LaTeX section on tailings k prediction models"
Synthesis Agent → gap detection across Aubertin/Ghirian → Writing Agent → latexEditText(density-k equation) → latexSyncCitations(5 papers) → latexCompile(PDF with figure).
"Find CFD codes for tailings deposition simulation"
Research Agent → searchPapers('tailings CFD rheology') → paperExtractUrls → paperFindGithubRepo → githubRepoInspect(yield stress solver repo with OpenFOAM scripts).
Automated Workflows
Deep Research workflow scans 50+ papers on 'tailings rheology' via searchPapers → citationGraph → structured report with k/rheology matrices from Aubertin (1996) to Cui (2015). DeepScan applies 7-step CoVe to verify THMC model parameters in Ghirian and Fall (2013), outputting GRADE-scored evidence table. Theorizer generates empirical yield stress correlations from Yilmaz (2014) curing data.
Frequently Asked Questions
What defines tailings hydraulic conductivity?
Hydraulic conductivity (k) measures tailings permeability, typically 10^-9 to 10^-6 m/s for homogenized hard rock mines, critical for consolidation analysis (Aubertin et al., 1996).
What methods measure rheological properties?
Rheology uses viscometers for yield stress and viscosity in CPB, with consolidation via oedometer tests tracking curing effects (Yilmaz et al., 2014); THMC column experiments quantify coupled processes (Ghirian and Fall, 2013).
What are key papers?
Foundational: Aubertin et al. (1996, 144 citations) on k; Yilmaz et al. (2014, 198 citations) on curing; recent: Cui and Fall (2015, 112 citations) on THMC models.
What open problems exist?
Scaling lab k to field deposition, rheology under chemical gradients, and climate effects on desaturation remain unsolved (Blight, 2009; Lyu et al., 2019).
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