🎓 Lesson 16
D5
Critical Moisture Thresholds and Stickiness Index Development
Critical moisture threshold is the exact amount of water in crushed ore or blast muck where material changes from flowing freely to sticking together and causing blockages in chutes, bins, or conveyors.
🎯 Learning Objectives
- ✓ Calculate critical moisture threshold using empirical correlations and laboratory shear test data
- ✓ Design bin hopper angles and feeder parameters based on measured stickiness index values
- ✓ Analyze moisture–flow relationships to diagnose and prevent flow blockages in ROM and crusher feed systems
- ✓ Apply the Stickiness Index (SI) classification system to select appropriate flow aid technologies (e.g., vibrators, air cannons, liner materials)
📖 Why This Matters
In mining operations, 23% of unplanned downtime in primary crushing and stockpile reclaim systems stems from moisture-induced flow failures—not equipment breakdowns. A single 4-hour blockage in a 10,000 tpd copper concentrator can cost over $180,000 in lost production and emergency labor. Understanding when blasted ore or crushed material becomes 'sticky' isn’t academic—it’s the difference between continuous operation and costly, hazardous manual clearing.
📘 Core Principles
Flow behavior in granular mining materials follows a moisture-dependent continuum: dry (free-flowing), damp (intermediate cohesion), and saturated (slurry-like). The critical moisture threshold sits at the inflection point where capillary forces dominate interparticle friction, triggering cohesive bridging. The Stickiness Index (SI) quantifies this transition on a 0–10 scale: SI < 2 = non-sticky; SI 3–5 = moderately sticky (requires design mitigation); SI ≥ 6 = highly sticky (demands active flow intervention). SI integrates moisture content, fines fraction (<75 µm), and clay activity (e.g., smectite vs. kaolinite), making it superior to moisture % alone for predictive design.
📐 Stickiness Index (SI) Calculation
The Stickiness Index (SI) is a dimensionless, empirically calibrated metric that combines moisture, fines, and clay content into a single predictive value. It is derived from regression analysis of Jenike shear test data across >120 mining materials and validated against field flow performance. SI ≥ 6 triggers mandatory hopper slope increases or flow aid installation per CEMA and FMC guidelines.
Stickiness Index (SI)
SI = 0.32 × (0.85 × M + 0.42 × F + 1.35 × C)Empirical index predicting flow obstruction risk based on moisture, fines, and smectite clay content.
Variables:
| Symbol | Name | Unit | Description |
|---|---|---|---|
| M | Gravimetric moisture content | % w/w | Measured moisture by loss-on-ignition or Karl Fischer titration |
| F | Fines fraction | % w/w | Mass percent of particles <75 µm (dry sieve analysis) |
| C | Smectite clay content | % w/w | Quantified by X-ray diffraction (XRD) or ethylene glycol expansion test |
Typical Ranges:
Hard rock (granite, porphyry): 2.0 – 5.5
Clay-rich shale or weathered schist: 5.0 – 9.0
💡 Worked Example
Problem: Given: Gravimetric moisture = 9.2%, fines (<75 µm) = 18.5%, smectite clay content = 4.1% (measured by XRD), bulk density = 1.82 g/cm³.
1.
Step 1: Compute base moisture term: M = 0.85 × moisture (%) = 0.85 × 9.2 = 7.82
2.
Step 2: Compute fines amplification factor: F = 0.42 × fines (%) = 0.42 × 18.5 = 7.77
3.
Step 3: Compute clay sensitivity multiplier: C = 1.35 × smectite (%) = 1.35 × 4.1 = 5.54
4.
Step 4: Apply SI formula: SI = 0.32 × (M + F + C) = 0.32 × (7.82 + 7.77 + 5.54) = 0.32 × 21.13 = 6.76
Answer:
The result is SI = 6.76, which falls within the highly sticky range (≥6.0), requiring conical hopper angles ≥65° and installation of pneumatic flow aids per FMC Standard 101.
🏗️ Real-World Application
At the Bingham Canyon Mine (Rio Tinto), ROM ore with 7.8% moisture and 22% fines triggered repeated ratholing in the 12-m-diameter surge bin feeding the primary gyratory crusher. Post-failure analysis revealed SI = 6.9—exceeding the design threshold of 5.0. Remediation included installing 6 air cannons (2.5 bar pulse, 15 s interval), relining with UHMW-PE (μ < 0.15), and adding real-time moisture monitoring with inline NIR sensors. Flow reliability improved from 78% to 99.2% over 18 months.
📋 Case Connection
📋 Pacific Northwest Wheat Export Terminal Conveyor Segregation Control
Size- and protein-based segregation causing grade noncompliance in railcar loading