SkillsLib.ai

Flotation Performance Analyzer

Diagnose flotation circuit performance issues and recommend evidence-based optimizations

3.7(37 reviews)
500+ downloads
Updated Nov 2026
Verified SafeSecurity VerifiedThis skill was analyzed by our AI security scanner for harmful content including data exfiltration, system manipulation, credential theft, and prompt injection. No threats were detected.

What You Can Do

You can systematically diagnose why flotation recovery or grade is deteriorating by synthesizing assay results, reagent consumption, equipment metrics, and concentrate performance data. The skill guides you through a structured decision framework to evaluate the impact of reagent or process parameter changes, model recovery-grade trade-offs before implementation, and generate documented recommendations validated against metallurgical first principles—output suitable for operations teams and management approval.

Features

Root cause diagnosis framework

systematically isolate whether performance decline stems from ore mineralogy, liberation, reagent chemistry, equipment wear, or operating parameters

Recovery-grade trade-off modeling

quantify the economic impact of competing process changes before implementation

Reagent impact assessment

evaluate whether proposed collector, frother, or depressant changes will improve circuit selectivity or throughput

Shift and feed type analysis

identify performance variance across operating conditions and correlate with assay or reagent consumption data

Metallurgical validation checklist

cross-check recommendations against flotation first principles (particle size, hydrophobicity, bubble size, residence time)

Operations-ready documentation

format findings and recommendations for seamless handoff to plant teams and management review

Comparative baseline analysis

benchmark current performance against historical baselines to quantify degradation severity

Example Output

Example 1: Recovery decline diagnosis

  • Input: Rougher recovery dropped 3.2 percentage points; feed grade stable; reagent dosing unchanged
  • Output: Structured analysis identifying probable causes (ore liberation, bubble coalescence, circuit residence time) ranked by likelihood, with 2–3 recommended tests and process adjustments

Example 2: Reagent optimization recommendation

  • Input: Frother consumption rising 8%; concentrate grade holding; flotation cell froth unstable
  • Output: Economic trade-off model comparing three frother alternatives, projected recovery impact ±1.5%, implementation cost, and risk assessment

Example 3: Selectivity improvement plan

  • Input: Recovery stable at 85%; concentrate grade drifting down 1.8%; iron and silica deportment increasing
  • Output: Depressant dosing strategy, collector selectivity enhancement options, and phased implementation timeline with assay monitoring checkpoints

What's Included

  • SKILL.md: structured flotation performance analysis instruction file
  • Flotation diagnostics worksheet: data input template (ore type, assay results, reagent usage, equipment metrics)
  • Root cause analysis decision tree: flowchart for isolating performance decline sources
  • Recovery-grade trade-off calculator: framework for modeling process change economics
  • Recommendations documentation template: formatted output for operations and management communication

Who It's For

  • Minerals Processing Engineers — optimizing flotation circuits and troubleshooting performance drift
  • Plant Operations Managers — diagnosing production shortfalls and evaluating process modifications
  • Metallurgists — validating reagent or equipment changes against first principles
  • Process Improvement Teams — conducting root cause analysis on recovery or grade variability
  • Mining Technical Services — supporting remote site optimization and performance benchmarking

Best For

  • Diagnosing recovery or grade decline when root cause is unclear
  • Evaluating reagent or process parameter changes before full-scale implementation
  • Modeling recovery-grade trade-offs and economic impact of optimization proposals
  • Comparing performance across shifts, feed ore types, or equipment configurations
  • Documenting findings in a format suitable for operations teams and management approval

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