Flotation Optimization Using Froth Characteristics

29-Sep-2017·
Allyson Stoll
Allyson Stoll
· 1 min read
Abstract
Froth cameras have become a common technology in flotation circuits to collect key performance data in the minerals processing industry. The information they collect can be used to optimize circuit performance and metal recovery through measurement of variables such as bubble size, froth depth and froth velocity. Currently, very few of the actual parameters are used to make decisions while operating the circuit. The challenge is to determine what the optimum flotation conditions would be based on all the data measurements available.
Date
29-Sep-2017 — 1-Oct-2017
Event
Location

In-Person

Vancouver, BC

events

Currently, flotation circuit performance is driven by measured on-stream assays supported by operator observations of froth characteristics. Operator observations are subjective in nature (Is the froth slow? Dry? Too dark?) and inexperienced operators are typically unsure of their observations. In addition, operators cannot watch the circuit constantly as they have other duties to perform. The installed froth cameras allow for continuous monitoring of the cells and clear objective measurements of color, texture, collapse rate and other variables. Optimization of the flotation circuit could lead to USD 4.6 million revenue annually for every 0.5% increase in recovery at $1.10/lb. of zinc.

Hackathon
Allyson Stoll
Authors
Allyson Stoll (she/they)

Mining Generalist ⛏️

AI Strategist & Evangelist 💻

Neurodivergent Unicorn 🦄

Allyson is a highly motivated mining and mineral processing engineer with 15 years of experience. Her background is primarily in comminution and flotation optimization utilizing advanced process controls and expert systems.

She completed a Masters of Data Science from the University of British Columbia to hone and utilize her data science skills and subject matter expertise to transform the mining industry.

Her research interests are concentrated on decentralized task allocation for haulage fleets and the use of advanced simulation and optimization for data-driven decision support.