Online Superficial Gas Velocity, Holdup, and Froth Depth Sensor for Flotation Cells

Abstract

In flotation process, the efficiency and selectivity depend on mineralogy, particle size distribution and liberation, reagents added, mixing, and particle coverage. However, the kinetics of particle recovery is highly dependent on cell hydrodynamic and circuit configuration and operational strategy. Controlling froth depth and gas flow rate, measured as superficial gas velocity, is a straightforward alternative related to kinetics in the froth and collection zones. However, these parameters are not measured accurately. Froth depth measurement is based on a floating device coupled with a sonic sensor; this configuration presents hysteresis and deviation due to variation in the gas holdup and pulp density. In self-aspirated machines, there is no technology to measure gas velocity. To address this problem, the intelligent online gas dispersion sensor based on two concentric HDPE cylindres is proposed. The intelligent online gas dispersion sensor is based on two concentric HDPE cylinders. The methodology improves the accuracy of gas velocity calculation with a new algorithm. Froth depth measurement is based on two pressure transducers, reducing the uncertainty of the floating sonic sensor to 1 cm. Pulp bulk density is directly measured, and gas holdup can be estimated. Experimental results and industrial device validation indicate that the new intelligent system can measure superficial gas velocity (Jg) online and self-calibrate, with a 2% error, the froth depth error being ±1 cm. Therefore, a multiparameter sensor for measuring gas dispersion in industrial flotation cells was experimentally designed and validated in an industrial environment (TRL 8). In this context, the proposed online gas dispersion sensor emerges as a robust technology to improve the operation of the flotation process.

Enhancing bubble bize prediction in flotation processes: a drift flux model accounting for frother type

“…Bubble size estimation in flotation processes can be approached using practical techniques like artificial vision and acoustics, which offer alternative avenues for measurement (Sovechles & Waters, 2015;Grau & Heiskanen, 2005;Hernandez-Aguilar et al, 2004;Leiva et al, 2010). Additionally, mathematical methods provide a theoretical framework for estimation, leveraging operational parameters such as gas holdup, superficial gas velocity, and flow density (Leiva et al, 2022;Leiva et al, 2023;Vinnett et al, 2012;Wills & Napier-Munn, 2006;Wills & Finch, 2016).…”