The application of Machine Learning in Mineral Processing and Extractive Metallurgy has important benefits in terms of increasing the predictability and controllability of the processes, optimizing their performance, and improving maintenance. However, this application has significant implementation challenges. This paper analyzes these challenges and proposes ways of addressing them. Among the main identified challenges are data scarcity and the difficulty in characterizing abnormal events/conditions as well as modeling processes, which require the creative use of different learning paradigms as well as incorporating phenomenological models in the data analysis process, which can make the learning process more efficient. Other challenges are related to the need of developing reliable in-line sensors, adopting interoperability data models and tools, and implementing the continuous measurement of critical variables. Finally, the paper stresses the need for training of advanced human capital resources with the required skills to address these challenges.

Aqueous Pb(II) adsorption on pine bark (Pinus Radiata D.Don), an inexpensive and sustainable natural sorbent material, has been evaluated and the mechanism of metal retention characterized by scanning electron microscopy (SEM), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and X‐ray photoelectron spectroscopy (XPS). Bark pulp densities >1.5 g l ⁻¹ achieve near 100% Pb(II) removal from aqueous solutions containing 100 mg l ⁻¹ Pb(II). Adsorption rates increased with pulp density, although adsorption capacity diminished at high densities because of blockage of adsorption sites. The effects of washing and sulfuric acid activation were assessed and found to be less important than in previous metal sorption studies. Pb(II) sorption takes place mainly at the lignocellulosic C―O groups, with adsorption at phenolic sites appearing to be most significant. © 2016 The Authors. Surface and Interface Analysis published by John Wiley & Sons, Ltd.

Constructed wetlands (CW) have been widely used to treat different types of water, including acid mine drainage (AMD). However, little is known about their performance in the removal of As from AMD. In this study, a laboratory-scale horizontal subsurface flow (HSSF) CW system was tested to evaluate its capacity to treat highly acidic, As-rich contaminated water resembling AMD. Vegetated and non-vegetated cells, having limestone or zeolite as the main supporting media, were built and operated to evaluate the effect of the media type and the presence of Phragmites australis on the removal of arsenic, iron (Fe), lead (Pb), and zinc (Zn), and on the neutralization capacity. The four types of cells were highly effective in the removal of As and Pb (removal > 99%), and Fe (removal > 98%), whereas Zn removal rates depended on the cell type. Limestone cells raised the pH from ~1.9 to ~7.5, while zeolite cells raised it to ~4. These results suggest that the media type has a key role in the neutralization capacity, and that the presence of vegetation affected mainly the removal of Zn. Knowledge from this study will contribute to guiding the implementation of HSSF CW for treating As-rich AMD.