Mineral Leaching Modeling Through Machine Learning Algorithms − A Review

Saldaña, Manuel Romero; Neira, Purísima; Gallegos, Sandra; Salinas‐Rodríguez, Eleazar; Pérez-Rey, Ignacio; Toro, Norman

doi:10.3389/feart.2022.816751

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…There are several copper recovery models in literature [53,54], while the analytical model used in this work is given by Eq. ( 1) [55][56][57][58], which is based on the hypothesis that the leaching process could be modelled by using a system of first-order differential equation (1).…”

Section: Analytical Models For Heap Leachingmentioning

confidence: 99%

Development of an analytical model for copper heap leaching from secondary sulfides in chloride media in an industrial environment

Saldaña

Salinas‐Rodríguez

Castillo

et al. 2022

Hem Ind

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In multivariate analysis, a predictive model is a mathematical/statistical model that relates a set of independent variables to dependent or response variable(s). This work presents a descriptive model that explains copper recovery from secondary sulfide minerals (chalcocite) taking into account the effects of time, heap height, superficial velocity of leaching flow, chloride concentration, particle size, porosity, and effective diffusivity of the solute within particle pores. Copper recovery is then modelled by a system of first-order differential equations. The results indicated that the heap height and superficial velocity of leaching flow are the most critical independent variables while the others are less influential under operational conditions applied. In the present study representative adjustment parameters are obtained, so that the model could be used to explore copper recovery in chloride media as a part of the extended value chain of the copper sulfides processing.

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Section: Analytical Models For Heap Leachingmentioning

confidence: 99%

Development of an analytical model for copper heap leaching from secondary sulfides in chloride media in an industrial environment

Saldaña

Salinas‐Rodríguez

Castillo

et al. 2022

Hem Ind

Self Cite

View full text Add to dashboard Cite

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Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction

Chen,

Ma,

Wang

et al. 2024

Earth Sci Inform

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The integration of machine learning (ML) and deep learning (DL) into geoscience has experienced a pronounced uptick in recent years, a trend propelled by the intricate nature of geosystems and the abundance of data they produce. These computational methods have been harnessed across a spectrum of geoscientific challenges, from climate modeling to seismic analysis, exhibiting notable efficacy in extracting valuable insights from intricate geological datasets for applications such as mineral prediction. A thorough analysis of the literature indicates a marked escalation in AI-centric geoscience research starting in 2018, characterized by a predictive research orientation and a persistent focus on key computational terms. The thematic network and evolution analyses underscore the enduring prominence of “deep learning” and “machine learning” as pivotal themes, alongside progressive developments in “transfer learning” and “big data”. Despite these advancements, other methodologies have garnered comparatively lesser focus. While ML and DL have registered successes in the realm of mineral prediction, their amalgamation with domain-specific knowledge and symbolic reasoning could further amplify their interpretability and operational efficiency. Neuro-Symbolic AI (NSAI) emerges as a cutting-edge approach that synergizes DL’s robust capabilities with the precision of symbolic reasoning, facilitating the creation of models that are both powerful and interpretable. NSAI distinguishes itself by surmounting traditional ML constraints through the incorporation of expert insights and delivering explanatory power behind its predictive prowess, rendering it particularly advantageous for mineral prediction tasks. This literature review delves into the promising potential of NSAI, alongside ML and DL, within the geoscientific domain, spotlighting mineral prediction as a key area of focus. Despite the hurdles associated with infusing domain expertise into symbolic formats and mitigating biases inherent in symbolic reasoning, the application of NSAI in the realm of critical mineral prediction stands to catalyze a paradigm shift in the field. By bolstering prediction accuracy, enhancing decision-making processes, and fostering sustainable resource exploitation, NSAI holds the potential to significantly reshape geoscience’s future trajectory.

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PRIMENENIE BLOChNO-MODUL'NOGO METODA DLYa OPISANIYa I ISSLEDOVANIYa MEKhANIZMA PROTEKANIYa GETEROGENNYKh REAKTsIY VYShchELAChIVANIYa OKISLENNYKh TsINKOVYKh MATERIALOV

Zarochentsev,

Rutkovskiy

2024

Metally

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Mineral Leaching Modeling Through Machine Learning Algorithms − A Review

Cited by 3 publications

References 28 publications

Development of an analytical model for copper heap leaching from secondary sulfides in chloride media in an industrial environment

Development of an analytical model for copper heap leaching from secondary sulfides in chloride media in an industrial environment

Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction

PRIMENENIE BLOChNO-MODUL'NOGO METODA DLYa OPISANIYa I ISSLEDOVANIYa MEKhANIZMA PROTEKANIYa GETEROGENNYKh REAKTsIY VYShchELAChIVANIYa OKISLENNYKh TsINKOVYKh MATERIALOV

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