Evidence map›Paper›PMID 37241440›Full record

ReviewMaterials (Basel, Switzerland)2023

Bioleaching Modeling-A Review.

Manuel Saldaña, Matías Jeldres, Felipe M Galleguillos Madrid, Sandra Gallegos, Iván Salazar, Pedro Robles, Norman Toro

Open access · goldAbstract readReview
In one paragraph

Review in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 28 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Manuel SaldañaFaculty of Engineering and Architecture, Arturo Prat University, Iquique 1110939, Chile.ORCID 0000-0001-9265-1529
Matías JeldresDepartamento de Ingeniería Química y Procesos de Minerales, Universidad de Antofagasta, Antofagasta 1270300, Chile.
Felipe M Galleguillos MadridCentro de Desarrollo Energético Antofagasta, Universidad de Antofagasta, Antofagasta 1271155, Chile.ORCID 0000-0002-1347-3726
Sandra GallegosFaculty of Engineering and Architecture, Arturo Prat University, Iquique 1110939, Chile.
Iván SalazarDepartamento de Ingeniería Civil, Universidad Católica del Norte, Antofagasta 1270709, Chile.
Pedro RoblesEscuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340000, Chile.ORCID 0000-0002-8312-7554
Norman ToroFaculty of Engineering and Architecture, Arturo Prat University, Iquique 1110939, Chile.ORCID 0000-0003-4273-3563
Universidad de Antofagasta · CLArturo Prat University · CLPontificia Universidad Católica de Valparaíso · CLUniversidad Católica del Norte · CL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The leaching of minerals is one of the main unit operations in the metal dissolution process, and in turn it is a process that generates fewer environmental liabilities compared to pyrometallurgical processes. As an alternative to conventional leaching methods, the use of microorganisms in mineral treatment processes has become widespread in recent decades, due to advantages such as the non-production of emissions or pollution, energy savings, low process costs, products compatible with the environment, and increases in the benefit of low-grade mining deposits. The purpose of this work is to introduce the theoretical foundations associated with modeling the process of bioleaching, mainly the modeling of mineral recovery rates. The different models are collected from models based on conventional leaching dynamics modeling, based on the shrinking core model, where the oxidation process is controlled by diffusion, chemically, or by film diffusion until bioleaching models based on statistical analysis are presented, such as the surface response methodology or the application of machine learning algorithms. Although bioleaching modeling (independent of modeling techniques) of industrial (or large-scale mined) minerals is a fairly developed area, bioleaching modeling applied to rare earth elements is a field with great growth potential in the coming years, as in general bioleaching has the potential to be a more sustainable and environmentally friendly mining method than traditional mining methods.

Indexed as

machine learningmicroorganismsmineral bioleachingmineral processingtheoretical and empirical modeling

Identifiers

PMID37241440
PMCPMC10224567
OpenAlexW4377104514

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.