Representation for a prototype of recommendation system of operation mode in copper mining

Saldaña, Manuel Romero; Flores, Víctor; Toro, Norman; Leiva, Claudio

doi:10.23919/cisti.2019.8760884

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…On the other hand, contractor and provider companies are a fundamental part for the industry development. Nevertheless, under this revolution towards mining 4.0, they could also be strongly affected by this new stage of changes (Rylnikova et al 2017;Saldana et al 2019). For example, technologies like robotic, AI or machine learning could increase unemployment or blockchain could also disrupt contracts management in the future 8 .…”

Section: Kagan Et Al (2021) Metals Russiamentioning

confidence: 99%

Contractor Companies’ Small Projects Facing Mining 4.0 in Chile: How Risky Is the Implementation of Industry 4.0 for Them?

Peña Ramírez,

Ríos Muñoz,

Espinoza Brito

2024

Journal of Technology Management & Innovation

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Despite the relevance of Chile's mining industry, risk models applied to contracts have not been intensively developed. This is a quantitative descriptive research, which focuses on the results of a risk assessment methodology applied for the analysis of potential industry 4.0 technologies to be implemented by contractor companies in Chile's mining sector. In this paper, an overview about mining 4.0 was done and Chile's large mining companies were analyzed during 2021-2022, obtaining contracts information from a relevant copper producer. A case was selected to apply the model and semi-structured in-depth interviews were conducted with the stakeholders involved in this industry. Based on our results we found small contracts concentrated the largest number of low and moderate-priority projects, but also the largest number of high-priority contracts. However, from a mining company's perspective, it seems that big projects are riskier than small projects under the future effects of Industry 4.0 technologies.

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Section: Kagan Et Al (2021) Metals Russiamentioning

confidence: 99%

Contractor Companies’ Small Projects Facing Mining 4.0 in Chile: How Risky Is the Implementation of Industry 4.0 for Them?

Peña Ramírez,

Ríos Muñoz,

Espinoza Brito

2024

Journal of Technology Management & Innovation

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“…To summarize, mineral leaching process modeling contributes to generating a better understanding of the process dynamic through an abstraction of its operation and expressing the mathematical functions that represent its behavior in an integral way. The different models developed in the literature have also contributed to identifying the impact of the variables and/or operational parameters on the copper minerals leaching, and new approaches, such as the application of machine learning techniques [ 137 , 138 ], could lead to significant improvements in the study of the inherent dynamics in mineral processing, or in the generation of systems that support the mineral leaching process [ 139 , 140 ].…”

Section: Mineral Leaching Modelingmentioning

confidence: 99%

Copper Mineral Leaching Mathematical Models—A Review

et al. 2022

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Mineral leaching is the key unit operation in metallurgical processes and corresponds to the dissolution of metals. The study of leaching is carried out in many areas, such as geology, agriculture and metallurgy. This paper provides an introduction to the theoretical background regarding the mathematical modelling of the leaching process of copper minerals, establishing an overall picture of the scientific literature on technological developments and the generation of representative mathematical and theoretical models, and indicating the challenges and potential contributions of comprehensive models representing the dynamics of copper mineral leaching.

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“…Following the example of Saldaña et al [10], the generated stochastic model can be incorporated into a simulation framework that can quantify the benefits derived from the incorporation of probabilistic models in estimating the expected value of mineral recovery, or it could have the potential to include it in a system of support for decision making in the mining industry, as presented in Saldaña et al [36].…”

Section: Bayesian Network Validationmentioning

confidence: 99%

A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

Saldaña

González

Jeldres

et al. 2019

Metals

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Multivariate analytical models are quite successful in explaining one or more response variables, based on one or more independent variables. However, they do not reflect the connections of conditional dependence between the variables that explain the model. Otherwise, due to their qualitative and quantitative nature, Bayesian networks allow us to easily visualize the probabilistic relationships between variables of interest, as well as make inferences as a prediction of specific evidence (partial or impartial), diagnosis and decision-making. The current work develops stochastic modeling of the leaching phase in piles by generating a Bayesian network that describes the ore recovery with independent variables, after analyzing the uncertainty of the response to the sensitization of the input variables. These models allow us to recognize the relations of dependence and causality between the sampled variables and can estimate the output against the lack of evidence. The network setting shows that the variables that have the most significant impact on recovery are the time, the heap height and the superficial velocity of the leaching flow, while the validation is given by the low measurements of the error statistics and the normality test of residuals. Finally, probabilistic networks are unique tools to determine and internalize the risk or uncertainty present in the input variables, due to their ability to generate estimates of recovery based upon partial knowledge of the operational variables.

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Representation for a prototype of recommendation system of operation mode in copper mining

Cited by 5 publications

References 10 publications

Contractor Companies’ Small Projects Facing Mining 4.0 in Chile: How Risky Is the Implementation of Industry 4.0 for Them?

Contractor Companies’ Small Projects Facing Mining 4.0 in Chile: How Risky Is the Implementation of Industry 4.0 for Them?

Copper Mineral Leaching Mathematical Models—A Review

A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

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Representation for a prototype of recommendation system of operation mode in copper mining

Cited by 5 publications

References 10 publications

Contractor Companies&#8217; Small Projects Facing Mining 4.0 in Chile: How Risky Is the Implementation of Industry 4.0 for Them?

Contractor Companies&#8217; Small Projects Facing Mining 4.0 in Chile: How Risky Is the Implementation of Industry 4.0 for Them?

Copper Mineral Leaching Mathematical Models—A Review

A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks

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Product

Resources

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Contractor Companies’ Small Projects Facing Mining 4.0 in Chile: How Risky Is the Implementation of Industry 4.0 for Them?

Contractor Companies’ Small Projects Facing Mining 4.0 in Chile: How Risky Is the Implementation of Industry 4.0 for Them?