This page has only limited features, please log in for full access.

Ms. Javier González
Universidad Católica del Norte, Chile

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Mining Engineering
0 Rock Mechanics
0 rock mechanics through project-based learning
0 rock slope engineering
0 intact properties of rock

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 18 May 2021 in Metals
Reads 0
Downloads 0

The low grade of copper deposits and the use of the froth flotation process have caused excessive tailing production. In recent years, experts have looked for new alternative methods to improve this situation. Black copper minerals are abundant resources not exploited by large-scale copper mining and possess high Mn concentrations. On the other hand, manganese nodules are submarine resources and show high concentrations of Cu, Ni, Fe, and, mainly, Mn. However, both mineral resources are refractory to conventional leaching processes, and so a reducing agent is necessary for their treatment. We studied the use of tailings obtained from the flotation of foundry slags with a high content of Fe3O4 as reducing agents at different MnO2/tailings ratios and H2SO4 concentrations. Mn dissolution was compared in marine nodule and black copper minerals samples. It was found that higher Mn dissolutions are obtained from marine nodules, likely due to the acid consumption created by Cu dissolution from black copper minerals. The remnant elements in manganese nodules were leached under an oxidant condition.

ACS Style

Carlos Moraga; Eduardo Cerecedo-Saenz; Javier González; Pedro Robles; Francisco Carrillo-Pedroza; Norman Toro. Comparative Study of MnO2 Dissolution from Black Copper Minerals and Manganese Nodules in an Acid Medium. Metals 2021, 11, 817 .

AMA Style

Carlos Moraga, Eduardo Cerecedo-Saenz, Javier González, Pedro Robles, Francisco Carrillo-Pedroza, Norman Toro. Comparative Study of MnO2 Dissolution from Black Copper Minerals and Manganese Nodules in an Acid Medium. Metals. 2021; 11 (5):817.

Chicago/Turabian Style

Carlos Moraga; Eduardo Cerecedo-Saenz; Javier González; Pedro Robles; Francisco Carrillo-Pedroza; Norman Toro. 2021. "Comparative Study of MnO2 Dissolution from Black Copper Minerals and Manganese Nodules in an Acid Medium." Metals 11, no. 5: 817.

Journal article
Published: 19 April 2021 in Applied Sciences
Reads 0
Downloads 0

Determining the uniaxial compressive strength of intact rock is the primary objective of a geomechanical project, and a reliable estimate in the early phases saves time and costs for more sophisticated laboratory tests. The problem is knowing which of the correlations between the resistance to uniaxial compression and point load index are reliable, those that cover one or several types of rock (depending on the type of statistical adjustment). In this work, they were evaluated with respect to limestone and travertine from experimental results, and the statistical models of the scale effect of the point load index were determined, and the uniaxial compressive strength being estimated from correlations reported in literature. The limestone model was ascending (strength increases as diameter increases), while the travertine model was descending (strength decreases as diameter increases), obtaining similar exponents for the scale effect equations modeled from the uniaxial compressive strength and point load index in both cases.

ACS Style

Solange Contreras; Manuel Saldaña; Norman Toro; Ignacio Pérez-Rey; Manuel González; Javier González. Scale Effect and Correlation between Uniaxial Compressive Strength and Point Load Index for Limestone and Travertine. Applied Sciences 2021, 11, 3672 .

AMA Style

Solange Contreras, Manuel Saldaña, Norman Toro, Ignacio Pérez-Rey, Manuel González, Javier González. Scale Effect and Correlation between Uniaxial Compressive Strength and Point Load Index for Limestone and Travertine. Applied Sciences. 2021; 11 (8):3672.

Chicago/Turabian Style

Solange Contreras; Manuel Saldaña; Norman Toro; Ignacio Pérez-Rey; Manuel González; Javier González. 2021. "Scale Effect and Correlation between Uniaxial Compressive Strength and Point Load Index for Limestone and Travertine." Applied Sciences 11, no. 8: 3672.

Journal article
Published: 30 June 2020 in Applied Sciences
Reads 0
Downloads 0

In the rock mechanics and rock engineering field, the strength parameter considered to characterize the rock is the uniaxial compressive strength (UCS). It is usually determined in the laboratory through a few statistically representative numbers of specimens, with a recommended minimum of five. The UCS can also be estimated from rock index properties, such as the effective porosity, density, and P-wave velocity. In the case of a porous rock such as travertine, the random distribution of voids inside the test specimen (not detectable in the density-porosity test, but in the compressive strength test) causes large variations on the UCS value, which were found in the range of 62 MPa for this rock. This fact complicates a sufficiently accurate determination of experimental results, also affecting the estimations based on regression analyses. Aiming to solve this problem, statistical analysis, and machine learning models (artificial neural network) was developed to generate a reliable predictive model, through which the best results for a multiple regression model between uniaxial compressive strength (UCS), P-wave velocity and porosity were obtained.

ACS Style

Manuel Saldaña; Javier González; Ignacio Pérez-Rey; Matías Jeldres; Norman Toro. Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine. Applied Sciences 2020, 10, 4565 .

AMA Style

Manuel Saldaña, Javier González, Ignacio Pérez-Rey, Matías Jeldres, Norman Toro. Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine. Applied Sciences. 2020; 10 (13):4565.

Chicago/Turabian Style

Manuel Saldaña; Javier González; Ignacio Pérez-Rey; Matías Jeldres; Norman Toro. 2020. "Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine." Applied Sciences 10, no. 13: 4565.

Journal article
Published: 03 December 2019 in Applied Sciences
Reads 0
Downloads 0

Rock mechanics and rock engineering projects require determining, among other parameters, the uniaxial compressive strength (UCS) of rock. For such a purpose, it is not uncommon to perform ultrasonic pulse laboratory tests. Many researchers have found experimental relationships between strength and P-wave velocity, but these relationships are based mainly on dry conditions and without considering any other physical or chemical characteristics of the studied rock. Specifically, for limestone, there are 11 correlations reported in the literature, eight of which are simple and the remaining three are multiple, and, among the latter, only two of them consider the saturation. In order to evaluate the combined effect of P-wave velocity, density, and porosity on the UCS of saturated limestone, simple and multiple regression analyses were carried out on the test results of 13 saturated limestone specimens to determine the parameters of both previously mentioned predictive models. The results showed that density is not correlated with strength.

ACS Style

Javier González; Manuel Saldaña; Javier Arzúa. Analytical Model for Predicting the UCS from P-Wave Velocity, Density, and Porosity on Saturated Limestone. Applied Sciences 2019, 9, 5265 .

AMA Style

Javier González, Manuel Saldaña, Javier Arzúa. Analytical Model for Predicting the UCS from P-Wave Velocity, Density, and Porosity on Saturated Limestone. Applied Sciences. 2019; 9 (23):5265.

Chicago/Turabian Style

Javier González; Manuel Saldaña; Javier Arzúa. 2019. "Analytical Model for Predicting the UCS from P-Wave Velocity, Density, and Porosity on Saturated Limestone." Applied Sciences 9, no. 23: 5265.

Journal article
Published: 07 November 2019 in Metals
Reads 0
Downloads 0

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.

ACS Style

Manuel Saldaña; Javier González; Ricardo I. Jeldres; Ángelo Villegas; Jonathan Castillo; Gonzalo Quezada; Norman Toro. A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks. Metals 2019, 9, 1198 .

AMA Style

Manuel Saldaña, Javier González, Ricardo I. Jeldres, Ángelo Villegas, Jonathan Castillo, Gonzalo Quezada, Norman Toro. A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks. Metals. 2019; 9 (11):1198.

Chicago/Turabian Style

Manuel Saldaña; Javier González; Ricardo I. Jeldres; Ángelo Villegas; Jonathan Castillo; Gonzalo Quezada; Norman Toro. 2019. "A Stochastic Model Approach for Copper Heap Leaching through Bayesian Networks." Metals 9, no. 11: 1198.

Journal article
Published: 18 October 2019 in Metals
Reads 0
Downloads 0

Exotic type deposits include several species of minerals, such as atacamite, chrysocolla, copper pitch, and copper wad. Among these, copper pitch and copper wad have considerable concentrations of manganese. However, their non-crystalline and amorphous structure makes it challenging to recover the elements of interest (like Cu or Mn) by conventional hydrometallurgical methods. For this reason, black copper ores are generally not incorporated into the extraction circuits or left unprocessed, whether in stock, leach pads, or waste. Therefore, to dilute MnO2, the use of reducing agents is essential. In the present research, agitated leaching was performed to dissolve Mn of black copper in an acidic medium, comparing the use of ferrous ions and tailings as reducing agents. Two samples of black copper were studied, of high and low grade of Mn, respectively, the latter with a high content of clays. The effect on the reducing agent/black copper ratio and the concentration of sulfuric acid in the system were evaluated. Better results in removing Mn were achieved using the highest-grade black copper sample when working with ferrous ions at a ratio of Fe2+/black copper of 2/1 and 1 mol/L of sulfuric acid. Besides, the low-grade sample induced a significant consumption of H2SO4 due to the high presence of gangue and clays.

ACS Style

Kevin Pérez; Norman Toro; Eduardo Campos; Javier González; Ricardo I. Jeldres; Amin Nazer; Mario H. Rodriguez. Extraction of Mn from Black Copper Using Iron Oxides from Tailings and Fe2+ as Reducing Agents in Acid Medium. Metals 2019, 9, 1112 .

AMA Style

Kevin Pérez, Norman Toro, Eduardo Campos, Javier González, Ricardo I. Jeldres, Amin Nazer, Mario H. Rodriguez. Extraction of Mn from Black Copper Using Iron Oxides from Tailings and Fe2+ as Reducing Agents in Acid Medium. Metals. 2019; 9 (10):1112.

Chicago/Turabian Style

Kevin Pérez; Norman Toro; Eduardo Campos; Javier González; Ricardo I. Jeldres; Amin Nazer; Mario H. Rodriguez. 2019. "Extraction of Mn from Black Copper Using Iron Oxides from Tailings and Fe2+ as Reducing Agents in Acid Medium." Metals 9, no. 10: 1112.