Metallurgical Processing of Polymetallic Ocean Nodules

Das, Rajashree; Anand, S.

doi:10.1007/978-3-319-52557-0_12

Cited by 13 publications

(13 citation statements)

References 48 publications

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“…The exclusion of areas with slopes > 3° resulted in 8 km 2 mineable seafloor surface. Assuming a constant 80% collection efficiency (Volkmann & Lehnen, 2018) and a 30% reduction of the Mn-nodule weight by removal of water (Das & Anand, 2017), the dry mass of Mn-nodules that can be extracted from the surface and the first 15 cm of the sediment column amounts to ca. 190,000 t. In a back-of-the-envelope calculation this quantity, assuming constant metal content inside the study area , equal to the average metal concentrations inside the CCZ (Table 7) Mo ( Table 7).…”

Section: Estimation Of Abundance (Kg/m²) Of Mn-nodulesmentioning

confidence: 99%

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

Gazis¹,

Schoening²,

Alevizos³

et al. 2018

Preprint

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Abstract. In this study, high-resolution bathymetric multibeam and optical image data, both obtained within the Belgian manganese (Mn) nodule mining license area by the autonomous underwater vehicle (AUV) Abyss, were combined in order to create a predictive Random Forests (RF) machine learning model. AUV bathymetry reveals small-scale terrain variations, allowing slope estimations and calculation of bathymetric derivatives such as slope, curvature, and ruggedness. Optical AUV imagery provides quantitative information regarding the distribution (number and median size) of Mn-nodules. Within the area considered in this study, Mn-nodules show a heterogeneous and spatially clustered pattern and their number per square meter is negatively correlated with their median size. A prediction of the number of Mn-nodules was achieved by combining information derived from the acoustic and optical data using a RF model. This model was tuned by examining the influence of the training set size, the number of growing trees (ntree) and the number of predictor variables to be randomly selected at each RF node (mtry) on the RF prediction accuracy. The use of larger training data sets with higher ntree and mtry values increases the accuracy. To estimate the Mn-nodule abundance, these predictions were linked to ground truth data acquired by box coring. Linking optical and hydro-acoustic data revealed a non-linear relationship between the Mn-nodule distribution and topographic characteristics. This highlights the importance of a detailed terrain reconstruction for a predictive modelling of Mn-nodule abundance. In addition, this study underlines the necessity of a sufficient spatial distribution of the optical data to provide reliable modelling input for the RF.

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Section: Estimation Of Abundance (Kg/m²) Of Mn-nodulesmentioning

confidence: 99%

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

Gazis¹,

Schoening²,

Alevizos³

et al. 2018

Preprint

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“…Research on the beneciation and metallurgy process of manganese nodules are conducted in many countries throughout the world since the early 1950s. Now, there have been reports on some representative processing methods, such as reduction roasting-ammonia leaching, 11 cuprous ion catalytic reduction-ammoniacal leaching, 12 reduction acid leaching in H 2 SO 4 or HCl system, 13,14 rusting-leaching process, 15 and bioleaching. 16 A critical review of the literature, especially in terms of energy consumption, carbon emission, and selective extraction, indicates that the reductive leaching of manganese nodules in the sulphuric acid system is a relatively promising and inexpensive approach.…”

Section: Introductionmentioning

confidence: 99%

The effect of Fe³⁺ ions on the electrochemical behaviour of ocean manganese nodule reduction leaching in sulphuric acid solution

2022

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“…(3) hydrometallurgical processing followed by pyrometallurgical treatment for recovery of Mn from leaching residue [6][7][8]. Various hydrometallurgical approaches included reductive ammonia leaching [9][10][11][12][13][14], reduction and sulphuric acid leaching [15][16][17][18], reduction and hydrochloric acid leaching [19,20], and other integrated or synergistic solvent extractions [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Thermodynamic and Experimental Study on Efficient Extraction of Valuable Metals from Polymetallic Nodules

Parianos³

et al. 2020

Minerals

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Polymetallic nodules are promising resources for the extraction of valuable metals such as copper, nickel, and cobalt, as well as manganese alloys. To achieve efficient extraction of useful metals from the emerging resource, high-temperature carbothermic reduction of nodules was investigated by optimizing the reductant addition, slag and alloy systems. Thermochemical software FactSage was used to predict the liquidus temperature of the slag system, which is not sensitive to FeO, CaO and Al2O3, but decreases significantly with decreasing MnO/SiO2 mass ratio. The experiments were designed to reduce the oxides of Cu, Co and Ni completely, and reduce FeOx partially depending on the amount of graphite addition while leaving the residual slag for further processing into ferromanganese and/or silicomanganese alloys. Co, Cu and Ni concentrations in the alloy decreased with increasing graphite addition. The optimal reduction condition was reached by adding 4 wt% graphite at the MnO/SiO2 mass ratio of 1.6 in slag. The most effective metal-slag separation was achieved at 1350 °C, which enables the smelting reduction to be carried out in various furnaces.

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Metallurgical Processing of Polymetallic Ocean Nodules

Cited by 13 publications

References 48 publications

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

The effect of Fe³⁺ ions on the electrochemical behaviour of ocean manganese nodule reduction leaching in sulphuric acid solution

Thermodynamic and Experimental Study on Efficient Extraction of Valuable Metals from Polymetallic Nodules

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Metallurgical Processing of Polymetallic Ocean Nodules

Cited by 13 publications

References 48 publications

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

Quantitative mapping and predictive modelling of Mn-nodules' distribution from hydroacoustic and optical AUV data linked by Random Forests machine learning

The effect of Fe3+ ions on the electrochemical behaviour of ocean manganese nodule reduction leaching in sulphuric acid solution

Thermodynamic and Experimental Study on Efficient Extraction of Valuable Metals from Polymetallic Nodules

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The effect of Fe³⁺ ions on the electrochemical behaviour of ocean manganese nodule reduction leaching in sulphuric acid solution