Review Ofchromite Deposits of Indonesia

Ernowo, Ernowo; Oktaviani, Penny

doi:10.47599/bsdg.v5i1.250

Cited by 4 publications

(5 citation statements)

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“…Bijih kromit merupakan mineral besi kromium oksida dengan rumus kimia FeCr2O4 (Ernowo & Oktaviani, 2010). Dominan bijih kromit yang ditambang dimanfaatkan untuk pembuatan ferokrom, sisanya untuk refraktori dan bahan cat (Pariser, 2014).…”

Section: Pendahuluanunclassified

Benefisiasi Bijih Kromit Daerah Paludda Dengan Metode Konsentrasi Gravitasi

Bakri¹,

Iqbal²,

Nurhawaisyah³

et al. 2023

JMEPT

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Bijih kromit merupakan mineral besi kromium oksida dengan rumus kimia FeCr2O4. Dominan bijih kromit yang ditambang dimanfaatkan untuk pembuatan ferokrom, sisanya untuk refraktori dan bahan cat. Daerah Palludda Barru terdapat bijih kromit dengan kadar rendah atau belum bisa dimanfaatkan lebih lanjut. Metode konsentrasi yang cocok untuk pengolahan bijih kromit adalah metode konsentrasi gravitasi, salah satunya dengan meja goyang. Penelitian dilakukan dengan tujuan untuk mengetahui besar peningkatan kadar dan recovery bijih kromit asal Palludda Barru menggunakan konsentrasi gravitasi meja goyang. Sampel dari lapangan Palludda Barru terlebih dahulu dikarakterisasi untuk mengetahui mineral-mineral dan berapa kadar mineral atau unsur yang terkandung dalam bijih kromit. Bijih sebelum diumpankan, direduksi ukurannya menggunakan jaw crusher dan ball mill. Percobaan dilakukan dengan memvariasikan besar sudut kemiringan meja yaitu rentang 5° -15°. Hasil penelitian menunjukkan bahwa sudut kemiringan mempengaruhi kadar dan recovery Cr yang diperoleh. Kemiringan optimum diperoleh pada sudut kemiringan 10° dengan kadar 35,89% dan recovery 59,06%.

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Section: Pendahuluanunclassified

Benefisiasi Bijih Kromit Daerah Paludda Dengan Metode Konsentrasi Gravitasi

Bakri¹,

Iqbal²,

Nurhawaisyah³

et al. 2023

JMEPT

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“…Penelitian dilakukan pada endapan laterit zona limonit di wilayah kecamatan Wasile Selatan, kabupaten Halmahera Timur Propinsi Maluku Utara (Gambar 1). Daerah penelitian secara geologi terletak di bagian lengan timur pulau Halmahera yang sebagian besar ditempati oleh kelompok batuan ultrabasa sebagai batuan dasar (Ernowo and Oktaviani, 2010). Batuan ultrabasa ini terdiri dari harzburgit terserpentinisasi dan dunit (Kadarusman, 2013).…”

Section: Bahan Dan Metode Lokasi Dan Geologi Daerah Penelitianunclassified

“…Gambar 1: Lokasi penelitian dan geologi regional Halmahera (Ernowo and Oktaviani, 2010) Pengolahan data dimulai dari validasi data dengan melakukan pengecekan data pada setiap lokasi titik bor untuk mengidentifikasi kemungkinan kesalahan nilai kadar dan koordinat yang tidak logis pada setiap data. Dalam proses pengolahan data diperlukan satu nilai kadar komposit pada setiap lokasi titik bor untuk dijadikan basis data, sehingga perlu dilakukan penentuan nilai kadar rata-rata tertimbang pada setiap titik bor.…”

Section: Sampling Dan Analisa Kadarunclassified

Pemetaan Spasial Kadar Kobal pada Endapan Laterit dengan Metode Ordinary Cokriging dan Inverse Distance Weighting

Purnomo

Rande²,

Prastowo³

2022

jstl

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Cobalt is a metallic element that can be found in laterite deposits with varying concentration in each lithology zone. Objective of this study was to compare the performance of the ordinary cokriging (OCK) and inverse distance weighting (IDW) in mapping the cobalt content of laterite deposits. Cross variogram analysis used the covariable Ni and Fe, and the selection of the variogram model used the residual sum of square parameter. The IDW interpolation process uses power 1 and 2. The determination of the best method is based on the mean error parameter, and the root mean square error. Meanwhile, to determine the accuracy of the OCK estimation results, the root mean square standardized prediction error is used. The study indicates that the performance of the OCK interpolation is better than IDW, with overestimated predictions. The results of OCK interpolation showed that the distribution of 0.1-0.18% cobalt spread in the northern part and narrowed in the southern part of the study area.

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“…Therefore, these methods are suitable for moderate-to well-sampled areas characterised by known deposit types [21]. Several modelling methods such as the weights-of-evidence [22][23][24][25], frequency ratio (FR) [26,27], logistic regression (LR) [4,22,28] and artificial neural network (ANN) [29,30] methods were tested for mapping numerous mineral resources across different parts of the world. Surip [31] conducted a work in Penjom-Merapoh, Pahang (Malaysia) by using eight factors such as geology, faults, granite bodies, arsenic (As), tungsten (W), lead (Pb) and copper (Cu) concentrations in their research.…”

Section: Introductionmentioning

confidence: 99%

“…Bonham-Carter [44] and Raines [45] conducted several studies on gold mineralisation mapping using a traditional weights-of-evidence technique that utilised a regional geoscience and GIS database. Several models, such as FR, LR, ML and deep learning (DL) models, are also used for potential zone mapping [22][23][24][25][26][27][28]. The database consists of geological bedrock and surficial maps, geochemical sediment samples, and airborne geophysical and mineral occurrence information.…”

Section: Introductionmentioning

confidence: 99%

A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model

et al. 2022

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Geoscientists have extensively used machine learning for geological mapping and exploring the mineral prospect of a province. However, the interpretation of results becomes challenging due to the complexity of machine learning models. This study uses a convolutional neural network (CNN) and Shapley additive explanation (SHAP) to estimate potential locations for gold mineralisation in Rengali Province, a tectonised mosaic of volcano-sedimentary sequences juxtaposed at the interface of the Archaean cratonic segment in the north and the Proterozoic granulite provinces of the Eastern Ghats Belt in Eastern India. The objective is to integrate multi-thematic data involving geological, geophysical, mineralogical and geochemical surveys on a 1:50 K scale with the aim of prognosticating gold mineralisation. The available data utilised during the integration include aero-geophysical (aeromagnetic and aerospectrometric), geochemical (national geochemical mapping), ground geophysical (gravity), satellite gravity, remote sensing (multispectral) and National Geomorphology and Lineament Project structural lineament maps obtained from the Geological Survey of India Database. The CNN model has an overall accuracy of 90%. The SHAP values demonstrate that the major contributing factors are, in sequential order, antimony, clay, lead, arsenic content and a magnetic anomaly in CNN modelling. Geochemical pathfinders, including geophysical factors, have high importance, followed by the shear zones in mineralisation mapping. According to the results, the central parts of the study area, including the river valley, have higher gold prospects than the surrounding areas. Gold mineralisation is possibly associated with intermediate metavolcanics along the shear zone, which is later intruded by quartz veins in the northern part of the Rengali Province. This work intends to model known occurrences with respect to multiple themes so that the results can be replicated in surrounding areas.

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Review Ofchromite Deposits of Indonesia

Cited by 4 publications

References 0 publications

Benefisiasi Bijih Kromit Daerah Paludda Dengan Metode Konsentrasi Gravitasi

Benefisiasi Bijih Kromit Daerah Paludda Dengan Metode Konsentrasi Gravitasi

Pemetaan Spasial Kadar Kobal pada Endapan Laterit dengan Metode Ordinary Cokriging dan Inverse Distance Weighting

A New Method to Evaluate Gold Mineralisation-Potential Mapping Using Deep Learning and an Explainable Artificial Intelligence (XAI) Model

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