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All Journal Jurnal Rekayasa Proses IJOG : Indonesian Journal on Geoscience JLBG (Jurnal Lingkungan dan Bencana Geologi) (Journal of Environment and Geological Hazards) Jurnal Geomine Metal Indonesia Civil Engineering Journal Buletin Sumber Daya Geologi Jurnal Rekayasa Proses Jurnal GEOSAPTA Jurnal Fisika Indonesia Indonesian Journal on Geoscience Indonesian Journal on Geoscience Jurnal Teknik Kimia Indonesia Journal of Applied Geology Jurnal Rekayasa Proses LEMBARAN PUBLIKASI MINYAK DAN GAS BUMI (LPMGB)
Ferian Anggara
Departemen Teknik Geologi, Fakultas Teknik, Universitas Gadjah Mada, Daerah Istimewa Yogyakarta, Indonesia
Astronomy Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Education Energy Engineering Environmental Science Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences

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IDENTIFIKASI KANDUNGAN LITIUM PADA BATUAN FILIT KOMPLEKS LUK ULO, JAWA TENGAH: LITHIUM IDENTIFICATION ON PHYLLITE FROM THE LUK ULO COMPLEX, CENTRAL JAVA Isyqi; Setiawan, Nugroho Imam; Anggara, Ferian
Buletin Sumber Daya Geologi Vol 19 No 2 (2024): Buletin Sumber Daya Geologi
Publisher : Pusat Sumber Daya Mineral Batubara dan Panas Bumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47599/bsdg.v19i2.470

Abstract

Production of lithium-ion batteries in Indonesia will be more feasible if Indonesia has its own lithium resources. Metapelitic rocks have the potential to serve as an alternate raw material for lithium. Phyllite, a type of metapelitic rock, was found in the Luk Ulo Complex, Central Java. The objective of this study is to determine the mineral composition and lithium concentration of the phyllite rocks from the Luk Ulo Complex. This will serve as a representative model for understanding the occurrence of lithium in similar rock types. The techniques employed to accomplish this objective include field surveys, petrographic, XRD, and ICP-AES/MS analysis. Phyllite in the Luk Ulo Complex is found in several locations and exhibits well-developed schistosity, characterized by a predominant mineral composition of quartz, graphite, chlorite, and white mica. The concentration of lithium in the phyllite from the Luk Ulo Complex exceeds the average concentration of lithium in the earth's crust, which typically ranges from 18.2 to 84.7 ppm. The presence of lithium in the phyllite rock is believed to be associated with the white mica minerals in the rock because the principal component analysis reveals a significant correlation between the lithium level in phyllite and the major elements K2O, CaO, Na2O, MgO, and Al2O3, which are responsible for the formation of the white mica. The phyllite rocks in the Luk Ulo Complex have a lithium enrichment that is five times more than the concentration of lithium in the earth's crust. It is believed that this enrichment is generated from the protoliths of the phyllite rocks, which are pelitic sedimentary rocks of continental origin. The presence of lithium in the phyllite rocks of the Luk Ulo Complex makes them suitable as lithium sources. However, due to their location within the Karangsambung Geological Reserve Area, these rocks are not to be exploited.
Unsupervised Machine Learning for Determining Exploration Areas of Valuable Elements and Potential Toxicology Elements: A Case Study of the Bowen Basin Coal, Australia Addintamma, Fajri Zakka; Amaranggana, Adzani Nareswari; Kusuma, Anindya Ayu; Aviliana, Aviliana; Solikh, Mochammad Wildanun; Patria, Aulia Agus; Anggara, Ferian
Journal of Applied Geology Vol 10, No 1 (2025)
Publisher : Geological Engineering Department Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jag.104595

Abstract

Global coal production and demand have increased anually. In addition to its potential as an alternative source of critical elements, coal also has environmental risks through toxicology elements. Australia is the world’s second-largest producer of rare earth elements (REEs) and critical elements, making coal exploration a key focus of the country’s mining strategy. An unsupervised Machine learning algorithm was applied to 56 coal samples from three pits in Bowen Basin, e.g., Blake Central Pit, Blake West Pit, and Bowen No. 2 Pit, to correlate trace elements with the geochemical characteristics of coal, such as proximate and major oxides. Blake West Pit is enriched in Ba, Br, and Sr, which associated with inherent moisture and phosphor (P), extending SE-trend. Blake Central Pit and Blake West Pit are enriched in Hf, Mo, Ta, Th, Y, and REY, which are associated with ash and major elements such as Si, Al, Ti, and K, with a trend of potential exploration towards N-NW. However, both pits show the risk of contamination from the toxic element Zn, which is associated with volatile matter, and major elements e.g., Fe, Mg, and Mn, with a trend of distribution towards S-SW. Based on the correlation analysis and regional geology, trace element enrichment in Bowen Basin is controlled by two main factors: 1) the transgressive phase during Early-Late Permian, which enriched inherent moisture, P, Ba, Br, and Sr, and 2) volcanic activity during Early Permian, which enriched silicate minerals and elements such as Hf, Ta, Th, W, and REY. Unsupervised machine learning has proven effective for preliminary coal characterization to support further exploration.
Rock Typing Pada Reservoar Gas Upper Eastern View Coal-Measures (EVCM), Cekungan Bass, Australia Dinar Hananto Kurniawan; Sarju Winardi; Ferian Anggara
Lembaran publikasi minyak dan gas bumi Vol 56 No 3 (2022)
Publisher : BBPMGB LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/LPMGB.56.3.1100

Abstract

Kesulitan terkadang muncul ketka menentukan permeabilitas, terutama pada interval yang tidak memiliki data batuan inti (core). Pada penelitian ini, digunakan teknik sedimentary rock type (SRT) dan petrophysical rock type (PRT) dengan tujuan untuk mengurai permasalahan tersebut. SRT terdapat 4 rock type, yaitu thoroughly bioturbated sandstone and mudstone, fl aser bedded sandstone, laminated sandstone and mudstone, dan thoroughly bioturbated mudstone. Teknik PRT dilakukan dengan mengkomparasikan metode Hydraulic Flow Unit, Global Hydraulic Element, Winland R35, dan Pore Geometry Structure untuk mendapatkan metode yang paling cocok. Hasilnya, metode GHE yang menghasilkan nilai permeabilitas prediksi paling mendekati nilai permabilitas dari data core (koefi sien korelasi 0,9139). Aplikasi metode GHE pada interval yang tidak memiliki data core dilakukan dengan menggunakan data wireline log menghasilkan 4 RT (RT 1, RT 2, RT 3, dan RT 4). Setiap rock type tersebut digunakan rumusan poro-perm transform-nya untuk mementukan nilai permeabilitas. Dipadukan dengan data DST, dapat diambil nilai cut-off , yaitu porositas 0,183, permeabilitas 0,55 mD, volume shale 0,6, dan saturasi air 0,99. Dengan nilai cut-off tersebut menghasilkan satu zona potensial pada kedalaman 2216 mRT - 2234 mRT. Dengan demikian maka manfaat yang didapatkan dari penelitian ini antara lain: mengetahui metode PRT yang cocok diaplikasikan di reservoir ini, jumlah rock type beserta karakteristiknya, serta zona yang potensial.
Unsupervised Machine Learning for Determining Exploration Areas of Valuable Elements and Potential Toxicology Elements: A Case Study of the Bowen Basin Coal, Australia Fajri Zakka Addintamma; Adzani Nareswari Amaranggana; Anindya Ayu Kusuma; Aviliana Aviliana; Mochammad Wildanun Solikh; Aulia Agus Patria; Ferian Anggara
Journal of Applied Geology Vol 10, No 1 (2025)
Publisher : Geological Engineering Department Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jag.104595

Abstract

Global coal production and demand have increased anually. In addition to its potential as an alternative source of critical elements, coal also has environmental risks through toxicology elements. Australia is the world’s second-largest producer of rare earth elements (REEs) and critical elements, making coal exploration a key focus of the country’s mining strategy. An unsupervised Machine learning algorithm was applied to 56 coal samples from three pits in Bowen Basin, e.g., Blake Central Pit, Blake West Pit, and Bowen No. 2 Pit, to correlate trace elements with the geochemical characteristics of coal, such as proximate and major oxides. Blake West Pit is enriched in Ba, Br, and Sr, which associated with inherent moisture and phosphor (P), extending SE-trend. Blake Central Pit and Blake West Pit are enriched in Hf, Mo, Ta, Th, Y, and REY, which are associated with ash and major elements such as Si, Al, Ti, and K, with a trend of potential exploration towards N-NW. However, both pits show the risk of contamination from the toxic element Zn, which is associated with volatile matter, and major elements e.g., Fe, Mg, and Mn, with a trend of distribution towards S-SW. Based on the correlation analysis and regional geology, trace element enrichment in Bowen Basin is controlled by two main factors: 1) the transgressive phase during Early-Late Permian, which enriched inherent moisture, P, Ba, Br, and Sr, and 2) volcanic activity during Early Permian, which enriched silicate minerals and elements such as Hf, Ta, Th, W, and REY. Unsupervised machine learning has proven effective for preliminary coal characterization to support further exploration.
Co-Authors Addintamma, Fajri Zakka Adzani Nareswari Amaranggana Afrina Septantia Agung Harijoko Agung Rizki Perdana Agus Prasetya Amaranggana, Adzani Nareswari Andre Nouval Anggun Purnama Edra Anindya Ayu Kusuma Arifudin Idrus Arsha Maulana Aulia Agus Patria Aulia Agus Patria Aviliana Aviliana Aviliana, Aviliana Aya Shika Bangun Ayu Safira Mariska, Nanda Ayu Safira Mariska, Nanda Bangun, Aya Shika Beny Wiranata Beny Wiranata Brilian R. Sadewo Cikasimi, Mutiara Dagus Resmana Djuanda Dea Anisa Ayu Besari Deddy Tanggara Dinar Hananto Kurniawan Dinar Hananto Kurniawan Diyan Pamungkas Donatus Hendra Amijaya Fahrialam, Aldian Fajri Zakka Addintamma Febry Yulindra Abdi Saputra Fenny Tamba Friederich, Mike C. Giwangkara, Atifa Maritza Guritno Safitri Muchitawati Gussyak, Selasian Handika M. Prabu Haryo Edi Wibowo Hidayat, Tantan Himawan T.B.M. Petrus Himawan T.B.M. Petrus Himawan Tri Bayu Murti Petrus Hotden Manurung Hotden Manurung I Made Bendiyasa Isnadiyati, Oyinta Fatma Isyqi Janna Azizah Wijayanti Jarot Setyowiyoto Jessica Trofimovs Jyalita, Jasmin Kezia Kartika Windya Kezia Kartika Windya Kusuma, Anindya Ayu Kyuro Sasaki Laksono Trisnantoro Lucas Donny Setijadji Mardhani Riasetiawan Mike C. Friederich Mochammad Wildanun Solikh Moore, Tim Allen Moore, Tim. A. Muchitawati, Guritno Safitri Nanda Ayu Safira Mariska Nugroho Imam Setiawan Oyinta Fatma Isnadiyati Pamungkas, Diyan Patria, Aulia Agus Patria, Aulia Agus Perdana, Agung Rizki Petrus, Himawan Tri Bayu Prakoso, Wildan Guntur Purnama Edra, Anggun Putra, Agik Dwika Putra, Ilham Satria Raditya Sarju Winardi Sasaki, Kyuro Septantia, Afrina Shelia F. Hunarko Sinabang, Paul Vito Parluhutan Slamet Sumardi, Slamet Solikh, Mochammad Wildanun Sugai, Yuichi Sujoto, Vincent Sutresno Hadi Supardin Nompo Susilawati, Rita Tamba, Fenny Tangkas, I Wayan Christ Widhi Herman Tim Allen Moore Tim. A. Moore Trofimovs, Jessica Vanisa Syahra Wahyu Wilopo Wibisono, Sigit Arso Widi Astuti Widi Astuti Widya Rosita Wildan Guntur Prakoso Winda Putri Anggraeni Wiranata, Beny Yuichi Sugai Yusup Iskandar