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Studi pembakaran spontan batubara menggunakan metode pemanasan adiabatik pada skala laboratorium

2020· article· id· W3035477418 on OpenAlexaff
Nuhindro Priagung Widodo, Edo Syawaludin, Zaenal Arifin

Bibliographic record

VenueJurnal Teknologi Mineral dan Batubara · 2020
Typearticle
Languageid
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsAnalytical Chemistry (journal)ChemistryEnvironmental chemistry

Abstract

fetched live from OpenAlex

Untuk mengatasi kejadian pembakaran spontan batubara yang merugikan, dibutuhkan suatu metode yang dapat mengenali potensi pembakaran spontan batubara. Pada penelitian ini Metode Oksidasi Adiabatik dipelajari untuk menggambarkan proses reaksi oksidasi batubara pada suhu 40-70 °C. Percontoh yang digunakan adalah batubara high-volatile C bituminous. Parameter yang diamati adalah ukuran butir, debit suplai oksigen (pada 100% O2) dan kompaksi. Satu buah percontoh memiliki berat 220 gram. Sebanyak 24 percontoh batubara di uji dengan alat pemanas oksidasi adiabatik dan dicatat temperaturnya selama waktu pengujian. Dari hasil penelitian terlihat bahwa nilai laju pembakaran spontan (R70) terbesar adalah 13,2719 °C/jam pada perontoh dengan ukuran 10-14 mesh (1,410 mm) tanpa kompaksi dengan debit oksigen 0,1 L/menit. Pada percontoh dengan ukuran 170-200 mesh (0,081 mm) tanpa kompaksi dengan debit oksigen 0,05 L/menit, nilai laju pembakaran spontan (R70) terbesar adalah 14,75 °C/jam. Selain itu, nilai energi aktivasi pada kedua percontoh tersebut merupakan yang terendah pada masing-masing kelompok pengujian, yaitu 13,10 kJ/mol dan 11,22 kJ/mol. Semakin kecil ukuran butir dan pada kondisi tanpa kompaksi, semakin meningkat nilai indeks R70 dan semakin mudah batubara terbakar. Dari kedua pengujian terlihat bahwa ukuran butir dan kompaksi memiliki pengaruh besar terhadap terjadinya pembakaran spontan batubara. Pengaruh debit oksigen tidak memperlihatkan kecenderungan (korelasi) pada kedua pengujian.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.250
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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