PERANCANGAN KOMPOR BERBAHAN BAKAR OLI BEKAS UNTUK PENGERINGAN GARAM INDUSTRI
Bibliographic record
Abstract
Garam sebagai salah sumber mineral penting banyak dibutuhkan oleh masyarakat maupun industry. Dalam proses pembuatannya garam harus melalui proses pengeringan. Pengeringan di workshop garam Badan Riset Nasional (BRIN) yang masih menggunakan bahan bakar pelet kayu dinilai belum optimal karena masih adanya pemborosan waktu tunggu dan proses. Alternatif bahan bakar lain diperlukan untuk memaksimalkan proses produksi garam, salah satunya menggunakan limbah oli bekas. Pemanfaatan limbah oli bekas sebagai bahan bakar proses pengeringan garam memerlukan penyesuaian kompor, sebagai media konversi energi. Penelitian ini bertujuan merancang kompor berbahan bakar oli bekas untuk pengeringan garam industri di workshop garam BRIN. Perancangan kompor menggunakan metode French. Kompor oli bekas yang sudah dibangun dibandingkan dengan kompor pelet kayu dalam hal waktu tunggu sampai mencapai suhu pengeringan garam yang diinginkan, biaya operasional bahan bakar, dan kapasitas garam yang dihasilkan. Kompor oli berbahan dasar baja ST-44 yang berdiameter 17 cm dan tinggi 13 cm dapat mencapai suhu pengeringan 18 menit lebih cepat dibanding kompor pelet kayu tanpa perlunya supervisi proses feeding. Biaya operasional harian bahan bakar dengan oli bekas juga lebih ekonomis dengan tonase garam kering yang lebih banyak 33% dibanding menggunakan kompor pelet kayu.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".