Potensi Limbah Biomassa Menjadi Karbon Aktif Sebagai Upaya Resources Recovery : Studi Literatur
Bibliographic record
Abstract
Reutilization of natural resources as a resource recovery effort is one aspect of sustainable development, one of which is the utilization of biomass waste. Biomass waste is waste from agricultural, plantation, livestock, and forestry processes that contain organic matter composed of carbon dioxide bonds, air, water, soil, and sunlight from plants and animals. Pollution by the accumulation or untreated biomass waste will result in the potential formation of greenhouse gases (GHG). One of the efforts is to utilize biomass waste into activated carbon by carbonizing and activating the biomass waste. The indication of why biomass waste can be used as a material for making activated carbon is because biomass waste contains lignocellulosic materials that have the ability to absorb metals and colors. The purpose of this paper is to see the potential of various kinds of biomass waste that can be utilized into activated carbon by conducting a journal review. The results of the review of various journals show that various kinds of biomass waste can be utilized into activated carbon. The lignocellulosic content of biomass waste consists of lignin in the range of 8%-53.85%, cellulose in the range of 6.92%-81%, and hemicellulose 11%-41%.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".