Perhitungan Beban Emisi Particulate Matter berdasarkan Data CEMS dari PLTU Batu Bara Milik PT PLN (Persero)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the Coal-Fired Power Plant (CFPP) operation, various pollutants will be generated, including Particulate Matter (PM), which can be monitored with the Continuous Emission Monitoring System (CEMS). The parameters monitored include PM concentration, volumetric flow rate, % O2, and the operation hours and/or CEMS monitoring hours. Emission load calculation by utilizing CEMS data is a Tier 3 calculation method that uses real operation data from a CFPP unit. Information related to PM emission load from CFPP can be considered for corporate decision making in developing emission control strategies. This research utilizes secondary data from January 2021 to July 2023 period, from 52 PT PLN (Persero)’s CFPP units with various boiler, installed capacity, and types of Air Pollution Control Devices specifications. The largest average/month emission load value is 139,632 kg PM/month for CFPP unit that uses PC boiler, have a capacity of > 600 MW, and use low NOx burners and ESP as its APCD; while the smallest average/month emission load value is 525 kg PM/month for CFPP unit that uses PC boiler, have a capacity of 101-300 MW, and use ESP as its APCD. If the average emission load/month from January 2021 to July 2023 is compared with the maximum emission load/month with a PM quality standard of 100 mg/Nm3, all CFPP units do not exceed it and are generally ready to face the tightening of PM quality standard up to 75 mg/Nm3.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it