A new computational control strategy for leachate management in bioreactor landfills
Bibliographic record
Abstract
A novel computational measurement-based control strategy (CMCS) was developed to manage leachate recirculation based on monitoring ofkey system parameters. The proposed framework identifies the operational phase ofthe controlled bioreactor, and accordingly determines quantities ofleachate, buffer, supplemental water, and nutritional amendments required to provide the temporally changing landfill microbial consortia with their growth requirements. The CMCS was tested in a pilot-scale bioreactor cell (0.5 m3) for a period of nine months, and compared to a conventional open-loop leachate control scheme (fixed recirculation rate of produced leachate) in an identical cell. Overall, positive results confirmed the applicability and benefits of the control strategy to optimize the leachate recirculation and manipulation processes. Throughout the 21 operational cycles of the controlled bioreactor cell, the maximum specific daily recirculation volume (24 L/t/d) was sixfold the minimum volume determined. The amounts of inoculum/nutrient, buffer, and supplemental water were also computed cyclically, and ranged between 2% and 11%, 1% and 9%, and 3% and 16% of the total volume recycled, respectively. The recirculated volumes of leachate and other amendments were highly variable, and did not follow any predictable trends. In general, CMCS achieved proper control with a minimal use of resources, e.g. it utilized about 55% less buffer compared with the leachate neutralization practice commonly used in lab- and pilot-scale studies. The greater degradation rates of organic matter combined with relative increases in biogas production (1.7-fold greater) demonstrate that calculation-based recirculation stimulated the methanogenic activity and accelerated the bioreactor evolution.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".