Upgrading ADM1 by Addition of Lag Phase Sub-model to Simulate Acidic Inhibition of Methanogenic Reactor
Bibliographic record
Abstract
This study is aimed at improving calculation quality for IWA Anaerobic Digestion Model No.1 (ADM1) to simulate acidic failure of the methane fermentation systems and its performance recovery. The methanogen collected from digestate at a municipal wastewater treatment plant was cultivated in a lab-scale continuous reactor receiving acetate as a sole organic source. By varying the influent concentration during 400 days of operation, 6 datasets of acidification events were obtained to simulate the concentrations of acetate, VSS, pH, and the methane production rate. The ADM1 equipped with either pH sub-model or undissociated acetate sub-model could reproduce the deterioration of reactor performance in the acidic failure but totally failed to simulate the recovery in the subsequent lowered volumetric loading rate. The statistical analysis revealed both ADM1 models had relatively low correlation coefficient (Nash-Sutcliffe model efficiency coefficient (NSE)) of 0.31–0.38 although these were considerably improved from that without parameter calibration (NSE = −0.04). To cope with the mismatch, a lag phase sub-model was developed. The sub-model is composed of the remaining relative activity of the microorganism at which the inhibition peaked, and the half-saturation coefficient to express the specific length of lag phase. By adding the sub-model into the calibrated ADM1, NSE was significantly improved to 0.49–0.53.
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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 | 0.000 |
| 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".