Extending the Applications of the ADM1 to Predict Performance of the Induced Bed Reactor (IBR) Co-Digesting Municipal Sludge with Bakery Waste
Bibliographic record
Abstract
The goal of this research was to examine the stability of the induced bed reactor (IBR) digesting municipal sludge (MS) mixed with bakery waste (BW) by experiment and modeling. It was necessary to modify the Anaerobic Digestion Model number1(ADM1) to accurately predict the performance of the IBR for this mixed waste. The total mixed influent COD was 50 g/L with hydraulic retention times that varied from 27 to 6 days at mesophilic temperatures. The reactor reached the steady state at each HRT with no sign of inhibition or failure, however, the COD removal efficiency of the digester decreased from 92% to 72% with decreasing HRT. The modified ADM1 outputs agreed well with the measured stability indicators (pH, total volatile fatty acid (TVFA), Q (gas production), percent CH<sub>4</sub> at the longer retention times of 27, and 20 days. The model overestimated the pH, and methane percentage and underestimated the TVFA when the HRT was shorter (12, 9 and 6 days). However, the model predicted well the trends of the observed data and the overall stability process of the digester until 6 d HRT. This research provided an alternative for the disposal of industrial bakery waste and also pointed out the ability of the IBR to manage high waste loads stably, while providing high energy production.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".