Modelling anaerobic digestion of agricultural waste: From lab to full scale
Bibliographic record
Abstract
• The ADM1 model effectively represented four reactors fed with different substrates. • Co-digestion of maize silage and cow slurry showed the synergy of both substrates. • Maximum operating ranges were determined through simulations. • A scale-up case was studied with the ADM1 model. Biogas production through anaerobic digestion offers a promising alternative to address climate change. In this study, the ADM1 model was used to simulate the digestion of four different substrates: a mixture of rye and maize silage, a mixture of cow slurry and maize silage, cow slurry alone, and food waste. Furthermore, the determination of total solids (TS) content was integrated into the model. Based on experimental data from 5 L Continuous Stirred Tank Reactors (CSTR), ADM1 model parameters were calibrated for each substrate, primarily differing in hydrolysis and inhibition constants. These parameters, along with two additional sets of parameters from the literature, were subsequently applied in simulations to assess methane productivity, yield, and TS under increasing organic loading rates (OLR) for each substrate. Among the substrates, food waste showed the highest productivity, yield, and solids removal, while rye and maize silage substrate was the most unstable, with system failure at the lowest OLR (7 kgVS . m - 3 . d - 1 ) compared to the other substrates. In addition, co-digestion of cow slurry and maize silage showed synergies between maize silage and cow slurry, achieving a productivity of 2.62 N m 3 . m - 3 . d - 1 . Moreover, the parameters determined for rye and maize silage mixture were further used to simulate a full-scale anaerobic digestion unit fed with rye and maize silage as substrate. A difference in volatile fatty acid accumulation was found between the lab- and full-scale systems, suggesting a possible better microbial adaptation to inhibitory factors in the full-scale system. Further investigation into inhibition effects is recommended to improve the predictive accuracy of the ADM1.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".