ADM1 application for tuning and performance analysis of a multi-model observer-based estimator
Bibliographic record
Abstract
Anaerobic digestion model no.1 (ADM1) was used for tuning and performance analysis of the multi-model observer based estimator (mmOBE). The mmOBE was based on the variable structure model (VSM) of the anaerobic digestion model, which consists of several local submodels, each of which describes a typical process state. Depending on the hydraulic retention time, ADM1 simulated the methanogenic, organic overload, and acidogenic states of the process. These simulations allowed for optimising tunable parameters of the mmOBE. Owing to relatively slow process dynamics, a data acquisition interval as large as one day was sufficient to obtain acceptable accuracy. The simulations of mmOBE performance showed excellent rate of mmOBE convergence to ADM1 outputs. Moreover, mmOBE successfully estimated key kinetic parameters, such as maximal transformation rates of CODs, VFAs, and methane. These estimations can be used in the development of the advanced knowledge-based process system, which uses both available measurements and estimations of key kinetic parameters for extended diagnosis of failures and process trend analysis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".