Fundamentals of Operation of the Induced Bed Reactor (IBR) Anaerobic Digester
Bibliographic record
Abstract
The Induced Bed Reactor (IBR) was developed at Utah State University to apply high-rate anaerobic digestion techniques to high solids content substrates. This technology has been successfully implemented at full-scale multiple installations in the United States and Canada as a waste treatment and energy production technology, but the physical processes necessary to further optimize the system were not well understood. Bench scale IBRs were operated as anaerobic digesters at 35°, 45°, and 55° C under three organic loading rates and three corresponding hydraulic retention times. Reactor performance was monitored at steady state for residence time distribution and substrate reduction. The results show that the IBR behaves as a retained biomass reactor with fluid mixing that most closely approximates Completely Stirred Tank Reactor (CSTR) behavior when operated under the study conditions. A compartment real CSTR model, incorporating elements of dead zone and bypass flow, appears to be the most appropriate representation of the data. Mixing is likely due to a combination of energy inputs from thermal gradients induced by heat flux through the reactors and reactor and shear rates induced by gas evolution in the sludge bed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".