Enhancing Methane Yield via Biomethanation and Kinetic Modelling
Bibliographic record
Abstract
This investigation explores the enhancement of CH4 generation in anaerobic digesters (AD) via in-situ renewable hydrogen injection utilising four exotic crop wastes and a crop (five feedstocks).The substrates are yam, cassava, and cocoyam peel (YP, CP and CYP), rice husk (RH) and finger millet seeds (FMS).Biomethane Potential (BMP) Tests, followed by AD experiments with food waste inoculum (FWI), were conducted in triplicate under mesophilic conditions (37°C), utilising an anaerobic model (ANM) test rig.The last phase of the experimental campaign is bio-methanation to upgrade CH4 purity.CYP and YP showed 233% and 81.5% higher gas yields, respectively, with CH4 content improvements up to 38.5%.However, CP emerged as the optimal feedstock, hence the primary substrate utilised in the AD, supporting hydrogenotrophic methanogenesis (HM) and CO₂ to CH₄ conversion.Consequently, MATLAB-based kinetic modelling confirmed the Richard equation as the best fit predictor.The novelty of this study lies in the innovative incorporation of insitu H₂ injection (0.67 ml/min), bubble mixing and mass transfer to enhance CH₄ from tropical crop waste (cassava peel), a widely available yet underutilised feedstock specific to Plateau State, Nigeria.Additionally, integrating computational fluid dynamics (CFD) and bioprocess kinetic modelling provides a comprehensive framework for understanding the parameterisation and optimising system dynamics.This consolidates the research contribution to the experimental optimisation of decentralised biogas systems, facilitating sustainable energy solutions for pipeline quality in tropical regions.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".