Emerging trends of biohydrogen ecosystem on environmental sustainability: A case study
Bibliographic record
Abstract
The greatest threat to humanity is now considered climate change. Biomass as a renewable energy source is treated as one of the clean energy sources that help meet humanity's energy needs. In the transition to a new energy system based on renewable energies, biomass can be crucial. This paper particularly focuses on a new biohydrogen (bioH 2 ) ecosystem development concept for communities to provide global and local sustainable and green energy, considering the biomass-to-bioenergy nexus. In this regard, the paper further discusses the different bioH 2 ecosystem concepts and emerging trends where biomass and renewable resources are utilized for energy production. In addition, the bioenergy production potentials of different agricultural crop wastes are evaluated for different end-use purposes like electricity, heat, cogeneration, and transport. In parallel to its high bioenergy yield, the highest total energy (83,686.8 GJ) and gross electricity (4686.5 MWh) production values were observed for the olive cake waste. Moreover, the biomethane and bioethanol production potentials of the crop wastes are evaluated. The highest biomethane yield of 253.7 m 3 /ha with a total bioenergy production of 40,662.6 GJ was obtained for the maize stover waste, while its bioethanol production was 505.7 L/ha. Consequently, the bioH 2 ecosystem with biomass utilization reveales as a sustainable and green way of providing future energy for communities owing to the great potential of crop wastes for bioenergy production. • The unique biohydrogen ecosystem concepts are evaluated for sustainable bioenergy. • The bioenergy potentials of the crop wastes are projected for various end uses. • The highest total energy production of 83,686.8 GJ is observed for the olive cake waste. • The highest biomethane yield of 253.7 m 3 /ha is obtained for the maize stover waste. • Bioenergy utilization appears to be environmentally benign and sustainable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".