A parametric study through the modelling of hydrothermal gasification for hydrogen production from algal biomass
Bibliographic record
Abstract
Abstract Hydrothermal gasification (HTG) is applicable to high moisture content biomass feedstock such as wet microalgae. The key interests of this thermochemical processing are its ability to use whole algae instead of simply lipid extracts and to use a wide range of algal feedstocks. It employs water in the form of a reaction medium to disintegrate biomass into hydrogen gas. The products' composition and yields are a function of process parameters, namely feed concentration, pressure, and temperature. There is very limited literature available on model development to understand the impacts of various input parameters on the products of HTG. This study presents development of a detailed process model for HTG and the illustration of process parameters on the gas product yields. The approach includes developing the system model, identifying the key process parameters in the reactor setup that affect syngas yield, and understanding the overall process in terms of final product yield. A simulation of hydrothermal gasification based on thermodynamic equilibrium is studied. Based on the developed process model about 52.1 t/day of hydrogen can be produced from 500 t/day of wet algal biomass. This shows the potential of large‐scale hydrogen production through this process for hydrogen economy. The results from this study could be used by the gas processing industry and policymakers to determine the most feasible means of converting biomass‐based resources into gaseous fuels.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".