Development of a novel renewable energy-based integrated system coupling biomass and H2S sources for clean hydrogen production
Bibliographic record
Abstract
The present work aims to develop a novel integrated energy system to produce clean hydrogen, power, and biochar. The Palmaria palmata, a type of seaweed, and hydrogen sulfide from the industrial gaseous waste streams are taken as potential feedstock. A combined thermochemical approach is employed for the processing of both feedstocks. For clean hydrogen production, the zinc sulfide thermochemical cycle is employed. Both stoichiometric and non-stoichiometric equilibrium-based models of the proposed plant design are developed in the Aspen Plus software, and a comprehensive thermodynamic analysis of the system is also performed by evaluating energy and exergy efficiencies. The study further explores and covers the modeling, simulation, and parametric analyses of various subsections to enhance the hydrogen and biochar production rate. The parametric analyses show that the first step of the thermochemical cycle (desulfurization reaction) follows a stoichiometric pathway and the optimal conversion is achieved at a ZnO/H 2 S ratio equal to 1. On the other hand, the second step of the thermochemical cycle (regeneration reaction) does not follow a stoichiometric pathway, and ZnS conversion of 12.87 % is achieved at a high temperature of 1400 °C. It is found that a hydrogen production rate of 0.71 mol/s is achieved with the introduction of 0.27 mol/s of H 2 S. The energy and exergy efficiencies of the zinc sulfide thermochemical cycle are found to be 65.23 % and 35.58 % respectively. A biochar production rate of 0.024 kg/s is obtained with the Palmaria palmata feed rate of 0.097 kg/s. The Palmaria to biochar energy and exergy efficiencies are found to be 55.43 % and 45.91 % respectively. The overall energy and exergy efficiencies of the proposed plant are determined to be 72.88 % and 50.03 % respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".