Incorporating Energy Generation into Volatile Organic Compound (VOC) Emission Treatment Using a Solid Oxide Fuel Cell: A Model-Based Approach
Bibliographic record
Abstract
Industrial processes that use solvent-based coatings emit volatile organic compounds (VOCs), which when released into the environment are smog precursors. The purpose of this work is to develop a VOC abatement technology that not only destroys such VOCs with high efficiency but also extracts the energy found in these compounds to do useful work elsewhere in the facility. To this end, a model-based design approach using Aspen HYSYS was used to develop and optimize an abatement system that consisted of three separate technologies: an adsorber, a reformer, and a solid oxide fuel cell (SOFC). A model was developed that integrated the technologies, allowing for optimization of the overall operating conditions and performance. The reformer and SOFC portion of the model was validated by a comparison to published literature results for methane as a feedstock. After optimization, the model indicated that this system could achieve 95% VOC removal efficiency, with an electrical efficiency of 49%. When heat integration is considered, the portion of energy recovered increases to 85%.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".