Insights into the integrated effects of polymeric pretreatment and catalytic hydrotreatment of light gas oil
Bibliographic record
Abstract
Abstract A study has been carried out in detail to measure the effects of the removal of nitrogen compounds on the hydrotreatment of light gas oil (LGO) using a synthesized polymer consisting of a polymer support, copolymer of glycidyl methacrylate and ethylene glycol dimethacrylate (PGMA‐co‐EDGMA), a π‐acceptor moiety (2,4,5,7‐tetranitrofluorenone, TENF), and a three‐carbon linker (diaminopropane, DAP (3)). The primary focus of this paper is first to study the effects of selectively removing nitrogen compounds on the hydrotreatment of LGOs. Removal of these catalyst inhibiting and poisoning compounds prior to hydrotreatment will help in improving the hydroprocessing efficiency as well as in reducing the chemical fouling, thus improving the catalyst life. Second, the effectiveness on the reusability of bulk polymer after regeneration was studied. To achieve this, nitrogen compounds from LGO were adsorbed on the synthesized polymer by mixing polymer and LGO. The polymer was regenerated by washing with toluene in a Soxhlet apparatus. Hydrotreating experiments were performed in a pilot scale trickle‐bed reactor. To create a baseline for monitoring the hydrotreatment activity, untreated LGO (without polymer adsorption) was hydrotreated with a commercial NiMo/γ‐Al2O3 catalyst and analyzed for nitrogen, sulfur, and aromatics content. The pretreated feed (with polymer adsorption) was also hydrotreated, and the results were compared. The results show that prior selective removal of nitrogen compounds improved overall hydrotreatment activity and resulted in additional decrease of 18.7%, 8.3%, and 9.4% in total nitrogen, sulfur, and aromatics content, 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".