In-situ characterization of the reaction progress of the fluid catalytic cracking reactions by laser diagnostic techniques
Bibliographic record
Abstract
Sophisticated analytical techniques, such as mass spectrometry and high-performance liquid chromatography (HPLC), can be used to measure aromatic and saturate contents of the Fluid Catalytic Cracking (FCC) feedstock and products. However, optical methods have the advantage of being rapid and non-intrusive, operating in contact-less mode. Laser-absorption measurements of fuel concentration are often made at mid-infrared (MIR) wavelengths near 3.4 µm, which overlap with the strong C-H stretch vibrational transitions of hydrocarbons and guarantee sensitive detection even for short measurement path lengths. The MIR spectra for individual hydrocarbons can be found in different databases, however the information is limited to being used with species having low carbon number. In this sense, a Group Contribution Method (GCM) is proposed for the spectra prediction of the different compounds present in the catalytic cracking reaction in the region 3200-2800 cm-1. This PhD thesis considers the development of a laser diagnostic methodology for “in-situ-free of particles” monitoring of fluid catalytic cracking (FCC) reaction progress using model compounds. The aim is to contribute to the characterization of the chemistry and chemical species involved in FCC through the discrete evaluation of the infrared (IR) spectra. The methodology proposed considers the in-situ MIR analysis of the change in the concentration of functional groups present in the model compound 1,3,5-triisopropylbenzene (1,3,5-TIPB) as indicator of FCC reaction progress. This is performed in the annulus of a CREC Riser Simulator. As well, the considered approach postulates the application of this in-situ MIR methodology for the characterization of the light gases and gasoline lumps in the context of Industrial FCC unit using a sampling bottle with two cameras under vacuum. This research was carried out at the Grupo de Investigación Bioprocesos y Flujos Reactivos at Universidad Nacional de Colombia. An internship in the group of Professor Hugo de Lasa (Western University - Ontario Canada) allowed the development of a Group Contribution Method (GCM) for the characterization of the reaction progress of a model FCC reaction.
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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) | 0.002 | 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".