RF-ICP Thermal Plasma for Thermoplastic Waste Pyrolysis Process with High Conversion Yield and Tar Elimination
Bibliographic record
Abstract
This paper demonstrates an RF thermal plasma pyrolysis reaction apparatus that achieves 89 wt.% reaction conversion yield with no tar content. The demonstrated experimental apparatus consists of a 1100 W RFVII Inc. (104 Church St, Newfield, NJ 08344, United States) @ 13.56 MHz RF thermal plasma generator, a Navio matching network, intelligent feedback controller, and an 8-turn copper RF-ICP torch embedded in a 12 L thermochemical reactor. The intelligent feedback controller optimizes the thermal performance based on feedback signals from three online gas analyzers: CO, CO2 and NOx. The feedback controller output signal controls the RF thermal plasma torch current that provides real-time temperature control. The proposed reaction system achieves precise temperature profiles for both pyrolysis and gasification as well as increases end-product yield and eliminates undesired products such as tar and char. The identified hydrocarbon liquid mixture is 90 wt.% gasoline and 10 wt.%. diesel. The 8-turn RF-ICP thermal plasma torch has an average heating rate of +35 °C/min and a maximum operating temperature of 2000 °C and is able to sustain stable flame for more than 30 min. The reaction operating parameters are (550–990 °C τ = 30 min for pyrolysis and (1300 °C τ = 1 sec) for the gasification process. The identified hydrocarbon liquid products are 90 wt.% of a n-butyl-benzene (C6H5C4H9) and oluene (C7H8) mixture with less than 10 wt.% decane diesel fuel (C10 H22). Comsol simulation is used to assess the RF-ICP thermal plasma torch’s thermal performance.
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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 | 0.001 |
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".