CAT-TRAP exhaust after treatment system for diesel engine
Bibliographic record
Abstract
This paper presents development of new developed cost effective CAT -TRAP system for diesel engine to reduce NOx and particulate matter. CAT -TRAP system is a combination of pellets type catalytic converter (CAT) and foam type particulate Trap (TRAP). The CAT was developed based on catalyst materials consisting of combination of metal catalyst such as cerium oxide (CeO2), zirconium dioxide (ZrO2) and silver nitrate (AgNO3) with pellets substrate. These catalyst materials are inexpensive in comparison with convectional catalysts (noble metals) such as palladium or platinum. The Trap was developed with indigenous materials for minimum pressure drop and maximum filtration efficiency. The CAT -TRAP (CAT C2D1L1 (C2 = Ag /CeO2/ZrO2 catalysts, D1 =132 mm and L1=20 mm) + TRAP P1D2L1 (P1 = 70-75%, D2 =125 mm and L1=20 mm) gives back pressure range (50-266 mbar). Minimum increase in brake specific fuel consumption was (0.4 - 3.70%), minimum decrease in brake thermal efficiency was (0.38- 2.26%) and loss in brake power was (0.57- 1.60%). The CAT -TRAP (C2D1L1 + P1D2L1) gives filtration efficiency range (65-72%) and NOx conversion efficiency was (65%). The objective of this paper is to develop cost effective CAT-TRAP system to reduce NOx and particulate matter from the exhaust of diesel engine. Detailed review on catalytic converter, Trap, inexpensive CAT-TRAP development, performance evaluation and engine test results have been presented with discussions. Key words: Catalyst, emissions, trap, C.I engine, spherical pellets, Ag/CeO2 /ZrO2.
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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) | 0.005 | 0.002 |
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".