Development and Field Performance Validation of a Retrofit SCR System for On-Road Heavy-Duty Application
Bibliographic record
Abstract
Selective catalytic reduction (SCR) technology is being considered as the potential strategy for significant reduction of NOx emissions from diesel engines. Many challenges exist in the development of an On-Road SCR retrofit system in terms of system integration and optimization of control strategy in order to achieve highest NOx reduction given the diversity of duty cycles. The main considered challenges are: - The development of a generic control strategy that would work for a broad range of engines, - Development of a reliable and durable injection system that would be able to withstand the harsh environments on a heavy-duty vehicle, - Packaging of the system to be able to fit on a number of vehicles with different configurations, - Controlling ammonia slip and assurance of reducing agent (Urea) availability and quality. In this study a prototype SCR system was evaluated over engine and chassis dynamometer test cycles. Two heavy-duty diesel engines were used during engine dynamometer evaluations using transient and steady state test cycles; a 2004 Caterpillar C11 and 2000 Cummins ISM highway diesel engines. At the completion of the engine test program, an in-use single-axle heavy-duty truck was obtained with a similar Caterpillar C11 engine and the same SCR system was installed for further evaluation during chassis dynamometer tests. These tests were conducted over transient and constant speed test cycles at standard and lowered ambient temperature (-15°C). The engine test results are reported for 4 urea injection calibrations over transient testing showing 84% NOx reduction with Calibration-04 at 2 ppm NH₃ slip when run on Caterpillar C11 engine and urea injection Calibration-04. During the chassis dynamometer testing with system flashed with Calibration-01, the NOx emissions were lowered by 50% over the transient cycle and 50, 80, and 98 percent under steady state tests at 50, 80, and 100 kilometers per hour, respectively. The goal of this work is to outline the development and optimization of the control strategy and hardware integration of the retrofit system; - System development and integration aspects. - The test results from the calibration of the control strategy using a Cummins ISM engine over FTP transient cycle. - Test results of the optimized system that was tested on a Caterpillar C11 engine over FTP cycle. - A summary of the performance of the field demo program highlighting component durability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".