Investigation of the Potential for Biofuel Blends in Residual Oil-Fired Power Generation Units as an Emissions Reduction Strategy for New York State
Bibliographic record
Abstract
There is a significant amount of oil, about 12.6 million barrels per year, used for power generation in New York State. The majority of it is residual oil. The primary reason for using residual oil probably is economic, as these fuels are cheaper than distillates. However, the stack emissions from the use of such fuels, especially in densely populated urban areas, can be a cause for concern. The emissions of concern include sulfur and nitrogen oxides and particulates, particularly PM 2.5. Blending with distillate (ASTM No.2) fuels may not reduce some or all of these emissions. Hence, a case can be made for blending with biofuels, such as biodiesel, as they tend to have very little fuel bound sulfur and nitrogen and have been shown in prior work at Brookhaven National Laboratory (BNL) to reduce NOx emissions as well in small boilers. Some of the research carried out at CANMET in Canada has shown potential reductions in PM with blending of biodiesel in distillate oil. There is also the benefit obtaining from the renewable nature of biofuels in reducing the net carbon dioxide emitted thus contributing to the reduction of green house gases that would otherwise be emitted to the atmosphere. The present project was conceived to examine the potential for such benefits of blending biofuels with residual oil. A collaboration was developed with personnel at the New York City Poletti Power Plant of the New York Power Authority. Their interest arose from an 800 MW power plant that was using residual oil and which was mandated to be shut down in 2010 because of environmental concerns. A blend of 20% biodiesel in residual oil had also been tested for a short period of about two days in that boiler a couple of years back. In this project, emission measurements including particulate measurements of PM2.5 were made in the commercial boiler test facility at BNL described below. Baseline tests were done using biodiesel as the blending biofuel. Biodiesel is currently and probably in the foreseeable future more expensive than residual fuel. So, another task was to explore potential alternative biofuels that might confer emission benefits similar to those of biodiesel, while being potentially significantly cheaper. Of course, for power plant use, availability in the required quantities is also a significant criterion. A subsidiary study to determine the effect of the temperature of the filter used to collect and measure the PM 2.5 emissions was conducted. This was done for reasons of accuracy in a residential boiler using distillate fuel blends. The present report details the results obtained in these tests with the baseline ASTM No. 6 fuel and blends of biodiesel with it as well as the results of the filter temperature study. The search for the alternative 'cheaper' biofuel identified a potential candidate, but difficulties encountered with the equipment during the testing prevented testing of the alternative biofuel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".