LOW SULFUR HOME HEATING OIL DEMONSTRATION PROJECT SUMMARY REPORT.
Bibliographic record
Abstract
This project was funded by NYSERDA and has clearly demonstrated many advantages of using low sulfur content heating oil to provide thermal comfort in homes. Prior laboratory research in the United States and Canada had indicated a number of potential benefits of using lower sulfur (0.05%) heating oil. However, this prior research has not resulted in the widespread use of low sulfur fuel oil in the marketplace. The research project described in this report was conducted with the assistance of a well-established fuel oil marketer in New York State (NYS) and has provided clear proof of the many real-world advantages of marketing and using low sulfur content No. 2 fuel oil. The very positive experience of the participating marketer over the past three years has already helped to establish low sulfur heating oil as a viable option for many other fuel marketers. In large part, based on the initial findings of this project and the experience of the participating NYS oilheat marketer, the National Oilheat Research Alliance (NORA) has already fully supported a resolution calling for the voluntary use of low sulfur (0.05 percent) home heating oil nationwide. The NORA resolution has the goal of converting eighty percent of all oil-heated homes to the lower sulfur fuel (0.05 percent by weight) by the year 2007. The Oilheat Manufacturers Association (OMA) has also passed a resolution fully supporting the use of lower sulfur home heating oil in the equipment they manufacture. These are important endorsements by prominent national oil heat associations. Using lower sulfur heating oil substantially lowers boiler and furnace fouling rates. Laboratory studies had indicated an almost linear relationship between sulfur content in the oil and fouling rates. The completed NYSERDA project has verified past laboratory studies in over 1,000 occupied residential homes over the course of three heating seasons. In fact, the reduction in fouling rates so clearly demonstrated by this project is almost the same as predicted by past laboratory studies. Fouling deposition rates are reduced by a factor of two to three by using lower sulfur oil. This translates to a potential for substantial service cost savings by extending the interval between labor-intensive cleanings of the internal surfaces of the heating systems in these homes. In addition, the time required for annual service calls can be lowered, reducing service costs and customer inconvenience. The analyses conducted as part of this field demonstration project indicates that service costs can be reduced by up to $200 million a year nationwide by using lower sulfur oil and extending vacuum cleaning intervals depending on the labor costs and existing cleaning intervals. The ratio of cost savings to added fuel costs is economically attractive based on past fuel price differentials for the lower sulfur product. The ratio of cost savings to added costs vary widely as a function of hourly service rates and the additional cost for lower sulfur oil. For typical values, the expected benefit is a factor of two to four higher than the added fuel cost. This means that for every dollar spent on higher fuel cost, two to four dollars can be saved by lowered vacuum cleaning costs when the cleaning intervals are extended. Information contained in this report can be used by individual oil marketers to estimate the benefit to cost ratio for their specific applications. Sulfur oxide and nitrogen oxide air emissions are reduced substantially by using lower sulfur fuel oil in homes. Sulfur oxides emissions are lowered by 75 percent by switching from fuel 0.20 percent to 0.05 percent sulfur oil. This is a reduction of 63,000 tons a year nationwide. In New York State, sulfur oxide emissions are reduced by 13,000 tons a year. This translates to a total value of $12 million a year in Sulfur Oxide Emission Reduction Credits for an emission credit cost of $195 a ton. While this ''environmental cost'' dollar savings is smaller than the potential service costs reduction, it is very significant. It represents an important reduction in air pollutants that contribute directly to acid rain and other adverse impacts in the United States. When all air emissions are included, low sulfur content home heating oil and utility gas are virtually equal in their environmental impacts.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.023 |
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".