Comparison between HFC-134a and alternative refrigerants in mobile air conditioners using the GREEN-MAC-LCCP© model.
Bibliographic record
Abstract
The transition from CFC-12 (GWP=10,900) to HFC-134a (GWP=1,430) in the 1990’s in new vehicle air conditioners eliminated the potential contribution to ozone depletion from new vehicles and reduced the direct Global Warming Potential (GWP) by over 80%. One proposed alternative is HFC-1234yf (GWP=4). Despite the phase-in success of HFC-134a as a zero ODP automotive refrigerant it is still a potent greenhouse gas and the European Union (EU) issued Directive 2006/40/EC that prohibits the use of automotive refrigerants with GWP greater than 150, starting from January 1st, 2011. Due to such regulations, the automotive Original Equipment Manufacturers (OEMs), chemical manufacturers and Mobile Air Conditioning (MAC) industry have evaluated several alternative refrigerants considering a range of selection criteria that include: refrigerant engineering performance, system design impact, MAC system changes to optimize the use of new refrigerants, cost, flamability, and environmental impacts including global warming, and human toxicity risks. There has been remarkable success in eliminating refrigerant fluids that deplete the ozone layer, but many of their replacements have high GWP. There is now a major international effort for a third generation refrigerant fluids that are safe both for ozone depletion and climate protection. During 2013, the United States and China proposed phasing out high GWP HFCs that have been introduced to replace ODP substances through the provisions of the Montreal Protocol. Europe is currently debating between two alternative fluids for vehicle air conditioners, and the outcome is being watched closely. This paper will compare the alternatives. New MAC systems that meet the low GWP requirements of the EU Directive refrigerants should also be equally or more efficient than HFC-134a designs. Life Cycle Analysis (LCA) adds a step in the understanding of the dynamics of the industrial activities as a system and not as individual components, with implications for better policy decisions at the technological and environmental levels. This was recognized by the MAC industry and government and Life Cycle Climate Performance (LCCP) was accepted as one of the methods for selecting among alternative refrigerants. We consider and implement LCA for developing the Global Refrigerants Energy & Environmental-Mobile Air Conditioning-Life Cycle Climate Performance (GREEN-MAC-LCCP)© model which is the tool that evaluates the full cycle of Greenhouse Gas emissions of alternative refrigerant systems. The goal of this tool is to provide a superior basis for engineers and policy makers to make wise decisions of alternative competing technologies. In this presentation, we summarize the evolution of refrigerant fluids and how the world has arrived at the present point. The interplay between the evolution of technology and the regulatory system that governs it, the economic drivers and the environmental health and safety implications will be elucidated. We will also provide a short overview of the model and the results obtained by evaluating various alternative refrigerant MAC systems and compare them with the HFC-134a production baseline. Using GREEN-MAC-LCCP© we estimate the energy consumption and GHG of current vehicle MACs that operate with HFC-134a and compare these results with new systems that operate with alternative refrigerants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".