• Stan Andrea
• S Aggarwal

The future of aviation transitioning to net-zero depends on a credible, rapid scale-up of sustainable aviation fuels (SAF) within increasingly stringent chemical and process requirements, as well as unrelenting fuel specifications and safety standards. The growing policy focus in the EU, UK, and the US has made incentives and obligations more acute. Nonetheless, producers continue to face feedstock limitations, excessive expenses, and complex compliance interfaces between fuel standards and chemical regulatory frameworks. The following paper suggests and evaluates a combined, policy-regulatory analysis-techno-economic and lifecycle assessment (TEA/LCA) and machine learning (ML) framework involving property prediction, reactor surrogates, and integration with a digital-twin, which can be called an integrated, regulation-aware ML. According to the report, EU/UK/US is a stringency/alignment index, 122-SF pathways, and the ML models are trained on ASTM conformance gates, which are empirical design codes. Findings have revealed that aligned requirements and precise sustainability requirements will minimize scale risk. Physics-based surrogates will result in fewer experiments and faster compliance with specifications.

Sustainability, Machine Learning, SAF, TEA, LCA

"Synergizing Sustainable Aviation Fuel Innovation, Chemical Industry Regulation, and Machine Learning: Towards a Low-Carbon Future", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.a175-a183, November-2025, Available :http://www.jetir.org/papers/JETIR2511022.pdf

"Synergizing Sustainable Aviation Fuel Innovation, Chemical Industry Regulation, and Machine Learning: Towards a Low-Carbon Future", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppa175-a183, November-2025, Available at : http://www.jetir.org/papers/JETIR2511022.pdf