Using Artificial Intelligence (AI) and Bioprocessing for Drop-in Fuel

Abstract

Across global energy systems, the adoption of drop-in fuels offers a practical pathway to decarbonization while preserving the value of existing petroleum infrastructure. Their molecular similarity to fossil fuels eliminates the compatibility issues that hindered earlier biofuels such as ethanol and biodiesel. As a result, they can be used directly in current engines, pipelines, and refineries, enabling a rapid transition toward renewable energy with minimal operational disruption. Air transport, oceanic freight, and overland haulage sectors operate under stringent reliability, endurance, and economic constraints, favoring compatible renewable energy carriers that bypass major infrastructural changes. Rising energy demand driven by increased consumption, fossil fuel depletion, and environmental challenges necessitates the development of clean, reliable, and sustainable alternatives. Economically and environmentally sustainable biofuel technologies offer a promising solution to meet growing energy demands. Hydrocarbons are produced from pre-treated feedstocks by engineered microbes and are refined and upgraded for efficient production. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) gene editing technique overcomes metabolic bottlenecks and low yields, enabling microbes to efficiently produce hydrocarbons for enhanced fuel production. Internet of Things (IoT) integration enables real-time process control, enhancing efficiency, yield, and scalability in fuel production. Predictive models and automated strain development enhance process efficiency, reduce costs, and support scalable fuel production, providing a practical pathway toward sustainable and decarbonized energy solutions.