The Transition to Global Renewable Chemical Production: AI Enhanced Pathway Prediction

Global bulk chemical production is responsible for huge amounts of carbon emissions, withammonia alone responsible for nearly 2% of global CO2. The transition to renewableproduction is a key to meeting net zero goals and also has the potential to allow the storage ofrenewably generated energy. By combining technoeconomic, life cycle, and social life cycleassessment with AI-enhanced probabilistic prediction of future development, the mostcomplete assessment of renewable production technologies can be undertaken, predicting bothpresent and future performance. We find hydrogen to be cost competitive by 2030, but thatammonia production will require substantially more capacity for planned 2050 production.This thesis is presented in publication format with each chapter self-contained and severalfeaturing journal articles. Chapter one provides a literature review, assessing prior work toidentify strengths, weaknesses, and opportunities for future research. Chapters two to fourpresent papers which focus on the renewable production of hydrogen, ammonia, and other bulkchemicals. These chapters present technoeconomic, life cycle, and social life cycle assessmentsof production routes for these chemicals to better define the most sustainable technology bothcurrently and over time as their development proceeds. These works explore gaps such as theneed for a robust integrated mechanistic and economic model for chemical production, thefuture price distribution of hydrogen production, the optimal renewable ammonia productiontechnology and deployment route.Chapter five provides two papers that collectively analyse the need for responsiblegenerative AI use in chemical engineering, covering its impacts in both the private sector andeducation. Finally, Chapter six contains the thesis conclusions and outlines directions for futurework.