Digitalisation of catalytic processes for sustainable production of biobased chemicals and exploration of wider chemical space

Abstract

Global warming and the depletion of petroleum resources require immediate and focused attention, and there is a pressing need to accelerate progress. Digital approaches can be leveraged in these efforts, for example in exploring effective replacements for petrochemicals or effectively identifying molecules with better performance. One such potential replacement is lignocellulosic biomass: a sustainable feedstock for producing chemicals and fuels that does not compete with essential food supply. However, the inherent complexity of lignocellulosic biomass and the technical challenges in its transformation pose significant obstacles that require data-driven approaches to solve. Here, we use the catalytic transformation of lignocellulose to value added chemicals as a case study highlighting the critical role of digital technologies, including improved data integration, process optimization, and system-level decision-making in catalyst design, synthesis, and characterization. Data-driven approaches work hand-in-hand with technology: the integration of machine learning (ML) and artificial intelligence (AI) allows for efficient molecule design and optimization; coupling ML/AI with the use of flow chemistry and high-throughput synthesis techniques enhances scalability and sustainability. Together, these innovations can facilitate a more resilient and sustainable chemical industry, reducing dependency on fossil fuels and mitigating environmental impact.