Quick estimation and prediction of product carbon footprint (PCF) with the tool of ChemBoost Carbon Intelligence® (CBCI)

Abstract: As a small-molecule CDMO company, we keenly understand that this field is characterized by urgent development cycles, diverse synthetic routes and process options, but generally very high product carbon footprints. We have a strong commitment to continuously reducing our product carbon footprint through process optimization and lean production management. However, in practice we encountered two major challenges. Large computation cost. We execute more than ten thousand chemical steps in production plant each year. If we were to rely on traditional lifecycle carbon footprint calculation methods, it would require enormous human and material resources, making the approach impractical to implement; Poor timeliness. Traditional LCA can usually provide reasonably accurate carbon footprint figures only after massive production, and therefore cannot guide researchers in optimizing direction during the development phase. To address these pain points, we build CBCI tool in our internal information platform, which provides two core functions. Carbon footprint estimation for production batches: Taking structured electronic batch records as input, it retrieves two categories of standard carbon intensity parameters (equipment catagory such as reactors, centrifuges, dryers; and chemical catagory such as raw materials and solvents) to quickly output the estimated carbon footprint for that batch. Carbon footprint prediction at the R&D stage: Using the tech package from the process development stage as input, and applying the same standard database and algorithms, it predicts the carbon footprint once the process moves into scaleup production. The CBCI tool was deployed this year. In numerous cases where it has been applied, the margins of error for the two scenarios are no more than 10 and 5% respectively, fully meeting our business requirements. Overall, we have built a fit-for-purpose and handy tool for carbon footprint estimation & prediction. It not only enables rapid, precise carbon footprint assessment across our operations, but also integrates directly into the process development workflow. Overall it empowers every stakeholder in decarbonization and sustainability.