While we have presently focused on refining the AqSO biorefinery concept, our work also highlights how BO can function as the cornerstone of a holistic framework for developing sustainable materials technologies. The key advantage provided by BO in this context is the ability to establish surrogate models from a small set of data that is automatically curated by the algorithm. Here, we showed how the data can be efficiently collected in batches and shared between multiple surrogate models to accelerate convergence and provide predictive power over the entire design space. A Pareto front analysis can then be applied as a general tool to optimize products and increase the the resource efficiency, without the need to conduct further experiments. This will in turn help increase the long term sustainability of the technology. Our work thus shows the potential of machine learning methods, such as BO, to both improve and expedite the development of sustainable materials processing and engineering.