Guiding Environmental Sustainability of Emerging Bioconversion Technology for Sophorolipid Production By Adopting a Dynamic Life Cycle Assessment (dLCA) Approach

Microbial biosurfactants have been gaining attention as a potential replacement to synthetic surfactants as they can be produced from renewable feedstocks, have lower environmental toxicity and are highly biodegradable. Sophorolipids (SL) is one such microbially produced glycolipid biosurfactant representing the largest market share of the 27 billion USD global surfactant market. Though SL production is based on renewable feedstocks challenges concerning the production of electricity, enzymes, and materials that are primarily fossil based, still exist. From a scale-up perspective, it is imperative to quantify the environmental impacts associated with the SL production pathway and inform improvements at the research and development (R&D) stage, to facilitate commercial exploitation of these new generation biosurfactants. LCA is considered a power tool to evaluate environmental sustainability, its application to emerging technologies is different and is challenged with problems like data scarcity and rapid changes in the technology itself. This study adopts a dynamic LCA (dLCA) framework consisting of two traversals that emphasizes iterative evaluations and collaborative efforts with the experimentalists to tackle these problems. The dLCA framework is used to analyze SL production from organic waste streams, identify hotspots, derive recommendations to reduce environmental impacts at lab scale, to avoid unintended consequences while scaling up. LCA in the first traversal identified food waste as the most suitable feedstock. After accounting for experimental results with food waste as feedstock, two separation technologies were evaluated in the second traversal to find out that fed batch fermentation integrated with in-situ separation resulted in lesser environmental impacts compared to conventional separation technique. Results obtained from each traversal will inform the experimentalists to optimize those processes, resultant data sets can be iteratively used in subsequent traversals to account for the technological changes and mitigate the impacts before scaling up.