Abstract
Most biomass reaction studies have focused on optimizing product yield without considering cost and emissions as key metrics; they consider pure feeds without accounting for process integration resulting from separation and recycling and often change one parameter at a time to maximize yield. Here, we propose a framework to overcome the above issues. We demonstrate it for the hydrodeoxygenation (HDO) of 5-hydroxymethylfurfural (HMF) to produce 2,5-dimethylfuran (DMF), a crucial reaction in making lignocellulosic biomass-based platform chemicals. We consider the impact of water in the feed stemming from the sugar dehydration reactor on the HDO in 2-pentanol over a Ru/C catalyst, guided by an active learning experimental design (NEXTorch toolkit), combined with Aspen Plus process flowsheet simulation, techno-economic analysis and life cycle assessment. We demonstrate that Bayesian optimization of process flowsheets significantly reduces production costs by 26% and greenhouse gas emissions by 15% after striking a balance between raw material usage, solvent loss, and utility consumption. Such reductions are strongly correlated to the product yield due to the dominant cost of the feed. Interestingly, a slight amount of water negatively impacts greenhouse gas emissions more than production costs, requiring relatively high purity in process integration.
Published Version
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