Abstract: The development of biomass pretreatment approaches that, next to (hemi)cellulose valorization, aim at the conversion of lignin to chemicals is essential for the long-term success of a biorefinery. Herein, we discuss a dithionite-assisted organosolv fractionation (DAOF) of lignocellulose in n-butanol and water to produce cellulosic pulp and mono-/oligo-aromatics. The study frames the technicalities of this biorefinery process and relates them to the features of the obtained product streams. We comprehensively identify and quantify all products of interest: solid pulp (acid hydrolysis-HPLC, ATR-FTIR, XRD, SEM, enzymatic hydrolysis-HPLC), lignin derivatives (GPC, GC-MS/FID, 1H-13C HSQC NMR, ICP-AES), and carbohydrate derivatives (HPLC). These results were used for inspecting the economic feasibility of DAOF. In the best process configuration, a high yield of monophenolics was reached (~20%, based on acid insoluble lignin in birch sawdust). Various other lignocellulosic feedstocks were also explored, showing that DAOF is particularly effective on hardwood and herbaceous biomass. Overall, this study demonstrates that DAOF is a viable fractionation method for the sustainable upgrading of lignocellulosic biomass.

Abstract: Multi-objective optimization is an important decision-making tool for energy processes, as multiple targets need to be achieved. These objectives are usually conflicting since a single solution cannot be optimal for all objectives, resulting in a set of Pareto-optimal solutions. Multiple indicators might be available to describe a sustainability objective, such as the environmental impact which is commonly evaluated by performing a life cycle assessment. In this study, Pareto aggregation is proposed as a method which employs a novel multi-objective optimization-based approach as an alternative to the classically used aggregation in life cycle assessment. This method identifies conflicting environmental indicators and performs an aggregation among those that require a trade-off. An environmental-economic optimization of a second-generation bioethanol plant is used to illustrate and evaluate the proposed method. Process parameters from a biochemical conversion pathway flowsheet simulation model are chosen as optimization variables. To reduce the computational time, surrogate models, based on artificial neural networks, are used. Out of the eighteen ReCiPe Midpoint environmental indicators, five were identified as conflicting, resulting in an aggregated environmental objective, which was then traded off with the economic objective function, chosen as the levelized cost of ethanol. Comparison with the widely used single-score EcoIndicator99 showed that the Pareto aggregation method can reduce most of the environmental indicators by up to 6.5%. This research provides an insight on non-redundant objective functions, aiming at reducing the dimensionality of multi-objective optimization problems, while taking into consideration decision-makers’ preferences.

Abstract: Lignocellulosic biomass is the most abundant source of renewable biomass and is seen as a high-potential replacement for petroleum-based resources. The conversion technologies to advanced biofuels are still at a low maturity level, thus allowing for future cost reductions through technological learning. This fact is barely considered in state-of-the-art techno-economic assessments and a structured approach to account for technological learning in techno-economic assessments is needed. In this study, a framework for techno-economic assessments of advanced biofuels, integrating learning curves, is proposed. As a validation of this framework, the economic feasibility of the valorization of corn stover for the production of second-generation bioethanol in Belgium is studied. Process flowsheet simulations in Aspen Plus are developed, with an emphasis on the comparison of four different pretreatment technologies and two plant capacities at 156 dry kt biomass/y and 667 dry kt/y. The dilute acid pretreatment model of the large-scale biorefinery required the lowest minimum learning rate to reach an economically feasible biorefinery by 2030, being 3.9%, almost half as the one calculated for the smaller scale plant. This learning rate seems to be achievable based on learning rates commonly estimated in literature. We conclude that there is a potential for advanced ethanol production in Belgium under the current state of technology for large-scale biorefineries, which require additional biomass imports, when accounting for future cost reductions through learning

Abstract: Herein, we report a transition metal-free approach for the regioselective functionalisation of benzylic C(sp3)-H bonds using alcohols and carboxylic acids as the nucleophiles. This approach provides a straightforward route for the synthesis of various benzylic ethers and esters to provide a wide generality of this system. Expediently, twelve pharmaceutically relevant compounds have been synthesized using this strategy.