Following the anticipated successful completion of Phase I, the SCCER BIOSWEET is committed to its vision that sustainable biomass can contribute to achieving the objectives of the Swiss Energy Strategy 2050 with an estimated “100 PJ” consumption of final energy from biomass, which would represent more than twice as much as today. The SCCER BIOSWEET develops and implements biomass valorization technologies to make the Swiss energy turnaround happen. While Phase I had a strong focus on research and development, the work structure of the SCCER BIOSWEET for Phase II is re-organised to focus on Knowledge and Technology Transfer (KTT) of the most promising technologies for biomass conversion, considering the potential impact to the energy transition. The objective to achieve concrete utilisation of biomass by the private sector and benefit Swiss SMEs, larger industries and utilities in the short to medium term.
On the one hand, the technological goal is to exploit biomass resources to the highest sustainable extent. This is pursued by pushing the conversion and efficiency limits of existing bioenergy technologies, by improving the feedstock utilization, by creating new and innovative biomass value chains, and by designing better integrated energy systems. On the other hand, the SCCER BIOSWEET reaches out to promote alternative energy carriers for e.g. mobility or heat and power applications and it offers knowledge to support the energy policy and market development.
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Kinetic Network Modeling of the Catalytic Upgrading of Biomass’s Acetate Fraction to Aromatics
2019-06-28.29th European Symposium on Computer Aided Process Engineering, Eindhoven, The Netherlands, 16-6, 2019. p. 1729-1734. DOI : 10.1016/B978-0-128-18634-3.50289-7.
2016.The 29th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, Portoroz, Slovenia, June 19-23, 2016. DOI : 10.1016/j.energy.2017.05.027.
Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP)
