Heteropolyacid promoted lignin-MOF derived spherical catalyst for catalytic hydrogen transfer of 5-hydroxymethylfurfural.

Catalytic conversion of biomass-derived value-added chemicals was of great significance for the utilization of renewable biomass resources to instead of fossil chemicals. Biomass-derived lignin was regarded as an important support and 5-hydroxymethylfurfural (HMF) was a vital platform chemical derived from cellulose. Herein, a series of lignin-MOF hybrid catalysts were prepared and modified with different heteropolyacids (HPAs), which were then successfully introduced into the selective conversion of HMF to 5-hydroxymethylfurfuryl alcohol (MFA). The effect of different HPA, calcination temperature, etc. were all studied, and all catalysts were well characterized. It was confirmed that silicotungstic acid modified catalyst (Ni3Co-MOF-LS@HSiW) exhibited the best catalytic performance, while the highest conversion of HMF was up to 100%, with the best MFA yield of 86.5%. The finding in this study could provide novel insights for the utilization of lignin and preparation of value-added biomass-derived chemicals.

SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array.

Wind power is a clean and renewable energy, yet it poses integration challenges to the grid due to its variable nature. Thus, Wind Power Forecasting (WPF) is crucial for its successful integration. However, existing WPF datasets often cover only a limited number of turbines and lack detailed information. To bridge this gap and advance WPF research, we introduce the Spatial Dynamic Wind Power Forecasting dataset (SDWPF). The SDWPF dataset not only provides information on power generation and wind speed but also details the spatial distribution of the wind turbines and dynamic contextual factors specific to each turbine. These factors include weather information and the internal status of each wind turbine, thereby enriching the dataset and improving its applicability for predictive analysis. Further leveraging the potential of SDWPF, we initiated the ACM KDD Cup 2022, a competition distinguished as the foremost annual event in data mining, renowned for presenting cutting-edge challenges and attracting top talent from academia and industry. Our event successfully draws registrations from over 2400 teams around the globe.