• Energy Research
• Embargo close
• B2FIND close

Materials:Rice straw, pine sawdust and Phoenix Tree's leaf were selected as the main biomass of this study. Algorithms and methods:Coats-Redfern integral method,Doyle method,Distribution Activation Energy Model (DAEM): The database contains all the original data, intermediate data and final results used in the paper. Fig. 1 was schematic diagram of WRT-3P high temperature TGA and gas flow routes Fig. 2 was influence of particle size on biomass pyrolysis kinetics (a) TG curves of rice straw (b) DTG curves of rice traw (c) TG curves of pine sawdust (d) DTG curves of pine sawdust (e) TG curves of Phoenix Tree's leaf (f) DTG curves of Phoenix Tree's leaf Fig. 3 was influence of heating rate on different biomass (rice straw, pine sawdust and Phoenix Tree's leaf) pyrolysis kinetics (a) TG curves of rice straw (b) DTG curves of rice traw (c) TG curves of pine sawdust (d) DTG curves of pine sawdust (e) TG curves of Phoenix Tree's leaf (f) DTG curves of Phoenix Tree's leaf Fig. 4 was potassium concentration of initial and soaked rice straw Fig. 5 was influence of K+ on rice straw pyrolysis kinetics (a) TG curves (b) DTG curves Fig. 6 was the relationship between and 1/T of three kinds of biomass with a particle size of 0.150 - 0.180 mm at different heating rates. (a) 5℃/min (b) 10℃/min (c) 20℃/min (d) 40℃/min Fig. 7 was the apparent activation energy of biomass pyrolysis obtained by DAEM.

The recently proposed in the energy literature approach to short-term electricity price forecasting, based on explicit accounting for the long-term price dynamic (i.e. its independent modeling), has demonstrated its efficiency in gaining forecast accuracy. But the practical implementation of this approach has certain impediments, because the "true" trend-cyclical component is unknown in most cases, while the choice of the method and the degree of smoothing of a time-series to estimate this component can only be made by experts on an a priori basis. If such choice is made incorrectly, this eliminates the mentioned advantage of this approach, and may lead to accuracy loss as compared even to less sophisticated forecasting methods. In the current research we call it the a priori knowledge issue and study some possible solutions of this problem. We show that the adaptive methods of trend estimation, which are based on different algorithms of the empirical mode decomposition, while not requiring any a priori setups, still, do not solve the studied issue. In turn, forecast combining conducted for individual models (based on different methods and degrees of time-series smoothing) allows not only to mitigate the need of making a priori choices, but also has lower forecast error and, thus, performs better than individual models. We also propose a new approach to forecast combining (based on p-values of a model confidence set) and show that it outperforms a number of well-established classic forecast averaging schemes (simple averaging, constrained OLS, inverted root mean square errors). Finally, our research indicates that making an model confidence set based trimming of the pool of models before averaging of their forecasts does not lead to lower prediction errors relative to their untrimmed averaging. Hence, conducting such trimming does not provide any extra advantages in solving the a priori knowledge issue.

This dataset is the raw data from Fig. 8 to Fig. 15.

The uploaded files include the results of the research presented in this paper.

CO2 activation energy; CO2 adsorption and activation over Fe_FeC; CO2 and H2 adsorption energy; PDOS_Fe5C2 and Fe3C

The supplementary material is the complete database extracted from the 40 articles selected for the systematic review. The excel file has two spreadsheets. The spreadsheet named "Articles database" is the complete information extracted from the articles according to the Review Protocol. The spreadsheet named "Bottom-up detailed database" presents the detailed information of the 17 articles classified as "bottom-up" and gives transport-related data type and sources according to the ASIF model

Using a 33-year-long dataset spanning a 1267-meter semi-arid elevational gradient in the southwestern United States, we test whether flowering phenology diverged among subpopulations within species and among five communities comprising 590 species. Applying circular statistics to test for changes in year-round flowering, we show flowering has become earlier for all communities except at the highest elevations. However, flowering times shifted at different rates across elevations likely due to elevation-specific changes in temperature and precipitation, indicating diverging phenologies of neighboring communities. Subpopulations of individual species also diverged at mid-elevation but converged in phenology at high elevation. phenology_data: Data on flowering phenology of 590 taxa collected by C. David Bertelsen over 33 years (1984-2016). A total 169,030 observations were recorded during 1,639 surveys. Data were collected along an 8.05-km trail in Finger Rock Canyon ascending from 945-2212 m, to Mt. Kimball in the Santa Catalina Mountains of Arizona, USA. The trail was partitioned into five elevation bands with distinct flowering assemblages (communities) during the growing seasons: 1) 945-1079 m, 2) 1079-1372 m, 3) 1372-1671 m, 4) 1671-1939 m, and 5) 1939-2212 m. Every species seen in anthesis (angiosperms) or releasing pollen (gymnosperms), together referred to as “flowering,” was recorded for each community along each 1.6-km-long trail segment on every survey. temperature_precipitation_data: Temperature and precipitation data for three locations within Finger Rock Canyon in the Santa Catalina Mountains of Arizona, USA. The 1930-2016 Parameter-elevation Regressions on Independent Slopes Model (PRISM) data was downloaded from the PRISM Climate Group, Oregon State University, (http://www.prism.oregonstate.edu/explorer/), created October 25, 2017. The gauge data was collected by C. David Bertelsen.

Data of oedometer test of natural gas hydrate-bearing sands with particle-scale simulation