Energy Conversion and Management

Description GloWiSMo is applicable for mapping the wind resource available for wind power use worldwide on a 250 m spatial grid. For a detailed description of the underlying methods of GloWiSMo please refer to: “Jung C, Schindler D. Integration of small-scale surface properties in a new high resolution global wind speed model. Energy Conversion and Management 2020;210:112733. https://doi.org/10.1016/j.enconman.2020.112733” The dataset contains: 1. Mean wind speed in 10 m The values of the grids are provided as integers. Please divide the values by 10 to obtain wind speed in m/s. 2. Mean wind speed in 120 m The values of the grids are provided as integers. Please divide the values by 10 to obtain wind speed in m/s. 3. Power law exponent The power law exponent can be used to extrapolate the 10 m wind speed to any wind turbine hub height. Please refer to “Jung C, Schindler D. The role of the power law exponent in wind energy assessment: A global analysis. International Journal of Energy Research 2021;45:8484-8496. https://doi.org/10.1002/er.6382” for a detailed description of how to extrapolate wind speed. The values of the grids are provided as integers. Please divide the values by 1000 to obtain the power law exponents. Files 1. Mean wind speed in 10 m (250 m x 250 m) 2. Mean wind speed in 120 m (250 m x 250 m) 3. Power law exponent (250 m x 250 m)

Results of the modelled indicators.

Acetone-butanol-ethanol (ABE) fermentation process is a promising bioenergy option amid rising concerns over the environmental impact of fossil fuel usage. However, the commercialization of the ABE process has been marred by challenges of low product yield and titer, thereby non-competitive process economics. Here, we coupled low-cost reducing agents with a controlled feeding strategy to improve both product titer and yield. Reducing agents promote cofactor dependent butanol production while fed-batch operation enhances glucose consumption, final ABE titer, and partly mitigates product toxicity. We investigated the effects of ascorbic acid, L-cysteine, and dithiothreitol (DTT) on fed-batch ABE production using Clostridium acetobutylicum. Moreover, to study the metabolic modifications triggered by these reducing agents, we performed NADH, ATP, extracellular amino acid secretion, and NADH- dependent butanol dehydrogenase (BDH) assays. L-cysteine and DTT improved ABE solvent titer by 2-fold, producing 24.33 and 22.98 g/L ABE with solvent yields of 0.38 and 0.37 g/g, respectively. NADH, BDH, and ATP levels increased significantly which also reflected in elevated ABE titer and yield. Furthermore, histidine secretion emerged as an important factor in Clostridial acid stress in this study. The results demonstrate that reducing agents and the fed-batch combination enables efficient utilization of glucose and remarkably enhances ABE production.

the experimental data used in the 'Parameter estimation of photovoltaic modules using a heuristic iterative algorithm'.

Matlab code

This repository contains data for: Opportunities and limitations for existing CHP plants to integrate polygeneration of drop-in biofuels with onsite hydrogen production

The dataset contains the Monte Carlo simulations results presented in the article "Uncertainty and influence of input parameters and assumptions on the design and analysis of thermochemical waste conversion processes: A stochastic approach"

Wind speed times series related to 10min sampling from 02/jan /2019 up to 05/jan/2019. Also there is daily sampled average wind speed data related to 01/nov/2018 up to 31/jan/2019. The wind speed time series are related to Guanambi city, on Bahia State, Brazil.

This dataset provides reader to get real-time turbulence intensity data corresponding to the wind speed time series on multiple time resolutions. The dataset is comprised of two parts: original wind speed series, and multi-resolution wind speed and turbulence intensity. The original wind speed dataset gives the wind observation on different altitudes on the wind farm, such as speed, fluctuation, direction, maximum, minimum, as well as air conditions, with a sampling rate of 10 minutes from Jan. 1,2013 to Dec. 31, 2013. Multi-resolution wind speed and turbulence intensity dataset provides the average wind speed and turbulence intensity on different time scales (1 hour, 2 hours, 4 hours, 6 hours, 12 hours and 24 hours), which is calculated from the highest altitude where the wind speed is obtained.

1. Temperature of every channel (including 3-stage water in troughs, circular outer surface, circulating water and ambient); 2. Yield rate; 3. Volume rate of circulating water; 4. Electric conductivity of raw water, etc.