Experimental Investigation on Mixing and Segregation Behavior of Oxygen Carrier and Biomass Particle in Fluidized Bed

In this work, lab-scale cold fluidization equipment is designed and constructed to investigate the mixing and segregating phenomena of binary fluidized beds. The focus of the investigation is carbon reduction with the fluidized bed technology-based Chemical Looping Combustion (CLC). Nowadays, aspiration to carbon reduction focuses on the solid fuels. Therefore, it is of great importance to integrate the benefits of CLC technology with the use of solid fuels. The measurements of fuel particles in the fluidized bed are extended from the homogeneous and spherical shape to the inhomogeneous, non-spherical shape. During the tests, an iron-based oxygen carrier (OC) for chemical looping combustors is examined with different particle sizes. In addition, the tests included the examination of three different fuel samples (crushed coal, agricultural pellet, and Solid Recovered Fuel (SRF)), which can be utilized in chemical looping combustion with In-situ gasification. The experiments are carried out using the bed-frozen method. With this method, the vertical concentration of active particles could be measured. The results show that the particle size of the oxygen carrier does fundamentally influence its vertical placement, and the non-spherical character of most alternative fuels must also be considered for optimal reactor design

Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning

The increasing penetration of weather-dependent renewable energy generation calls for high-resolution modeling of the possible future energy mixes to support the energy strategy and policy decisions. Simulations relying on the data of only a few years, however, are not only unreliable but also unable to quantify the uncertainty resulting from the year-to-year variability of the weather conditions. This paper presents a new method based on artificial neural networks that map the relationship between the weather data from atmospheric reanalysis and the photovoltaic and wind power generation and the electric load. The regression models are trained based on the data of the last 3 to 6 years, and then they are used to generate synthetic hourly renewable power production and load profiles for 42 years as an ensemble representation of possible outcomes in the future. The modeled profiles are post-processed by a novel variance-correction method that ensures the statistical similarity of the modeled and real data and thus the reliability of the simulation based on these profiles. The probabilistic modeling enabled by the proposed approach is demonstrated in two practical applications for the Hungarian electricity system. First, the so-called Dunkelflaute (dark doldrum) events, are analyzed and categorized. The results reveal that Dunkelflaute events most frequently happen on summer nights, and their typical duration is less than 12 h, even though events ranging through multiple days are also possible. Second, the renewable energy supply is modeled for different photovoltaic and wind turbine installed capacities. Based on our calculations, the share of the annual power consumption that weather-dependent renewable generation can directly cover is up to 60% in Hungary, even with very high installed capacities and overproduction, and higher carbon-free electricity share targets can only be achieved with an energy mix containing nuclear power and renewable sources. The proposed method can easily be extended to other countries and used in more detailed electricity market simulations in the future