Numerical modeling of bed-to-wall heat transfer in a circulating fluidized bed combustor based on cluster energy balance

Heat transfer process in gas–solid fluidized bed combustors: A review

“…CFB combustion is one of the most advanced energy technologies available today for power generation, cogeneration and process heat supply because of their remarkable features like fuel flexibility, high combustion efficiency and efficient pollution control, using high ash, high sulfur coals, with reduced emissions of sulfur and nitrogen oxides. In addition to, other solid materials such as biomass, municipal waste and wood chips can also be used as the primary fuel to produce high pressure and temperature steam for power generation and process utilities [30,31]. The importance of CFBs in industry has increased with time, since the heat from fuel combustion can be removed with external or internal heat exchangers.…”

“…As a consequence, a higher concentration of particles results in a higher heat transfer [32]. From the available FB literature, most of the theoretical investigations on cluster and bed-to-wall heat transfer are based on mechanistic models except a few based on mathematical and numerical approaches like energy balance model, cluster-based approach, and radiation model [24,30,70,[79][80][81]. In the mechanistic approach, the cluster heat transfer coefficient is estimated using the correlations, deduced from experimental data by the local estimation of cluster properties combined with the mechanism of cluster behavior.…”

“…In the mechanistic approach, the cluster heat transfer coefficient is estimated using the correlations, deduced from experimental data by the local estimation of cluster properties combined with the mechanism of cluster behavior. The mathematical approach is done by developing a model by building governing equations from the fundamental mechanism and solving the equations using numerical methods with appropriate assumptions and boundary conditions [30].…”

Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction

“…The selection of the most related features from a large variety of sensors is important for the realization of effective control [8]. Traditional feature selections are normally developed on the basis of mass balance, energy balance, and dynamic principles, which rely greatly on human expertise and normally require a long modeling time [9][10][11]. Recently, researchers have increasingly adopted the data-driven methodology that extracts features directly from huge amounts of accumulated process data [12][13][14].…”