Microwave drying is a promising and effective way to drying and upgrading lignite. The influence of temperature (100-140 • C) and microwave power levels (500-800 W) on thin-layer drying characteristics of Zhaotong lignite under microwave irradiation were investigated. Fourteen thin-layer drying models were used to analyze the microwave drying process while six thin-layer drying models were used to analyze the hot-air drying process. The microwave drying processes at all temperature (100-140 • C) or low microwave power levels (500-700 W) exhibited four periods: a warm-up period, a short constant period, the first and second falling rate period, while one falling rate period was found during hot-air drying. The effective diffusion coefficient of lignite were calculated and it increases with increasing temperature and microwave power levels. During microwave drying, the two-term exponential model is the most suitable model for all applied conditions, while the Modified Page model is the most suitable model to describe the hot-air drying experiments. The apparent activation energy were determined from Arrhenius equation and the values for the first and second falling rate period are 3.349 and 20.808 kJ·mol −1 at different temperatures, while they are 13.455 and 19.580 W·g −1 at different microwave power levels. This implies the apparent activation energy is higher during the second falling rate period, which suggest that the dewatering of absorbed water is more difficult than capillary water. The value of apparent activation energy in hot-air drying is between the first and second falling rate period of microwave drying. Results indicate that microwave drying is more suitable to dewatering free water and capillary water of lignite.Processes 2019, 7, 74 2 of 18 high reactivity, and low pollution impurities [3], it will be used more widely in the future. Thus, moisture removal is the first essential step to improve the quality of lignite by drying technologies in downstream utilization, such as pyrolysis, gasification, liquefaction, and combustion.Various lignite dehydration technologies have been developed and researched by evaporation or non-evaporation methods [4]. Solar drying [5], steam-fluidized bed drying [6], and flue gas drum drying [7] are based on evaporation drying, while mechanical thermal expression [8,9] and hydrothermal dewatering [10,11] are based on non-evaporation drying. In traditional drying technologies, heat is transferred from the surface to the interior of the material by convection and conduction while the moisture transferred from the inside of the material to the surface. Most of thermal drying process are operated with combustion gas or superheated water vapor and the configuration of the drying reactor are complicated, which induces high costs of construction. In addition, traditional methods will lead to heating inhomogeneity, which is not beneficial for lignite upgrade. Among these dehydration technologies, microwave drying of low-rank coal is a very promising method due to its unique he...
The uncertainty principle restricts our ability to simultaneously predict the measurement outcomes of two incompatible observables of a quantum particle. However, this uncertainty could be reduced and quantified by a new Entropic Uncertainty Relation (EUR). By the open quantum system approach, we explore how the nature of de Sitter space affects the EUR. When the quantum memory A freely falls in the de Sitter space, we demonstrate that the entropic uncertainty acquires an increase resulting from a thermal bath with the Gibbons-Hawking temperature. And for the static case, we find that the temperature coming from both the intrinsic thermal nature of the de Sitter space and the Unruh effect associated with the proper acceleration of A also brings effect on entropic uncertainty, and the higher temperature, the greater uncertainty and the quicker the uncertainty reaches the maxima value. And finally the possible mechanism behind this phenomenon is also explored.
An important and hard problem in signal processing is the estimation of parameters in the presence of observation noise. In this paper, adaptive finite impulse response (FIR) filtering with noisy input-output data is considered and two developed bias compensation least squares (BCLS) methods are proposed. By introducing two auxiliary estimators, the forward output predictor and the backward output predictor are constructed respectively. By exploiting the statistical properties of the cross-correlation function between the least squares (LS) error and the forward/backward prediction error, the estimate of the input noise variance is obtained; the effect of the bias can thereafter be removed. Simulation results are presented to illustrate the good performances of the proposed algorithms.adaptive FIR filtering, recursive least squares algorithm, bias compensation, forward prediction, backward prediction
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