Effects of atmosphere on the oxidation state of iron and viscosity behavior of coal ash slag

He, Chao; Bai, Jin; Kong, Lingxue; Xu, Jiang; Guhl, Stefan; Li, Xiaoming; Ge, Zefeng; Cao, Xi; Bai, Zongqing; Li, Wen

doi:10.1016/j.fuel.2019.01.020

Cited by 51 publications

(20 citation statements)

References 29 publications

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“…Leaky ReLU (f(x) = max(αx, x)) is a variant of ReLU that can restore the transmission of values in the negative region. The training process of a BP neural network is realized by two processes: forward transfer of input variables and back propagation of errors [25,39]. The model continuously adjusts the weights and thresholds through experimental data to reduce the error.…”

Section: Topological Structure Of Neural Networkmentioning

confidence: 99%

“…In order to establish a model with extensive applicability, a large amount of slag viscosity data was collected from the literature [1,[5][6][7]9,[12][13][14][15][16]18,19,23,25,[31][32][33]38,. It is widely accepted that the gas atmosphere affects the viscosity of hazardous waste slag at elevated temperatures.…”

Section: Model Development For the Classification Of Slagsmentioning

confidence: 99%

“…Due to insufficient samples and a small chemical component range, many models cannot be applied to all compositions and temperatures [7][8][9]. In addition, empirical equations cannot explain factors that interfere with viscosity, such as atmospheres [25]. Fortunately, the neural network models that were trained with a large amount of real experimental data have the potential to improve the overall prediction efficiency [26].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the existing neural network models for predicting ash viscosity did not employ a wide range of data from various compositions for training. It has been revealed that the measurement of slag viscosity under different atmospheres will give different results, leading to the difficulties in the accurate prediction of the viscosity [25,31,32]. Moreover, the existing neural network models only consider the influences of ash composition and temperature on viscosity, without taking the other operating parameters such as gas atmosphere into account [8,25,[31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…It has been revealed that the measurement of slag viscosity under different atmospheres will give different results, leading to the difficulties in the accurate prediction of the viscosity [25,31,32]. Moreover, the existing neural network models only consider the influences of ash composition and temperature on viscosity, without taking the other operating parameters such as gas atmosphere into account [8,25,[31][32][33]. Consequently, to improve the performance and applicability of the slag viscosity and type prediction model based on a neural network, it is imperative to use a larger amount of data from a wider range of hazardous wastes under various atmospheres to train the neural network.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Lin

Dai

et al. 2020

Energies

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Molten gasification is considered as a promising technology for the processing and safe disposal of hazardous wastes. During this process, the organic components are completely converted while the hazardous materials are safely embedded in slag via the fusion-solidification-vitrification transformation. Ideally, the slag should be glassy with low viscosity to ensure the effective immobilization and steady discharge of hazardous materials. However, it is very difficult to predict the characteristics of slag using existing empirical equations or conventional mathematical methods, due to the complex non-linear relationship among the phase transformation, vitrification transition and chemical composition of slag. Equipped with a strong nonlinear mapping ability, an artificial neural network may be able to predict the properties of slags if a large amount of data is available for training. In this work, over 10,000 experimental data points were used to train and develop a slag classification model (glassy vs. non-glassy) based on a neural network. The optimal structure of the neural network was figured out and validated. The results suggest that the classification accuracy for the independent test samples reached 93.3%. Using 1 and 0 as model inputs to represent mildly reducing and inert atmospheres, a double hidden layer structure in the neural network enabled the accurate classification of slags under various atmospheres. Furthermore, the neural network for the prediction of glassy slag viscosity was optimized; it featured a double hidden layer structure. Under a mildly reducing atmosphere, the absolute error from the independent test data was generally within 4 Pa·s. By adding a gas atmosphere into the input of the neural network using a simple normalization method, a multi-atmosphere slag viscosity prediction model was developed. Said model is much more accurate than its counterpart that does not consider the effect of the atmosphere. In summary, the artificial neural network proved to be an effective approach to predicting the slag properties under different atmospheres. The data-driven models developed in this work are expected to facilitate the commercial deployment of molten gasification technology.

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Section: Topological Structure Of Neural Networkmentioning

confidence: 99%