2016
DOI: 10.1016/j.energy.2016.09.034
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Influence of carbothermic reduction on submerged arc furnace energy efficiency during silicon production

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Cited by 38 publications
(13 citation statements)
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“…Due to the high partial pressure of oxygen at the initial stage and the high activity of O 2 , a small amount of O 2 in N 2 will rst participate in the reaction and react with SiO(g) and internal Si to form SiO 2 , as shown in Eq. (2,3). As the partial pressure of oxygen gradually decreased, Si 2 N 2 O began to form through the Eq.…”
Section: Analysis Of Ni Accelerated Nitridation Mechanismmentioning
confidence: 99%
See 1 more Smart Citation
“…Due to the high partial pressure of oxygen at the initial stage and the high activity of O 2 , a small amount of O 2 in N 2 will rst participate in the reaction and react with SiO(g) and internal Si to form SiO 2 , as shown in Eq. (2,3). As the partial pressure of oxygen gradually decreased, Si 2 N 2 O began to form through the Eq.…”
Section: Analysis Of Ni Accelerated Nitridation Mechanismmentioning
confidence: 99%
“…Recently, the global energy crisis and environmental problems are becoming increasingly serious, due to the rapid growth of fossil energy consumption (Chen et al, 2016). So, it is imperative to nd a new type of renewable energy, in which solar energy is emerging and promising (Liu et al, 2019b;Wu et al, 2017).…”
Section: Introductionmentioning
confidence: 99%
“…Over 90 % of today`s photovoltaic cells still use silicon as conversion material. The reason is its abundance (Si constitutes 27 mass % of earth's crust), non−toxicity, acceptable band gap and reasonable energy conversion efficiency (around 20 %) [1][2][3]. One of the current hurdles against solar energy becoming a major contributor to the energy basket is still its high cost.…”
Section: Introductionmentioning
confidence: 99%
“…In previous studies, we determined the correlation coefficients between the different raw material consumption and exergetic efficiency via linear regression on industrial silicon production in the furnace [25,26]. We used several artificial neural network (ANN) models to simulate and evaluate the approximate composition fluctuations of carbon materials and the final power strain and combustion efficiency under different petroleum coke, soft coal, silica and electrode [27][28][29].…”
Section: Introductionmentioning
confidence: 99%