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Kinetics and thermodynamics dataset of iron oxide reduction using torrefied microalgae for chemical looping combustion

机译:使用焙烧微藻进行化学循环燃烧还原氧化铁的动力学和热力学数据集

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摘要

The reduction of iron oxides transpires through the application of heat wherein a carbon source known as reductant is required. In order to design a chemical looping combustion using iron as an oxygen carrier and torrefied microalgae biomass as a reductant, the kinetics and thermodynamics dataset must be determined. Using the Arrhenius law of reaction, the kinetics dataset was obtained employing the three chemical reaction model such as the first order (C1), the reaction order 1.5 (C1.5), and the second-order (C2). The iron oxide reduction from hematite to metallic iron was sub-divided into three phases wherein phase 1 (Fe O → Fe O ) is from 365 °C to 555 °C, phase 2 (Fe O → FeO) is from 595 °C to 799 °C, and phase 3 (FeO → Fe) is from 800 °C to 1200 °C. Two torrefied microalgae (Chlamydomonas sp. JSC4 and Chlorella vulgaris ESP-31) were considered as a reducing agent. The kinetics dataset comprise of the activation energy (E), pre-exponential factor (A), and the reaction rate (k) while the thermodynamic dataset consists of the change in enthalpy (ΔH), change in Gibbs energy (ΔG), and change in entropy (ΔS). These kinetics and thermodynamics parameters are essential in understanding the reaction mechanisms of the reduction process of iron oxides enabling process optimization and improvement. Current literature lacks the kinetics and thermodynamics datasets for the reduction of iron oxides using the two torrefied microalgae as reductants. This work provides these datasets which are useful for the design of reactors in chemical looping combustion.
机译:氧化铁的还原通过加热来实现,其中需要一种称为还原剂的碳源。为了设计使用铁作为氧载体和焙烧的微藻生物质作为还原剂的化学循环燃烧,必须确定动力学和热力学数据集。使用阿伦尼乌斯反应定律,使用三种化学反应模型(例如一阶(C1),1.5阶(C1.5)和二阶(C2))获得动力学数据集。从赤铁矿到金属铁的氧化铁还原分为三个阶段,其中阶段1(Fe O→FeO)从365°C到555°C,阶段2(Fe O→FeO)从595°C到799°C,第3相(FeO→Fe)为800°C至1200°C。两种焙干的微藻(衣藻(Chlamydomonas sp。)JSC4和小球藻(Chlorella vulgaris)ESP-31)被认为是还原剂。动力学数据集包括活化能(E),指数前因子(A)和反应速率(k),而热力学数据集则包括焓变(ΔH),吉布斯能(ΔG)和熵变化(ΔS)。这些动力学和热力学参数对于理解氧化铁还原过程的反应机理至关重要,从而可以优化和改进工艺。当前文献缺乏使用两种焙烧的微藻作为还原剂还原铁氧化物的动力学和热力学数据集。这项工作提供了这些数据集,这些数据集可用于设计化学循环燃烧反应堆。

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