A simulation study of reduction kinetics for sponge iron production in a rotary hearth furnace

ABSTRACT

A mathematical model has been developed by coupling genetic algorithm (GA) with heat and material balance equations to estimate rate parameters and solid-phase evolution related to the reduction of iron ore-coal composite pellets in a multi-layer bed Rotary hearth Furnace (RHF). The present process involves treating iron ore-coal composite pellets in a crucible over the hearth in RHF. The various solid phases evolved at the end of the process are estimated experimentally, and are used in conjunction with the model to estimate rate parameters. The predicted apparent activation energy for the wustite reduction step is found to be lower than those of the reduction of higher oxides. The thermal efficiency is found to decrease significantly with an increase in the carbon content of the pellet. Thermal efficiency was also found to increase mildly up to three layers. Multilayer bed remains as a potential design parameter to increase thermal efficiency.     

Rate equation theory for the hydrogenation kinetics of Mg-based materials

A uniform solid product layer normally assumed in the shrinking-core model cannot predict the kinetic transition behavior of the H₂ adsorption reactions. In this study, the concept of a uniform solid product layer has been replaced by that of the inward growth of solid products on the solid surface. A rate equation is established to calculate the inward growth of the solid product and was implemented into the shrinking-core model to calculate the H₂ adsorption kinetics for various shapes of Mg-based materials. The prediction accuracy of the developed model is verified from the detailed experimental data. To account for the external gas diffusion around the particle and the intraparticle gas diffusion, an analytical equation is derived using the Thiele modulus method. This model can be used to analyze various kinetic aspects and to analyze the effect of change in the particle microstructure on intraparticle diffusion.     

A review study on titanium niobium oxide-based composite anodes for Li-ion batteries: Synthesis,structure, and performance

The growing demands for Li-ion batteries (LIBs) in the electrification revolution, require the development of advanced electrode materials. Recently, intercalating titanium niobium oxide (TNO) anode materials with the general formula of TiNb_xO_2+2.5x have received lots of attention as an alternative to graphite and Li₄Ti₅O₁₂ commercial anodes. The desirability of this family of compounds stems from their high theoretical capacities (377–402 mAh/g), high safety, high working voltage, excellent cycling stability, and significant pseudocapacitive behavior. However, the rate performance of TNO-based anodes is poor owing to their low electronic and ionic conductivities. TNO-based composites generally are prepared with two aims of enhancing the conductivity of TNO and achieving a synergic effect between the TNO and the other component of the composite. Compositing with carbon matrices, such as graphene and carbon nanotubes (CNTs) are the most studied strategy for improving the conductivity of TNO and optimizing its high-rate performance. Also, for obtaining anode materials with high capacity and high long-term stability, the composites of TNO with transition metal dichalcogenides (TMDs) materials were proposed in previous literature. In this work, a comprehensive review of the TNO-based composites as the anodes for LIBs is presented which summarizes in detail the main recent literature from their synthesis procedure, optimum synthesis parameters, and the obtained morphology/structure to their electrochemical performance as the LIBs anode. Finally, the research gaps and the future perspective are proposed.     

Unique CNTs-chained Li4Ti5O12 nanoparticles as excellent high rate anode materials for Li-ion capacitors

Composite anode materials with a unique architecture of carbon nanotubes (CNTs)-chained spinel lithium titanate (Li₄Ti₅O₁₂, LTO) nanoparticles are prepared for lithium ion capacitors (LICs). The CNTs networks derived from commercial conductive slurry not only bring out a steric hindrance effect to restrict the growth of Li₄Ti₅O₁₂ particles but greatly enhance the electronic conductivity of the CNTs/LTO composites, both have contributed to the excellent rate capability and cycle stability. The capacity retention at 30 C (1 C = 175 mA g^?1) is as high as 89.7% of that at 0.2 C with a CNTs content of 11 wt%. Meanwhile, there is not any capacity degradation after 500 cycles at 5 C. The LIC assembled with activated carbon (AC) cathode and such a CNTs/LTO composite anode displays excellent energy storage properties, including a high energy density of 35 Wh kg^?1 at 7434 W kg^?1, and a high capacity retention of 87.8% after 2200 cycles at 1 A g^?1. These electrochemical performances outperform the reported data achieved on other LTO anode-based LICs. Considering the facile and scalable preparation process proposed herein, the CNTs/LTO composites can be very potential anode materials for hybrid capacitors towards high power-energy outputs.     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

基于模式间混淆关系自适应构建层次化分类器

为了解决层次化分类器的设计难点——子分类器的层属关系及其内部组成的确定,本文首先定义了模式间混淆关系,用于描述不同模式在判决域中的相互作用;进而提出了基于混淆矩阵度量模式间混淆关系的方法。设计并实现了多模式混淆关系分析机MPCRAM,将有师指派和无师自组两种常用的模式重组方法有机结合,遵循Fisher准则,自适应地产生层次化分类器结构。大量综合测试证实了该方法有效、实用,可显著提高分类器的识别性能和稳健性。     

多带抗噪声语音识别算法研究

根据Flether等人的研究,基于感知独立性假设的子带识别方法被用于抗噪声鲁棒语音识别。本文拓展子带方法,采用基于噪声污染假定的多带框架来减少噪声影响。论文不仅从理论上分析了噪声污染假定多带框架在识别性能上的潜在优势,而且提出了多带环境下的鲁棒语音识别算法。研究表明：多带框架不仅回避了独立感知假设要求,而且与子带方法相比,多带方法能更好的减少噪声影响,提高系统识别性能。     

Evaluation of Monkman-Grant parameters for type 316LN and modified 9Cr-Mo stainless steels

The Monkman-Grant (M-G) and its modified parameters were evaluated for type 316LN and modified 9Cr-Mo stainless steels prepared with minor element variations. Several sets of creep data for the two alloy systems were obtained by constant-load creep tests in 550-650°C temperature range. The M-G parameters,m, m’,C, andC’ were proposed and discussed for the two alloy systems. Them value of the M-G relation was 0.90 in type 316LN steel and 0.84 in modified 9Cr-Mo steel. Them’, value of the modified relation was 0.94 in type 316LN steel and 0.89 in 9Cr-Mo steel. Although creep fracture modes and creep properties between type 316LN and modified 9Cr-Mo steels showed a basic difference, the M-G and its modified relations demonstrated linearity quite well. Them’ of modified relation almost overlapped regardless of the creep testing conditions and chemical variations in the two alloy systems, and the parameterm’ was closer to unity than that of the M-G relation.     

挤压力对晶体生长速度及枝晶间距的影响

研究了挤压铸造时挤压力对宏观晶体生长速度的影响，给出了晶体生长速度表达式。结果表明：挤压力对合金宏观晶体生长速度的影响除与合金成分有关外，在某些挤压条件下，当形核率的增加幅度超过长大速度时，则挤压力可使宏观晶粒细化；反之，会使宏观晶粒变粗。随着挤压力的提高，合金凝固速度加快，一次枝晶间距和二次枝晶间距都随之减小，从而细化了树枝晶。