超重力强化臭氧高级氧化技术的研究进展

臭氧高级氧化技术因其绿色高效、适用性广、操作简便等优势，成为当前水处理领域前沿技术之一，但臭氧在传统反应器内普遍存在吸收效果差，臭氧利用率低等缺陷。旋转填料床（RPB）利用高速旋转的填料产生超重力场，将液体剪切破碎为细小的液膜、液丝或液滴，其较高的相界面积、不断更新的界面以及内部流体的强制湍动，加快了臭氧的传质与分解，该技术对于传质受限的臭氧高级氧化过程的强化有着突出的优势。本文简述了超重力强化臭氧氧化过程的原理，介绍了RPB与O₃、O₃/H₂O₂、O₃/Fenton、O₃/PS（过硫酸盐）、催化臭氧氧化等高级氧化法耦合应用处理有机废水的研究现状，并对超重力技术的优势及技术突破进行了述评，总结了超重力应用臭氧高级氧化技术的潜在经济效益和环境效益，提出功能化填料及大型RPB的开发需求，以期为超重力技术在废水处理领域的拓展应用提供理论基础和技术参考。     

基于改进ReaxFF 力场的全氟己酮热分解机理研究

采用基于密度泛函的紧束缚模型（DFTB）和改进的反作用力场（ReaxFF）对全氟己酮的热分解机理进行反应分子动力学仿真研究，分析了热分解的初始反应路径、微观反应过程以及体系内气体的生成机理，探讨了温度对全氟己酮热分解的影响，建立了以仿真数据为基础的一级动力学方程并对热分解过程进行一级动力学评估。结果表明，全氟己酮热分解的起始反应为：全氟己酮→全氟异戊烷+一氧化碳，全氟己酮热分解的主要产物为一氧化碳、四氟甲烷、六氟丙烯、全氟异丁烯、十氟丁烷、全氟异戊烷及大量活性基团。经一级动力学评估得到指前因子和表观活化能分别为5.021 7×1014 s-1和1 771.96 kJ/mol。     

白云石热分解动力学机理探究

以白云石粉为原料，利用TG、DTG、DSC联用的方法对白云石进行非等温热分解动力学研究，并进行了物相分析。结果表明，在Ar气保护和升温速率10 ℃/min的条件下，白云石的热分解是分两步进行的，第一步(CO32的扩散为控制步骤)、第二步(CO2的逸出为控制步骤)开始分解的温度分别在470、700 ℃，与纯物质的理论值相差较大；白云石的分解温度范围主要在470~590 ℃、700~820 ℃，在800 ℃时有个最大的吸热峰，此后白云石热分解吸热逐渐减小，温度达到820 ℃时失重率达到最大值45.27%，也几乎不再吸热，说明没有晶格转变在消耗能量。利用Freeman-Carroll的差减微分法对白云石非等温热分解计算分析，得到活化能E分别约为62.488、73.880 kJ/mol，指前因子A分别约为14.360、 ，两阶段的反应级数n分别约为0.490、0.819，白云石热分解过程属于相界反应模型。     

磷酸高效分解磷矿实验研究

为改进湿法磷酸生产工艺,提高副产石膏的品质,减少湿法磷酸固体副产物磷石膏堆存产生的经济和环保压力,进行了磷酸分解磷矿制磷酸的实验研究。采用贵州某磷矿为主要原料,研究了湿法磷酸预分解磷矿的反应条件。通过单因素实验确定了湿法磷酸分解磷矿最优反应条件：反应温度为75 ℃,磷酸与磷矿的质量比（液固比）为9,反应时间为3 h,磷酸质量分数为30%（以五氧化二磷计）。在此条件下,磷矿的分解率为98.62%。     

回转窑人工智能专家系统的应用实践

根据水泥熟料煅烧过程操作控制原理研发的"回转窑人工智能专家系统"采用了人工智能模拟技术、实时数据清洗技术、控制数据关联匹配技术。该系统由分解炉出口温度自动控制系统、篦速自动控制系统和氨水用量自动控制系统三个模块组成。该系统投运后,分解炉出口温度控制有效率由之前人工控制的平均60%~70%提高至平均80%~95%;篦下压力和二次风温稳定性提高;氨水消耗量下降显著;窑系统投料量增加10 t/h左右,熟料标准煤耗降低2.85 kg/t,熟料强度均有提高。     

MIMO-GFDM系统中低复杂度动态禁忌搜索检测算法的改进

针对多输入多输出的广义频分复用（MIMO-GFDM）系统的等效信道矩阵维度极大，传统的MIMO检测算法复杂度高且性能不佳的问题，将大规模MIMO系统中的动态禁忌搜索（RTS）检测算法运用到MIMO-GFDM系统中，并解决了RTS算法初始值的求解复杂度高的问题。首先利用最小均方误差（MMSE）检测算法所用到矩阵的正定对称性将矩阵Cholesky分解，并结合Sherman-Morrison公式迭代计算初始值，降低了初始值求逆的高复杂度；然后以改进的MMSE检测结果作为RTS算法的初始值，从初始值逐步全局搜索最优解；最后通过仿真，对不同算法的迭代次数和误码率（BER）性能进行了研究。理论分析与仿真结果表明：在MIMO-GFDM中，所提改进RTS信号检测算法误码率远低于传统信号检测算法。在4QAM时，RTS算法大约有低于MMSE检测6 dB的信噪比性能增益（误码率在10^-3时）；在16QAM时，RTS算法大约有低于MMSE检测4 dB的信噪比性能增益（误码率在10^-2时）。与传统RTS算法性相比，所提改进算法在不影响误码率性能的同时降低了算法复杂度。     

鲸鱼优化支持向量机的短期风电功率预测

为提高风电预测的精度,提出一种鲸鱼优化支持向量机SVM(support vector machine)的组合预测模型。该模型针对风电序列的非平稳波动特性,首先应用集合经验模态分解技术EEMD(ensemble empirical mode de?composition)将原始风电序列分解为一系列不同特征尺度的子序列;并引入鲸鱼优化算法WOA(whales optimiza?tion algorithm)解决SVM中学习参数选择难的问题,进而对各子序列建立WOA_SVM预测模型;最后,叠加各子序列的预测值以得到最终预测值。仿真表明,所提EEMD_WOA_SVM模型具有较高的风电预测精度,显著优于其他基本模型。     

Relative vibration identification of cutter and workpiece based on improved bidimensional empirical mode decomposition

Frontiers of Mechanical Engineering - In the process of cutting, the relative vibration between the cutter and the workpiece has an important effect on the surface topography. In this study, the...     

MgFe and Mg–Co–Fe mixed oxides derived from hydrotalcites: Highly efficient catalysts for COx free hydrogen production from NH3

Mg–Fe Layered Double Hydroxide (LDH) with M²⁺: M³⁺ 3:1 stoichiometric ratio was synthesized and employed as catalyst precursor for CO_x-free hydrogen production from ammonia. The resulting catalyst showed good catalytic activity. A series of Mg/Co–Fe layered double hydroxides were synthesized by replacing Mg²⁺ with Co²⁺ without disturbing M²⁺:M³⁺ ratio. The influence of nature and extent of Co(II) substitution on structure, morphology and surface properties were studied. A systematic study was carried out using these materials as catalyst precursors for ammonia decomposition. BET, XRD, TPR, XPS, CO₂-TPD and TEM techniques were used to characterize the synthesized catalysts. These Fe-based catalysts are highly active, highly stable and not promoting any stable surface nitridation during the ammonia decomposition reaction. Among all catalysts, the Mg₃Co₃Fe₂ catalyst showed the highest activity i.e. 100% conversion at 6,000 h⁻¹ and 60% at 50,000 h⁻¹ space velocities at 550 °C. The registered superior catalytic activity was result of the formed specific catalyst's properties like high surface area, high surface Co and Fe atomic concentration and suitable basicity. These Fe-based materials are, cost-effective, easily synthesize and highly stable, thus attractive for large-scale operation.     

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.