湿法磷酸净化脱砷的研究现状

随着食品、医药及电子行业的不断发展，磷酸及磷酸盐的市场需求量不断增大，并且对磷酸的品质要求也越来越高，尤其是对湿法磷酸中砷杂质的要求，湿法磷酸中砷的含量将直接影响其在磷精细化工领域的应用，因此湿法磷酸中砷的高效、安全去除是亟待解决的一大难题。本文主要对湿法净化磷酸脱砷技术的研究情况进行综述，比较了几种湿法净化磷酸脱砷工艺的优缺点，介绍了较常见的化学沉淀法、结晶法，以及新兴的电沉积法、垂直区域熔融法和微反应器脱砷工艺的应用前景，并指出这些新兴的脱砷工艺将成为今后湿法磷酸的净化脱砷的发展方向，如能解决将这些脱砷工艺在实际中的问题，将会对磷酸工业的发展提供一个更有力的支持。     

滚齿工艺对齿根粗糙度的影响

针对国内外风电和工业齿轮箱轻量化的设计趋势,较低的安全系数对齿轮齿根弯曲强度提出了更为严格的要求。研究表明,在材料技术和齿形设计难以再优化的情况下,滚齿加工的粗糙度,尤其是齿根部位的粗糙度对齿轮弯曲强度的影响尤为敏感。针对滚齿工艺过程,从滚齿加工参数、刀具前刀面粗糙度和刀具刃口圆角三方面进行分析,验证其对齿根粗糙度的影响。     

一种复杂产品总装工艺流程设计方法研究

围绕复杂产品总装工艺流程设计问题,从装配技术和企业经营管理的角度提出复杂产品总装工艺流程设计不仅是技术上的最优化,还应综合引入开工面限制、人力资源平衡、生产节拍需求以及投入产出比等因素,寻求技术与经营管理的平衡。提出了一种复杂产品总装工艺流程设计总体方案,设计了该方案的实现思路,并通过某型飞机总装工艺流程设计项目验证了方法的可行性和有效性。     

排风扇前盖注射模设计

分析了排风扇前盖塑件的工艺特点,介绍了排风扇前盖注射成型模结构及模具的工作过程。     

某贫磁赤铁矿石选矿工艺试验研究

为了使某矿山贫磁赤铁矿资源得到合理的开发利用，针对现开采部位矿石的性质和可选性问题，进行了工艺矿物学和可选性试验研究。试验结果表明：采用弱磁、强磁—阴离子反浮选工艺可得到精矿铁品位68.55%、精矿产率36.41%、金属回收率79.92%、尾矿品位9.86%的较好选别指标，说明矿石的可选性较好，为该矿石的合理开发利用提供了可靠的技术依据。     

基于多层感知器的气液两相流流型识别方法

通过对电阻层析成像数据采集原理和深度学习网络的研究,提出了一种基于阵列电阻值和多层感知器深度学习网络相结合的流型识别方法。利用电阻层析成像系统中的16个电极传感器来获取流型样本数据,并构建出流型识别数据库,然后对多层感知器深度学习网络进行训练,获得可以识别不同流型的网络。实验结果表明,采用阵列电阻值结合多层感知器网络对流型进行学习和识别的方法,流型识别准确率可以达到95%,解决了流型图像生成过程与数据特征预选过程中流型特征损失的问题,流型识别性能得到了提高。     

Tributyl phosphate additive enhancing catalytic absorption of NO2 for simultaneous removal of SO2/NOx in wet desulfurization system

Removal of SO₂ and NO emissions from coal-fired power plants have always been the focus in coal's utilization industries for which traditional wet desulfurization system hold the potential to achieve simultaneous removal of SO₂/NO_x. In this work, a novel liquid catalyst (tributyl phosphate, TBP) was investigated for the simultaneous removal of SO₂ and NO_x in a NO pre-oxidation (ozone oxidation) assisted process. The absorption process and reaction mechanism of SO₂ and NO₂ were studied in a small-scale experimental system, and the removal efficiency of pollutant and the key parameters were examined in a pilot-scale system. The results show that the removal efficiency of NO₂ in traditional wet desulfurization system is only 20–40%; however greatly increases to >90% when TBP is added. Moreover, TBP can enhance the NO₂ removal performance in the presence of SO₂ due to the formation of TBP–NO₂–SO₃²⁻ complex. Considering the oily nature of TBP that can be easily separated, such addition strategy hold great potential for industrial SO₂/NO_x simultaneously removal.     

王坡煤矿煤层顶板稳定性主控因素分析

煤炭开采过程中极易发生顶板事故。基于王坡煤矿的地质条件，利用钻孔、井下钻探、物探、生产揭露资料，分别对该矿煤层顶板沉积特征、煤层顶板岩石结构特征进行系统分析研究，通过实验室室内测试，获取了煤层顶板岩石抗压强度、泊松比、坚固性系数、黏聚力等物理力学性质指标，最终确定了影响煤层顶板稳定性相关因素；运用层次分析法确定影响因素的权重值，得出岩石力学性质是影响煤层顶板稳定性的主控因素。研究可为类似煤矿顶板稳定性因素分析提供借鉴。     

Chemical process fault diagnosis based on enchanted machine‐learning approach

In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F₁‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.