Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO₂-NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.     

我国中空吹塑行业发展现状及“十四五”期间重点产品、工艺和设备发展方向

从原料、中空成型机和吹塑工艺等方面介绍了我国中空吹塑行业发展现状，并对电子化学品专用超洁净桶、高压储氢四型瓶、全电动中空成型机、微发泡中空成型技术和挤吹聚对苯二甲酸乙二醇酯（PET）容器等在“十四五”期间的重点产品、工艺和设备发展方向进行了展望。     

UV curing-assisted 3D plotting of ceramic feedstock containing thermo-regulated phase-separable,photocurable vehicle for dual-scale porosity structure

We propose a novel approach for manufacturing dual-scale porosity alumina structures by UV curing-assisted 3D plotting of a specially formulated alumina feedstock using a thermo-regulated phase separable, photocurable camphene/triethylene glycol dimethacrylate (TEGDMA) vehicle. In particular, 3D plotting process was conducted at - 5 °C, and thus an alumina suspension prepared using liquid camphene/TEGDMA at room temperature could undergo phase separation, resulting in camphene crystals surrounded by walls comprised of liquid photopolymer enclosing alumina particles. To enhance the shape retention ability of extruded filaments, polystyrene (PS) polymer was used as the tackifier. The phase-separated feedrod could be extruded favorably through a nozzle and rapidly photopolymerized by UV light during the 3D plotting process. Three-dimensionally interconnected macropores were tightly constructed, which were separated by microporous alumina filaments, where micropores were created by the removal of camphene crystals via freeze-dying. The macroporosity of porous alumina ceramics was controlled by adjusting the distance between deposited filaments, while their microporosity was kept constant, leading to tightly tailored overall porosity and mechanical properties.     

Effect of ball-milling parameters on the rheological properties of mold powder slurry

In this study, mold powder slurries with high solid loading and low viscosity were prepared during the ball-milling process for improving the homogeneity and mechanical properties of granules after spray-drying. The effect of ball-milling parameters, such as solid loading, binder/dispersant content, and ball-milling time, on the flowability, dispersibility, stability, and rheological behavior of mold powder slurries was systematically investigated by rheology observation and sedimentation tests. As these parameters varied, the slurry exhibited the shear-thinning behavior of a non-Newtonian fluid with a shear rate range of 0–50 s^?1, which was adequately described by the Herschel-Bulkley model. The optimal parameters that optimized the flowability, dispersibility, and stability of the slurry, along with its rheological behavior, were chosen as follows: solid loading, 60 wt%; modified sodium carboxymethyl cellulose binder content, 1.0 wt%; sodium tripolyphosphate dispersant content, 0.5 wt%; ball-milling time, 60 min.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

分析铁路改良粉细砂填料应用性能试验

在设计铁路路基的填料时,最常出现的是在进行铁路建设的地方就地取材。在以粉细砂为主的地区通常会选择用粉细砂作为铁路的填料,在所有的填料中,粉细砂填料属于C组类型填料,但是因为两个方面的原因需要对其进行一定的改良,一方面是因为粉细砂的粒径比较单一、级配不好,K30不能够满足规范标准的要求;另一方面则是因为大型机械不能够进行施工。本文通过掺入不同体积A、B组砾砂填料到粉细砂填料中进行试验,结果显示这种操作可以在不同程度上提高K30的值,从而在达到相关设计要求的同时解决施工方面的难题。     

基于改进相干增强扩散与纹理能量测度和高斯混合模型的导光板表面缺陷检测方法

针对液晶屏(LCD)导光板表面缺陷检测方法存在漏检率和误检率较高,对产品表面复杂渐变的纹理结构适应性差的问题,提出一种基于改进相干增强扩散(ICED)与纹理能量测度和高斯混合模型(TEM-GMM)的LCD导光板表面缺陷检测方法。首先,构建ICED模型,基于结构张量引入平均曲率流扩散(MCF)滤波,使得相干增强扩散(CED)模型对缺陷的细线状纹理有良好的边缘保持效果,并利用相干性得到缺陷纹理增强和背景纹理抑制的滤波后图像;然后,根据Laws纹理能量测度(TEM)提取图像纹理特征,将图像的背景纹理特征作为离线阶段高斯混合模型(GMM)的训练数据,使用期望最大化(EM)算法估计GMM参数;最后,计算待检测图像各像素的后验概率,并将其作为在线检测阶段缺陷像素的判断依据。实验结果表明,该检测方法在导光颗粒随机、规则两种分布的缺陷图像测试数据组上的漏检率和误检率分别为3.27%、4.32%和3.59%、4.87%。所提检测方法适用范围广,可有效检测出LCD导光板表面划痕、异物、脏污和压伤等类型的缺陷。     

Effect of laver powder on textual,rheological properties and water distribution of squid (Dosidicus gigas) surimi gel

In order to ameliorate the gel quality of Dosidicus gigas surimi, the effects of laver powder on gel properties, rheological properties, and water-holding capacity (WHC) were investigated. Results indicated that the addition of laver powder could significantly increase the hardness, chewiness, and breaking force of surimi gels. However, the texture indexes and gel strength began to decline when additional amount exceeded 0.6%. Rheological results demonstrated that the addition of laver powder increased the storage modulus (G′) and viscosity of surimi, prolonged protein denaturation temperature in surimi gels. Moreover, the WHC of surimi gel was improved with the increase of laver powder. Further analyses in low-field nuclear magnetic resonance revealed that laver powder could shorten the transverse relaxation time, enhanced the combination with water, and altered the distribution of different water categories. The proportion of bound water and immobilized water reached its maximum and minimum at 0.6% of laver powder, respectively. Correlation analyses showed that WHC of surimi gel was negatively correlated well with the proportion of loose-bound water, but positively correlated with the strong-bound water and free water. In conclusion, the results supported that 0.6% was the optimal additional amount of laver powder for the squid-based surimi production based on the current ingredients of surimi products.