利用煤层气发展山西合成油工业探讨

介绍了国外利用天然气生产油口的生产工艺，探讨了山西省利用丰富的煤层气资源发展合成油的可能性和优势，并提出了相应的发展建议。     

高转速、无稳心搅拌装置介绍

本文介绍了一种特殊的高转速、无稳心搅拌系统，具备简洁、高效、稳定、安全的特点，经使用证明，效果良好，性能可靠，为高速搅拌及无稳心反应釜的应用提供了一条新的设计及运用的途径。     

离子色谱法测定配煤、焦炭和焦油中的氯含量

研究了用带电导检测器的离子色谱对配煤、焦炭和焦油中的Cl- 进行测定的方法。在YSA型8126A-4# 阴离子分离柱上用3.5mmol/LNa2CO3、5.3mmol/LNaHCO3做淋洗液对Cl-进行测定,方法简便快速。Cl-的检出限为2×10-6。该法也适用于F-、NO2-、NO3-、SO42-等的测定。     

化肥厂造气污水处理新装置

介绍了一套新型造气污水处理装置，该装置具有处理工艺流程合理，设备布置紧凑，装置自动化运行的特点，造气污水经处理后重复循环使用，污泥送沸腾炉作燃料燃烧，实现了污水零排放的目的。     

提高液态烃中丙烯收率的技术

简要介绍了国内外增产丙烯的工艺技术，如SCC，MGG，MOI等，并针对特定装置提出了如何采用提高吸收压力，降低吸收温度，改善补充吸收油质量等措施增加液态烃中丙烯收率，以满足市场对丙烯的需求。     

UV photo‐stabilization of tetrabutyl titanate for aramid fibers via sol–gel surface modification

Tetrabutyl titanate was used as sol–gel precursor of a nanosized TiO₂ coating to improve the photo‐stability of aramid fibers. The nanosized TiO₂ coating was characterized by XRD and XPS. The influence of the TiO₂ coating on photo‐stability of aramid fibers was investigated by an accelerated photo‐ageing method. The photo‐stability of aramid fiber showed obvious improvement after coating. After 156 h of UV exposure, the coated fibers showed less deterioration in mechanical properties with the retained tensile strength and elongation at break greater than 36 and 50% of the original values, respectively, whereas the uncoated fibers degraded completely and became powdery. SEM analysis showed no significant surface morphological change on the coated fiber after the exposure, while some latitudinal crack fractures appeared on the uncoated aramid fiber. The effect of the nanosized TiO₂ coating was also well demonstrated by examining the difference of distributions of C1s in XPS deconvolution analysis on the surface of uncoated/coated fibers with increasing UV exposure time. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 3113–3119, 2007     

A Rough RBF Neural Network Based on Weighted Regularized Extreme Learning Machine

Adjusting parameters iteratively is a traditional way of training neural networks, and the Rough RBF Neural Networks (R-RBF-NN) follows the same idea. However, this idea has many disadvantages, for instance, the training accuracy and generalization accuracy etc. So how to change this condition is a hot topic in Academics. On the basis of Extreme Learning Machine (ELM), this paper proposes a Weighted Regularized Extreme Learning Machine (WRELM), taking into account both minimizing structured risk and weighted least-squares principle, to train R-RBF-NN. The traditional iterative training method is replaced by the minimal norm least-squares solution of general linear system. The method proposed in this paper, increasing controllability of the entire learning process and considering the structured risk and empirical risk, can improve the performance of learning and generalization. Experiments show that it can reach a very superior performance in both time and accuracy when WRELM trains the Rough RBF Neural Networks in pattern classification and function regression, especially in pattern classification, which can improve the generalization accuracy more than 3.36 % compared with ELM. Obviously, the performance of the method proposed in this paper is better than the traditional methods.     

Image super-resolution based on multi-space sparse representation

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.     

Prediction-based realistic 3D model compression

The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced.     

新型尿素产品的开发

介绍了国内外新型尿素产品（大颗粒尿素、涂层尿素、包膜尿素、长效尿素）的生产工艺和产品特点。