Two-zone simulated moving bed (SMB) systems with a storage tank were developed for binary separation. Simulations were done with Aspen Chromatography for the linear dextran T6raffinose mixture and the nonlinear binaphthol enantiomers mixture. The results for these linear isotherms show that two-zone SMBs give purities comparable to those obtained for a four-zone SMB with the same productivity and desorbent-to-feed ratio (D/F). At D/F ) 2.794, the purities obtained in a two-zone decoupled SMB for dextran T6 and raffinose are 94.5% and 97.6%, respectively, whereas in a two-zone coupled SMB, the purities are 95.0% and 97.5%, respectively, compared to a four-zone SMB's purities of 96.1% and 96.6%, respectively. The results for the nonlinear isotherms show that these two-zone SMB systems can obtain good separation but with more desorbent than required for a four-zone SMB. Simulations for two-zone SMBs with one column per zone show that partial feed can improve the product purities and recoveries considerably, particularly at the optimum feed length. Early feed introduction increases the raffinose purity, whereas late feed introduction increases the dextran T6 purity. Partial-feed operation shows very modest improvement in raffinate purity for the binaphthol enantiomers system. The results for two-zone SMBs with multiple columns per zone show that adding more columns per zone is not always beneficial for separation in two-zone SMBs for both linear and nonlinear isotherms.

The scaling rules for chromatographic and adsorption columns and for standard four-zone simulated moving beds (SMBs) are extended to two-zone SMB systems with linear and nonlinear isotherms. Once an old or base design is available, the operational parameters can be predicted by simple algebraic equations. The scaling rules are applied to use the entire available pressure drop in four-zone, two-zone, and parallel twozone SMB systems. Adding booster pumps between columns to operate columns at their pressure limits and the simultaneous reduction of the particle diameter allows for higher productivity designs with constant product purities and a constant desorbent-to-feed ratio (D/F). The feed rate of a four-zone SMB with one column per zone can be increased by approximately 69% with four pumps between the columns for both linear and nonlinear isotherms compared to the base case with one pump. For systems with one column per zone, the productivities of two-zone and parallel two-zone SMB systems are from 16% to 32% higher than the corresponding four-zone SMBs (with two or four pumps between columns) with constant product purities, a constant D/F, and maximum pressure drop for both linear and nonlinear isotherms. Four-zone SMBs with multiple columns per zone can also be scaled to fully use the available pressure drop and greatly increase productivity with constant product purities. Simulations done with Aspen Chromatography showed that the scaling rules are effective in predicting operating conditions to increase productivity with the same product purities. Operating the high productivity systems at reduced feed rates increases product purities.

Based on the classic filter of progressive triangulated irregular network (TIN) densification, an improved filter is proposed in this paper. In this method, we divide ground points into grids with certain size and select the lowest points in the grids to reconstruct TIN in the process of iteration. Compared with the classic filter of progressive TIN densification (PTD), the improved method can filter out attached objects, avoid the interference of low objects and obtain relatively smooth bare-earth. In addition, this proposed filter can reduce memory requirements and be more efficient in processing huge data volume. The experimental results show that the filtering accuracy and efficiency of this method is higher than that of the PTD method.Airborne light detection and ranging (LiDAR) technology can obtain high-precision and high-density point cloud data of surface [1] , which is a fast growing geodetic imaging technology. LiDAR point cloud has been widely used in many fields, such as cultural heritage documentation, reverse engineering, three-dimensional (3D) object reconstruction, and digital elevation model (DEM) generation. For the production of DEM, filtering must be executed to separate ground points and non-ground points. The existing filtering methods can be classified into four categories: morphological-based filter [2][3][4] , interpolation-based filter [5][6][7][8] , slope-based filter [9][10][11] and segmentation/cluster-based filter [12][13][14] . Thereinto, the filter of progressive triangulated irregular network (TIN) densification belongs to the interpolation-based filter [8] . By analyzing several existing filters, Sitbole and Vosselman [15] pointed out that the filter of progressive TIN densification (PTD) proposed by Axelsson [6] performed best, but it could not remove attached objects [16] , such as bridge and ramp. Zhang and Lin [8] proposed a developed PTD using a point cloud segmentation method, namely segmentation using smoothness constraint (SUSC). Both the PTD and the developed PTD are invalid for attached objects and require lots of memory to construct TIN. Insufficient memory poses great challenges to the TIN-based filters, due to huge data volume and complexity of objects.In order to remove attached objects and reduce memory requirements, we propose an improved filter of

Satellite telemetry data contains satellite status information, and ground-monitoring personnel need to promptly detect satellite anomalies from these data. This paper takes the satellite power subsystem as an example and presents a reliable anomaly detection method. Due to the lack of abnormal data, the autoencoder is a powerful method for unsupervised anomaly detection. This study proposes a novel stage-training denoising autoencoder (ST-DAE) that trains the features, in stages. This novel method has better reconstruction capabilities in comparison to common autoencoders, sparse autoencoders, and denoising autoencoders. Meanwhile, a cluster-based anomaly threshold determination method is proposed. In this study, specific methods were designed to evaluate the autoencoder performance in three perspectives. Experiments were carried out on real satellite telemetry data, and the results showed that the proposed ST-DAE generally outperformed the autoencoders, in comparison.

No abstract