Structural Reliability Based Dynamic Positioning of Turret-Moored FPSOs in Extreme Seas

FPSO is widely used during the deep-sea oil and gas exploration operations, for which it is an effective way to keep their position by means of positioning mooring (PM) technology to ensure the long-term reliability of operations, even in extreme seas. Here, a kind of dynamic positioning (DP) controller in terms of structural reliability is presented for the single-point turret-moored FPSOs. Firstly, the mathematical model of the moored FPSO in terms of kinematics and dynamics is established. Secondly, the catenary method is applied to analyze the mooring line dynamics, and mathematical model of one single mooring line is set up based on the catenary equation. Thereafter, mathematical model for the whole turret mooring system is established. Thirdly, a structural reliability index is defined to evaluate the breaking strength of each mooring line. At the same time, control constraints are also considered to design a state feedback controller using the backstepping technique. Finally, a series of simulation tests are carried out for a certain turret-moored FPSO with eight mooring lines. It is shown in the simulation results that the moored FPSO can keep its position well in extreme seas. Besides, the FPSO mooring line tension is reduced effectively to ensure mooring lines safety to a large extent in harsh sea environment

Novel Polymeric Membranes Preparation and Membrane Process

Polymer-based membranes have advanced or novel functions in the various membrane separation processes for liquid and gaseous mixtures, such as gas separation, pervaporation (PV), reverse osmosis (RO), nanofiltration (NF), ultrafiltration (UF), microfiltration (MF), and in other critical applications of membranes such as water purification, solvent concentration, and recovery [...

Novel Polymeric Membranes Preparation and Membrane Process

Polymer-based membranes have advanced or novel functions in the various membrane separation processes for liquid and gaseous mixtures, such as gas separation, pervaporation (PV), reverse osmosis (RO), nanofiltration (NF), ultrafiltration (UF), microfiltration (MF), and in other critical applications of membranes such as water purification, solvent concentration, and recovery [...

Multi-Plant Disease Identification Based on Lightweight ResNet18 Model

Deep-learning-based methods for plant disease recognition pose challenges due to their high number of network parameters, extensive computational requirements, and overall complexity. To address this issue, we propose an improved residual-network-based multi-plant disease recognition method that combines the characteristics of plant diseases. Our approach introduces a lightweight technique called maximum grouping convolution to the ResNet18 model. We made three enhancements to adapt this method to the characteristics of plant diseases and ultimately reduced the convolution kernel requirements, resulting in the final model, Model_Lite. The experimental dataset comprises 20 types of plant diseases, including 13 selected from the publicly available Plant Village dataset and seven self-constructed images of apple leaves with complex backgrounds containing disease symptoms. The experimental results demonstrated that our improved network model, Model_Lite, contains only about 1/344th of the parameters and requires 1/35th of the computational effort compared to the original ResNet18 model, with a marginal decrease in the average accuracy of only 0.34%. Comparing Model_Lite with MobileNet, ShuffleNet, SqueezeNet, and GhostNet, our proposed Model_Lite model achieved a superior average recognition accuracy while maintaining a much smaller number of parameters and computational requirements than the above models. Thus, the Model_Lite model holds significant potential for widespread application in plant disease recognition and can serve as a valuable reference for future research on lightweight network model design

Sensitivity parameters of tight sand gas: A case study of Lower Cretaceous Yingcheng Formation of Yingtai gas field in Songliao Basin, NE China

In tight sandstone, the gas-bearing sensitivity parameters are studied to improved prediction accuracy of thin gas layer due to the small impedance differences between gas-bearing layers and surrounding rocks. In this paper, we propose a new combined elastic parameter, i.e., the ratio of the first Lame coefficient to S-wave velocity based on elastic parameters sensitivity analysis for tight sandstone gas. By considering different geological conditions, we introduce the extending attribute (the ratio of Russell fluid phase to S-wave velocity), which can reduce to the ratio of the first Lame coefficient to S-wave velocity in specific condition. Both Gassmann equation and Brie empirical relationship are applied to calculate elastic parameters of different gas saturation in fluid replacement process. The results verify the validity of the new combined elastic parameter, which is more sensitive to gas saturation than conventional parameters, such as the product of the first Lame coefficient and density and the ratio of P-wave to S-wave velocity. The pre-stack inversion is applied in the second member of Lower Cretaceous Yingcheng Formation in Yingtai gas field. Compared to the section of the product for the first Lame coefficient and density, the results show the new combined elastic parameter presented improves the accuracy of identifying gas-bearing layers, well conforms to the logging interpretation, and greatly enhances the identification ability and prediction accuracy of gas-bearing layers. Key words: Tight sand gas, Gas-bearing, Elastic parameters, Sensitivity parameters, Pre-stack inversio

Progress in microalgae cultivation photobioreactors and applications in wastewater treatment: A review

Using microalgae to treat wastewater has received growing attention in the world because it is regarded as a novel means for wastewater treatment. It is commonly recognized that large-scale cultivation and commercial application of microalgae are limited by the development of photobioreactor (PBR). Although there are a lot of PBRs for microalgae pure cultivation which used culture medium, specialized PBRs designed for wastewater treatment are rare. The composition of wastewater is quite complicated; this might cause a very different photosynthetic effect of microalgae compared to those grown in a pure cultivation medium. Therefore, PBRs for wastewater treatment need to be redesigned and improved based on the existing PBRs that are used for microalgae pure cultivation. In this review, different PBRs for microalgae cultivation and wastewater treatment are summarized. PBR configurations, PBR design parameters and types of wastewater are presented. In addition, the wastewater treatment efficiency and biomass productivity were also compared among each type of PBRs. Moreover, some other promising PBRs are introduced in this review, and a two-stage cultivation mode which combines both closed and open system is discussed as well. Ultimately, this article focuses on current problems and gives an outlook for this field, aiming at providing a primary reference for microalgae cultivation by using wastewater

Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains

Obtaining accurate forest coverage of tree species is an important basis for the rational use and protection of existing forest resources. However, most current studies have mainly focused on broad tree classification, such as coniferous vs. broadleaf tree species, and a refined tree classification with tree species information is urgently needed. Although airborne LiDAR data or unmanned aerial vehicle (UAV) images can be used to acquire tree information even at the single tree level, this method will encounter great difficulties when applied to a large area. Therefore, this study takes the eastern regions of the Qilian Mountains as an example to explore the possibility of tree species classification with satellite-derived images. We used Sentinel-2 images to classify the study area’s major vegetation types, particularly four tree species, i.e., Sabina przewalskii (S.P.), Picea crassifolia (P.C.), Betula spp. (Betula), and Populus spp. (Populus). In addition to the spectral features, we also considered terrain and texture features in this classification. The results show that adding texture features can significantly increase the separation between tree species. The final classification result of all categories achieved an accuracy of 86.49% and a Kappa coefficient of 0.83. For trees, the classification accuracy was 90.31%, and their producer’s accuracy (PA) and user’s (UA) were all higher than 84.97%. We found that altitude, slope, and aspect all affected the spatial distribution of these four tree species in our study area. This study confirms the potential of Sentinel-2 images for the fine classification of tree species. Moreover, this can help monitor ecosystem biological diversity and provide references for inventory estimation

Environmentally regulated intrinsic oxygen-ion transport for oxide-ion conductors

Oxide-ion conductors have been widely used as catalytic, conductive, detecting and other materials under oxidizing, reducing, inert, mixed environments and the like. However, so far the evaluation of their oxygen-ion transport (such as oxide-ion conductivity and oxygen permeability) either is extrinsic or is limited only in oxidizing or inert environment. Herein, the evaluation of intrinsic oxygen-ion transport for oxide-ion conductors in all environments seems especially important. In this work, a new test system was designed to enable the oxide-ion conductors placing in single oxidizing, reducing, inert or mixed environment separately, which also realized all the oxygen-vacancy concentrations of oxide-ion conductors are in equilibrium in all environments. The intrinsic oxide-ion conductivity and oxygen permeability were evaluated in all environments, and the influencing factors regulated by environments also were analyzed to correlate the variation of oxygen-ion transport