No abstract

The accuracy of the process model directly affects the performance of the model‐based controller. In zinc hydrometallurgy, the overall dynamics of the cobalt removal process can hardly be described by a fixed model since there are a series of interconnected reactors working together under time‐varying inlet and reaction conditions. In this study, an interacting continuously stirred tank reactors (ICSTR) model is developed to describe the cooperative relationship of these cascaded reactors. Considering the time‐varying inlet and reaction conditions, the reaction surface conversion coefficient is defined and incorporated into the ICSTR model, and the kernel partial least squares (KPLS) is employed to update the dynamic model online. The effectiveness of the time‐varying ICSTR model is validated using industrial data. Based on the proposed time‐varying ICSTR model, a predictive controller is designed to realize the optimal operation of the cobalt removal process. Simulation results indicate that compared with conventional predictive control and manual manipulation, the time‐varying ICSTR model‐based predictive control method can not only maintain the outlet cobalt ion concentration but also reduce the zinc dust dosage.

The residue hydrotreating process plays a significant role in the petroleum refining industry. In this process, modeling and simulation have critical importance for process development, control, and optimization. However, there is a lack of relevant reports of plant scale due to complexity in characterizing feedstock and determining reaction mechanisms. In this paper, reaction and fractionation models are constructed and simulated for a real-life industrial residue hydrotreating process based on Aspen HYSYS/Refining. Considering the heavier and inferior residue, analytical characterization is carried out for feedstock characterization based on laboratory analysis data. Moreover, two reactor models with parallel structures are proposed to implement the intricate reaction network, namely, a hydrocracker reactor and a plug flow reactor. The former simulates lighter petroleum hydrotreating based on the built-in reaction network. The latter emulates the conversion of a peculiar, heavier resin and asphaltene, using a six-lump model, which expands the scope of the feedstock and improves the accuracy of the model. To obtain a realistic simulation of fractionation, the database-based delumping method is adopted to model it with proper pseudo-components. The simulation results, including temperature rise, hydrogen consumption, temperature distribution, product yield, product properties, indicate that the model is capable of reflecting the realistic process accurately.

Hepatocarcinogenesis is associated with epigenetic changes, including histone deacetylases (HDACs). Epigenetic modulation by HDAC inhibition is a potentially valuable approach for hepatocellular carcinoma treatment. In present study, we evaluated the anticancer effects of sodium valproate (SVP), a known HDAC inhibitor, in human hepatocarcinoma cells. The results showed SVP inhibited the proliferation of Bel-7402 cells in a dose-dependent manner. Low dose SVP treatment caused a large and flat morphology change, positive SA-β-gal staining, and G0/G1 phase cell cycle arrest in human hepatocarcinoma cells. Low dose SVP treatment also increased acetylation of histone H3 and H4 on p21 promoter, accompanied by up-regulation of p21 and down-regulation of RB phosphorylation. These observations suggested that a low dose of SVP could induce cell senescence in hepatocarcinoma cells, which might correlate with hyperacetylation of histone H3 and H4, up-regulation of p21, and inhibition of RB phosphorylation. Since the effective concentration inducing cell senescence in hepatocarcinoma cells is clinically available, whether a clinical dose of SVP could induce cell senescence in clinical hepatocarcinoma is worthy of further study.

Clusters of dead trees are forest fires-prone. To maintain ecological balance and realize its protection, timely detection of dead trees in forest remote sensing images using existing computer vision methods is of great significance. Remote sensing images captured by Unmanned aerial vehicles (UAVs) typically have several issues, e.g., mixed distribution of adjacent but different tree classes, interference of redundant information, and high differences in scales of dead tree clusters, making the detection of dead tree clusters much more challenging. Therefore, based on the Multipath dense composite network (MDCN), an object detection method called LLAM-MDCNet is proposed in this paper. First, a feature extraction network called Multipath dense composite network is designed. The network’s multipath structure can substantially increase the extraction of underlying and semantic features to enhance its extraction capability for rich-information regions. Following that, in the row, column, and diagonal directions, the Longitude Latitude Attention Mechanism (LLAM) is presented and incorporated into the feature extraction network. The multi-directional LLAM facilitates the suppression of irrelevant and redundant information and improves the representation of high-level semantic feature information. Lastly, an AugFPN is employed for down-sampling, yielding a more comprehensive representation of image features with the combination of low-level texture features and high-level semantic information. Consequently, the network’s detection effect for dead tree cluster targets with high-scale differences is improved. Furthermore, we make the collected high-quality aerial dead tree cluster dataset containing 19,517 images shot by drones publicly available for other researchers to improve the work in this paper. Our proposed method achieved 87.25% mAP with an FPS of 66 on our dataset, demonstrating the effectiveness of the LLAM-MDCNet for detecting dead tree cluster targets in forest remote sensing images.