Dynamic modelling of catalytic SO2 converter in a sulfuric acid plant of an industrial smelter

In industrial nickel and copper production, sulfur dioxide (SO2) is generated from the combustion of sulfide ores. With increasingly tightened regulations on SO2 emissions, a sulfuric acid plant has become a crucial part of industrial smelters. It converts environmentally harmful SO2, which is generated in smelter furnaces, roasters, and Cu-reactors, into commercially beneficial sulfuric acid. This method is recognized as one of the most effective ways to ensure that smelters are able to satisfy the SO2 emission regulations. A sulfuric acid plant is primarily comprised of a central catalytic SO2 converter, SO3 (sulfur trioxide) absorption towers and a series of interconnected heat exchangers. The catalytic SO2 converter is the key component and the focus of this research. Both steady-state and dynamic models of the converter are developed in this thesis. A steady-state model of the converter is established in accordance with steady-state mass and energy balances. The developed model provides an explicit relation between SO2 conversion ratio and gas temperature, which is denoted as the heat-up path of the converter. By combining the heat-up path with the equilibrium curve of the SO2 oxidation reaction, an equilibrium state for every converter stage can be obtained. Using the developed steady-state model, simulations are performed to investigate the effect of inlet SO2 molar fraction and gas temperature on the equilibrium conversion ratio. In an industrial SO2 converter, the SO2 concentration and conversion ratio out of each bed are important variables but are not measured in real time. To monitor these unmeasured variables in industrial operations, a soft sensor is proposed by combining the derived steadystate model with dynamic data analysis. The obtained soft sensor provides a real-time estimation of outlet SO2 concentration and the conversion ratio from measured temperatures. For synchronization between the inlet SO2 concentration and outlet temperature, a first-order exponential data filter is applied to the feed SO2 data. With the filtered signal being used, the proposed soft sensors give a satisfactory estimation of both outlet SO2 concentration and conversion ratio in the converter stages. Dynamic modelling is carried out using two different model forms: ordinary differential equation (ODE) and partial differential equation (PDE) models. The ODE model is obtained by applying dynamic mass and energy conservation to the SO2 converter. The resulting model can be used in industrial applications and describes the converter performance even if information of reaction kinetics is not available. A good fit with collected industrial data verifies the validity of the developed ODE model. The effect of process input variables is studied using simulations with the ODE model. Dynamic modelling is performed by implementing mass and energy balances on both fluid and solid-phase gas flows. The proposed two-phase dynamic model, which takes the PDE form, is able to generate detailed profiles of the SO2 converter within time and space. With the estimated parameters, this two-phase dynamic model generates a good fit between the simulated and measured outlet temperatures. Based on the PDE model, simulations are run to investigate the detailed mechanistic performance of the converter. The detailed PDE model provides useful explanation of, and prediction for the converter behaviour.Doctor of Philosophy (PhD) in Natural Resources Engineerin

Exploring Training Effect in 42 Human Subjects Using a Non-invasive Sensorimotor Rhythm Based Online BCI

Electroencephalography based brain-computer interfaces (BCIs) show promise of providing an alternative communication channel between the brain and an external device. It is well acknowledged that BCI control is a skill and could be improved through practice and training. In this study, we explore the change of BCI behavioral performance as well as the electrophysiological properties across three training sessions in a pool of 42 human subjects. Our results show that the group average of BCI accuracy and the information transfer rate improved significantly in the third session compared to the first session; especially the significance reached in a smaller subset of a low BCI performance group (average accuracy <70%) as well. There was a significant difference of event-related desynchronization (ERD) lateralization for BCI control between the left- and right-hand imagination task in the last two sessions, but this significant difference was not revealed in the first training sessions. No significant change of R2 value or event-related desynchronization and synchronization (ERD/ERS) for either channel C3 or channel C4, which were used for online control, was found across the training sessions. The change of ERD lateralization was also not significant across the training sessions. The present results indicate that BCI training could induce a change of behavioral performance and electrophysiological properties quickly, within just a few hours of training, distributed into three sessions. Multiple training sessions might especially be beneficial for the low BCI performers

Editorial: Machine learning in studies of atmospheric environment and climate change

ISSN:2296-665

Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization

Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2

Resource Management for Device-to-Device Communications in Heterogeneous Networks Using Stackelberg Game

Device-to-device (D2D) communications and femtocell systems can bring significant benefits to users’ throughput. However, the complicated three-tier interference among macrocell, femtocell, and D2D systems is a challenging issue in heterogeneous networks. As D2D user equipment (UE) can cause interference to cellular UE, scheduling and allocation of channel resources and power of D2D communication need elaborate coordination. In this paper, we propose a joint scheduling and resource allocation scheme to improve the performance of D2D communication. We take UE rate and UE fairness into account by performing interference management. First, we construct a Stackelberg game framework in which we group a macrocellular UE, a femtocellular UE, and a D2D UE to form a two-leader one-follower pair. The cellular UE are leaders, and D2D UE is the follower who buys channel resources from the leaders. We analyze the equilibrium of the game and obtain solutions to the equilibrium. Second, we propose an algorithm for joint scheduling of D2D pairs based on their utility. Finally, we perform computer simulations to study the performance of the proposed scheme

Analytical and computational method of structure-borne noise and shock resistance of gear system

An approach to synthetically evaluate structure-borne noise and shock resistance of gear system is proposed. Firstly, dynamic finite element mesh model of gear system which includes shafts, bearings, gears and housing is established by using spring element, tetrahedral element and hexahedral element. Then dynamic finite element analysis model of gear system is gotten by loading the dynamic excitation force which can be calculated via the computation program of gear pair stiffness excitation, error excitation and impact excitation onto the tooth meshing line as boundary conditions. And the dynamic response of gear system is analyzed by using modal superposition method, and the vibration response experimental study of gear system is performed on the gearbox test-bed. The comparative analysis shows that computational results of the vibration response are in good agreement with the data of experiment tests and it could verify the rationality of dynamic finite element mesh model of gear system. Finally, taking acceleration shock excitation load into account on the basis of the dynamic finite element mesh model, the impact response of gear system is solved, and the shock resistance is analyzed based on the strength decision criterion

Power allocation for D2D communications in heterogeneous networks

In this paper, we study power allocation for D2D communications in heterogeneous networks utilizing game theory approach to improve the performance of the whole system. Given D2D's underlay status in the system, Stackelberg game framework is well suited for the situation. In our scheme, macrocell system and femtocell system are considered as two leaders and D2D pairs are considered as the follower, forming a two-leader-one-follower Stackelberg game. The leaders act first, charging some fees from the follower for using the channel and causing interference to jeopardize their communication equality. The follower observes the leaders' behavior and develops its strategy based on the prices offered by the leaders. We analyse the procedure and obtain the Stackeberg equilibrium, which determines the optimal prices for the leaders and optimal transmit power for the follower. In the end, simulations are executed to validate the proposed allocation method, which significantly improves data rate of user equipments. ? 2014 Global IT Research Institute (GIRI).EICPCI-S(ISTP)

Identification of Molecular Signatures in Epicardial Adipose Tissue in Heart Failure With Preserved Ejection Fraction

AIMS: The molecular signatures in epicardial adipose tissue (EAT) that contribute to the pathogenesis of heart failure with preserved ejection fraction (HFpEF) are poorly characterized. In this study, we sought to elucidate molecular signatures including genetic transcripts and long non-coding RNAs (lncRNAs) in EAT that might modulate HFpEF development. METHODS: RNA sequencing (RNA-seq) was performed to identify differentially expressed lncRNAs and mRNAs in EAT samples from patients with HFpEF (n = 5) and without HF (control, n = 5) who underwent coronary artery bypass grafting. The sequencing results were validated using quantitative real-time PCR (qRT-PCR). Bioinformatics analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) of differentially expressed RNAs was performed to predict enriched functions. RESULTS: HFpEF patients had higher EAT thickness and NT-proBNP levels than the control group. A total of 64 471 transcripts were detected including 35 395 protein-coding sequences, corresponding to 16 854 genes in EAT. RNA-seq identified a total of 741 dysregulated mRNA transcripts (394 up-regulated and 347 down-regulated) and 334 differentially expressed lncRNA transcripts (222 up-regulated and 112 down-regulated) in the HFpEF group compared with the control group (P \u3c 0.05). qRT-PCR analysis confirmed that two lncRNAs ENST00000561775 (P = 0.0194) and ENST00000519093 (P = 0.027) and an mRNA POSTN (P = 0.003) were differentially expressed. Functional enrichment analysis of the differentially expressed mRNAs suggested their potential roles in immune response involving cytokine interaction and chemokine signalling. CONCLUSIONS: We are the first group to report on the lncRNA and mRNA landscape in EAT in HFpEF patients. Our study suggests the possible role of lncRNAs in EAT