Optimisation of the bolt profile configuration for load transfer enhancement

Both bolt profile shape and profile spacing (rib spacing) have been found to influence the bonding capacity of the grouted rock bolt. The bolt surface profile configuration has greater importance to rock bolt than the steel rebar used in civil engineering construction, because the rock bolt is subjected to greater dynamic loading than the steel rebar. The increased bonding capacity of bolts is important when supported ground is either heavily fractured, faulted or the supported ground is of soft formation, typically that of coal measure rocks. Past laboratory studies have identified the bolt profile spacing as of significant relevance to bolt resin rock bonding increase, however, no attempt has been made to determine the optimum spacing between the bolt profiles spacing. Accordingly, a series of laboratory tests were carried out on 22 core diameter bolts installed in cylindrical steel sleeve. The study was carried out by both push and pull testing. The push testing was carried out in 150 mm long sleeves while the pull testing was made in 115 mm long sleeves. Profile spacing tested include, 12.5, 25.0mm, 37.5 mm and 50 mm lengths. The profile spacing of 37.5 mm wide was found to provide the optimum bearin

Prediction of rock strength parameters for an Iranian oil field using neuro-fuzzy method

Uniaxial compressive strength (UCS) and internal friction coefficient (µ) are the most important strength parameters of rock. They could be determined either by laboratory tests or from empirical correlations. The laboratory analysis sometimes is not possible for many reasons. On the other hand, Due to changes in rock compositions and properties, none of the correlations could be applied as an exact universal correlation. In such conditions, the artificial intelligence could be an appropriate candidate method for estimation of the strength parameters. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) which is one of the artificial intelligence techniques was used as dominant tool to predict the strength parameters in one of the Iranian southwest oil fields. A total of 655 data sets (including depth, compressional wave velocity and density data) were used. 436 and 219 data sets were randomly selected among the data for constructing and verification of the intelligent model, respectively. To evaluate the performance of the model, root mean square error (RMSE) and correlation coefficient (R2) between the reported values from the drilling site and estimated values was computed. A comparison between the RMSE of the proposed model and recently intelligent models shows that the proposed model is more accurate than others. Acceptable accuracy and using conventional well logging data are the highlight advantages of the proposed intelligent model

Numerical modelling of a fast pyrolysis process in a bubbling fluidized bed reactor

In this study, the Eulerian-Granular approach is applied to simulate a fast pyrolysis bubbling fluidized bed reactor. Fast pyrolysis converts biomass to bio-products through thermochemical conversion in absence of oxygen. The aim of this study is to employ a numerical framework for simulation of the fast pyrolysis process and extend this to more complex reactor geometries. The framework first needs to be validated and this was accomplished by modelling a lab-scale pyrolysis fluidized bed reactor in 2-D and comparing with published data. A multi-phase CFD model has been employed to obtain clearer insights into the physical phenomena associated with flow dynamics and heat transfer, and by extension the impact on reaction rates. Biomass thermally decomposes to solid, condensable and non-condensable and therefore a multi-fluid model is used. A simplified reaction model is sued where the many components are grouped into a solid reacting phase, condensable/non-condensable phase, and non-reacting solid phase (the heat carrier). The biomass decomposition is simplified to four reaction mechanisms based on the thermal decomposition of cellulose. A time-splitting method is used for coupling of multi-fluid model and reaction rates. A good agreement is witnessed in the products yield between the CFD simulation and the experiment

Bacterial co-infections with SARS-CoV-2

The pandemic coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has affected millions of people worldwide. To date, there are no proven effective therapies for this virus. Efforts made to develop antiviral strategies for the treatment of COVID-19 are underway. Respiratory viral infections, such as influenza, predispose patients to co-infections and these lead to increased disease severity and mortality. Numerous types of antibiotics such as azithromycin have been employed for the prevention and treatment of bacterial co-infection and secondary bacterial infections in patients with a viral respiratory infection (e.g., SARS-CoV-2). Although antibiotics do not directly affect SARS-CoV-2, viral respiratory infections often result in bacterial pneumonia. It is possible that some patients die from bacterial co-infection rather than virus itself. To date, a considerable number of bacterial strains have been resistant to various antibiotics such as azithromycin, and the overuse could render those or other antibiotics even less effective. Therefore, bacterial co-infection and secondary bacterial infection are considered critical risk factors for the severity and mortality rates of COVID-19. Also, the antibiotic-resistant as a result of overusing must be considered. In this review, we will summarize the bacterial co-infection and secondary bacterial infection in some featured respiratory viral infections, especially COVID-19. © 2020 International Union of Biochemistry and Molecular Biolog

Modelling of Sheared Behaviour Bolts Across Joints

A three dimensional numerical model was developed to simulate the shearing of reinforced joints. Reinforcement of the shearing surfaces is effected with pretensioned bolts installed perpendicular to the sheared joint surface. The influence of bolt pretension forces examined included 20 kN, 50 kN and 80 kN respectively and aimed to complement the experimental work on double shearing of bolts installed in two different strength concrete blocks. Post shear stresses were analysed for both linear and nonlinear regions of the load - deflection curve. Simulation of several models in varying conditions provided a better understanding of the role of bolt pretensioning in sheared joint and bedding plane reinforcement. There was a clear relationship between the level of bolt pretensioning and the shear load applied. It was shown that the strength of the sheared composite medium was influenced by the applied shear load. The modeling study is part of a comprehensive programme of research work aimed at providing a better understanding of load transfer mechanisms in bolt /resin /rock for effective strata reinforcement

Determination of hepatitis C virus genotypes among blood donors in Ahvaz, Iran

This study aims to determine the genotypes of hepatitis C virus (HCV) among blood donors at Ahvaz Blood Transfusion Centre. Blood samples were taken from 2376 blood donors - 1795 (75.54%) male and 581(24.45%) female - who referred to Ahvaz Blood Transfusion Centre during 2007-2008. Detection of anti-HCV antibody for all the donors was carried out by ELISA and the confirmatory RIBA tests. HCV RT-PCR followed by RFLP test was carried out for anti-HCV positive samples. Out of 2376 blood donors, only 55 (2.3%) male donors showed to be positive for HCV antibody by ELISA and RIBA tests out of which 45(1.8%) donors were positive for RT-PCR test. Female donors were negative for HCV antibody. The result of HCV genotyping by RFLP test showed 24 (53.3%) for 1a, 17 (37.7%) for 3a (a) and 4 (8.8%) for 3a (b) genotypes respectively. In conclusion, high prevalence of 53.3% HCV 1a genotype was observed among blood donors in Ahvaz city