a b s t r a c tIn this research, a simplified dynamic model (SDM) is developed to predict the dynamic characteristics during the reclosing process and blowdown of a conventional pressure relief valve (PRV). The principle of the SDM is based on the equation of motion for one degree of freedom system. The damping effect is ignored due to its little effect on the reclosing time; a pressure drop condition based on lots of experiments is assumed. In addition, it is very novel that the SDM combines the static CFD analysis result, that is, the lift force coefficient of the valve at several fixed lifts is calculated with static CFD analysis, and then imported into the SDM as the inherent characteristics of the valve. The case study proved that the SDM is very reliable to predict the dynamic characteristics and blowdown of the conventional PRV.Crown
Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.
Microorganisms play an important role in the geochemical cycles, industry, environmental cleanup, and biotechnology among other fields. Given the high microbial diversity, identification of the microorganism is essential in understanding and managing the processes. One of the most popular and powerful method for microbial identification is comparative 16S rRNA gene analysis. Due to the highly conserved nature of this essential gene, sequencing and later comparison of it against known rRNA databases can provide assignment of the bacteria into the taxonomy, and the identity of its closest relatives. Isolation and sequencing of 16S rRNA genes directly from natural environments (either from DNA or RNA) can also be used to study the structure of the whole microbial community. Nowadays, novel sequencing technologies with massive outputs are giving researchers worldwide the chance to study the microbial world with a depth that was previously too expensive to achieve. In this article we describe commonly used research approaches for the study of individual microorganisms and microbial communities using the tools provided by Ribosomal Database Project website.
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