Leak-before-break analysis of Prototype Fast Breeder Reactor main vessel

In Prototype Fast Breeder Reactor, the core is immersed in a sodium pool contained within the main vessel (MV). There is an argon cover gas space over the sodium. Top Shield supports the control plug which houses important components such as Control and Safety Rod Driving Mechanism. A core support structure welded to the bottom of MV carries the Grid plate which in turn supports the fuel subassemblies. MV is a cylindrical shell with a bottom dished end and supported at the top. Double ended guillotine rupture of MV will lead to downward movement of the core, which is in effect equivalent to withdrawal of control and safety rod and thus causing a series safety concern. It is essential to demonstrate Leak Before Break (LBB) argument for MV. LBB argument is justified if leak detection is ensured with adequate margin on critical crack length corresponding to failure by tearing instability. The material of construction of MV is SS316 LN. Towards assuring the justification of LBB argument, an isolated 5 mm deep X 200 mm long semi elliptical flaw is taken as the initial flaw and analysed under fatigue loading. A through wall crack in the argon space can be detected by sampling the nitrogen in the annular space between MV and safety vessel surrounding it. Accordingly the detectable crack length is arrived at. The critical crack length is obtained considering stable tearing. More details of the analysis and justification of LBB argument as per RCC-MR Appendix A16 are presented in the paper.     

Microstructural refinement and tensile properties enhancement of Mg-3Al alloy using charcoal additions

Significant grain refinement in Mg-3Al alloy is achieved with the addition of charcoal due to the formation of Al₄C₃ particles, which act as effective nuclei for magnesium grains. Addition of 0.5 wt% charcoal has lead to reduced grain size of Mg-3Al alloy from 500 to 80 μm and no substantial grain refinement is obtained on further addition of charcoal. The results further reveal that the prolonged holding of the melt after the addition of charcoal has not affected the grain refining efficiency of Al₄C₃. Steady increase in tensile properties observed with increasing amount of charcoal addition has been attributed to the grain refinement and the presence of fine Al₄C₃ particles. The strengthening mechanisms due to charcoal addition are discussed in terms of Hall-Petch relation and dispersion strengthening. The predicted values are in good agreement with experimental results.     

Antihyperlipidemic effect of diosmin: A citrus flavonoid on lipid metabolism in experimental diabetic rats

The present study was hypothesized to evaluated the antihyperlipidemic effect of diosmin (DS) on lipid metabolism in experimental diabetic rats. Diabetes was induced male albino Wistar rats by intraperitoneal administration of streptozotocin (STZ) (45 mg/kg b.w.) 15 min after the ip administration of nicotinamide (NA) (110 mg/kg b.w.). DS were administered to diabetic rats intragastrically at 100 mg/kg b.w. for 45 days. The levels of plasma and tissue lipids (cholesterol, triglycerides) (TGs), free fatty acids (FFAs) and phospholipids (PLs), low density, very low-density lipoproteins (LDL and VLDL), and high-density-cholesterol (HDL-C) were measured. The activities of 3-hydroxy 3-methylglutaryl coenzyme A (HMG-CoA) reductase, lipoprotein lipase (LPL) and lecithin cholesterol acyl transferase (LCAT) were assayed. The levels of plasma and tissue lipids decreased with significant increase in HDL-C levels. The altered activities of lipid metabolic enzymes were restored to near normal. The present findings suggest that DS can potentially ameliorate lipid abnormalities in experimental diabetes.     

Novel genetic algorithm for short-term scheduling of sequence dependent changeovers in multiproduct polymer plants

Polymer plants generally operate to produce different grades of product from the same reactor. Such systems commonly require short-term scheduling to meet market demand. One important requirement in continuous-time scheduling of such systems is to satisfy a variety of constraints, including identifying feasible sequences of the predecessor and successor jobs to effectively handle changeovers. In this study, a new genetic algorithm (GA) is proposed to solve such job sequencing problems. The proposed GA uses real-coded chromosome to represent job orders and their sequences in the schedule. The novelty is that the representation ensures that all constraints are satisfied a priori, except the sequence constraint which is handled by penalizing violations. Three important problems relevant to polymer industry are solved to obtain optimal schedules. The first deals with the sequencing constraint between individual product orders, the second with sequencing constraint between groups of product orders, while the third incorporates batching with scheduling.     

Graph theory augmented math programming approach to identify minimal reaction sets in metabolic networks

Bioprocesses are of growing importance as an avenue to produce chemicals. Microorganisms containing only desired catalytic and replication capabilities in their metabolic pathways are expected to offer efficient processes for chemical production. Realizing such minimal cells is the holy grail of metabolic engineering. In this paper, we propose a new method that combines graph-theoretic approaches with mixed-integer liner programming (MILP) to design metabolic networks with minimal reactions. Existing MILP based computational approaches are computationally complex especially for large networks. The proposed graph-theoretic approach offers an efficient divide-and-conquer strategy using the MILP formulation on sub-networks rather than considering the whole network monolithically. In addition to the resulting improvement in computational complexity, the proposed method also aids in identifying the key reactions to be knocked-out in order to achieve the minimal cell. The efficacy of the proposed approach is demonstrated using three case studies from two organisms, Escherichia coli and Saccharomyces cerevisiae.     

IPS-MAC: an informative preamble sampling MAC protocol for wireless sensor networks

Radio transceivers are the main source of energy consumption in wireless sensor networks (WSNs) where the source of energy supply is non-rechargeable battery. Several MAC protocols have been proposed in order to efficiently conserve energy in the link layer via duty-cycling. Low power listening (LPL) methods have been shown to outperform other schemes in lightly loaded situations which are common in environment monitoring applications. Nonetheless, as the network becomes dense, in LPL protocols such as BMAC a large number of nodes stay awake for each transmission, resulting in high levels of energy consumption. This paper introduces the informative preamble sampling (IPS) protocol in which a transmitter implicitly embeds information about its intended receiver via the power at which the preamble is transmitted. This results in far fewer nodes staying awake for each preamble. Upon hearing the preamble, a receiver executes a decision-making algorithm to decide whether to stay awake. If the decision-making algorithm is too lax, then more nodes stay awake following the preamble. On the other hand if the algorithm is too strict, it is likely that the intended receiver misses the preamble. In this paper we derive the optimal operating points for the IPS protocol. We show analytically that the IPS protocol can achieve a gain in energy by at least a factor of 2 over BMAC. We also conduct extensive simulations to show that IPS can achieve significant energy gains compared to BMAC.     

Smart utilization of renewable energy sources in a microgrid system integrated with plug‐in hybrid electric vehicles

Mass roll‐out of plug‐in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi‐zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real‐time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm‐based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously.     

Integrated decision support system for waste minimization analysis in chemical processes

The need to build and operate environmentally friendly plants has challenged the chemical industry to consider waste minimization or even elimination starting from the early stages of process development. A thorough waste minimization analysis requires specialized expertise and is laborious, time-consuming, expensive, and knowledge-intensive. This has caused a major technical barrier for implementing waste minimization programswithin the industry. Previously, we had reported a systematic methodology and a knowledge-based system, called ENVOPExpert, for identifying waste minimization opportunities in chemical processes. In this paper, we propose an integrated qualitative-quantitative methodology to identify waste minimization alternatives and assess their efficacy in terms of environmental impact and process economics. A qualitative analysis is first conducted to identify the sources of wastes and to propose alternatives for eliminating or minimizing them. Environmental impact of each alternative is then calculated by doing a quantitative pollutant balance. The capital expenditure required for implementing the alternative and the resulting plant operating costs are also calculated and used in the evaluation of the waste minimization alternatives. Through this, practical and cost-effective options can be identified. This methodology has been implemented as an integrated decision support system and tested using the hydrodealkylation process case study with satisfactory results.     

Coupling fiber optics to a permeation liquid membrane for heavy metal sensor development

We present the first sensing system for metal ions based on the combination of separation/preconcentration by a permeation liquid membrane (PLM) and fluorescence detection with an optical fiber. As a model, a system for the detection of Cu(II) ions was developed. The wall of a polypropylene hollow fiber serves as support for the permeable liquid membrane. The lumen of the fiber contains the strip solution in which Cu(II) is accumulated. Calcein, a fluorochromic dye, acts as stripping agent and at the same time as metal indicator. The quenching of the calcein fluorescence upon metal accumulation in the strip phase is detected with a multimode optical fiber, which is incorporated into the lumen. Fluorescence is excited with a blue LED and detected with a photon counter. Taking advantage of the high selectivity and sensitivity of PLM preconcentration, a detection limit for Cu(II) of approximately 50 nM was achieved. Among five tested heavy metal ions, Pb(II) was the only major interfering species. The incorporation of small silica optical fibers into the polypropylene capillary allows for real-time monitoring of the Cu(II) accumulation process.     

Fast Distributed Outlier Detection in Mixed-Attribute Data Sets

Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinical diagnosis, from detecting anomalous defects in materials to fraud and intrusion detection. Over the past decade, researchers in data mining and statistics have addressed the problem of outlier detection using both parametric and non-parametric approaches in a centralized setting. However, there are still several challenges that must be addressed. First, most approaches to date have focused on detecting outliers in a continuous attribute space. However, almost all real-world data sets contain a mixture of categorical and continuous attributes. Categorical attributes are typically ignored or incorrectly modeled by existing approaches, resulting in a significant loss of information. Second, there have not been any general-purpose distributed outlier detection algorithms. Most distributed detection algorithms are designed with a specific domain (e.g. sensor networks) in mind. Third, the data sets being analyzed may be streaming or otherwise dynamic in nature. Such data sets are prone to concept drift, and models of the data must be dynamic as well. To address these challenges, we present a tunable algorithm for distributed outlier detection in dynamic mixed-attribute data sets.