Modeling of ion transport reactor for oxy‐fuel combustion

This paper aims at investigating the performance of a cylindrical ion transport reactor designed for oxy‐fuel combustion. The cylindrical reactor walls are made of dense, nonporous, mixed‐conducting ceramic membranes that only allow oxygen permeation from the outside air into the combustion chamber. The sweep gas (CO₂ and CH₄) enters the reactor from one side and mixes with the oxygen permeate, and the products are discharged from the other side. The process of oxygen permeation through the reactor walls is influenced by the flow condition and composition of air at the feed side (inlet air side) and the gas mixture at the permeate side (sweep gas side). The modeling of the flow process is based on the numerical solution of the conservation equations of mass, momentum, energy, and species in the axisymmetric flow domain. The membrane is modeled as a selective layer in which the oxygen permeation depends on the prevailing temperatures as well as the oxygen partial pressure at both sides of the membrane. The CFD calculations were carried out using fluent 12.1 (ANSYS, Inc., Canonsburg, PA, USA), whereas the mass transfer of oxygen through the membrane is modeled by a set of user defined functions. The model results were validated against previous experimental data, and the comparison showed a good agreement. The study focused on the effect of oxygen partial pressure and temperature on the resulting combustion zones inside the reactor for the two cases of co‐current and counter‐current flow regimes. The results indicated that the oxygen to fuel mass ratio increases as the percentage of CO₂ increases in the inflow sweep gas for both co‐current and counter‐current flows. The obtained sweep mixture ratio (CO₂/CH₄) of 24 is found within the stoichiometric limit over most of the reactor length in the co‐current configuration, whereas the sweep mixture ratio of 15.67 is found in the counter‐current configuration owing to the high O₂ permeation. Copyright © 2012 John Wiley & Sons, Ltd.     

A distributed algorithm for energy efficient and fault tolerant routing in wireless sensor networks

Wireless Networks - Energy conservation and fault tolerance are the most two important challenging issues for the development of large scale wireless sensor networks (WSNs). Failure of cluster...     

Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems

The use of artificial neural networks (ANN) in fault detection analysis is widespread. This paper aims to provide an overview on its application in the field of fault identification and diagnosis (FID), as well as the guiding elements behind their successful implementations in engineering-related applications. In most of the reviewed studies, the ANN architecture of choice for FID problem-solving is the multilayer perceptron (MLP). This is likely due to its simplicity, flexibility, and established usage. Its use managed to find footing in a variety of fields in engineering very early on, even before the technology was as polished as it is today. Recurrent neural networks, while having overall stronger potential for solving dynamic problems, are only suggested for use after a simpler implementation in MLP was attempted. Across various ANN applications in FID, it is observed that preprocessing of the inputs is extremely important in obtaining the proper features for use in training the network, particularly when signal analysis is involved. Normalization is practically a standard for ANN use, and likely many other decision-based learning methods due to its ease of use and high impact on speed of convergence. A simple demonstration of ANN’s ease of use in solving a unique FID problem was also shown.

     

Rational Nanotoolbox with Theranostic Potential for Medicated Pro‐Regenerative Corneal Implants

Cornea diseases are a leading cause of blindness and the disease burden is exacerbated by the increasing shortage around the world for cadaveric donor corneas. Despite the advances in the field of regenerative medicine, successful transplantation of laboratory‐made artificial corneas is not fully realized in clinical practice. The causes of failure of such artificial corneal implants are multifactorial and include latent infections from viruses and other microbes, enzyme overexpression, implant degradation, extrusion or delayed epithelial regeneration. Therefore, there is an urgent unmet need for developing customized corneal implants to suit the host environment and counter the effects of inflammation or infection, which are able to track early signs of implant failure in situ. This work reports a nanotoolbox comprising tools for protection from infection, promotion of regeneration, and noninvasive monitoring of the in situ corneal environment. These nanosystems can be incorporated within pro‐regenerative biosynthetic implants, transforming them into theranostic devices, which are able to respond to biological changes following implantation.     

A cluster based charging schedule for wireless rechargeable sensor networks using gravitational search algorithm

Wireless Networks - Wireless sensor networks have a short network lifetime due to limited battery life. External power supplies are utilised to extend the life of sensor nodes. The previous works...     

Spectroscopic and electrochemical studies on the molecular interaction between copper sulphide nanoparticles and bovine serum albumin

The interaction of covellite hexagonal phase of copper sulphide nanoparticles (CuS NPs) with bovine serum albumin (BSA) was examined systematically by using fluorescence, UV–visible, circular dichroism (CD), Fourier transform infrared (FTIR), dynamic light scattering (DLS) and molecular modelling techniques. Electrochemical method was studied to further confirm the interaction of BSA with CuS NPs. The results of fluorescence studies demonstrated that fluorescence of BSA was quenched by CuS NPs via a static quenching mechanism. The negative values of thermodynamic parameters (ΔG, ΔH and ΔS) indicated that the binding process is spontaneous, exothermic and van der Waals force or hydrogen bonding plays major roles in the interaction of CuS NPs with BSA. The interaction of CuS NPs with Trp residue was established by synchronous studies, and competitive binding studies revealed that Trp-212 of subdomain IIA was involved in the interaction with these nanoparticles. Further, the efficiency of energy transferred and the distance between fluorophore (BSA) and acceptor (CuS NPs) were calculated using Forster’s resonance energy transfer theory. The results of UV–visible, CD, FTIR and DLS revealed that the CuS NPs interact with BSA by inducing the conformational changes in secondary structure and reducing the α-helix content of BSA. Molecular modelling studies suggested that CuS NPs bind to site I of sub domain IIA of BSA. The results of spectroscopic and molecular docking studies were complimented by the electrochemical techniques.     

A fuzzy-clustering-based hierarchical i-vector/probabilistic linear discriminant analysis system for text-dependent speaker verification

In the i-vector/probabilistic linear discriminant analysis (PLDA) technique, the PLDA backend classifier is modelled on i-vectors. PLDA defines an i-vector subspace that compensates the unwanted variability and helps to discriminate among speaker-phrase pairs. The channel or session variability manifested in i-vectors are known to be nonlinear in nature. PLDA training, however, assumes the variability to be linearly separable, thereby causing loss of important discriminating information. Besides, the i-vector estimation, itself, is known to be poor in case of short utterances. This paper attempts to address these issues using a simple hierarchy-based system. A modified fuzzy-clustering technique is employed to divide the feature space into more characteristic feature subspaces using vocal source features. Thereafter, a separate i-vector/PLDA model is trained for each of the subspaces. The sparser alignment owing to subspace-specific universal background model and the relatively reduced dimensions of variability in individual subspaces help to train more effective i-vector/PLDA models. Also, vocal source features are complementary to mel frequency cepstral coefficients, which are transformed into i-vectors using mixture model technique. As a consequence, vocal source features and i-vectors tend to have complementary information. Thus using vocal source features for classification in a hierarchy tree may help to differentiate some of the speaker-phrase classes, which otherwise are not easily discriminable based on i-vectors. The proposed technique has been validated on Part 1 of RSR2015 database, and it shows a relative equal error rate reduction of up to 37.41% with respect to the baseline i-vector/PLDA system.     

Priority-based Energy Management Technique for Integration of Solar PV,Battery, and Fuel Cell Systems in an Autonomous DC Microgrid

Recent times have witnessed a significant positive trend in adoption of renewable energy sources and DC-based loads, which questions the efficiency and reliability of existing structure of electrical power system. DC microgrids are identified as potential solutions for addressing India's and other developing nations' rural electrification. This paper presents an energy management technique for isolated DC microgrids for academic readers, as well as provides an introduction to not-familiarized readers for understanding the common design challenges for implementing off-grid power systems. The principle of the proposed technique is based on law of energy conservation defined by the energy balance equation and is implemented by synthesizing the instantaneous current reference and hysteresis control with source priority, which was validated by rigors simulation studies in Matlab/Simulink. Furthermore, in order to demonstrate the modularity and scalability of the algorithm, it was tested on a prototype hardware test setup at the Solar Energy Research Centre, VIT University.     

A novel approach for designing delay efficient path for mobile sink in wireless sensor networks

In the recent years, the use of mobile sink has drawn enormous attention for data collection in wireless sensor networks (WSNs). Mobile sink is well known for solving hotspot or sinkhole problem. However, the design of an efficient path for mobile sink has tremendous impact on network lifetime and coverage in data collection process of WSNs. This is particularly an important issue for many critical applications of WSNs where data collection requires to be carried out in delay bound manner. In this paper, we propose a novel scheme for delay efficient trajectory design of a mobile sink in a cluster based WSN so that it can be used for critical applications without compromising the complete coverage of the target area. Given a set of gateways (cluster heads), our scheme determines a set of rendezvous points for designing path of the mobile sink for critical applications. The scheme is based on the Voronoi diagram. We also propose an efficient method for recovery of the orphan sensor nodes generated due to the failure of one or more cluster heads during data collection. We perform extensive simulations over the proposed algorithm and compare its results with existing algorithms to demonstrate the efficiency of the proposed algorithm in terms of network lifetime, path length, average waiting time, fault tolerance and adaptability etc. For the fault tolerance, we simulate the schemes using Weibull distribution and analyze their performances.     

Performance assessment of drop tube reactor for biomass fast pyrolysis using process simulator

Biomass pyrolysis process from a drop tube reactor was modelled in a plug flow reactor using Aspen Plus process simulation software. A kinetic mechanism for pyrolysis was developed considering the recent improvements and updated kinetic schemes to account for different content of cellulose, hemicellulose, and lignin. In this regard, oak, beechwood, rice straw, and cassava stalk biomasses were analyzed. The main phenomena governing the pyrolysis process are identified in terms of the characteristic times. Pyrolysis process was found to be reaction rate controlled. Effects of pyrolysis temperature on bio-oil, gases, and char yields were evaluated. At optimum pyrolysis conditions (i.e., 500°C), a bio-oil yield of 67.3, 64, 43, and 52 wt.% were obtained from oak, beechwood, rice straw, and cassava stalk, respectively. Oak and beechwood were found to give high yields of bio-oil, while rice straw produced high gas and char yields compared to other biomasses. Although temperature is the main factor that plays a key role in the distribution of pyrolysis products, the composition of cellulose, hemicellulose, and lignin in the feedstock also determines the yield behaviour and composition of products. With the rise in pyrolysis temperature, further decomposition of intermediate components was initiated favouring the formation of lighter fractions. Comparably, species belonging to the aldehyde chemical family had the highest share of bio-oil components in all the investigated feedstocks. Overall, the present study shows a good agreement with the experimental study reported in the literature, confirming its validity as a predictive tool for the biomass pyrolysis process.