Ru(4+) ion in CeO(2) (Ce(0.95)Ru(0.05)O(2-delta)): A non-deactivating, non-platinum catalyst for water gas shift reaction

Hydrogen is a clean energy carrier and highest energy density fuel. Water gas shift (WGS) reaction is an important reaction to generate hydrogen from steam reforming of CO. A new WGS catalyst, Ce(1-x)Ru(x)O(2-delta) (0 <= x <= 0.1) was prepared by hydrothermal method using melamine as a complexing agent. The Catalyst does not require any pre-treatment. Among the several compositions prepared and tested, Ce(0.95)Ru(0.05)O(2-delta) (5% Ru(4+) ion substituted in CeO(2)) showed very high WGS activity in terms of high conversion rate (20.5 mu mol.g(-1).s(-1) at 275 degrees C) and low activation energy (12.1 kcal/mol). Over 99% conversion of CO to CO(2) by H(2)O is observed with 100% H(2) selectivity at >= 275 degrees C. In presence of externally fed CO(2) and H(2) also, complete conversion of CO to CO(2) was observed with 100% H(2) selectivity in the temperature range of 305-385 degrees C. Catalyst does not deactivate in long duration on/off WGS reaction cycle due to absence of surface carbon and carbonate formation and sintering of Ru. Due to highly acidic nature of Ru(4+) ion, surface carbonate formation is also inhibited. Sintering of noble metal (Ru) is avoided in this catalyst because Ru remains in Ru(4+) ionic state in the Ce(1-x)Ru(x)O(2-delta) catalyst.

Fault-Diagnostic System using Support Vector Machines for Power Transmission Systems

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Support Vector Machine Approach to Downscale Precipitation in Climate Change Scenarios

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.

New Insights into Selective Heterogeneous Nucleation of Metal Nanoparticles on Oxides by Microwave-Assisted Reduction: Rapid Synthesis of High-Activity Supported Catalysts

Microwave-based methods are widely employed to synthesize metal nanoparticles on various substrates. However, the detailed mechanism of formation of such hybrids has not been addressed. In this paper, we describe the thermodynamic and kinetic aspects of reduction of metal salts by ethylene glycol under microwave heating conditions. On the basis of this analysis, we identify the temperatures above which the reduction of the metal salt is thermodynamically favorable and temperatures above which the rates of homogeneous nucleation of the metal and the heterogeneous nucleation of the metal on supports are favored. We delineate different conditions which favor the heterogeneous nucleation of the metal on the supports over homogeneous nucleation in the solvent medium based on the dielectric loss parameters of the solvent and the support and the metal/solvent and metal/support interfacial energies. Contrary to current understanding, we show that metal particles can be selectively formed on the substrate even under situations where the temperature of the substrate Is lower than that of the surrounding medium. The catalytic activity of the Pt/CeO(2) and Pt/TiO(2) hybrids synthesized by this method for H(2) combustion reaction shows that complete conversion is achieved at temperatures as low as 100 degrees C with Pt-CeO(2) catalyst and at 50 degrees C with Pt-TiO(2) catalyst. Our method thus opens up possibilities for rational synthesis of high-activity supported catalysts using a fast microwave-based reduction method.

Spatial Decision Support System for Assessing Micro, Mini and Small Hydel Potential

This study describes the design and implementation of DSS for assessment of Mini, Micro and Small Schemes. The design links a set of modelling, manipulation, spatial analyses and display tools to a structured database that has the facility to store both observed and simulated data. The main hypothesis is that this tool can be used to form a core of practical methodology that will result in more resilient in less time and can be used by decision-making bodies to assess the impacts of various scenarios (e.g.: changes in land use pattern) and to review, cost and benefits of decisions to be made. It also offers means of entering, accessing and interpreting the information for the purpose of sound decision making. Thus, the overall objective of this DSS is the development of set of tools aimed at transforming data into information and aid decisions at different scales.

Decision support system for regional electricity planning

Electricity appears to be the energy carrier of choice for modern economics since growth in electricity has outpaced growth in the demand for fuels. A decision maker (DM) for accurate and efficient decisions in electricity distribution requires the sector wise and location wise electricity consumption information to predict the requirement of electricity. In this regard, an interactive computer-based Decision Support System (DSS) has been developed to compile, analyse and present the data at disaggregated levels for regional energy planning. This helps in providing the precise information needed to make timely decisions related to transmission and distribution planning leading to increased efficiency and productivity. This paper discusses the design and implementation of a DSS, which facilitates to analyse the consumption of electricity at various hierarchical levels (division, taluk, sub division, feeder) for selected periods. This DSS is validated with the data of transmission and distribution systems of Kolar district in Karnataka State, India.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable to strictly sparse signal recovery from noisy measurements. The support recovery embedded in the approach makes it suitable for recovery of signals with same sparsity profiles, as in the problem of multiple measurement vectors (MMV). Simulation results show that the proposed algorithm, termed as JSSR-MP (joint support and signal recovery via message passing) algorithm, achieves performance comparable to that of sparse Bayesian learning (M-SBL) algorithm in the literature, at one order less complexity compared to the M-SBL algorithm.

Growth of silicon nanowires by electron beam evaporation using indium catalyst

For the first time silicon nanowires have been grown on indium (In) coated Si (100) substrates using e-beam evaporation at a low substrate temperature of 300 degrees C. Standard spectroscopic and microscopic techniques have been employed for the structural, morphological and compositional properties of as grown Si nanowires. The as grown Si nanowires have randomly oriented with an average length of 600 nm for a deposition time of 15 min. As grown Si nanowires have shown indium nanoparticle (capped) on top of it confirming the Vapor Liquid Solid (VLS) growth mechanism. Transmission Electron Microscope (TEM) measurements have revealed pure and single crystalline nature of Si nanowires. The obtained results have indicated good progress towards finding alternative catalyst to gold for the synthesis of Si nanowires. (C) 2011 Elsevier B.V. All rights reserved.

Kinetics of carbon monoxide oxidation with Sn(0.95)M(0.05)O(2-delta) (M = Cu, Fe, Mn, Co) catalysts

Base metal substituted Sn(0.95)M(0.05)O(2-delta) (M = Cu, Fe, Mn, Co) catalysts were synthesized by the solution combustion method and characterized by XRD, XPS, TEM and BET surface area analysis. The catalytic activities of these materials were investigated by performing CO oxidation. The rates and the apparent activation energies of the reaction for CO oxidation were determined for each catalyst. All the substituted catalysts showed high rates and lower activation energies for the oxidation of CO as compared to unsubstituted SnO(2). The rate was found to be much higher over copper substituted SnO(2) as compared to other studied catalysts. 100% CO conversion was obtained below 225 degrees C over this catalyst. A bifunctional reaction mechanism was developed that accounts for CO adsorption on base metal and support ions and O(2) dissociation on the oxide ion vacancy. The kinetic parameters were determined by fitting the model to the experimental data. The high rates of the CO oxidation reactions at low temperatures were rationalized by the high dissociative chemisorption of adsorbed O(2) over these catalysts.

Catalyst-Packed Non-Thermal Plasma Reactor for Removal of Nitrogen Oxides

A single-stage plasma-catalytic reactor in which catalytic materials were packed was used to remove nitrogen oxides. The packing material was scoria being made of various metal oxides including Al2O3, MgO, TiO2, etc. Scoria was able to act not only as dielectric pellets but also as a catalyst in the presence of reducing agent such as ethylene and ammonia. Without plasma discharge, scoria did not work well as a catalyst in the temperature range of 100 °C to 200 °C, showing less than 10% of NOx removal efficiency. When plasma is produced inside the reactor, the NOx removal efficiency could be increased to 60% in this temperature range.