Treatment of NOx from Diesel Engine Exhaust by Dielectric Barrier Discharge Method

This paper reports improved performance of discharge plasma in filtered engine exhaust treatment. Our paper deals about the removal of NOX emissions from the diesel exhaust by electric discharge plasma. For the treatment of diesel exhaust a new type of reactor referred to as crossflow dielectric barrier discharge reactor has been used, where the gas flow is perpendicular to the corona electrode. Experiments were conducted at different flow rates ranging from 2 l/min to 10 l/min. The discharge plasma assisted barrier discharge reactor has shown promising results in NOX removal at high flow rates.

Application of Solar Powered High Voltage Discharge Plasma for NOX Removal in Diesel Engine Exhaust

This paper proposes a compact electric discharge plasma source for controlling NOX emission in diesel engine exhaust. Boost converter is used to boost to solar powered battery voltage to 24V, further an automobile ignition coil was used to generate the high voltage pulse using fly-back topology. This design is aimed at retrofitting the existing catalytic converters with pulse assisted cleaning technique. In this paper we bring out a relative comparison of discharge plasma and plasma-adsorbent process at different gas flow rates. Activated alumina was used as adsorbent. The main emphasis is laid on the development of a compact pulse source from 12V battery, which is powered by the solar, for the removal of NOX from the filtered diesel engine exhaust.

Application of Pulsed Discharge Plasma For Methanol Synthesis

Synthesis of methanol using pulsed discharge plasma process is gaining significance. We report the production of methanol from methane and CO2/H2O, using pulsed discharge. Experiments were conducted at very low temperature (-192°C) in addition to normal room temperature. Two types of plasma reactors, cylindrical and rectangular, were tested for methanol production with different corona electrodes (straight wire, helical wire and barbed plate). Gas analysis was carried out by GC-MS and the yield of methanol at various operating conditions was compared and discussed. Experiments were carefully conducted laboratory environment

Application of Solar Powered High Voltage Discharge Plasma for NOX Rem

This paper proposes a compact electric discharge plasma source for controlling NOX emission in diesel engine exhaust. Boost converter is used to boost to solar powered battery voltage to 24V, further an automobile ignition coil was used to generate the high voltage pulse using fly-back topology. This design is aimed at retrofitting the existing catalytic converters with pulse assisted cleaning technique. In this paper we bring out a relative comparison of discharge plasma and plasma-adsorbent process at different gas flow rates. Activated alumina was used as adsorbent. The main emphasis is laid on the development of a compact pulse source from 12V battery, which is powered by the solar, for the removal of NOX from the filtered diesel engine exhaust.

Treatment of NOx from Diesel Engine Exhaust by Dielectric Barrier Discharge Method

This paper reports improved performance of advantages when compared to its counterpart as it is cost discharge plasma in filtered engine exhaust treatment. Our effective, low capital and operation costs, salable by- paper deals about the removal of NOX emissions from the diesel products, and integration with the existing systems. In this exhaust by electric discharge plasma. For the treatment of diesel paper we describe an alternate reactor geometry referred to exhaust a new type of reactor referred to as cross-flow dielectric as cross-flow DBD reactor, where the exhaust gas flow barrier discharge reactor has been used, where the gas flow is perpendicular to the wire-cylinder reaction chamber. This perpendicular to the corona electrode. Experiments were reactor is used to treat the actual exhaust of a 3.75 kW diesel- conducted at different flow rates ranging from 2 l/min to 10 l/ generator set. The main emphasis is laid on the NOX treatment min. The discharge plasma assisted barrier discharge reactor of diesel engine exhaust. Experiments were carried out at has shown promising results in NOX removal at high flow rates.

A core study on NOX removal in diesel exhaust by pulsed/ac/dc electric discharge plasma

In this paper a study on effect of different energization on removal of NOX in diesel engine exhaust has been presented. Here we made a detailed qualitative study of effect of pulsed/ac/dc voltage energizations on the NOX treatment of using conventional wire-cylinder reactor configuration. It was observed that amongst different energizations, pulse energization exhibits maximum NOX removal efficiency when compared to ac and dc energizations. For a given specific energy density, wire-cylinder reactor filled with BaTiO3 pellet gives higher NOX removal efficiency when compared to reactor without pellets under both pulse and ac energization. The dc energization does not have much impact on the removal processes. The paper further discusses the individual energization cases in detail.

Dielectric Barrier Discharge Cascaded with Red Mud Waste to Enhance NOX Removal from Diesel Engine Exhaust

Red mud is a waste by-product generated during the processing of bauxite, the most common ore of aluminium. With the presence of ferric oxide, high surface area, resistance to poisoning and low cost, red mud made itself a good alternative to the existing commercial automobile catalyst. The cascading of dielectric barrier discharge plasma with red mud improved the NOX removal from diesel engine exhaust significantly. The DeNO(X) efficiency with discharge plasma was 74% and that with red mud was 31%. The efficiency increased to 92% when plasma was cascaded with red mud catalyst operating at a temperature of 400 degrees C. The NOX removal was dominated by NO2 removal. The studies were conducted at different temperatures and the results were discussed.

Simulating the daily discharge of the Mandovi River, west coast of India

A hydrological modelling framework was assembled to simulate the daily discharge of the Mandovi River on the Indian west coast. Approximately 90% of the west-coast rainfall, and therefore discharge, occurs during the summer monsoon (June-September), with a peak during July-August. The modelling framework consisted of a digital elevation model (DEM) called GLOBE, a hydrological routing algorithm, the Terrestrial Hydrological Model with Biogeochemistry (THMB), an algorithm to map the rainfall recorded by sparse rain-gauges to the model grid, and a modified Soil Conservation Service Curve Number (SCS-CN) method. A series of discharge simulations (with and without the SCS method) was carried out. The best simulation was obtained after incorporating spatio-temporal variability in the SCS parameters, which was achieved by an objective division of the season into five regimes: the lean season, monsoon onset, peak monsoon, end-monsoon, and post-monsoon. A novel attempt was made to incorporate objectively the different regimes encountered before, during and after the Indian monsoon, into a hydrological modelling framework. The strength of our method lies in the low demand it makes on hydrological data. Apart from information on the average soil type in a region, the entire parameterization is built on the basis of the rainfall that is used to force the model. That the model does not need to be calibrated separately for each river is important, because most of the Indian west-coast basins are ungauged. Hence, even though the model has been validated only for the Mandovi basin, its potential region of application is considerable. In the context of the Prediction in Ungauged Basins (PUB) framework, the potential of the proposed approach is significant, because the discharge of these (ungauged) rivers into the eastern Arabian Sea is not small, making them an important element of the local climate system.

Causality analysis of groundwater dynamics based on a Vector Autoregressive model in the semi-arid basin of Gundal (South India)

Causal relationships existing between observed levels of groundwater in a semi-arid sub-basin of the Kabini River basin (Karnataka state, India) are investigated in this study. A Vector Auto Regressive model is used for this purpose. Its structure is built on an upstream/downstream interaction network based on observed hydro-physical properties. Exogenous climatic forcing is used as an input based on cumulated rainfall departure. Optimal models are obtained thanks to a trial approach and are used as a proxy of the dynamics to derive causal networks. It appears to be an interesting tool for analysing the causal relationships existing inside the basin. The causal network reveals 3 main regions: the Northeastern part of the Gundal basin is closely coupled to the outlet dynamics. The Northwestern part is mainly controlled by the climatic forcing and only marginally linked to the outlet dynamic. Finally, the upper part of the basin plays as a forcing rather than a coupling with the lower part of the basin allowing for a separate analysis of this local behaviour. The analysis also reveals differential time scales at work inside the basin when comparing upstream oriented with downstream oriented causalities. In the upper part of the basin, time delays are close to 2 months in the upward direction and lower than 1 month in the downward direction. These time scales are likely to be good indicators of the hydraulic response time of the basin which is a parameter usually difficult to estimate practically. This suggests that, at the sub-basin scale, intra-annual time scales would be more relevant scales for analysing or modelling tropical basin dynamics in hard rock (granitic and gneissic) aquifers ubiquitous in south India. (c) 2012 Elsevier B.V. All rights reserved.

Ganga-Brahmaputra river discharge from Jason-2 radar altimetry: An update to the long-term satellite-derived estimates of continental freshwater forcing flux into the Bay of Bengal

This paper discusses the use of Jason-2 radar altimeter measurements to estimate the Ganga-Brahmaputra surface freshwater flux into the Bay of Bengal for the period mid-2008 to December 2011. A previous estimate was generated for 1993-2008 using TOPEX-Poseidon, ERS-2 and ENVISAT, and is now extended using Jason-2. To take full advantages of the new availability of in situ rating curves, the processing scheme is adapted and the adjustments of the methodology are discussed here. First, using a large sample of in situ river height measurements, we estimate the standard error of Jason-2-derived water levels over the Ganga and the Brahmaputra to be respectively of 0.28 m and 0.19 m, or less than similar to 4% of the annual peak-to-peak variations of these two rivers. Using the in situ rating curves between water levels and river discharges, we show that Jason-2 accurately infers Ganga and Brahmaputra instantaneous discharges for 2008-2011 with mean errors ranging from similar to 2180 m(3)/s (6.5%) over the Brahmaputra to similar to 1458 m(3)/s (13%) over the Ganga. The combined Ganga-Brahmaputra monthly discharges meet the requirements of acceptable accuracy (15-20%) with a mean error of similar to 16% for 2009-2011 and similar to 17% for 1993-2011. The Ganga-Brahmaputra monthly discharge at the river mouths is then presented, showing a marked interannual variability with a standard deviation of similar to 12500 m(3)/s, much larger than the data set uncertainty. Finally, using in situ sea surface salinity observations, we illustrate the possible impact of extreme continental freshwater discharge event on the northern Bay of Bengal as observed in 2008.

Impact of pit-toilet leachate on groundwater chemistry and role of vadose zone in removal of nitrate and E-coli pollutants in Kolar District, Karnataka, India

Assessment of chemistry of groundwater infiltrated by pit-toilet leachate and contaminant removal by vadose zone form the focus of this study. The study area is Mulbagal Town in Karnataka State, India. Groundwater level measurements and estimation of unsaturated permeability indicated that the leachate recharged the groundwater inside the town at the rate of 1 m/day. The average nitrate concentration of groundwater inside the town (148 mg/L) was three times larger than the permissible limit (45 mg/L), while the average nitrate concentration of groundwater outside the town (30 mg/L) was below the permissible limit. The groundwater inside the town exhibited E. coli contamination, while groundwater outside the town was free of pathogen contamination. Infiltration of alkalis (Na+, K+) and strong acids (Cl-, SO4 (2-)) caused the mixed Ca-Mg-Cl type (60 %) and Na-Cl type (28 %) facies to predominate groundwater inside the town, while, Ca-HCO3 (35 %), mixed Ca-Mg-Cl type (35 %) and mixed Ca-Na-HCO3 type (28 %) facies predominated groundwater outside/periphery of town. Reductions in E. coli and nitrate concentrations with vadose zone thickness indicated its participation in contaminant removal. A 4-m thickness of unsaturated sand + soft, disintegrated weathered rock deposit facilitates the removal of 1 log of E. coli pathogen. The anoxic conditions prevailing in the deeper layers of the vadose zone (> 19 m thickness) favor denitrification resulting in lower nitrate concentrations (28-96 mg/L) in deeper water tables (located at depths of -29 to -39 m).

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.