Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores

The ability of Coupled General Circulation Models (CGCMs) participating in the Intergovernmental Panel for Climate Change's fourth assessment report (IPCC AR4) for the 20th century climate (20C3M scenario) to simulate the daily precipitation over the Indian region is explored. The skill is evaluated on a 2.5A degrees x 2.5A degrees grid square compared with the Indian Meteorological Department's (IMD) gridded dataset, and every GCM is ranked for each of these grids based on its skill score. Skill scores (SSs) are estimated from the probability density functions (PDFs) obtained from observed IMD datasets and GCM simulations. The methodology takes into account (high) extreme precipitation events simulated by GCMs. The results are analyzed and presented for three categories and six zones. The three categories are the monsoon season (JJASO - June to October), non-monsoon season (JFMAMND - January to May, November, December) and for the entire year (''Annual''). The six precipitation zones are peninsular, west central, northwest, northeast, central northeast India, and the hilly region. Sensitivity analysis was performed for three spatial scales, 2.5A degrees grid square, zones, and all of India, in the three categories. The models were ranked based on the SS. The category JFMAMND had a higher SS than the JJASO category. The northwest zone had higher SSs, whereas the peninsular and hilly regions had lower SS. No single GCM can be identified as the best for all categories and zones. Some models consistently outperformed the model ensemble, and one model had particularly poor performance. Results show that most models underestimated the daily precipitation rates in the 0-1 mm/day range and overestimated it in the 1-15 mm/day range.

SWEETENING OF BIOGAS IN SPRAY TOWERS FOR POWER GENERATION

This paper presents the experience of the new design of using impinging jet spray columns for scrubbing hydrogen sulfide from biogas that has been developed by Indian Institute of Science and patented. The process uses a chelated polyvalent metal ion which oxidizes the hydrogen sulfide to sulfur as a precipitate. The sulfur generated is filtered and the scrubbing liquid recycled after oxidation. The process involves in bringing contact the sour gas with chelated liquid in the spray columns where H2S reacts with chelated Fe3+ and precipitates as sulfur, whereas Fe3+ gets reduced to Fe2+. Fe2+ is regenerated to Fe3+ by reaction of oxygen in air in a separate packed column. The regenerated liquid is recirculated. Sulfur is filtered and separated as a byproduct. The paper presents the experience in using the spray towers for hydrogen sulfide removal and further use of the clean gas for generating power using gas engines. The maximum allowable limit of H2S for the gas engine is 200 ppm (v/v) in order to prevent any corrosion of engine parts and fouling of the lubricating oil. With the current ISET process, the hydrogen sulfide from the biogas is cleaned to less than 100 ppm (v/v) and the sweet gas is used for power generation. The system is designed for 550 NM3/hr of biogas and inlet H2S concentration of 2.5 %. The inlet concentration of the H2S is about 1 - 1.5 % and average measured outlet concentration is about 30 ppm, with an average gas flow of about 300 - 350 NM3/hr, which is the current gas production rate. The sweet gas is used for power generation in a 1.2 MWe V 12 engine. The average power generation is about 650 - 750 kWe, which is the captive load of the industry. The plant is a CHP (combined heat power) unit with heat from the cylinder cooling and flue being recovered for hot water and steam generation respectively. The specific fuel consumption is 2.29 kWh/m(3) of gas. The system has been in operation for more than 13,000 hours in last one year in the industry. About 8.4 million units of electricity has been generated scrubbing about 2.1 million m3 of gas. Performance of the scrubber and the engine is discussed at daily performance level and also the overall performance with an environment sustenance by precipitating over 27 tons of sulfur.

BIOMASS GASIFICATION TO SUBSTITUTE FOSSIL FUEL IN FOOD INDUSTRY

Food industries like biscuit and confectionary use significant amount of fossil fuel for thermal energy. Biscuit manufacturing in India is carried out both by organized and unorganized sector. The ratio of organized to unorganized sector is 60 : 40 (1). The total biscuit manufacturing in the organized sector India in 2008 was about 1.7 million metric tons (1). Accounting for the unorganized sector in India, the total biscuit manufacturing would have been about 2.9 million metric tons/annum. A typical biscuit baking is carried in a long tunnel kiln with varying temperature in different zones. Generally diesel is used to provide the necessary heat energy for the baking purpose, with temperature ranging from 190 C in the drying zone to about 300 C in the baking area and has to maintain in the temperature range of +/- 5 C. Typical oil consumption is about 40 litres per ton of biscuit production. The paper discusses the experience in substituting about 120 lts per hour kiln for manufacturing about 70 tons of biscuit daily. The system configuration consists of a 500 kg/hr gasification system comprising of a reactor, multicyclone, water scrubbers, and two blowers for maintaining the constant gas pressure in the header before the burners. Cold producer gas is piped to the oven located about 200 meters away from the gasifier. Fuel used in the gasification system is coconut shells. All the control system existing on the diesel burner has been suitably adapted for producer gas operation to maintain the total flow, A/F control so as to maintain the temperature. A total of 7 burners are used in different zones. Over 17000 hour of operation has resulted in replacing over 1800 tons of diesel over the last 30 months. The system operates for over 6 days a week with average operational hours of 160. It has been found that on an average 3.5 kg of biomass has replaced one liter of diesel.

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

Novel Relay Selection Rules for Average Interference-Constrained Cognitive AF Relay Networks

Cooperative relaying combined with selection exploits spatial diversity to significantly improve the performance of interference-constrained secondary users in an underlay cognitive radio (CR) network. However, unlike conventional relaying, the state of the links between the relay and the primary receiver affects the choice of the relay. Further, while the optimal amplify-and-forward (AF) relay selection rule for underlay CR is well understood for the peak interference-constraint, this is not so for the less conservative average interference constraint. For the latter, we present three novel AF relay selection (RS) rules, namely, symbol error probability (SEP)-optimal, inverse-of-affine (IOA), and linear rules. We analyze the SEPs of the IOA and linear rules and also develop a novel, accurate approximation technique for analyzing the performance of AF relays. Extensive numerical results show that all the three rules outperform several RS rules proposed in the literature and generalize the conventional AF RS rule.

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.

Correlation equations for average deposition rate coefficients of nanoparticles in a cylindrical pore

Nanoparticle deposition behavior observed at the Darcy scale represents an average of the processes occurring at the pore scale. Hence, the effect of various pore-scale parameters on nanoparticle deposition can be understood by studying nanoparticle transport at pore scale and upscaling the results to the Darcy scale. In this work, correlation equations for the deposition rate coefficients of nanoparticles in a cylindrical pore are developed as a function of nine pore-scale parameters: the pore radius, nanoparticle radius, mean flow velocity, solution ionic strength, viscosity, temperature, solution dielectric constant, and nanoparticle and collector surface potentials. Based on dominant processes, the pore space is divided into three different regions, namely, bulk, diffusion, and potential regions. Advection-diffusion equations for nanoparticle transport are prescribed for the bulk and diffusion regions, while the interaction between the diffusion and potential regions is included as a boundary condition. This interaction is modeled as a first-order reversible kinetic adsorption. The expressions for the mass transfer rate coefficients between the diffusion and the potential regions are derived in terms of the interaction energy profile. Among other effects, we account for nanoparticle-collector interaction forces on nanoparticle deposition. The resulting equations are solved numerically for a range of values of pore-scale parameters. The nanoparticle concentration profile obtained for the cylindrical pore is averaged over a moving averaging volume within the pore in order to get the 1-D concentration field. The latter is fitted to the 1-D advection-dispersion equation with an equilibrium or kinetic adsorption model to determine the values of the average deposition rate coefficients. In this study, pore-scale simulations are performed for three values of Peclet number, Pe = 0.05, 5, and 50. We find that under unfavorable conditions, the nanoparticle deposition at pore scale is best described by an equilibrium model at low Peclet numbers (Pe = 0.05) and by a kinetic model at high Peclet numbers (Pe = 50). But, at an intermediate Pe (e.g., near Pe = 5), both equilibrium and kinetic models fit the 1-D concentration field. Correlation equations for the pore-averaged nanoparticle deposition rate coefficients under unfavorable conditions are derived by performing a multiple-linear regression analysis between the estimated deposition rate coefficients for a single pore and various pore-scale parameters. The correlation equations, which follow a power law relation with nine pore-scale parameters, are found to be consistent with the column-scale and pore-scale experimental results, and qualitatively agree with the colloid filtration theory. These equations can be incorporated into pore network models to study the effect of pore-scale parameters on nanoparticle deposition at larger length scales such as Darcy scale.

Direct Link-Aware Relay Selection for Average Interference-Constrained Underlay Cognitive Radio

Cooperative relaying combined with selection exploits spatial diversity to significantly improve the performance of interference-constrained secondary users in an underlay cognitive radio network. We present a novel and optimal relay selection (RS) rule that minimizes the symbol error probability (SEP) of an average interference-constrained underlay secondary system that uses amplify-and-forward relays. A key point that the rule highlights for the first time is that, for the average interference constraint, the signal-to-interference-plus-noise-ratio (SINR) of the direct source-to-destination (SI)) link affects the choice of the optimal relay. Furthermore, as the SINR increases, the odds that no relay transmits increase. We also propose a simpler, more practical, and near-optimal variant of the optimal rule that requires just one bit of feedback about the state of the SD link to the relays. Compared to the SD-unaware ad hoc RS rules proposed in the literature, the proposed rules markedly reduce the SEP by up to two orders of magnitude.