Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

Optimization of Biofuel Blends and Compression Ratio of a Diesel Engine Fueled with Calophyllum inophyllum Oil Methyl Ester

Abstract: The twin crises in depletion of fossil fuels and environmental degradation have motivated researches to consolidate the use of biofuels for internal combustion engine applications. In the present study, the performance and emission characteristic of Calophyllum inophyllum oil-based methyl ester and its diesel blends are analyzed at various compression ratios. Comprehensive optimization by considering the performance parameter along with emission characteristic is rather involved and is done carefully with designed set of experiments and analyzed statistically using design expert software. Higher compression ratio (CR) induces high cylinder temperature which enhances vaporization and thereby better performance only to a certain extent, that is, up to a CR of 19. However, due to high operating temperature, the oxides of nitrogen emission increase with CR and also for high biofuel blends, but better combustion phenomenon at these conditions reduces the emissions of carbon monoxide and unburned hydrocarbon. The designed empirical statistical model for optimum performance with lower emission is found to be B30 (30 % biofuel) at a CR of 19, which is then tested and validated.

Genetic programming model for forecast of short and noisy data

Abstract: Though forecasting of river flow has received a great deal of attention from engineers and researchers throughout the world, this still continues to be a challenging task owing to the complexity of the process. In the last decade or so, artificial neural networks (ANNs) have been widely applied, and their ability to model complex phenomena has been clearly demonstrated. However, the success of ANNs depends very crucially on having representative records of sufficient length. Further, the forecast accuracy decreases rapidly with an increase in the forecast horizon. In this study, the use of the Darwinian theory-based recent evolutionary technique of genetic programming (GP) is suggested to forecast fortnightly flow up to 4-lead. It is demonstrated that short lead predictions can be significantly improved from a short and noisy time series if the stochastic (noise) component is appropriately filtered out. The deterministic component can then be easily modelled. Further, only the immediate antecedent exogenous and/or non-exogenous inputs can be assumed to control the process. With an increase in the forecast horizon, the stochastic components also play an important role in the forecast, besides the inherent difficulty in ascertaining the appropriate input variables which can be assumed to govern the underlying process. GP is found to be an efficient tool to identify the most appropriate input variables to achieve reasonable prediction accuracy for higher lead-period forecasts. A comparison with ANNs suggests that though there is no significant difference in the prediction accuracy, GP does offer some unique advantages. Copyright © 2006 John Wiley & Sons, Ltd.