Performance - Evaluation of Lignite in a downdraft Gasifier
India is a developing country which lacks an adequate supply of electricity. For many of the interior regions of India, gasification is a good way to produce electricity with standalone gasifier. In many regions of India as well as in the world, a large reserve of lignite is available. Lignite could be a fuel for gasification system. This paper presents the result of the experimental investigations carried out using lignite to investigate the effect of fuel size variation on gas generation rate and performance parameter in terms of calorific value of producer gas and gasifier efficiency in downdraft gasifier. Based on the realization, while studying the effect of gas generation rate, the choice of particle size is playing an important role for flowability study and performance of gasifier. The present work investigates the effect of lignite size variation on temperature profile at different zones in gasifier. They were in the range of 16-19 mm, 19-22 mm, 22-25 mm particle sizes of lignite. For comparative studies, experiments were also carried out on uneven size wood and lignite. It was observed from three particle sizes of lignite that better calorific value (629 kcal/m 3 ) and efficiency of gasifier (46.092%)
A process development for gasification of rice husk
Abstract Rice husk has been recognized as a potential source of energy. Thus, the main objective of this work was to develop a rice husk gasification process. Diffuse reflectance infrared spectra reveal that partial oxidation of rice husk at reaction temperatures below 1000 K would also allow recover valuable of amorphous silica materials. In the laboratory-scale fixed-bed and bench-scale downdraft experimental approaches, gasification of rice husk was accompanied by a substantial production of syngas at 760–900 K. It appears that in order to generate 10 kW electric power, approximately 28 kg/h of rice husk must be gasified. The rice husk gasification process, in terms of heat requirements, can be self-sustaining.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.