26 research outputs found
Machine learning based predictive modelling of micro gas turbine engine fuelled with microalgae blends on using LSTM networks: An experimental approach
No full textAir transport plays an inevitable role in the transportation sector. In the modern world, the aviation contribution is very immense to establish worldwide developments. However, the emission released by the aviation industry is massively high. Due to the sudden increase in the air traffic the contribution of global CO2 and CO have increased in recent years. Hence the aviation sector seeks the replacement for fossil fuels. In this study, the micro gas turbine engine has been experimentally studied for different engine speeds and throttle position. The gas turbine was allowed to run in the different test fuels such as, Jet-A, A20 (20% microalgae 80% Jet-A) and A30 (30% microalgae 70% Jet-A) and the predicted results were compared. In addition to the typical experimental calibrations, machine learning has been applied to examine the differences in the both performance and emission characteristics of the biofuel blends with approximately 51 different fuel combinations using LSTM networks. Based on the predicted results, introduction of the biofuel affects the production of the static thrust. On the contrary, the emissions of the CO and CO2 were very low compared to Jet-A. With regard to the nitrogen of the oxides, no massive reduction has been witnessed despite running at different fuel conditions. Besides, the marginal decrease in the NOx was observed above 75000 rpm.King Saud University, KSU; Natural Science Foundation of Jiangsu Province: BK20200775, RSP-2021/257Natural Science Foundation of Jiangsu Province [BK20200775, RSP-2021/257]; King Saud University, Riyadh, Saudi Arabi
Ag nanoparticles agargel nanocomposites for SERS detection of cultural heritage interest pigments
No full textForest biomass density, utilization and production dynamics in a western Himalayan watershed
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No full textNot AvailableSustaining soil and land quality under intensive land
use and fast economic development is a major challenge
for improving crop productivity in the developing
world. Assessment of soil and land quality
indicators is necessary to evaluate the degradation
status and changing trends of different land use and
management interventions. During the last four decades,
the Indo-Gangetic Plains (IGP) which covers an
area of about 52.01 m ha has been the major food producing
region of the country. However at present, the
yield of crops in IGP has stagnated; one of the major
reasons being deterioration of soil and land quality.
The present article deals with the estimation of soil
and land quality indicators of IGP, so that, proper soil
and land management measures can be taken up to
restore and improve the soil health. Use of principal
component analysis is detailed to derive the minimum
dataset or indicators for soil quality. The article also
describes spatial distribution of soil and land quality
with respect to major crops of IGP.Not Availabl
