2,857 research outputs found
Source Apportionment and Forecasting of Aerosol in a Steel City - Case Study of Rourkela
Urban air pollution is one of the biggest problems ascending due to rapid urbanization and industrialization. The improvement of air quality in an urban area in general, constitutes of three phases, monitoring, modeling and control measures. The present research work addresses the requirements of the urban air quality management programme (UAQMP) in Rourkela steel city. A typical UAQMP contains three aspects: monitoring of air pollution, modeling of air pollution and taking control measures. The present study aims to conduct the modeling of particulate air pollution for a steel city. Modeling of particulate matter (PM)
pollution is nothing but the application of different mathematical models in source apportionment and forecasting of PM. PM (PM10 and TSP) was collected twice a week for
two years (2011-2012) during working hours in Rourkela. The seasonal variations study of PM showed that the aerosol concentration was high during summer and low during monsoon.
A detailed chemical characterization of both PM10 and TSP was carried out to find out the concentrations of different metal ions, anions and carbon content. The Spearman rank
correlation analysis between different chemical species of PM depicted the presence of both crustal and anthropogenic origins in particulate matter. The enrichment factor analysis highlighted the presence of anthropogenic sources. Three major receptor models were used for the source apportionment of PM, namely chemical mass balance model (CMB), principal component analysis (PCA) and positive matrix factorization (PMF). In selecting source profiles for CMB, an effort has been put to select the profiles which represent the local conditions. Two of the profiles, namely soil dust and road dust, were developed in the present
study for better accuracy. All three receptor models have shown that industrial (40-45%) and combustion sources (30-35%) were major contributors to particulate pollution in Rourkela. Artificial neural networks (ANN) were used for the prediction of particulate pollution using meteorological parameters as inputs. The emphasis is to compare the performances of MLP and RBF algorithms in forecasting and provide a rigorous inter-comparison as a first step
toward operational PM forecasting models. The training, testing and validation errors of MLP networks are significantly lower than that of RBF networks. The results indicate that both MLP and RBF have shown good prediction capabilities while MLP networks were better than that of RBF networks. There is no profound bias that can be seen in the models which may also suggest that there are very few or zero external factors that may influence the dispersion and distribution of particulate matter in the study area
Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4
Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences
Book of abstracts of the 16th International Symposium of Croatian Metallurgical Society - SHMD \u272023, Materials and metallurgy
Book of abstracts of the 16th International Symposium of Croatian Metallurgical Society - SHMD \u272023, Materials and metallurgy, Zagreb, Croatia, April 20-21, 2023. Abstracts are organized into five sections: Anniversaries of Croatian Metallurgy, Materials - Section A; Process Metallurgy - Section B; Plastic Processing - Section C and Metallurgy and Related Topics - Section D
A comparison between Recurrent Neural Networks and classical machine learning approaches In Laser induced breakdown spectroscopy
Recurrent Neural Networks are classes of Artificial Neural Networks that
establish connections between different nodes form a directed or undirected
graph for temporal dynamical analysis. In this research, the laser induced
breakdown spectroscopy (LIBS) technique is used for quantitative analysis of
aluminum alloys by different Recurrent Neural Network (RNN) architecture. The
fundamental harmonic (1064 nm) of a nanosecond Nd:YAG laser pulse is employed
to generate the LIBS plasma for the prediction of constituent concentrations of
the aluminum standard samples. Here, Recurrent Neural Networks based on
different networks, such as Long Short Term Memory (LSTM), Gated Recurrent Unit
(GRU), Simple Recurrent Neural Network (Simple RNN), and as well as Recurrent
Convolutional Networks comprising of Conv-SimpleRNN, Conv-LSTM and Conv-GRU are
utilized for concentration prediction. Then a comparison is performed among
prediction by classical machine learning methods of support vector regressor
(SVR), the Multi Layer Perceptron (MLP), Decision Tree algorithm, Gradient
Boosting Regression (GBR), Random Forest Regression (RFR), Linear Regression,
and k-Nearest Neighbor (KNN) algorithm. Results showed that the machine
learning tools based on Convolutional Recurrent Networks had the best
efficiencies in prediction of the most of the elements among other multivariate
methods
Assessing Atmospheric Pollution and Its Impacts on the Human Health
This reprint contains articles published in the Special Issue entitled "Assessing Atmospheric Pollution and Its Impacts on the Human Health" in the journal Atmosphere. The research focuses on the evaluation of atmospheric pollution by statistical methods on the one hand, and on the other hand, on the evaluation of the relationship between the level of pollution and the extent of its effect on the population's health, especially on pulmonary diseases
Process Modeling in Pyrometallurgical Engineering
The Special Issue presents almost 40 papers on recent research in modeling of pyrometallurgical systems, including physical models, first-principles models, detailed CFD and DEM models as well as statistical models or models based on machine learning. The models cover the whole production chain from raw materials processing through the reduction and conversion unit processes to ladle treatment, casting, and rolling. The papers illustrate how models can be used for shedding light on complex and inaccessible processes characterized by high temperatures and hostile environment, in order to improve process performance, product quality, or yield and to reduce the requirements of virgin raw materials and to suppress harmful emissions
The doctoral research abstracts. Vol:6 2014 / Institute of Graduate Studies, UiTM
Congratulations to Institute of Graduate
Studies on the continuous efforts to publish the 6th
issue of the Doctoral Research Abstracts which ranged
from the discipline of science and technology,
business and administration to social science and
humanities.
This issue captures the novelty of research from 52
PhD doctorates receiving their scrolls in the UiTM’s
81st Convocation. This convocation is very significant
especially for UiTM since we are celebrating the
success of 52 PhD graduands – the highest number
ever conferred at any one time.
To the 52 doctorates, I would like it to be known
that you have most certainly done UiTM proud by
journeying through the scholastic path with its endless
challenges and impediments, and by persevering
right till the very end.
This convocation should not be regarded as the end of
your highest scholarly achievement and contribution
to the body of knowledge but rather as the beginning
of embarking into more innovative research from
knowledge gained during this academic journey, for
the community and country.
As alumni of UiTM, we hold
you dear to our hearts. The
relationship that was once
between a student and
supervisor has now matured
into comrades, forging
and exploring together
beyond the frontier of
knowledge. We wish
you all the best in
your endeavour
and may I offer my
congratulations to
all the graduands.
‘UiTM sentiasa dihati
ku’
Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar ,
FASc, PEng
Vice Chancellor
Universiti Teknologi MAR
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