Abstract: Petroleum refineries discharged large amount of
wastewater -during the refining process- that contains hazardous
constituents that is hard to degrade. Anaerobic treatment process is
well known as an efficient method to degrade high strength
wastewaters. Up-flow Anaerobic Sludge Blanker (UASB) is a
common process used for various wastewater treatments. Two UASB
reactors were set up and operated in parallel to evaluate the treatment
efficiency of petroleum refinery wastewater. In this study four
organic volumetric loading rates were applied (i.e. 0.58, 0.89, 1.21
and 2.34 kg/m3·d), two loads to each reactor. Each load was applied
for a period of 60 days for the reactor to acclimatize and reach steady
state, and then the second load applied. The chemical oxygen demand
(COD) removals were satisfactory with the removal efficiencies at the
loadings applied were 78, 82, 83 and 81 % respectively.
Abstract: The purpose of this article is to analyze the market structure as well as the degree of concentration in insurance markets in new EU member states. The analysis was conducted using several most commonly used concentration indicators such as concentration ratio, Herfindahl-Hirschman index and entropy index. These indicators were calculated for the 2000-2010 period on the basis of total gross written premium as the most relevant indicator of market power in insurance markets. The results of the analysis showed that in all observed countries the level of concentration decreased, though with significantly different intensity. Yet, in some countries, the level of concentration remains very high.
Abstract: In order to evaluation the effects of soil organic
matter and biofertilizer on chickpea quality and biological
nitrogen fixation, field experiments were carried out in 2007
and 2008 growing seasons. In this research the effects of
different strategies for soil fertilization were investigated on
grain yield and yield component, minerals, organic compounds
and cooking time of chickpea. Experimental units were
arranged in split-split plots based on randomized complete
blocks with three replications. Main plots consisted of (G1):
establishing a mixed vegetation of Vicia panunica and
Hordeum vulgare and (G2): control, as green manure levels.
Also, five strategies for obtaining the base fertilizer
requirement including (N1): 20 t.ha-1 farmyard manure; (N2):
10 t.ha-1 compost; (N3): 75 kg.ha-1 triple super phosphate;
(N4): 10 t.ha-1 farmyard manure + 5 t.ha-1 compost and (N5):
10 t.ha-1 farmyard manure + 5 t.ha-1 compost + 50 kg.ha-1
triple super phosphate were considered in sub plots.
Furthermoree four levels of biofertilizers consisted of (B1):
Bacillus lentus + Pseudomonas putida; (B2): Trichoderma
harzianum; (B3): Bacillus lentus + Pseudomonas putida +
Trichoderma harzianum; and (B4): control (without
biofertilizers) were arranged in sub-sub plots. Results showed
that integrating biofertilizers (B3) and green manure (G1)
produced the highest grain yield. The highest amounts of yield
were obtained in G1×N5 interaction. Comparison of all 2-way
and 3-way interactions showed that G1N5B3 was determined
as the superior treatment. Significant increasing of N, P2O5,
K2O, Fe and Mg content in leaves and grains emphasized on
superiority of mentioned treatment because each one of these
nutrients has an approved role in chlorophyll synthesis and
photosynthesis abilities of the crops. The combined application
of compost, farmyard manure and chemical phosphorus (N5)
in addition to having the highest yield, had the best grain
quality due to high protein, starch and total sugar contents, low
crude fiber and reduced cooking time.
Abstract: The equilibrium, thermodynamics and kinetics of the
biosorption of Cd (II) and Pb(II) by a Spore Forming Bacillus (MGL
75) were investigated at different experimental conditions. The
Langmuir and Freundlich, and Dubinin-Radushkevich (D-R)
equilibrium adsorption models were applied to describe the
biosorption of the metal ions by MGL 75 biomass. The Langmuir
model fitted the equilibrium data better than the other models.
Maximum adsorption capacities q max for lead (II) and cadmium (II)
were found equal to 158.73mg/g and 91.74 mg/g by Langmuir model.
The values of the mean free energy determined with the D-R equation
showed that adsorption process is a physiosorption process. The
thermodynamic parameters Gibbs free energy (ΔG°), enthalpy (ΔH°),
and entropy (ΔS°) changes were also calculated, and the values
indicated that the biosorption process was exothermic and
spontaneous. Experiment data were also used to study biosorption
kinetics using pseudo-first-order and pseudo-second-order kinetic
models. Kinetic parameters, rate constants, equilibrium sorption
capacities and related correlation coefficients were calculated and
discussed. The results showed that the biosorption processes of both
metal ions followed well pseudo-second-order kinetics.
Abstract: This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.
Abstract: In this paper, we consider the problem of logic simplification for a special class of logic functions, namely complementary Boolean functions (CBF), targeting low power implementation using static CMOS logic style. The functions are uniquely characterized by the presence of terms, where for a canonical binary 2-tuple, D(mj) ∪ D(mk) = { } and therefore, we have | D(mj) ∪ D(mk) | = 0 [19]. Similarly, D(Mj) ∪ D(Mk) = { } and hence | D(Mj) ∪ D(Mk) | = 0. Here, 'mk' and 'Mk' represent a minterm and maxterm respectively. We compare the circuits minimized with our proposed method with those corresponding to factored Reed-Muller (f-RM) form, factored Pseudo Kronecker Reed-Muller (f-PKRM) form, and factored Generalized Reed-Muller (f-GRM) form. We have opted for algebraic factorization of the Reed-Muller (RM) form and its different variants, using the factorization rules of [1], as it is simple and requires much less CPU execution time compared to Boolean factorization operations. This technique has enabled us to greatly reduce the literal count as well as the gate count needed for such RM realizations, which are generally prone to consuming more cells and subsequently more power consumption. However, this leads to a drawback in terms of the design-for-test attribute associated with the various RM forms. Though we still preserve the definition of those forms viz. realizing such functionality with only select types of logic gates (AND gate and XOR gate), the structural integrity of the logic levels is not preserved. This would consequently alter the testability properties of such circuits i.e. it may increase/decrease/maintain the same number of test input vectors needed for their exhaustive testability, subsequently affecting their generalized test vector computation. We do not consider the issue of design-for-testability here, but, instead focus on the power consumption of the final logic implementation, after realization with a conventional CMOS process technology (0.35 micron TSMC process). The quality of the resulting circuits evaluated on the basis of an established cost metric viz., power consumption, demonstrate average savings by 26.79% for the samples considered in this work, besides reduction in number of gates and input literals by 39.66% and 12.98% respectively, in comparison with other factored RM forms.
Abstract: Predicting short term wind speed is essential in order
to prevent systems in-action from the effects of strong winds. It also
helps in using wind energy as an alternative source of energy, mainly
for Electrical power generation. Wind speed prediction has
applications in Military and civilian fields for air traffic control,
rocket launch, ship navigation etc. The wind speed in near future
depends on the values of other meteorological variables, such as
atmospheric pressure, moisture content, humidity, rainfall etc. The
values of these parameters are obtained from a nearest weather
station and are used to train various forms of neural networks. The
trained model of neural networks is validated using a similar set of
data. The model is then used to predict the wind speed, using the
same meteorological information. This paper reports an Artificial
Neural Network model for short term wind speed prediction, which
uses back propagation algorithm.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.
Abstract: The Niger Delta Region of Nigeria is home to about
20 million people and 40 different ethnic groups. The region has an
area of seventy thousand square kilometers (70,000 KM2) of
wetlands, formed primarily by sediments deposition and makes up
7.5 percent of Nigeria's total landmass. The notable ecological zones
in this region includes: coastal barrier islands; mangrove swamp
forests; fresh water swamps; and lowland rainforests. This incredibly
naturally-endowed ecosystem region, which contains one of the
highest concentrations of biodiversity on the planet, in addition to
supporting abundant flora and fauna, is threatened by the inhuman act
known as gas flaring. Gas flaring is the combustion of natural gas
that is associated with crude oil when it is pumped up from the
ground. In petroleum-producing areas such as the Niger Delta region
of Nigeria where insufficient investment was made in infrastructure
to utilize natural gas, flaring is employed to dispose of this associated
gas. This practice has impoverished the communities where it is
practiced, with attendant environmental, economic and health
challenges. This paper discusses the adverse environmental and
health implication associated with the practice, the role of
Government, Policy makers, Oil companies and the Local
communities aimed at bring this inhuman practice to a prompt end.
Abstract: In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF systems.
Abstract: DNA shuffling is a powerful method used for in vitro
evolute molecules with specific functions and has application in areas
such as, for example, pharmaceutical, medical and agricultural
research. The success of such experiments is dependent on a variety
of parameters and conditions that, sometimes, can not be properly
pre-established. Here, two computational models predicting DNA
shuffling results is presented and their use and results are evaluated
against an empirical experiment. The in silico and in vitro results
show agreement indicating the importance of these two models and
motivating the study and development of new models.
Abstract: An optimal power flow (OPF) based on particle swarm
optimization (PSO) was developed with more realistic generator
security constraint using the capability curve instead of only Pmin/Pmax
and Qmin/Qmax. Neural network (NN) was used in designing digital
capability curve and the security check algorithm. The algorithm is
very simple and flexible especially for representing non linear
generation operation limit near steady state stability limit and under
excitation operation area. In effort to avoid local optimal power flow
solution, the particle swarm optimization was implemented with
enough widespread initial population. The objective function used in
the optimization process is electric production cost which is
dominated by fuel cost. The proposed method was implemented at
Java Bali 500 kV power systems contain of 7 generators and 20
buses. The simulation result shows that the combination of generator
power output resulted from the proposed method was more economic
compared with the result using conventional constraint but operated
at more marginal operating point.
Abstract: Inverse kinematics analysis plays an important role in developing a robot manipulator. But it is not too easy to derive the inverse kinematic equation of a robot manipulator especially robot manipulator which has numerous degree of freedom. This paper describes an application of Artificial Neural Network for modeling the inverse kinematics equation of a robot manipulator. In this case, the robot has three degree of freedoms and the robot was implemented for drilling a printed circuit board. The artificial neural network architecture used for modeling is a multilayer perceptron networks with steepest descent backpropagation training algorithm. The designed artificial neural network has 2 inputs, 2 outputs and varies in number of hidden layer. Experiments were done in variation of number of hidden layer and learning rate. Experimental results show that the best architecture of artificial neural network used for modeling inverse kinematics of is multilayer perceptron with 1 hidden layer and 38 neurons per hidden layer. This network resulted a RMSE value of 0.01474.
Abstract: This paper features the modeling and design of a Fast
Output Sampling (FOS) Feedback control technique for the Active
Vibration Control (AVC) of a smart flexible aluminium cantilever
beam for a Single Input Single Output (SISO) case. Controllers are
designed for the beam by bonding patches of piezoelectric layer as
sensor / actuator to the master structure at different locations along
the length of the beam by retaining the first 2 dominant vibratory
modes. The entire structure is modeled in state space form using the
concept of piezoelectric theory, Euler-Bernoulli beam theory, Finite
Element Method (FEM) and the state space techniques by dividing
the structure into 3, 4, 5 finite elements, thus giving rise to three
types of systems, viz., system 1 (beam divided into 3 finite
elements), system 2 (4 finite elements), system 3 (5 finite elements).
The effect of placing the sensor / actuator at various locations along
the length of the beam for all the 3 types of systems considered is
observed and the conclusions are drawn for the best performance and
for the smallest magnitude of the control input required to control the
vibrations of the beam. Simulations are performed in MATLAB. The
open loop responses, closed loop responses and the tip displacements
with and without the controller are obtained and the performance of
the proposed smart system is evaluated for vibration control.
Abstract: In India, the quarrel between the budding human
populace and the planet-s unchanging supply of freshwater and
falling water tables has strained attention the reuse of gray water as
an alternative water resource in rural development. This paper
present the finest design of laboratory scale gray water treatment
plant, which is a combination of natural and physical operations such
as primary settling with cascaded water flow, aeration, agitation and
filtration, hence called as hybrid treatment process. The economical
performance of the plant for treatment of bathrooms, basins and
laundries gray water showed in terms of deduction competency of
water pollutants such as COD (83%), TDS (70%), TSS (83%), total
hardness (50%), oil and grease (97%), anions (46%) and cations
(49%). Hence, this technology could be a good alternative to treat
gray water in residential rural area.
Abstract: The use of renewable energy sources becomes more
necessary and interesting. As wider applications of renewable energy
devices at domestic, commercial and industrial levels has not only
resulted in greater awareness, but also significantly installed
capacities. In addition, biomass principally is in the form of woods,
which is a form of energy by humans for a long time. Gasification is
a process of conversion of solid carbonaceous fuel into combustible
gas by partial combustion. Many gasifier models have various
operating conditions; the parameters kept in each model are different.
This study applied experimental data, which has three inputs, which
are; biomass consumption, temperature at combustion zone and ash
discharge rate. One output is gas flow rate. For this paper, neural
network was used to identify the gasifier system suitable for the
experimental data. In the result,neural networkis usable to attain the
answer.
Abstract: The aim of this research is to use artificial neural networks computing technology for estimating the net heating value (NHV) of crude oil by its Properties. The approach is based on training the neural network simulator uses back-propagation as the learning algorithm for a predefined range of analytically generated well test response. The network with 8 neurons in one hidden layer was selected and prediction of this network has been good agreement with experimental data.
Abstract: Load forecasting has always been the essential part of
an efficient power system operation and planning. A novel approach
based on support vector machines is proposed in this paper for annual
power load forecasting. Different kernel functions are selected to
construct a combinatorial algorithm. The performance of the new
model is evaluated with a real-world dataset, and compared with two
neural networks and some traditional forecasting techniques. The
results show that the proposed method exhibits superior performance.
Abstract: Goat milk has an hypoallergenic effects, and allergic
diseases related to abnormal of intestinal flora. Probiotic microorganisms
do exert an activity on the immune system in the skin of
the individual.The purpose of this study are to determine the number
of leukocyte and lymphocyte proliferation in rat supplemented with
fermented goat milk (acidophilus milk and kefir) and sensitized with
dinitrochlorobenzene (DNCB). Female Wistar rats 6-8 weeks olds
were divided into 3 treatment groups. The first group supplemented
goat milk kefir, second group acidophilus goat milk, and third group
as control. During 28-day experiment, on day 15 rat sensitized with
allergen DNCB on the dorsal of the body, and on day 24 was
challenged with DNCB on the ear. Sampling of blood and tissue of
intestinal Peyer'patch (PP) were performed on day 14 (before DNCB
sensitized) and on day 28 (after DNCB sensitized). The results
showed the number of neutrophils in rats supplemented with
acidophilus milk was higher (P
Abstract: This paper presents a means for reducing the torque
variation during the revolution of a vertical-axis water turbine
(VAWaterT) by increasing the blade number. For this purpose, twodimensional
CFD analyses have been performed on a straight-bladed
Darrieus-type rotor. After describing the computational model and
the relative validation procedure, a complete campaign of
simulations, based on full RANS unsteady calculations, is proposed
for a three, four and five-bladed rotor architectures, characterized by
a NACA 0025 airfoil. For each proposed rotor configuration, flow
field characteristics are investigated at several values of tip speed
ratio, allowing a quantification of the influence of blade number on
flow geometric features and dynamic quantities, such as rotor torque
and power. Finally, torque and power curves are compared for the
three analyzed architectures, achieving a quantification of the effect
of blade number on overall rotor performance.