Al-Qadisiyah Journal for Engineering Sciences

This paper deals with the performance analysis of three phase Induction Motor
(IM) with Direct Torque Control based Space Vector Modulation (DTC-SVM). The DTC-SVM
scheme is a kind of high-performance control of IM drives to improve the ripples of torque
and flux in steady state, which one drawback of conventional DTC. DTC-SVM has three
Proportional-Integral (PI) controllers, one used as the PI speed controller and other PI flux
controller and PI torque controller, which are utilized to produce the stator voltage
references (???????????? and ????????????) respectively. To improve the performance of motor we used the
Self-Tuning Fuzzy PI (STFPI) controller and Adaptive Neuro Fuzzy Inference System
(ANFIS) controller instead of PI controllers in DTC-SVM. The simulation results carried out
using MATLAB/Simulink software package show that the response of speed motor with
ANFIS is improved, more robustness, and reaches to the reference value faster than a
conventional PI controller and STFPI controller and also reduced the torque ripple and less
steady state error.

In today\'s complex environment, a high responding ability represents a core for each organization
to survive in a competitive environment. To grip your position in intense competition market, the organization
must design high efficiency inventory system that has the ability to respond to changes in demand and at the
same time reduce holding cost of accommodation to the lowest possible value by controlling inventory
drivers such as safety stock level (SS). The traditional approaches of safety stock are limited to deal with
dynamic behavior of market. Advanced approaches based on soft computing allow the dynamic updating of
SS level. In this paper, a highly advanced dynamic fuzzy logic (DFL) has been suggested as an innovation
step to identify safety stock level in dairy industries with objective of minimizing total cost and meet with
customer requirements. The proposed approach consists of three main steps firstly, identifying demand
uncertainty conditions by applying fuzzy logic steps embedded by identifying dynamic (N) factor which
represents the increasing level in demand in period time. Secondly, identifying of raw material availability
conditions by applying fuzzy logic steps, and finally, identification of inventory on hand conditions by
applying fuzzy logic steps. It is necessary to identify the level of SS dynamically in fuzzy logic as an output
embedded with identifying of period specification concept which describes states of demand in a specific
period in which the demand is high, medium, or low which leads to identify maximum values of universe of
discourse of output (safety stock). Here Matlab program was used. The provided solution demonstrates the
proposed model validity. There has been a significant reduction in safety stock level ranging from (7-98)%
depending on product type and period specification with a reduction also in holding cost, while keeping the
requirements fulfillment of customers demand

In demand to the industrial needs to produce Cu-Al alloys with controlled chemical
compositions. The present research focuses on the production of Cu-Al alloys with different
aluminum additions of 1.9, 3.6, 5.8 and 7.7 wt. % by stir casting method in argon atmosphere. Then,
homogenized treatment was done for these alloys at 850 oC in different periods of soaking time of 2,
4, and 6 hrs to determine the best conditions for increasing the mechanical properties. This is
carried out in the α phase region. The microstructure of the samples is analyzed by using Optical
and Scanning Electron Microscopy (SEM) with (EDS). The results showed the mechanical
properties of copper were increased with increasing in Aluminum content. The microstructure of the
castings consist of the dendiritic structure, columinar grains and segregation. The results showed
that after homogenization, the microstructure changed to fine grains, elimination of the columnar
segregation and the clusters disappeared. The XRD results show that the phases are α (Cu-rich)
phase as highest peaks and the small amount of CuAl2 phase. The best Aluminum content value
was 7.7 wt % and the soaking time 4 hrs were given the high hardness 204 Hv and the highest
ultimate tensile stress value of 383.8 Mpa

Unsupported MoS2 catalysts were synthesized for the hydrodesulfurization
(HDS) of real feed gas oil using different temperatures and pressures. Hydrothermal
method was utilized to prepare by using molybdenum trioxide and sodium sulfide. The
characterization of the catalyst was identified by XRD, SEM, and BET techniques. It was
found that BET surface and pore volume were positively affected by pressure and
temperature that could improve the activity of MoS2. Kinetic analysis showed that HDS
reaction over MoS2 follow pseudo-first order kinetics. Experimental results revealed that
the HDS activity of the unsupported MoS2 catalyst was better than supported CoMo/Al2O3
catalyst under the same operating conditions.

Clathrate hydrates are complex molecules formed by the contact between
water and gas at high pressure and low temperatures. One of the important aims of
gas hydrate technology is to enhance the formation of hydrate and reduction the
induction time for clathrate formation. Effect of the different promoters (nonionic
surfactant Tween80) on R-134a gas hydrate formation has been investigated in the
isochoric system experiment. It is noted that the surfactants have a significant effect
of increasing the speed of nucleation as well as improving the growth of hydrate. In
the experiment, the effect of Tween80 surfactant was studied to determine its effect
on the composition and cooling capacity of 134 hydrates. From kinetic models,
many objective functions such as the amount of gas consumed (∆n), the growth rate
(r (t)), the apparent rate constant (Kapp) and conversion of the water to hydrate were
obtined. The improvement of hydrate growth is marked by the addition of
surfactants, which in turn increase the solubility of hydrate gas and reduce the
contact angle. In addition, surfactants positively the contact with the gas through
the increase of the gas diffusion in the water and this increases the rate of formation
of hydrate. Best value achieved at a surfactant concentration of 100 ppm with
maximum gas consumed amount (0.16 mol) and less indction time 32 min

Adsorption and desorption kinetic curves for equimolar hydrogen – methane
mixture on molecular sieve type 5A were experimentally obtained for pressure range
0.122 – 3.546 MPa.The linear driving force rate expression model was used to simulate
the dynamic of adsorption and desorption in adiabatic fixed bed adsorber. The model
takes into account the interference effects for non-linear isotherms and non-isothermal
system. The equations were solved by backward finite difference method with a fixed
gridding technique. The individual mass transfer parameters were obtained by matching
the theoretical with the experimental data and found to be equal to 8.510 s
-1 and 0.783 s
-1
for hydrogen and methane, respectively.The predicted effluent histories were shown to be
in close agreement with the experimental data for the system. The lowest relative capacity
of the bed for methane was almost approximately 95% of that predicted equilibrium
capacity. The predicted temperature profiles tracked the experimental temperature data
points, but with higher values. Furthermore, the maximum temperature increasing was
observed for the adsorption of methane onto 5A molecular sieve at 35 atmospheres and
was recorded as 44 K.