Drinking water is too neccessary for everyone .It must be pure
and healthy.Turbidity is one of the most important problems in
water .It may cause damage for humanbeings . So it must be
controlled. This search aims to determine the suitability of
dosing AL2(SO4)3·18 H2O, ,FeSO4.7H2O with the intention of
reducing turbidity levels to acceptable limits .
In the present study , a series of jar test was conducted to
evaluate the optimum pH, dosage and performance parameters
for coagulants,.We studied the effect of AL2(SO4)3·18 H2O,
,FeSO4.7H2O dosage on reducing of turbidity, The influence of
pH on turbidity reducing , and the effect of slow mixing time
on turbidity .
And turbidity reducing by AL2(SO4)3·18 H2O was removed 96
% of the total turbidity.
And turbiditu reducing by FeSO4.7H2O was removed 98 % of
the total turbidity.
This research was conducted to study the feasibility of using
Alkaline Flooding (AP) to increase the displacement factor from
the (AL- Rasein Field).
At first ,a literature review of the Enhanced Oil Recovery (EOR)
methods in general was conducted ,especially Chemical Methods
,including Alkaline Flooding Methods.
Modelling the relationship between drinking water turbidity and other indicators of water
quality in Al-Sin drinking water purification plant using Dynamic Artificial neural
networks could help in the implementation of the stabilization for the per
formance of the
plant because these neural networks provide efficient tool to deal with the complex,
dynamic and non-linear nature of purification processes. They have the ability to response
to various instant changes in parameters influencing water purification.
In this research, four models of feed-forward back-propagation dynamic neural network
were designed to predict the effluent turbidity from Al-Sin drinking water purification
plant. The models were built based on turbidity, pH and conductivity of raw water data
while the effluent turbidity data were used for verify the performance accuracy of each
network. The results of this research confirm the ability of dynamic neural networks in
modeling and simulating the non-linearity behavior of water turbidity as well as to predict
its values. They can be used in Al-Sin drinking water purification plant in order to achieve
the stabilization of its performance.
This research focused on one of the stages of the conventional treatment of water
in the purification stations, a process of coagulation, which enhanced by using
alternatives to alum; such as Ferric Chloride and Poly Aluminum-chloride (PACl),
whic
h play an important role to reducing the turbidity of drinking water through
the destabilization of colloids, which include organic and inorganic materials in
order to increase the efficiency of sterilization and disposal of the side effects of
sterilization (DBPS) and to minimize the problems of clogged sand filters due to
an increase of the turbidity of water inside it. According to that, three types of
coagulant agents were used for the purpose of comparison with each other to achieve
the best efficiency in the process of reducing water turbidity through a process
of coagulation improved by using (Jar-test). Different concentrations of coagulant
agents of irrigation water were used depending on experiments. The results found
that urinary chloride aluminum gave the highest efficiency in reducing turbidity by
(84, 82 and 81%) according to the addition of concentration for coagulation (20
ppm, 10 ppm and 5ppm), respectively. The reduction rates in turbidity for Ferric
chloride were (79, 78.2 and 78.1% ) by concentrations added, respectively, but for
alum, the reduction rates in turbidity were (58, 56, and, 54%) by concentrations
added, respectively.
In this research electrochemical treatment was used to treat Al-Sin water that feed Banias
thermal station boilers for generate electricity , this recycled pure water minimize
corrosion and wear of turbine, the current of (2A) and (12V) was applied
by
Transformer on metal electrodes of aluminum. The electrochemical treatment efficiency
was studied.
Results revealed that the turbidity decreased for about (98%), and that total dissolved
solids (TDS) and conductivity were reduced by about (61%) and (70.8%) respectively after
one hour of treatment process.
