Sandstorms (dust storms) are considered the most events which cause destructive and costly damages in lots of desert regions. These sandstorms may be a reason of huge disasters or damages on environmental as well as health aspects. The aim of this paper is to develop a mathematical model for predicting the Dust Storm in Republic of Iraq using Artificial Neural Network (ANN) technique. As a case study, four construction projects in Iraqi cities were selected (Baghdad, Basrah, Samawa, and Nasiriya) in order to identifying and prediction of the sandstorms, which significantly help to reduce the effects of damages. Only one ANN model was built to predict a dust storm. The datas of this model cited from Iraqi Meteorological Organization and Seismology. Four factors were adapted to develop the model (Max. Temperature, Min. Temperature, Rain and Wind), It was found that ANN has the ability to predict the dust storm with a high accuracys off the correlation coefficient (R) which is 90.00%, with a percentage of average accuracy is 89%.
In many industries especially oil companies in Iraq consumed large quantities of water which will produce oil-contaminated water which can cause major pollution in agricultural lands and rivers. The aim of the present work is to enhance the efficiency of dispersed air flotation technique by using highly effective and cost-efficient coagulant to treating gas oil emulsion. The experimental work was carried out using bubble column made of Perspex glass (5cm I.D, 120cm height). A liquid was at depth of 60cm. Different dosage of sawdust +bentonite at ratio 2:1 (0.5+ 0.25; 1+ 0.5 and 2+1) gm and alum at concentration (10,20and30mg/l) at different pH ( 4 and 7) were used to determine optimum dosages of coagulant. Jar test experiment has showed that optimum dosage of (sawdust +bentonite) was (1+0.5gm) and alum concentration was 30 mg/l at pH=4. The present study was conducted to evaluate the effect of various parameters pH (3, 4,7and 9); air flow rate (300, 500, 1000, and 1500 cc/min); initial oil concentration (300 up to 1000 ppm); concentration of Sodium dodecylsulphat surfactants ,SDS (25, 75and 150mg/l); and the effect of the addition coagulant (sawdust + bentonite at ratio 2:1) and alum (30mg/l) in the removal efficiency of oil from wastewater by coagulation –flotation process. The study has showed that the removal efficiency of COD, oil content and turbidity were related to the initial oil concentration; additive concentration of SDS and dosage of coagulants. It was found that the flotation rate increases when using coagulants, the fastest removal rate was obtained when pH 4 and also the higher removal efficiency achieved was for flotation (87%) and (95.7%) sawdust +bentonite; (97%) for alum in coagulation – flotation process.
Providing a clean and high quality drinking water to both rural as well as urban areas is a great challenge by itself, adding to it the large volume requirements of such water at high population areas means a very high cost for such industry because mainly of the cost of expensive commercially available adsorbent used in this process. This led inhabitants of the remote and/or rural areas to use less quality water with all its risks and health challenges. In this study, a locally collected rice husk is tested to be used as an alternative adsorbent to the expensive common commercial ones. Parameters like adsorbent dosage, initial concentration of turbidity, and pH level were tested to investigate their effects on the process. Treatment of synthetic turbid water was done after changing these parameters to measure the effect of each parameter alone and the results showed a set of parameters that can be used to achieve high efficiency of turbidity removal. The study concluded that rice husk can be used as a well cheap alternative adsorbent to reduce the river water turbidity due to its availability and low cost with a decent removal efficiency approaching 95%.
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