Corrosion of steel reinforcement is one of the biggest problems facing all countries in the world like bridges in the beach area and marine constructions which lead to study these problems and apply some economical solutions. According to the high cost of repair for these constructions, were studied the effect of using kind of chemical compounds sodium nitrite(NaNO₂) and sodium silicate(Na₂SiO₃) as corrosion inhibitors admixture for steel bars that immersed partially in electrolyte solution (water + sodium chloride in 3% conc.) (Approximately similar to the concentration of salt in sea water). The two inhibitors above added each one to the electrolyte solution at concentrations (0.5%, 1% and 2%) for both

The ability to inhibit corrosion of low carbon steel in a salt solution (3.5%NaCl) has been checked with three real expired drugs (Cloxacillin, Amoxicillin, Ceflaxin) with variable concentrations (0, 250, 500, 750) mg/L were examined in the weight loss. The inhibition efficiency of the Cloxacillin 750 mg/L showed the highest value (82.8125 %) and the best inhibitor of the rest of the antibiotics. The different concentrations of Cloxacillin drug (0, 250, 500, 750) mg/L and temperature (25, 35, 45, 55) ^oC were studied as variables with potentiodynamic polarization, Scanning Electron Microscopy (SEM) for surface morphology and electrochemical impedance spectroscopy (EIS) depending on current values and the resistance of charge to

ABSTRACT

         The effect of adding raw bacteriocin produced by Lactobacillus bulgaricus to cheese curd at an amount of (5 and 10 and 15) mL/kg cheese as a biological preservative to prolong the shelf life of soft cheese, in addition to the control treatment, knowing that each 1 mL of bacteriocin filter contains 15 units/ mL of bacteriocin. The results of the physicochemical, microbial and sensory tests for cheese stored at refrigerator temperature for a period (zero) to (21) d of adding bacteriocin showed the superiority of the treatment of cheese added to 15 mL/kg cheese of bacteriocin over the rest of the other treatments during the storage period, wh

Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

Rutting is a crucial concern impacting asphalt concrete pavements’ stability and long-term performance, negatively affecting vehicle drivers’ comfort and safety. This research aims to evaluate the permanent deformation of pavement under different traffic and environmental conditions using an Artificial Neural Network (ANN) prediction model. The model was built based on the outcomes of an experimental uniaxial repeated loading test of 306 cylindrical specimens. Twelve independent variables representing the materials’ properties, mix design parameters, loading settings, and environmental conditions were implemented in the model, resulting in a total of 3214 data points. The network accomplished high prediction accuracy with an R

ABSTRACT: Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of applied theories or predicted formulas deduced analytically from previous studies were feasible only for a particular testing environment and do not match other field or laboratory datasets. However, the performance of such techniques depends largely on input parameters that really affect the target output and missing of any parameter can lead to inaccurate results and give a false indicator. In the current study, data were collected from previous rel

The objective of the present study is to determine the effect of Kaolin as a fuel oil additive to minimize the fireside corrosion of superheater boiler tubes of ASTM designation (A213-T22) by increasing the melting point of the formed slag on the outside tubes surface, through the formation of new compounds with protective properties to the metal surface. The study included measuring corrosion rates at different temperatures with and without additive use with various periods of time, through crucible test method and weight loss technique.

      A mathematical model represents the relation between corrosion rate and the studied variables, is obtained using statistical regression analysis. Using this model,