In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and introduced. Optimal results showed that the optimum viscosity and thermal conductivity occurs at maximum temperature.
The research aims to derive the efficient industrial plans for Al – shaheed public company under risk by using Target MOTAD as a linear alternative model for the quadratic programming models.
The results showed that there had been a sort of (trade- off) between risk and the expected gross margins. And if the studied company strives to get high gross margin, it should tolerate risk and vice versa. So the management of Al- Shaheed Company to be invited to apply the suitable procedures in the production process, in order to get efficient plans that improves it's performance .
A spectrophotometric- reverse flow injection analysis (rFIA) method has been proposed for the determination of Nitrazepam (NIT) in pure and pharmaceutical preparations. The method is based upon the coupling reaction of NIT with a new reagent O-Coumaric acid (OCA) in the presence of sodium periodate in an aqueous solution. The blue color product was measured at 632 nm. The variation (chemical and physical parameters) related with reverse flow system were estimated. The linearity was over the range 15 - 450 µg/mL of NIT with detection limits and limit of quantification of 3.425 and 11.417 µg mL-1 NIT,respectively. The sample throughput of 28 samples
... Show MoreDiabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
... Show MoreThe subgrade soil is the foundation plate form of the roadway; it should sustain its structural characteristics throughout the design life of the roadway with minimal requirements for maintenance. When Gypseous soil is implemented in the construction of subgrade, problems regarding collapsibility and poor structural capacity usually occur when the subgrade came in touch with excess water. Asphalt stabilization could furnish a proper solution to such problems. In this investigation, an attempt has been made to monitor the variations in compressibility characteristics of asphalt stabilized subgrade soil subjected to 30 cycles of (freezing-thawing) and (heating-cooling). Data have been observed after each 10 cycles, and compared with that of r
... Show MoreSorption is a key factor in removal of organic and inorganic contaminants from their aqueous solutions. In this study, we investigated the removal of Xylenol Orange tetrasodium salt (XOTS) from its aqueous solution by Bauxite (BXT) and cationic surfactant hexadecyltrimethyl ammonium bromide modified Bauxite (BXT-HDTMA) in batch experiments. The BXT and BXT-HDTMA were characterized using FTIR, and SEM techniques. Adsorption studies were performed at various parameters i.e. temperature, contact time, adsorbent weight, and pH. The modified BXT showed better maximum removal efficiency (98.6% at pH = 9.03) compared to natural Bauxite (75% at pH 2.27), suggesting that BXT-HDTMA is an excellent adsorbent for the removal of XOTS from water. The equ
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