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1,089 results on '"Al-Ansari, Nadhir"'

133. Thermodynamic and structural investigation of oily wastewater treatment using peach kernel and walnut shell based activated carbon.

Author

El Jery, Atef, Khedher, Khaled Mohamed, Mahmood Salman, Hayder, Al-Ansari, Nadhir, Sammen, Saad Sh., and Scholz, Miklas

Subjects
ACTIVATED carbon, WASTEWATER treatment, PEACH, WALNUT, ADSORPTION isotherms, GASOLINE
Abstract

Despite the many articles about activated carbon with different precursors in adsorption process, no in-depth research has been carried out to understand the causes of the difference in surface adsorption characteristics of activated carbon with different precursors and different activation processes. In this work, the ability of two active carbon adsorbents made of walnut shell and peach kernel by two chemical and physical methods (totally 4 different types of activated carbon) in treatment of oily wastewater including diesel, gasoline, used oil or engine lubricant has been compared. The results show that the chemical activated peach carbon active with 97% hardness has provided the highest hardness and physical activated walnut carbon active has obtained the lowest hardness value (87%). It is also found that peach activated carbon has a higher iodine number than walnut activated carbon, and this amount can be increased using chemical methods; Therefore, the highest amount of Iodine Number is related to Peach activated carbon that is made by chemical method (1230 mg/g), and the lowest amount of iodine number is seen in walnut activated carbon that is made by physical method (1020 mg/g). moreover, the pore diameter of physical activated carbon is lower than chemical activated carbon in all cases. So that the pore diameter of chemical activated peach carbon active is equal to 22.08 μm and the measured pore diameter of physical activated peach carbon active is equal to 20.42 μm. These values for walnut are obtained as 22.74 μm and 21.86 μm, respectively. Furthermore, the temperature and pH effects on the adsorption of different synthesized oily wastewater was studied and it was found that a decrease in adsorption can be seen with an increase in temperature or decreasing the pH value, which can be referred to this fact that the process of adsorption is an exothermic process. Finally, to analyze the compatibility of adsorption isotherms with experimental data and to predict the adsorption process, three different isotherms named Langmuir, Temkin, and Freundlich isotherms were applied and their parameters were correlated. The correlation results show that the Langmuir isotherm had the best correlation in all cases compared to the Freundlich and Temkin isotherms, based on the correlation coefficient, and the calculated R2 values which was greater than 0.99 in all the studied cases. [ABSTRACT FROM AUTHOR]

Published
2024
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134. Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system's performance by artificial neural network.

Author

El Jery, Atef, Salman, Hayder Mahmood, Al-Ansari, Nadhir, Sammen, Saad Sh., Maktoof, Mohammed Abdul Jaleel, and AL-bonsrulah, Hussein A. Z.

Subjects
BIOCHEMICAL oxygen demand, CHEMICAL oxygen demand, WASTEWATER treatment, PETROLEUM industry, DEIONIZATION of water, ENVIRONMENTAL degradation, PETROLEUM refineries
Abstract

The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration. The experimental findings revealed that the most favorable conditions for COD removal were determined to be 24 mA/cm2 for current density, pH 8, a COD concentration of 500 mg/l, an electrode surface area of 25.26 cm2, and a NaCl concentration of 0.5 g/l. Correlation equations were proposed to describe the relationship between COD removal and the aforementioned parameters, and double-factorial models were examined to analyze the impact of COD removal over time. The most favorable outcomes were observed after a reaction time of 20 min. Furthermore, an artificial neural network model was developed based on the experimental data to predict COD removal from wastewater generated by the oil industry. The model exhibited a mean absolute error (MAE) of 1.12% and a coefficient of determination (R2) of 0.99, indicating its high accuracy. These findings suggest that machine learning-based models have the potential to effectively predict COD removal and may even serve as viable alternatives to traditional experimental and numerical techniques. [ABSTRACT FROM AUTHOR]

Published
2023
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