Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method.

Diesel oil is known to be one of the major petroleum products that can pollute water and soil. Soil pollution caused by petroleum hydrocarbons has substantially impacted the environment, especially in the Middle East. In this study, modeling and optimization of hexadecane removal from soil was performed using two pure cultures of Acinetobacter and Acromobacter and consortium culture of both bacterial species using artificial neural network (ANN) method. Then the best ANN structure was proposed based on mean square error (MSE) as well as correlation coefficient (R) for pure cultures of Acinetobacter and Acromobacter as well as their consortium. The results showed that the correlations between the actual data and the data predicted by ANN (R2) in Acromobacter, Acinetobacter and consortium of both cultures were 0.50, 0.47 and 0.63, respectively. Despite the low correlation between the experimental data and the data predicted by the ANN, the correlation coefficient and the precision of ANN for the consortium was higher. As a result, ANN had desirable precision to predict hexadecan removal by the cobsertium culture of Ochromobater and Acintobacter.

Application of sono-electrocoagulation in arsenic removal from aqueous solutions and the related human health risk assessment.

Among the contaminants found in groundwater, arsenic poses a great threat to human health and the ecosystem. Therefore, it is vital to eliminate arsenic from water sources. This study utilizes one of the most efficient and emerging decontamination techniques known as the sono-electrocoagulation method. In recent years, sono-electrocoagulation has attracted many scientists due to its unique features, such as being cost-effective, rapid process, and high efficiency. The required groundwater samples were artificially synthesized in the laboratory, where the anode and cathode were determined to be Fe, Ti/PbO2, and Al, respectively. During the experiment, the impact of pH (5,6,7,8), various initial concentrations (100, 200, 300,400, 500, 600 µg/l), exposure times of 5,10,15,20,25 min, electrode distances of 1.5,2,2.5,3,3.5 cm and different current intensities of 5,10,15,20,25 mA/cm2 were examined. The ambient temperature of the laboratory was kept at 30 and 40 °C. Furthermore, this study showed that the system containing Ti/PbO2 as the anode and Al as the cathode electrodes removed arsenic contamination more effectively in the base environment. The performance of arsenic removal was directly related to current intensity, pH, and time. Nevertheless, time elapse played a negative factor due to the corrosion of the electrodes' surface and the dissolution of floating materials in the solution. With the surge of arsenic concentration from 100 to 300 mg/L, the arsenic removal efficiency increased from 61.9 to 98.5 percent, where the maximum removal efficiency due to the rise of the current intensity was 84.16 percent. The sono-electrocoagulation method reduced the risk of carcinogenic and non-carcinogenicity from 5.15E-03 to 7.73E-05 and 26.71 to 0.40. Accordingly, it was found that a combination of ultrasonic and electrocoagulation processes is a promising approach for arsenic removal.