Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
Research Center for Health Sciences, Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
Sci Rep. 2022 Nov 16;12(1):19662. doi: 10.1038/s41598-022-21996-6.
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.
柴油是已知的主要石油产品之一，可能会污染水和土壤。石油碳氢化合物造成的土壤污染对环境造成了重大影响，特别是在中东地区。在这项研究中，使用人工神经网络 (ANN) 方法，使用两种纯培养的不动杆菌和产碱杆菌以及两种细菌的混合培养物对十六烷从土壤中的去除进行了建模和优化。然后，根据均方误差 (MSE) 和相关系数 (R)，提出了最佳 ANN 结构，用于纯培养的不动杆菌和产碱杆菌以及它们的混合培养物。结果表明，在产碱杆菌、不动杆菌和两种细菌混合培养物的 consortium 中，ANN 预测的实际数据与实际数据之间的相关性 (R2) 分别为 0.50、0.47 和 0.63。尽管 ANN 预测的实验数据与实际数据之间的相关性较低，但 consortium 的相关系数和 ANN 的精度更高。因此，ANN 对 Ochromobater 和 Acintobacter 混合培养物去除十六烷具有良好的预测精度。
