Biodegradation of Acenaphthene Using Two Different Isolated Bacteria: Comparative Analysis and Optimization Using Artificial Neural Network

Abstract

Polycyclic Aromatic Hydrocarbons are one of the toxic pollutants in nature having both carcinogenic and mutagenic effects and accumulate in the environment from industrial wastes, natural sources like volcanoes and human activities. Acenaphthene degradation efficiency of two isolated micro-organism Bacillus sp. PD5 and Halomonas sp.PD4; gram positive and gram negative bacteria were evaluated in batch experiments. The study was performed by different parameters like inoculum volume, pH, salinity, temperature, agitation speed and a comparative analysis was carried out. The objective of this study was to compare the efficiency of the isolated organisms to degrade acenaphthene under different experimental conditions. The maximum acenaphthene degradation was found to be around 82% by Bacillus sp. PD5 and 65.25% in case of Halomonas sp.PD4. Artificial neural network was applied to estimate the maximum degradation of acenaphthene by both microorganisms and it was observed that the developed ANN model significantly satisfied the experimental results.

Using Medium-Resolution Remote Sensing Satellite Images to Evaluate Recent Changes and Future Development Trends of Mangrove Forests on Hainan Island, China

“…However, the spectral characteristics of some sparse vegetation are similar to those of mangroves, leading to errors in the identification results. Subsequently, researchers began to investigate using newer machine learning algorithms, including artificial neural networks [21,22], maximum likelihood classifier [23], Random Forest [24], and XGBoost [25]. Compared with traditional algorithms, machine learning classification techniques are more economical and efficient in detecting mangrove cover areas over large areas.…”