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Developing a smart and clean technology for bioremediation of antibiotic contamination in arable lands
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Researchers developed a machine learning framework using the fungus Penicillium simplicissimum to break down azithromycin — a common antibiotic — contaminating agricultural soil. The system identified pH as the most critical factor for effective removal, with optimal conditions achieving efficient bioremediation over 100 days, offering a smart biological approach to soil antibiotic pollution.
This study presents a smart technological framework to efficiently remove azithromycin from natural soil resources using bioremediation techniques. The framework consists of several modules, each with different models such as Penicillium Simplicissimum (PS) bioactivity, soft computing models, statistical optimisation, Machine Learning (ML) algorithms, and Decision Tree (DT) control system based on Removal Percentage (RP). The first module involves designing experiments using a literature review and the Taguchi Orthogonal design method for cultural conditions. The RP is predicted as a function of cultural parameters using Response Surface Methodology (RSM) and three ML algorithms: Instance-Based K (IBK), KStar, and Locally Weighted Learning (LWL). The sensitivity analysis shows that pH is the most important factor among all parameters, including pH, Aeration Intensity (AI), Temperature, Microbial/Food (M/F) ratio, and Retention Time (RT), with a p-value of <0.0001. AI is the next most significant parameter, also with a p-value of <0.0001. The optimal biological conditions for removing azithromycin from soil resources are a temperature of 32 °C, pH of 5.5, M/F ratio of 1.59 mg/g, and AI of 8.59 m3/h. During the 100-day bioremediation process, RP was found to be an insignificant factor for more than 25 days, which simplifies the conditions. Among the ML algorithms, the IBK model provided the most accurate prediction of RT, with a correlation coefficient of over 95%.
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