Developing a smart and clean technology for bioremediation of antibiotic contamination in arable lands

This study presents a smart technological framework to efficiently remove azithromycin from natural soil resources using bioremediation techniques. The framework consists of several mod -ules, each with different models such as Penicillium Simplicissimum (PS) bioactivity, soft comput-ing models, statistical optimisation, Machine Learning (ML) algorithms, and Decision Tree (DT) control system based on Removal Percentage (RP). The first module involves designing experi-ments using a literature review and the Taguchi Orthogonal design method for cultural condi-tions. The RP is predicted as a function of cultural parameters using Response Surface Methodol-ogy (RSM) and three ML algorithms: Instance-Based K (IBK), KStar, and Locally Weighted Learn-ing (LWL). The sensitivity analysis shows that pH is the most important factor among all parame-ters, including pH, Aeration Intensity (AI), Temperature, Microbial/Food (M/F) ratio, and Reten-tion 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 re-sources are a temperature of 32 & DEG;C, pH of 5.5, M/F ratio of 1.59 mg/g, and AI of 8.59 m3/h. Dur-ing 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%.

Automated summary

This study presents a smart technological framework using bioremediation techniques to efficiently remove azithromycin from natural soil resources. The framework consists of multiple modules with different models, including bioactivity, soft computing, statistical optimization, machine learning algorithms, and a decision tree control system. The study used experiments and ML algorithms to predict the removal percentage of azithromycin based on cultural parameters, and found that pH and aeration intensity were the most significant factors. The optimal biological conditions for azithromycin removal were determined as a temperature of 32°C, pH of 5.5, M/F ratio of 1.59 mg/g, and AI of 8.59 m3/h. The IBK model provided the most accurate prediction of retention time.