使用机器学习工具对西普罗夫洛克萨的生物降解:动力学和建模
Neha Kamal1, Amal Krishna Saha2, Ekta Singh1
1Aquatic Toxicology Laboratory, Environmental Toxicology Group, Food, Drug & Chemical, Environment and Systems, Toxicology (FEST) Division, Council of Scientific and Industrial Research-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India.
这项研究探讨了常见的抗生素污染物西普洛素的微生物生物降解. 优化的细菌联盟实现了95.5%的降解,为抗生素废水整治提供了可持续的解决方案.
科学领域:
- 环境微生物学环境微生物学
- 生物技术是生物技术.
- 污水处理 污水处理 污水处理
背景情况:
- 抗生素污染带来了重大的生态和健康风险,推动了多药耐药细菌的出现.
- 传统的废水处理方法在去除新出现的有机污染物方面面临成本和有效性的限制.
- 生物降解为环境修复提供了一个可持续的,具有成本效益的替代方案.
研究的目的:
- 通过微生物联盟来研究普洛素的生物降解.
- 通过代谢途径确定西普罗夫洛克萨生物降解的最佳条件.
- 评估机器学习工具在优化生物降解过程中的效率.
主要方法:
- 用于生物降解西普洛素的微生物联盟.
- 采用人工神经网络 (ANN) 和响应表面方法 (RSM) 与盒子-Behnken设计 (BBD) 进行参数优化.
- 在优化培养条件下评估生物降解效率.
主要成果:
- 在最佳条件下,设计的细菌联盟在最佳条件下实现了95.5%的西普洛素降解.
- 模型预测与实验结果非常相匹配:95.20% (RSM) 和94.53% (ANN).
- 经过优化生物降解的过程,证明了在去除西普罗夫洛克萨的高效率.
结论:
- 微生物生物降解是一种高效的方法,可以从水性介质中去除西普罗夫洛克萨.
- 机器学习工具成功优化了生物降解参数,提高了效率.
- 这种方法为用抗生素污染的废水处理提供了更绿色,更可持续的解决方案.
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