机器学习合微生物的继承解读气体剥离强化硫酸盐的减少硫化的去除
Ting-Ting Zhang1, Ei Myat Mon Oo1, Bi-Long Chen1
1State Key Laboratory of Advanced Environmental Technology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Bioresource technology
|August 20, 2025
概括
机器学习优化了气体剥离以去除硫化 (H2S),增强了硫酸盐的减少. 这项研究模拟了硫酸盐减少率,在最佳条件下实现了高效率.
科学领域:
- 环境工程 环境工程
- 生物技术是生物技术.
- 化学工程是化学工程的重要组成部分.
背景情况:
- 在酸性生成过程中去除硫化 (H2S) 对于增强硫酸盐减少至关重要.
- 气体剥离是去除H2S的关键过程,但其优化是复杂的.
研究的目的:
- 通过气体剥离去除H2S后,使用机器学习来建模和优化硫酸盐减少率.
- 为了比较响应表面方法 (RSM) 和人工神经网络 (ANN) 对此优化的有效性.
主要方法:
- 响应表面方法 (RSM) 用于基于COD/SO4{2-}比率,温度和气体剥离流量来建模硫酸盐减少率.
- 人工神经网络 (ANN) 与遗传算法相结合,用于建模和优化.
- 进行基因表达分析以了解气体剥离对减少硫酸盐的细菌的影响.
主要成果:
- 在特定条件下,RSM建模实现了最大硫酸盐减少率为92.30% (COD/SO42-) 比率为8.84,33.81°C,0.02 L/min/L气体剥离流量).
- 在优化条件下 (COD/SO4(2-) 比率为8.98,30.07°C,0.01 L/min/L气体剥离流量) 的情况下,ANN建模产生了最大硫酸盐减少率为91.93%,误差较低 (2.19%).
- 优化的低COD/SO4(2-) 比率和气体剥离流量使Syntrophobacter优于其他减少硫酸盐的细菌,并增加了对同化硫酸盐减少的基因表达.
结论:
- 机器学习方法,特别是ANN,是优化气体剥离过程以提高硫酸盐减少的有效工具.
- 优化气体剥离对于提高硫酸盐减少效率和促进资源回收至关重要.
- 该研究提供了对微生物社区转移和由气体剥离参数影响的代谢途径的见解.
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