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酸盐氧化细菌抑制的智能FA/FNA交替策略:数据驱动的预测和过程控制

Chenlong Bu¹, Siyuan Wang², Bohan Yu³

¹Sino-European School of Technology, Shanghai University, 333 Nanchen Road, Shanghai, 200444, China; School of Environmental and Chemical Engineering, Shanghai University, 333 Nanchen Road, Shanghai, 200444, China.

Journal of environmental management

|May 14, 2025

概括

一个智能控制系统自动化了自由氨和自由酸处理,以抑制部分化中的酸氧化细菌. 该系统提高了废水处理过程的稳定性和效率.

科学领域:

环境工程环境工程
废水处理技术废水处理技术
生物技术是生物技术.

背景情况:

部分化 (PN) 对于废水处理至关重要,但抑制酸盐氧化细菌 (NOB) 传统上依赖于手动监测.
手动评估酸盐积累率 (NAR) 阻碍了自由氨 (FA) 和自由酸 (FNA) 抑制策略的自动化.

研究的目的:

开发一个智能控制系统,实现FA/FNA抑制策略的自动实时切换.
为了实现实时控制抑制度,以在PN过程中稳定地抑制NOB.

主要方法:

对比支持向量机 (SVM) 和多层感知器 (MLP) 用于分类和回归任务.
利用基于NAR值和MLP的SVM进行战略交替,通过化物预测实时控制FNA度.
从连续流PN反应堆的180天实验数据集上训练和测试模型.

主要成果:

优化的SVM实现了91.67%的分类准确度;MLP显示化物预测的R2为0.96和RMSE为53.16.
实时控制显示SVM分类准确度为97.5%,MLP R2为0.83 (RMSE 0.04) 对于FNA度.
该系统保持了NAR高于95.55%,氨去除效率高于52.99%.

结论:

智能控制系统成功地自动化FA/FNA交替抑制,并精确控制FNA度.
这种自动化简化了操作,确保了稳定高效的PN过程性能.
开发的系统促进PN/A工艺在废水处理中的更广泛应用.

关键词:

免费的氨是什么意思自由的酸是一种自由的酸.机器学习是机器学习.多层感知器多层感知器部分化部分化支持矢量机器的支持矢量机器.

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