支持数据增强的混合普通微分方程模型,用于市政废水处理中的酸盐动态
Guang-Yao Zhao1, Hiroaki Furumai2, Masafumi Fujita3
1Graduate School of Science and Engineering, Ibaraki University, Hitachi, Ibaraki 316-8511, Japan.
Bioresource technology
|August 8, 2024
概括
结合普通微分方程和人工神经网络的新混合模型准确模拟了生物去除. 这种方法通过结合关键的生物数据来增强酸盐估计,改善废水处理洞察力.
科学领域:
- 环境工程 环境工程
- 生物技术是生物技术.
- 计算科学 计算科学
背景情况:
- 生物去除 (BPR) 对于废水处理至关重要.
- 现有的模型,如激活泥模型号. 2d (ASM2d) 在准确模拟BPR方面存在局限性.
- 酸盐积聚生物 (PAO) 在BPR中起着关键作用,但很难建模.
研究的目的:
- 开发一种新的并行混合模型,将ASM2d与人工神经网络 (ANN) 集成,以实现精确的BPR模拟.
- 改进无氧/有氧测序批量反应器中的酸盐估计.
- 提高废水处理模型的解释性和准确性.
主要方法:
- 开发了一个平行混合普通微分方程 (ODE) 模型,集成ASM2d和ANN.
- 纳入支持数据,特别是酸盐释放活动,作为ANN的输入.
- 实施了三个具有选择性输出的ANN模型,并将其性能与ASM2d进行了比较.
主要成果:
- 这三种混合型号在酸盐估计中都超过了独立的ASM2d.
- 将四个关键BPR变量纳入ANN的模型实现了最高的性能 (R2 = 0.93).
- 通过准确地表示PAO动态,ANN有效地弥补了ASM2d的限制.
结论:
- 新的并行混合ODE模型准确地模拟了BPR,具有高可解释性.
- 结合支持的生物数据显著提高了酸盐估计的准确性.
- 这种混合方法为优化废水处理过程提供了一个有前途的工具.
关键词:
活性污泥模型编号 活动污泥模型编号 2d 2d 2d 2d 2d 2d 2d 2d 2d 2d 2d 2d 2d 2d 2d人工神经网络的人工神经网络生物酸盐去除 生物酸盐去除混合动力模型 混合动力模型物理意义 物理意义更多相关视频
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