在喷出床生物反应器中进行脱的ANN模型的洞察机制
Keshava Joshi1, Lokeshwari Navalgund2, Nabisab Mujawar Mubarak3,4,5
1Department of Chemical Engineering, SDM College of Engineering and Technology, (Visvesvaraya Technological University Belagavi), Dharwad, Karnataka, 580 002, India.
Scientific reports
|July 14, 2025
概括
这项研究使用人工神经网络 (ANN) 来建模废水中酸盐的去除. 该ANN模型准确预测了脱率,超过了这种复杂的环境过程的传统多重回归分析.
科学领域:
- 环境科学 环境科学
- 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:
- 废水中的酸盐污染对健康和环境构成重大风险.
- 有效的酸盐去除技术对于水质管理至关重要.
- 在废水处理中模拟复杂的生物和化学过程具有挑战性.
研究的目的:
- 为了研究Pseudomonas注射器的脱率,使用固定颗粒活性炭 (GAC) 在一个导管喷出床反应器中.
- 开发和评估人工神经网络 (ANN) 模型,用于预测废水中的酸盐度.
- 将ANN模型的性能与多重回归分析 (MRA) 模型进行比较.
主要方法:
- 使用Pseudomonas syringae的分离物进行脱.
- 用于固定化的颗粒活性炭 (GAC) 在导管喷出床反应堆中.
- 开发了一个三层前进后向传播的人工神经网络 (ANN),有3个输入,9个隐藏和1个输出神经元.
主要成果:
- 该ANN模型实现了高相关系数98.8%和低RMSE9.25×10−5%.
- 与MRA模型相比,ANN模型在所有指标 (相关性,RMSE,平均绝对误差,RSS) 上都表现出优异的性能.
- 该ANN模型有效地捕捉了脱过程的非线性动态.
结论:
- 人工神经网络 (ANN) 对模拟复杂的废水脱过程非常有效.
- 开发的ANN模型提供了比传统MRA更准确的废水酸盐度预测.
- 这种方法为优化废水处理系统提供了灵活而强大的工具.
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