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在工业废水处理厂使用人工神经网络预测COD

Özgül Çimen Mesutoğlu¹, Oğuzhan Gök²

¹Environmental Engineering Department, Aksaray University, Aksaray, Turkey. ozgulcimen@gmail.com.

Scientific reports

|June 14, 2024

概括

人工神经网络 (ANN) 准确地建模了废水处理效率,高精度地预测了化学氧气需求 (COD). 这项研究为优化工业废水管理提供了一个强大的工具.

科学领域:

环境工程环境工程
计算智能是一种计算智能.
水处理技术水处理技术

背景情况:

工业废水处理厂需要有效的监测和管理,以减轻环境污染.
化学氧气需求 (COD) 是废水质量和处理效率的关键指标.
人工神经网络 (ANN) 为复杂的环境建模提供了先进的计算能力.

研究的目的:

开发和验证人工神经网络 (ANN) 模型,用于预测阿克萨雷工业废水处理厂的性能.
根据化学氧气需求 (COD) 输出来评估处理效率.
为准确的性能预测确定最佳的ANN架构.

主要方法:

在9个月的时间内从阿克萨雷废水处理厂收集数据.
主要组件分析 (PCA) 用于输入特征选择的应用.
使用MATLAB.开发和评估Feedforward反向传播的人工神经网络 (FFBANN).
使用平均平方误差 (MSE),平均绝对误差 (MAE) 和相关系数 (R2) 的性能评估.

主要成果:

该ANN模型显示了对废水处理厂性能具有很高的预测能力.
最优的ANN架构在预测COD输出时实现了高达0.9997的相关系数 (R2).
该模型有效地比较了观察到的和预测的输出变量,表明了强大的预测准确性.

结论:

ANNs提供了一个强大的,高度准确的方法来建模和预测工业废水处理厂的性能.
开发的模型可用于实时监控和优化处理过程,特别是用于减少COD.
这种方法为提高废水管理系统的效率和有效性提供了巨大的潜力.

关键词:

人工神经网络的人工神经网络化学氧气需求化学氧气需求主要组件分析的主要组件分析.废水处理厂的废水处理厂.

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