相关实验视频

Updated: Jun 12, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

对于废水处理厂的数据驱动水质预测

Haitham Abdulmohsin Afan¹, Wan Hanna Melini Wan Mohtar^2,3, Faidhalrahman Khaleel^4,5

¹Upper Euphrates Basin Developing Center, University of Anbar, Iraq.

|September 23, 2024

概括

GFFR机器学习模型擅长预测废水处理厂 (WWTP) 的水质参数,特别是在使用多步建模时. 这种方法显著提高了导电性和其他参数的预测准确性.

科学领域:

环境工程环境工程
人工智能的人工智能
水质管理水质管理

背景情况:

污水处理厂 (WWTP) 的有效监测和管理对于环境保护至关重要.
精确预测经过处理的水质量对于优化WWTP运营中的能源效率至关重要.

研究的目的:

为了比较四个机器学习模型 (MLP,GFFR,MLP-PCA,RBF) 的性能,用于预测水质参数.
评估两个不同的建模场景:使用WWTP输入/输出的简单方法和结合中间定居者输出的多步方法.
评估模型在处理通过多步建模产生的高维数据的能力.

主要方法:

实现多层感知子 (MLP),通用前回归 (GFFR),MLP与主要组件分析 (MLP-PCA) 和辐射基函数 (RBF) 模型.
应用两个建模场景:直接输入-输出预测和多步预测,利用初级和二级定位器的中间过程数据.
基于相关性准确度 (R) 和预测偏差 (规范化根平均平方误差 - NRMSE,规范化平均绝对误差 - NMAE) 的模型性能评估.

主要成果:

在这两种情景中,GFFR模型表现出优异的性能,特别是在预测导电性的多步方案中 (R=0.893,NRMSE=0.091,NMAE=0.071).
所有模型都在努力准确预测其他水质参数,显示较低的相关性和较高的偏差.
多步建模技术显著提高了所有模型的预测准确性,改进范围从0.2%到157% (平均60%).

结论:

机器学习模型,特别是GFFR,显示出在WWTP中准确预测水质参数的巨大潜力.
多步建模方法,利用中间过程数据,大大提高了人工智能模型的预测能力.
需要进一步的研究,以提高超出导电性的更广泛的水质参数的预测准确度.

关键词:

机器学习是机器学习.神经网络的神经网络的神经网络废水处理厂废水处理厂预测水质水质预测

更多相关视频

Comparison of Scale in a Photosynthetic Reactor System for Algal Remediation of Wastewater

Comparison of Scale in a Photosynthetic Reactor System for Algal Remediation of Wastewater

Published on: March 6, 2017

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

相关概念视频

Factors Affecting Solubility

Factors Affecting Solubility

Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:

Typical Model Studies

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

Design Example: Creating a Hydraulic Model of a Dam Spillway

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...