城市污水处理厂的间接温室气体排放来源:整合机器学习模型以丰富的观测来弥补稀缺的参数
Yujun Huang1, Yifan Xie1, Yipeng Wu1
1School of Environment, Tsinghua University, 1 Qinghuayuan, Beijing 100084, China.
Environmental science & technology
|November 17, 2023
概括
机器学习模型准确地预测废水处理厂的电力消耗和污泥产量. 这些模型有助于管理温室气体排放,并揭示影响效率的因素,如处理量和设备年龄.
科学领域:
- 环境工程 环境工程
- 机器学习应用 机器学习应用
- 废水处理技术 废水处理技术
背景情况:
- 电力消耗和污泥产量是废水处理厂 (WWTP) 中的主要间接温室气体排放来源.
- 准确预测这些副产品对于有效的政策决策至关重要关于WWTP技术.
- 现有的模型在表示间变量关系方面存在局限性,或需要大量的操作数据.
研究的目的:
- 整合梯度增强树 (GBT) 和深度学习 (DL) 模型,精确预测中国水电工厂的电力消耗强度 (ECI) 和污泥产量 (SY).
- 通过利用机器学习来克服传统模型的局限性,以获得数据驱动的见解.
- 分析影响ECI和SY的因素及其相互依存关系.
主要方法:
- 整合了两个机器学习模型:渐变增强树 (GBT) 和深度学习 (DL).
- 应用这些模型来预测ECI和SY,使用来自WWTP的操作和设计参数.
- 对挖掘大型数据集中的模型性能进行分析,以弥补缺失的操作参数.
主要成果:
- GBT和DL模型有效地预测ECI和SY,补偿数据限制.
- 较低的ECI和SY与较高的处理废水量,宽松的废水标准和较新的设备相关.
- 在无氧-无氧-有氧过程中,ECI和SY表现出基于影响生化氧需求水平的独特模式.
结论:
- 管理ECI和SY需要理解生化反应中的合关系,而不是单独处理变量.
- 集成的ML模型提供了一个全面的方法,重点关注操作和设计参数.
- 通过这些模型实现水污染和温室气体排放管理的协同作用,可能会揭示潜在的经济不平等.
相关概念视频
01:23Linear Approximations
For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...
01:30Microbial Wastewater Treatment
Microbial communities in aquatic ecosystems play a key role in the natural breakdown of contaminants introduced through domestic and industrial effluents. Acting as biological catalysts, these microbes change and mineralize a wide range of organic and inorganic pollutants under different redox conditions.In oxygen-rich surface waters, aerobic heterotrophs lead organic matter breakdown, using oxygen as the terminal electron acceptor to efficiently oxidize substrates to carbon dioxide and water.
01:27Microbes and Climate Change
Microorganisms are pivotal agents in Earth's biogeochemical cycles, significantly influencing climate dynamics through their metabolic activities. These microbes modulate the levels of key greenhouse gases by both contributing to and helping mitigate climate change.Microbial Contributions to Greenhouse Gas EmissionsRising global temperatures accelerate microbial metabolism, which, in turn, speeds up the decomposition of organic matter. This process releases carbon dioxide (CO₂) through...