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Updated: Feb 9, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

一个多替代模拟优化框架用于设计和处理系统

Chaoqi Wang¹, Zhi Dou¹, Ning Chen²

¹School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China.

Journal of contaminant hydrology

|February 7, 2026

概括

使用和处理 (P&T) 优化地下水清洁涉及代用模型. 结合DNN,Kriging和SVR等多个模型,可以提高整治方案的效率和成本效益.

科学领域:

环境工程环境工程
水文地质学水文地质学
计算科学计算科学

背景情况:

和处理 (P&T) 是一个关键的地下水整治技术.
优化输送运输运营 (井口位置,速率) 对于提高效率和降低成本至关重要.
替代模型与优化算法相结合,是P&T方案制定的新兴工具.

研究的目的:

为了比较五个替代模型 (Kriging,多项式插值,SVR,RF,DNN) 的性能,用于预测P&T修复中的污染物去除.
探索结合多个代孕模型的潜力,以提高P&T优化.
开发和评估用于地下水整治的多替代优化框架.

主要方法:

评估了五个替代模型:Kriging,多项式插值,支持向量回归 (SVR),随机森林 (RF) 和深度神经网络 (DNN).
评估了各种P&T方案中污染物清除效率的模型预测准确性.
开发了一个多替代品优化框架,将所有五种模型与遗传算法集成在一起.

主要成果:

深度神经网络 (DNN) 显示了最高的整体预测准确性,但没有一个模型在所有情况下都始终优越.
结合多种模型的多替代框架利用了互补的优势.
与非优化方案 (19.2-21.7%) 相比,优化整治方案实现了更好的污染物去除 (17.5%的残留量).

结论:

结合多个替代模型为优化和处理补救方案提供了显著的好处.
开发的多替代框架为环境管理提供了一个强大的工具.
这项研究推进了基于替代品的地下水清理优化技术.

关键词:

遗传算法遗传算法遗传算法地下水污染物整治补救地下水污染物整治机器学习机器学习多模型框架多模型框架和处理优化的优化.代理模拟代理模拟

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