Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

25
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
25
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

13
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
13

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Design of Experiments for Dynamic Test Runs in Solvent-Based CO<sub>2</sub> Capture Pilot Plants.

Industrial & engineering chemistry research·2025
Same author

Reducing Hexavalent Chromium to Trivalent Chromium with Zero Chemical Footprint: Borohydride Exchange Resin and a Polymer-Supported Base.

ACS omega·2019
Same author

Binding of porphyrins to tubulin heterodimers.

Biomacromolecules·2007
查看所有相关文章

相关实验视频

Updated: May 10, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.3K

混合整数线性编程配方与嵌入式机器学习替代品,用于设计化学过程家族.

Georgia Stinchfield1, Natali Khalife1, Bashar L Ammari1

  • 1Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

Industrial & engineering chemistry research
|April 28, 2025
PubMed
概括

机器学习模型创建高效的过程家族设计,降低新能源和工艺技术的制造成本和部署时间. 这种方法解决了以前通过传统方法无法解决的复杂问题.

更多相关视频

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

9.9K

相关实验视频

Last Updated: May 10, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.3K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

9.9K

科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 工艺系统工程 工艺系统工程
  • 机器学习应用 机器学习应用

背景情况:

  • 传统和模块化设计方法与新能源和工艺技术的快速部署作斗争.
  • 流程家族设计为开发可适应系统提供了替代策略.
  • 过程设计中的大规模优化问题往往是计算难以解决的.

研究的目的:

  • 开发和演示基于机器学习 (ML) 代理的流程家族设计方法.
  • 降低新工艺技术的制造成本和部署时间表.
  • 为了克服复杂的过程设计问题的传统优化方法的局限性.

主要方法:

  • 制定了过程家族设计作为通用断层程序 (GDP).
  • 将GDP转化为一个大型混合整数非线性编程 (MINLP) 问题.
  • 开发了使用优化和机器学习工具包 (OMLT) 的零碎线性ML替代品来近似非线性.
  • 使用ML替代品生成了一种高效的混合整数线性编程 (MILP) 公式.

主要成果:

  • 成功地应用了ML替代方法来设计碳捕获和水淡化系统的家族.
  • 为复杂的设计问题在合理的计算时间内获得最佳解决方案.
  • 获得的解决方案质量与之前报告的方法相比.

结论:

  • 机器学习替代模型提供了一种有效的方法来解决大规模的过程家族设计问题.
  • 这种方法使新能源和工艺技术的部署更快,更具成本效益.
  • 该方法适用于具有不同操作条件的多种工艺系统.