Chemical Engineering
机器学习代理框架
Machine-Learning Surrogate FrameworkA machine-learning surrogate framework trained on first-principles mechanistic simulations to predict distillate and bottoms compositions for binary tert-butylamine/alcohol systems, eliminating the need for experimental data or commercial tools like ChemCad or AspenPlus. / 基于第一性原理机理模拟训练的机器学习代理框架，用于预测二元叔丁胺/醇体系的馏出液和釜液组成，无需实验数据或ChemCad或AspenPlus等商业工具。
- Knowledge type
- 工艺技术
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-10-29 20:07:13
Sub-knowledge points
Surrogate models for binary tert-butylamine/alcohol systemsCoupled Pareto optimizer for cost trade-offsSystem-agnostic architecture for LLM integration
Higher-order knowledge points
Continuous distillation designPharmaceutical process developmentDigital tooling for retrosynthetic route design
Main parameters and performance indicators
Binary tert-butylamine/alcohol systems: methanol, ethanol, isopropanolMean absolute error: < 0.005 mol/molR2: > 0.999Prediction time: < 1 msCapital-versus-operating-cost trade-off: Pareto optimizer
Open problems
No unresolved problem recorded yet
Related literature
ranked by community starsMachine-Learning-Enabled Design of Lab-Scale Continuous Distillation Systems for Pharmaceutical Process DevelopmentOpen source
Related products and links
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Creation and modification trail
1 recordsRecord title: Machine-Learning Surrogate Framework.
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