Automated Calibration of a Poly(oxymethylene) Dimethyl Ether Oxidation Mechanism Using the Knowledge Graph Technology

In
this paper, we develop a knowledge graph-based framework for
the automated calibration of combustion reaction mechanisms and demonstrate
its effectiveness on a case study of poly­(oxymethylene)­dimethyl ether
(PODEn, where n = 3)
oxidation. We develop an ontological representation for combustion
experiments, OntoChemExp, that allows for the semantic enrichment
of experiments within the J-Park simulator (JPS, theworldavatar.com), an
existing cross-domain knowledge graph. OntoChemExp is fully capable
of supporting experimental results in the Process Informatics Model
(PrIMe) database. Following this, a set of software agents are developed
to perform experimental result retrieval, sensitivity analysis, and
calibration tasks. The sensitivity analysis agent is used for both
generic sensitivity analyses and reaction selection for subsequent
calibration. The calibration process is performed as a sampling task,
followed by an optimization task. The agents are designed for use
with generic models but are demonstrated with ignition delay time
and laminar flame speed simulations. We find that calibration times
are reduced, while accuracy is increased compared to manual calibration,
achieving a 79% decrease in the objective function value, as defined
in this study. Further, we demonstrate how this workflow is implemented
as an extension of the JPS.

本研究构建了一种基于知识图谱（knowledge graph）的燃烧反应机理（combustion reaction mechanisms）自动校准（automated calibration）框架，并以聚甲氧基二甲醚（poly(oxymethylene)dimethyl ether，简称PODEn，其中n=3）的氧化反应为案例验证了该框架的有效性。我们开发了面向燃烧实验的本体表示模型OntoChemExp，该模型可实现现有跨域知识图谱J帕克模拟器（JPS，theworldavatar.com）内实验数据的语义富集。OntoChemExp可完全兼容过程信息学模型（Process Informatics Model，PrIMe）数据库中的实验结果。在此基础上，我们开发了一组软件智能体，用于实现实验结果检索、敏感性分析与校准任务。该敏感性分析智能体既可用于通用敏感性分析，也可用于为后续校准流程选取反应路径。校准流程分为采样任务与优化任务两个阶段。本框架的智能体设计面向通用模型，但本文以点火延迟时间与层流火焰速度模拟场景进行了验证。研究结果表明，相较于人工校准，该方法可缩短校准时长并提升校准精度，目标函数值较本研究定义的基准降低了79%。此外，我们还演示了该工作流如何作为JPS的扩展模块得以实现。