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

Abstract

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 (PODE n , 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.

Universal Digital Twin: Integration of national-scale energy systems and climate data

“…Agents parse data from the knowledge graph to interact with the physical world, for example by controlling actuators or displaying data in convenient human-readable forms. Update. Agents query the knowledge graph, calculate new information, for example optimized model parameters (Bai et al, 2021), and update the knowledge graph with the results, either through the modification of existing triples or by the creation of new triples. Such agents may also perform maintenance tasks such as the detection and deletion of invalid triples. …”

Numerical Investigation on the Effect of the Oxymethylene Ether-3 (OME3) Blending Ratio in Premixed Sooting Ethylene Flames

“…For instance, the cetane number of OME 3 to OME 5 is in the range of 72-93 (Lautenschütz et al, 2016;Deutsch et al, 2017) and the flash, boiling and melting points are comparable to those of diesel fuel (Zheng et al, 2013;Omari et al, 2019). These suitable characteristics explain the recent research interest in this specific group of oxygenated fuels in the field of kinetic mechanism development (Sun et al, 2017;He et al, 2018;Cai et al, 2019;Li et al, 2020;Bai et al, 2021;Niu et al, 2021), their application in engine simulations (Lin et al, 2019;Lv et al, 2019;Ren et al, 2019) or for different synthesis methods (Gierlich et al, 2020;Klokic et al, 2020), and the assessment of the overall carbon impact (Mahbub et al, 2019;Bokinge et al, 2020). The application of oxymethylene ethers in engines in combination with their emission propensity has been investigated in several studies (Pellegrini et al, 2013;Barro et al, 2018;Huang et al, 2018;Liu et al, 2019;Ren et al, 2019;LeBlanc et al, 2020;Parravicini et al, 2020;Pélerin et al, 2020), while fewer investigations have been conducted, notably, into soot formation for pure or blended OMEs in canonical flames (Ferraro et al, 2021;Tan et al, 2021).…”

From Platform to Knowledge Graph: Evolution of Laboratory Automation

“…OntoSpecies links other ontologies to provide unambiguous identification of the chemicals, enabling translation of chemical names when integrating chemical data gathered from different sources. 164 The ontologies are connected to many of those described in previous sections. For instance, the development of OntoCompChem is partly based on the CompChem terms as described in the CML and the Gainesville Core (GNVC) ontology.…”

“…All outputs from the World Avatar project are available in the public domain. Various agents were developed and released on Github to provide service in the chemistry domain, for example, automated DFT calculations to address inconsistent thermodynamic data, 167 automated mechanism calibration to improve the alignment between kinetic models and experimental data, 164 and a question answering system enabling intuitive human data interaction–natural language queries of chemical data covering data from different sources. 168 Work is in progress to integrate services provided by agents into the natural language processing system so that on-demand computations can be invoked when a question could not be answered with the current knowledge.…”

“…A core strength of the knowledge graph approach is interoperability. The knowledge graph provides a mechanism to combine data, descriptions of software, and hardware interfaces in a standardized way, facilitating automation and allowing communication between agents acting on data from different domains. , …”