A knowledge graph framework for digital twins of chemical processes

Abstract A digital twin, which virtually replicates a real system and fuses data, models and domain knowledge, is a key technology for accelerating chemical process development and addressing sustainability challenges. Despite its potential, one critical challenge lies in the lack of a systematic approach to integrate data, domain knowledge and predictive models to contextualize and represent chemical processes effectively. Here we propose a knowledge graph framework associated with autonomous functional agents to support the development of digital twins for chemical processes, enabling the seamless incorporation of chemical databases, artificial intelligence models and large language models. Ontologies are developed for physical models of chemical processes, allowing scalable model construction and calibration. We demonstrate the framework with practical case studies focusing on bottom-up model assembly, top-down model search and model-based reaction optimization. The framework presents an approach to manage models as a depository of chemical process knowledge, providing a foundation of digital twin technology for future chemical process development and manufacturing.