Boosting autonomous process design and intensification with formalized domain knowledge

Abstract

Conceptual process design deals with searching for optimal process flowsheets in a large design space. Effective approaches benefit from sensible search space restrictions, commonly carried out by a knowledgeable expert in the form of a superstructure optimization or heuristic rules. To achieve the goal of autonomous process design, knowledge has to be formalized in a machine-readable format. This contribution aims to incorporate an ontological representation of fundamental process knowledge to empower general-purpose design procedures while respecting problem-specific variability. Specifically, the presented framework leverages an ontology to express declarative knowledge (what-is) of processes, phenomena and design tasks to set up the search space and boost a hierarchical reinforcement learning agent which learns the required procedural knowledge (how-to) in order to find an optimal solution. The work is applied in a case study of an intensified steam methane reforming process. Results show that the automated treatment of domain knowledge allows for dynamic search space reduction and achieves better computational efficiency and solution quality, highlighting its potential in autonomous process design approaches.