Automated Interpretation of Chemical Engineering Diagrams Using Computer Vision

This paper presents state-of-the-art object detection and object identification algorithms for digitizing and interpreting chemical engineering diagrams, including, Block Flow Diagrams (BFDs), Process Flow Diagrams (PFDs), and Piping and Instrumentation Diagrams (P&IDs), using computer vision techniques. These diagrams are essential for visualizing plant processes and equipment but are often stored as image-based PDFs, making manual digitization/interpretation labor-intensive and error-prone. The proposed algorithm automates tasks such as detecting unit operations and identifying them using a set of rule-based and predefined approaches including edge and contour-based rules, spatial arrangement rules, and geometric rules. This method avoids data requirements and computational requirements of deep learning approaches, offering a scalable and efficient solution for preliminary extraction of complex process information.