Strategic Engineering: Transforming P&ID Documents into Digital Twins via Machine Learning and Cloud Computing

In strategic engineering, Piping and Instrumentation Diagrams (P&IDs) historically stand as static and manually curated documents. Their transition into dynamic digital twins through advanced machine learning techniques and cloud computing frameworks marks a significant evolution. Recognizing the pivotal role of P&IDs within sectors such as petrochemical, power generation, and manufacturing, transitioning these diagrams into interactive entities offers the potential to substantially amplify operational efficacy and refine decision-making paradigms. Developed during an intensive phase at a leading technological institution, the innovative methodology adroitly combines sophisticated machine learning algorithms with the robust infrastructure of Amazon Web Services to streamline the intricate process of P&IDs digitization. The approach harnesses a spectrum of techniques, including object detection, image processing, classification models, and optical character recognition, ensuring proficient discernment of symbols, extrapolation of lines, and demarcation of interconnections. Utilizing a diverse array of AWS services, a scalable and efficient digitization pipeline emerges. The culmination produces a comprehensive CSV file and an interactive digital twin endowed with rich visual attributes, both primed for integration into auxiliary systems. A subsequent cost-benefit analysis underscores the favorable equilibrium between system performance and financial expenditure. Despite the intricate challenges encountered, the demonstrated outcomes advocate for the synergistic integration of machine learning and cloud computing in P&IDs digitization, setting a precedent for future endeavors in industrial digital transformation.