Mona Alshahrani
AI Lead Scientist
Saudi Aramco
Dr. Mona Alshahrani received her PhD & MSc in Computer Science from KAUST, focusing on Artificial Intelligence and Machine Learning.
Prior to joining Saudi Aramco, she worked as an Artificial Intelligence (AI) Consultant at the Saudi Data and Artificial Intelligence Authority (SDAIA), where she led key projects and initiatives in developing AI solutions across various sectors, ranging from healthcare to smart cities and recently to the energy sector. Dr. Mona has published her work in top-tier conferences and high-impact journals.
Participates in
TECHNICAL PROGRAMME | Energy Technologies
The Energy Transition: The Role of Digitalisation, AI, and Cybersecurity
Forum 23 | Digital Poster Plaza 4
30
April
10:00
12:00
UTC+3
Objectives/Scope:
Piping and Instrumentation Diagrams (P&IDs) are schematic representations of functional relationships between equipment, pipelines, and instrumentation in industrial processes. They are critical for design, operation, and maintenance in industries like oil and gas and chemicals. This paper presents an AI-driven approach for digitizing P&IDs, transforming image-based diagrams into structured, analyzable data for efficient and accurate digital representation.
Methods, Procedures, Process:
This study employs a comprehensive AI pipeline integrating object detection, Optical Character Recognition (OCR), and advanced machine learning techniques. The process includes identifying symbols, instruments, and lines from scanned P&IDs and associating them with relevant textual data. Methods like template matching, advanced vessel detection, and OCR are combined with data augmentation and parallel processing to handle complex P&ID layouts while ensuring high accuracy and scalability.
Results, Observations, Conclusions:
The AI-driven system demonstrated exceptional precision and recall in digitizing P&IDs, achieving over 97% accuracy in symbol detection and nearly 100% accuracy in text recognition. Key outcomes include accurate identification of symbols, association with relevant metadata, and efficient handling of large-scale, complex diagrams. The system efficiently processed diverse diagram layouts and orientations, demonstrating robustness against variations in diagram styles, symbol sizes, and text placements. Advanced OCR methods mitigated common text recognition challenges, such as distinguishing similar characters, improving overall reliability.
The resulting structured digital data facilitates enhanced usability in applications like system modeling, operational analysis, and compliance reporting. Visual outputs, such as annotated diagrams, allow for seamless verification and validation of results. By significantly reducing manual effort and minimizing errors, this approach accelerates industrial workflows, supporting informed decision-making. The pipeline’s scalability ensures adaptability to extensive industrial datasets, offering a transformative solution for managing and digitizing P&IDs at scale.
Novel/Additive Information:
This paper introduces a novel end-to-end AI solution for P&ID digitization, combining advanced OCR, template matching, and object detection to achieve unparalleled accuracy and efficiency. It sets a new standard in industrial diagram digitalization, addressing long-standing challenges in processing complex P&ID layouts.
Piping and Instrumentation Diagrams (P&IDs) are schematic representations of functional relationships between equipment, pipelines, and instrumentation in industrial processes. They are critical for design, operation, and maintenance in industries like oil and gas and chemicals. This paper presents an AI-driven approach for digitizing P&IDs, transforming image-based diagrams into structured, analyzable data for efficient and accurate digital representation.
Methods, Procedures, Process:
This study employs a comprehensive AI pipeline integrating object detection, Optical Character Recognition (OCR), and advanced machine learning techniques. The process includes identifying symbols, instruments, and lines from scanned P&IDs and associating them with relevant textual data. Methods like template matching, advanced vessel detection, and OCR are combined with data augmentation and parallel processing to handle complex P&ID layouts while ensuring high accuracy and scalability.
Results, Observations, Conclusions:
The AI-driven system demonstrated exceptional precision and recall in digitizing P&IDs, achieving over 97% accuracy in symbol detection and nearly 100% accuracy in text recognition. Key outcomes include accurate identification of symbols, association with relevant metadata, and efficient handling of large-scale, complex diagrams. The system efficiently processed diverse diagram layouts and orientations, demonstrating robustness against variations in diagram styles, symbol sizes, and text placements. Advanced OCR methods mitigated common text recognition challenges, such as distinguishing similar characters, improving overall reliability.
The resulting structured digital data facilitates enhanced usability in applications like system modeling, operational analysis, and compliance reporting. Visual outputs, such as annotated diagrams, allow for seamless verification and validation of results. By significantly reducing manual effort and minimizing errors, this approach accelerates industrial workflows, supporting informed decision-making. The pipeline’s scalability ensures adaptability to extensive industrial datasets, offering a transformative solution for managing and digitizing P&IDs at scale.
Novel/Additive Information:
This paper introduces a novel end-to-end AI solution for P&ID digitization, combining advanced OCR, template matching, and object detection to achieve unparalleled accuracy and efficiency. It sets a new standard in industrial diagram digitalization, addressing long-standing challenges in processing complex P&ID layouts.
