Piping Design Optimization an AI approach

Hydrogen piping system optimization is a critical aspect of industrial infrastructure, ensuring efficiency, safety, and cost-effectiveness in transporting hydrogen gas. This research focuses on leveraging machine learning (ML) and reinforcement learning (RL) techniques to optimize hydrogen pipeline design, considering factors such as pipe diameter, wall thickness, pressure, temperature, factor of safety (FoS), and material selection. Traditional pipeline design approaches rely on standardized codes such as ASME B31.3 & B31.1, which provide generalized safety margins but lack dynamic adaptability for real-time optimizations. The project integrates deep Q-learning (DQN), a reinforcement learning algorithm, to automate and optimize pipeline parameters dynamically. A custom Gym-based environment is developed, trained using real-world piping datasets, and evaluated for performance. The RL model learns optimal pipeline configurations by adjusting design parameters to minimize material costs while ensuring compliance with safety regulations. The system also implements interactive visualization dashboards using Dash and Plotly, allowing users to explore optimization results dynamically. Experimental results demonstrate that the RL-driven optimization reduces material costs by up to 20% while maintaining an FoS above 3.0, ensuring safety and regulatory compliance. Comparative analysis with traditional methods highlights the superiority of ML-based approaches in handling complex parameter dependencies and real-time adaptation to varying operational conditions. This study presents a novel AI-driven approach to hydrogen pipeline design, paving the way for intelligent, cost-efficient, and scalable solutions in industrial piping systems. Future work will explore multi-agent reinforcement learning and real-time sensor integration for adaptive pipeline monitoring and predictive maintenance. Keywords: Deep Learning, Reinforcement learning, Hydrogene Piping system, Optimization