Call for papers/Topics Topics of interest for submission include any topics related to: 1. Core Independent Domains Before looking at the intersections, these are the fundamental pillars of each field. Artificial Intelligence: Machine Learning (Supervised/Unsupervised), Deep Learning, Reinforcement Learning, Natural Language Processing (NLP), and Computer Vision. Energy Engineering: Thermodynamics, Power Systems, Renewable Energy (Solar, Wind, Hydro), Grid Stability, and Energy Storage (Batteries, Thermal). Industrial Engineering: Operations Research, Supply Chain Management, Ergonomics, Quality Control (Six Sigma), and Facilities Planning. 2. Interrelated Topics: AI & Energy This intersection focuses on making energy systems "smarter" and more resilient. Smart Grid Management: * Demand response forecasting using Neural Networks. Automated load balancing and frequency control. Renewable Energy Forecasting: * Predictive modeling for solar irradiance and wind speed to reduce curtailment. Virtual Power Plants (VPPs): * AI-driven orchestration of distributed energy resources (DERs). Energy Storage Optimization: * AI algorithms to manage battery charge/discharge cycles to maximize lifespan and ROI. 3. Interrelated Topics: AI & Industrial Engineering This intersection, often called Industry 4.0, focuses on efficiency and automation in production. Predictive Maintenance: * Using sensor data (IoT) and AI to predict equipment failure before it occurs. Autonomous Robotics: * Computer vision and path-planning for AGVs (Automated Guided Vehicles) in warehouses. Quality 4.0: * Automated visual inspection using Deep Learning to detect micro-defects in manufacturing. Intelligent Supply Chains: * AI for inventory optimization and dynamic routing under uncertainty. 4. The "Triple Intersection": AI + Energy + Industrial These topics represent the cutting edge, where all three fields converge to solve complex global challenges. Sustainable Smart Manufacturing Energy-Aware Scheduling: Integrating industrial production schedules with energy market prices to minimize costs and carbon footprint. Digital Twins: Creating virtual replicas of factories that simulate both mechanical performance (Industrial) and energy consumption (Energy) using real-time data (AI). Industrial Decarbonization Carbon Accounting Automation: Using AI to track and optimize Scope 1, 2, and 3 emissions across industrial supply chains. Waste Heat Recovery Optimization: Using Machine Learning to identify patterns in thermal waste and redirecting that energy back into the industrial process. Circular Economy Systems Automated Disassembly & Sorting: AI-driven robotics for recycling industrial components, reducing the energy required to process raw materials.