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.

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.

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.

These topics represent the cutting edge, where all three fields converge to solve complex global challenges.

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).

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.

Automated Disassembly & Sorting: AI-driven robotics for recycling industrial components, reducing the energy required to process raw materials.