World Congress on Computational Intelligence, Energy & Industrial Engineering BCIEIE on January 20-22, 2027 in Pattaya, Thailand

Call for papers/Topics

All Abstracts, Reviews, short articles, Full articles, Posters are welcomed related with any of the following research fields:

1. Core Computational Intelligence

This track covers the core algorithmic frameworks that provide the "brains" for modern engineering systems.

Neural Networks and Deep Learning

Physics-Informed Neural Networks (PINNs) for modeling physical systems

Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) for time-series forecasting

Transformers and Large Language Models (LLMs) for industrial documentation and code generation

Evolutionary Computation and Swarm Intelligence

Genetic Algorithms (GA) for multi-objective optimization

Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)

Differential Evolution (DE) for complex engineering design spaces

Fuzzy Logic and Multi-Criteria Decision-Making

Fuzzy control systems for handling operational uncertainty

Type-2 fuzzy systems for highly non-linear environments

Rough sets and granular computing

Reinforcement Learning (RL)

Deep Q-Networks (DQN) for sequential decision-making

Actor-Critic models for continuous control in robotics and machinery

Multi-agent reinforcement learning (MARL) for decentralized systems

2. Energy Engineering

This track covers the physical infrastructure, thermodynamic principles, and generation mechanisms of energy systems.

Renewable Energy Generation

Photovoltaic (PV) cell physics, tracking systems, and materials

Wind turbine aerodynamics, offshore structures, and mechanical drivetrains

Bioenergy, green hydrogen production, and geothermal systems

Power Systems and Grid Infrastructure

High-Voltage Direct Current (HVDC) transmission

Substation automation and protection relays

Grid stability, power quality, and reactive power compensation

Energy Storage Technologies

Lithium-ion, solid-state, and flow battery chemistry

Pumped-storage hydropower and compressed air energy storage (CAES)

Thermal energy storage (TES) for industrial processes

Thermal and Fluid Systems

Advanced thermodynamics and heat exchanger design

Carbon Capture, Utilization, and Storage (CCUS) technologies

Co-generation and poly-generation plants

3. Industrial Engineering

This track focuses on the optimization of complex processes, logistics, human systems, and manufacturing operations.

Operations Research and Optimization

Linear, integer, and non-linear programming

Stochastic modeling and queuing theory

Network flow models and graph theory applications

Production Planning and Control

Material Requirements Planning (MRP) and Enterprise Resource Planning (ERP)

Just-In-Time (JIT) and Lean manufacturing methodologies

Shop-floor scheduling and assembly line balancing

Supply Chain and Logistics Management

Facility location selection and warehouse layout design

Vehicle Routing Problems (VRP) and fleet management

Inventory control theory and global logistics network design

Quality Engineering and Reliability

Six Sigma, Statistical Process Control (SPC), and Total Quality Management (TQM)

Failure Mode and Effects Analysis (FMEA)

Life-cycle analysis and system reliability modeling

4. Computational Intelligence + Energy Engineering (Interrelated)

This intersection focuses on using smart algorithms to manage the volatility, physics, and deployment of energy assets.

Smart Grid Analytics and Forecasting

AI-driven net-load forecasting (combining load and behind-the-meter solar)

Deep learning for ultra-short-term wind power ramp forecasting

Fuzzy-logic-based state estimation in distribution grids

Intelligent Energy Storage Management

Reinforcement learning for optimal battery charging/discharging cycles

Neural network estimators for State of Health (SoH) and State of Charge (SoC)

Machine learning for battery thermal runaway prediction

Microgrid and Virtual Power Plant (VPP) Control

Multi-agent systems for decentralized microgrid energy balancing

Genetic algorithms for optimal sizing and placement of distributed energy resources

Deep RL for real-time bidding strategies of VPPs in electricity markets

5. Computational Intelligence + Industrial Engineering (Interrelated)

This intersection deals with embedding intelligence into factories, supply chains, and operational workflows (Industry 4.0/5.0).

Smart Manufacturing and Cyber-Physical Systems

Computer vision for real-time automated quality inspection on assembly lines

Reinforcement learning for dynamic, real-time job-shop scheduling

Digital Twins powered by real-time machine learning data streams

Intelligent Logistics and Supply Chain AI

Heuristics and swarm intelligence for solving dynamic Vehicle Routing Problems

Machine learning for demand sensing and predictive inventory management

Natural Language Processing (NLP) for automated supplier contract analysis

Predictive Maintenance and Diagnostics

Deep learning anomalies detection in vibration, acoustic, and thermal sensor data

Survival analysis and machine learning for Remaining Useful Life (RUL) estimation

Fuzzy expert systems for root-cause failure diagnostics

6. Energy Engineering + Industrial Engineering (Interrelated)

This intersection focuses on the operational efficiency, sustainability, and management of energy within industrial plants.

Industrial Energy Management Systems (IEMS)

Waste heat recovery optimization in manufacturing plants

Energy-conscious scheduling of high-power industrial machinery

Peak-shaving and load-shifting strategies for manufacturing facilities

Sustainable Supply Chains and Circular Economy

Green logistics and carbon-footprint minimization in freight transport

Reverse logistics for recycling, remanufacturing, and battery second-life processing

Life Cycle Assessment (LCA) integrated into product design workflows

Facility Energy Efficiency

HVAC optimization for large-scale industrial warehouses and cleanrooms

Co-optimization of water and energy networks in process industries (Pinch Analysis)

7. The Three-Way Nexus: CI + Energy + Industrial Engineering

This represents the ultimate convergence, where intelligent algorithms optimize industrial operations precisely around energy constraints, market prices, and sustainability targets.

Demand Response Optimization

Using reinforcement learning to automatically adjust a factory's production schedule based on real-time electricity pricing and grid stress signals.

Smart Asset Lifecycle and Operations Management

Combining industrial reliability engineering with energy asset physics and CI algorithms to schedule predictive maintenance for wind farms or solar arrays, minimizing downtime during peak generation windows.

Decarbonized Industrial Cluster Co-Optimization

Deploying swarm intelligence and multi-agent RL to coordinate energy generation, carbon capture systems, hydrogen production, and manufacturing output across an entire localized industrial park.

Name: EARET
Website: http://earet.org