28th BANGKOK World Congress on Computational Intelligence, Energy & Industrial Engineering (BCIEIE-27) scheduled on Jan. 25-27, 2027 Bangkok (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.