An Industry 4.0 Framework for the Smart Production Management of Renewable Energy and Water Systems: An Application of AI, IoT, and Digital Twin Technologies

Abstract

This study develops an integrated Industry 4.0 framework for smart production management in renewable energy systems applied to water processes. The framework combines artificial intelligence, the Internet of Things, and digital twin technologies to improve production planning, system reliability, and environmental performance. A neural network model was implemented for predictive analytics and achieved high accuracy (MAE = 0.82, R² = 0.92), enabling precise forecasting for energy generation and operational scheduling. Optimization algorithms, including genetic algorithms and particle swarm optimization, increased energy utilization efficiency from 65% to 85% and reduced operational costs by 15%. The IoT utilization enhanced real-time monitoring and reduced fault detection time from 120 minutes to 15 minutes, significantly improving maintenance response. Digital twin simulations allowed process optimization and predictive maintenance, further increasing production efficiency to 92% and system uptime to 99.5%. The approaches also led to a 20% reduction in CO₂ emissions, demonstrating both economic and environmental benefits. Overall, this framework offers a practical and data-driven solution for improving the efficiency and sustainability of renewable energy systems in water applications and contributes to the advancement of smart manufacturing in industrial engineering.

Article history: Received (August 1, 2025); Revised (October 21, 2025); Accepted (October 30, 2025); Published online (November 20, 2025)