AI-Based Adaptive Control for Energy-Efficient Process Optimization in Smart Manufacturing Environments: A Systematic Review and Conceptual Framework
Published 2024-12-12
Keywords
- adaptive control; artificial intelligence; energy efficiency; smart manufacturing; Industry 4.0; reinforcement learning; digital twin; process optimization; deep learning; cyber-physical systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
The convergence of artificial intelligence (AI) and Industry 4.0 has catalyzed transformative advances in manufacturing process control, particularly in the domain of energy efficiency optimization. This paper presents a systematic review and conceptual framework examining the integration of AI-based adaptive control strategies for energy-efficient process optimization within smart manufacturing environments. Drawing upon a comprehensive analysis of 187 peer-reviewed publications from 2018 to 2025, the study synthesizes current knowledge on reinforcement learning, deep neural networks, digital twin-enabled control, and multi-agent systems as applied to real-time energy management in discrete and continuous manufacturing processes. The proposed conceptual framework, termed the Adaptive Intelligence for Energy-Efficient Manufacturing (AIEEM) framework, delineates five interrelated layers: sensing and data acquisition, feature extraction and state representation, AI-driven decision-making, actuator-level adaptive control, and performance feedback with continuous learning. Results from the systematic review indicate that AI-based adaptive control systems can achieve energy consumption reductions ranging from 12% to 38% compared to conventional proportional-integral-derivative (PID) controllers, with reinforcement learning approaches demonstrating the highest potential for multi-objective optimization balancing energy efficiency, production throughput, and product quality. Furthermore, the integration of digital twin technology with adaptive control architectures enables predictive energy management capabilities that anticipate process disturbances and pre-emptively adjust control parameters. The study identifies critical research gaps including limited scalability of current approaches to multi-process manufacturing lines, insufficient consideration of human-machine collaboration in adaptive control loops, and the need for standardized benchmarking protocols. This work contributes to the theoretical foundations of intelligent manufacturing by providing a unified taxonomy of AI-adaptive control methods, a validated conceptual framework for implementation, and a research agenda that addresses both technological and organizational dimensions of energy-efficient smart manufacturing.