AI-driven framework optimizes energy consumption and waste reduction in industries using Digital Twins and Industrial IoT (IIoT).
Authors
Ankit Agarwal, Dr. Akhilesh Das Gupta Institute of Professional Studies, New Delhi, India
Inderjeet Sinha, Poornima Institute of Engineering and Technology, Jaipur, India
Subarno Bhattacharyya, O.P. Jindal Global University, Sonipat, Haryana, India
Udit Mamodiya, Poornima University, India
Summary
It was based on the motivation to generate novel innovative technologies in order to promote energy efficiency with reduced waste through the growth in demand for industrial practices in an eco-friendly way. In this paradigm, Industrial IoT provides a promising platform for real-time monitoring, maintenance, and resource usage within Digital Twins. A new multi-layer architecture will be developed in this chapter to integrate digital twins and IIoT to mutually optimize energy consumption and waste reduction. Named the Adaptive Energy Optimization and Waste Reduction Framework, this novel architecture utilizes AIdriven self-learning models for dynamically adapting itself to various changes in industrial conditions. Simultaneously, efficiency is maintained through both energy usage as well as waste minimization due to EWCO. This framework is simulated to carry out industrial operations in virtual environments to identify inefficiencies and predict failures along with the automation of optimization strategies.
Published in: Accelerating Product Development Cycles With Digital Twins and IoT Integration
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