Digital Twin-Enabled Thermal Energy Management System for Sustainable Manufacturing Process Optimization
Otabek Mukhitdinov- Doniyor Jumanazarov
- Egambergan Khudoynazarov
- Abdusalom Umarov
- Ahmed Mohsin Alsayah
Published 2026-01-31
abstract views: 21 // FULL TEXT ARTICLE (PDF): 8
Keywords
- Digital twin,
- Energy efficiency,
- Manufacturing optimization,
- Sustainable production,
- Thermal management
How to Cite
Copyright (c) 2026 International Journal of Industrial Engineering and Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Manufacturing processes consume substantial thermal energy, yet siloed management approaches cannot exploit facility-wide synergies. This study develops and validates an integrated Digital Twin (DT) that fuses physics-based thermal models with machine-learning forecasts and multi-objective optimization to coordinate process heat, waste-heat recovery, thermal storage, and on-site renewables in real-time. Deployed across four heterogeneous manufacturing facilities, the DT generated operator-ready knee-point recommendations that balanced energy use, operating cost, and emissions under changing production and weather conditions. Across sites, deployment produced substantial, sustained gains in thermal-energy efficiency and marked reductions in carbon intensity (approximately 27% higher efficiency and about one-third lower emissions in aggregate), demonstrating that system-level orchestration outperforms isolated component upgrades. Novelty lies in plant-scale, real-time co-optimization of process heat, waste-heat recovery, thermal storage, and on-site renewables using a hybrid physics–ML digital twin with uncertainty-aware multi-objective control, field-validated across four heterogeneous manufacturing sites.
Article history: Received (August 19, 2025); Revised (October 21, 2025); Accepted (November 13, 2025); Published online (January 30, 2026)
