Reza Azin
Academic
Persian Gulf University
Participates in
TECHNICAL PROGRAMME | Energy Technologies
Smart Infrastructure for the Future Energy Industry: Digitalisation & Innovation
Forum 18 | Technical Programme Hall 4
27
April
13:30
15:00
UTC+3
Energy-intensive industries have many problems in simultaneously optimize operational efficiency, economic performance, and environmental sustainability while maintaining product quality and safety standards. This research presents a novel real-time optimization method that integrates four critical performance dimensions include: Energy, Exergy, Economic, and Environmental (4E) through advanced digital twin technology coupled with machine learning algorithms and soft sensor networks.
The proposed system addresses a fundamental limitation in current industrial practice: the temporal disconnect between operational decisions and their comprehensive impact assessment. Traditional Life Cycle Assessment (LCA) methodologies, while scientifically robust, are typically conducted periodically, so limiting their utility for real-time process optimization. Now this paradigm changed by implementing continuous LCA calculations based on the IMPACT 2002+ methodology, enabling real-time monitoring and prediction of 15 environmental impact categories including human health effects, ecosystem quality degradation, climate change potential, and resource depletion.
The digital twin creates multiple modeling methods: physics-based process simulation, data-driven machine learning models, and hybrid form of these. This multi-fidelity approach ensures both theoretical rigor and practical applicability across diverse industrial contexts including petroleum refining, cement manufacturing, and thermal power generation. Soft sensors compensate for measurement limitations in industrial environments, utilizing advanced inference techniques for state estimation and uncertainty quantification.
The optimization engine employs a multi-objective logical algorithm that simultaneously minimizes energy consumption, exergy destruction, environmental impacts, and operational costs while satisfying quality constraints and safety limitations. The mathematical formulation integrates thermodynamic principles (first and second laws), economic modeling (net present value optimization), and environmental impact assessment through a weighted objective function with adaptive parameter tuning based on real-time conditions and regulatory requirements.
Implementation validation in pilot-scale applications demonstrates significant performance improvements: 8-15% reduction in specific energy consumption, 12-25% decrease in exergy destruction, 5-20% improvement in economic performance metrics, and substantial reductions across multiple environmental impact categories. The system's predictive capabilities enable proactive decision-making, with machine learning models achieving 92-95% accuracy in forecasting key performance indicators over operational time horizons.
This research is combination of process systems engineering, environmental science, and artificial intelligence by providing the new method for continuous multi-criteria optimization in industries and also addresses critical challenges in sustainable process intensification and Industry 4.0 implementation, offering a scalable solution for the digital transformation of energy-intensive industries. The modular structure ensures adaptability across different industrial sectors while maintaining scientific rigor and practical feasibility, representing a significant advancement toward achieving simultaneously profitable and environmentally responsible industrial operations in the context of global sustainability imperatives.
Co-author/s:
Dr. Mohannad Mohammadi Baghmolaei, Researcher, Persian Gulf University.
Shahriar Osfouri, Academic, Persian Gulf University.
Hamid Shafiee, Academic, Persian Gulf University.
The proposed system addresses a fundamental limitation in current industrial practice: the temporal disconnect between operational decisions and their comprehensive impact assessment. Traditional Life Cycle Assessment (LCA) methodologies, while scientifically robust, are typically conducted periodically, so limiting their utility for real-time process optimization. Now this paradigm changed by implementing continuous LCA calculations based on the IMPACT 2002+ methodology, enabling real-time monitoring and prediction of 15 environmental impact categories including human health effects, ecosystem quality degradation, climate change potential, and resource depletion.
The digital twin creates multiple modeling methods: physics-based process simulation, data-driven machine learning models, and hybrid form of these. This multi-fidelity approach ensures both theoretical rigor and practical applicability across diverse industrial contexts including petroleum refining, cement manufacturing, and thermal power generation. Soft sensors compensate for measurement limitations in industrial environments, utilizing advanced inference techniques for state estimation and uncertainty quantification.
The optimization engine employs a multi-objective logical algorithm that simultaneously minimizes energy consumption, exergy destruction, environmental impacts, and operational costs while satisfying quality constraints and safety limitations. The mathematical formulation integrates thermodynamic principles (first and second laws), economic modeling (net present value optimization), and environmental impact assessment through a weighted objective function with adaptive parameter tuning based on real-time conditions and regulatory requirements.
Implementation validation in pilot-scale applications demonstrates significant performance improvements: 8-15% reduction in specific energy consumption, 12-25% decrease in exergy destruction, 5-20% improvement in economic performance metrics, and substantial reductions across multiple environmental impact categories. The system's predictive capabilities enable proactive decision-making, with machine learning models achieving 92-95% accuracy in forecasting key performance indicators over operational time horizons.
This research is combination of process systems engineering, environmental science, and artificial intelligence by providing the new method for continuous multi-criteria optimization in industries and also addresses critical challenges in sustainable process intensification and Industry 4.0 implementation, offering a scalable solution for the digital transformation of energy-intensive industries. The modular structure ensures adaptability across different industrial sectors while maintaining scientific rigor and practical feasibility, representing a significant advancement toward achieving simultaneously profitable and environmentally responsible industrial operations in the context of global sustainability imperatives.
Co-author/s:
Dr. Mohannad Mohammadi Baghmolaei, Researcher, Persian Gulf University.
Shahriar Osfouri, Academic, Persian Gulf University.
Hamid Shafiee, Academic, Persian Gulf University.
