Neural Network-enabled Domain-consistent Robust Optimisation for Global CO$_2$ Reduction Potential of Gas Power Plants

2025-10-15

AI Summary

To address physically infeasible solutions arising from domain inconsistency between neural networks and optimization solvers, this paper proposes a neural network-driven robust optimization framework. It introduces, for the first time, a domain-consistency constraint mechanism that embeds data-driven physical constraints into a nonlinear programming model, ensuring all optimal solutions reside strictly within the feasible operational domain of gas-fired power plants. Methodologically, the approach integrates parameterized neural networks with robust optimization techniques to unify energy-efficiency modeling and optimization solving. Evaluated on a representative 1180-MW unit, the framework achieves an average energy-efficiency improvement of 0.76 percentage points. Extrapolated globally, it enables annual CO₂ emission reductions of approximately 26 Mt—comprising 10.6 Mt in Asia, 9.0 Mt in the Americas, and 4.5 Mt in Europe—thereby providing the first systematic quantification of the global carbon-mitigation potential of this technical pathway.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

We introduce a neural network-driven robust optimisation framework that integrates data-driven domain as a constraint into the nonlinear programming technique, addressing the overlooked issue of domain-inconsistent solutions arising from the interaction of parametrised neural network models with optimisation solvers. Applied to a 1180 MW capacity combined cycle gas power plant, our framework delivers domain-consistent robust optimal solutions that achieve a verified 0.76 percentage point mean improvement in energy efficiency. For the first time, scaling this efficiency gain to the global fleet of gas power plants, we estimate an annual 26 Mt reduction potential in CO$_2$ (with 10.6 Mt in Asia, 9.0 Mt in the Americas, and 4.5 Mt in Europe). These results underscore the synergetic role of machine learning in delivering near-term, scalable decarbonisation pathways for global climate action.

Problem

• Addressing domain-inconsistent solutions in neural network optimization
• Improving energy efficiency of gas power plants globally
• Estimating CO2 reduction potential through robust machine learning framework

Innovation

• Neural network-driven robust optimization framework
• Integrates data-driven domain constraints into nonlinear programming
• Achieves domain-consistent solutions for global CO2 reduction