用神经网络优化燃气电厂能效，全球年减碳2600万吨。
Neural Network-enabled Domain-consistent Robust Optimisation for Global CO$_2$ Reduction Potential of Gas Power Plants
- 将神经网络与优化算法结合，确保解在物理可行域内
- 实测能效提升0.76个百分点，全球可减碳2600万吨/年
- 适合能源、气候政策与电力系统研究者参考
我们提出一种基于神经网络的鲁棒优化框架，将数据驱动的物理域约束融入非线性规划，解决了参数化神经网络模型与优化求解器交互导致的域不一致问题。以1180兆瓦联合循环燃气电厂为例，该框架实现了域一致的鲁棒最优解，能效平均提升0.76个百分点。首次将此效率增益推广至全球燃气电厂，估算每年可减少二氧化碳排放2600万吨（其中亚洲1060万吨，美洲900万吨，欧洲450万吨）。结果表明机器学习在推动近中期可扩展减排路径方面具有协同潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。