監査可能なメタン排出量定量化のための炭素制約付きニューラル逆解析

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:物理制約を組み込んだ逆問題解法が、スパース観測下の排出量推定精度をどう改善するかを示す実証事例として有用。

🏢実務担当者:高価なメタンセンサ網を敷かずとも、既存の燃料計測データを物理事前情報として活用し監査可能なScope1算定を構築できる可能性を示す。

🏛政策担当者:測定ベースMRVの制度設計において、センサ間隔や観測可能性の最低要件を定める際の技術的根拠になり得る。

The quantification of fugitive methane emissions remains a primary challenge in carbon management due to the "inverse problem": reconstructing source rates from sparse, noisy atmospheric concentrations under turbulent transport. This paper evaluates a physics- informed neural network (PINN) framework that solves this inverse problem by coupling upstream fuel metering with downstream concentration gradients, providing continuous, facility-level carbon-flux reconstruction that maintains physical consistency without requiring high-density, point-source methane sensing. The proposed solution reformulates emissions quantification as a constrained optimization problem within a PINN architecture. First- principles constraints — mass conservation, combustion stoichiometry, and advection-diffusion physics — are embedded directly in the network's loss function, and facility emissions are treated as latent source terms. Minimizing the residual between measured fuel input and thermodynamically consistent outlet states regularizes the atmospheric inversion while enforcing strict adherence to the carbon mass balance. Field validation was conducted over a 12-month period (2025) at an industrial manufacturing site, using a distributed sensor transect and spanning wind speeds from 3 to 58 km/h. Because deliberate methane release is restricted, the demonstration used CO2 — combustion-derived and released in 20 controlled surrogate trials — as the measured and inverted species, exercising the complete sensing-and-inversion chain end to end. The reconstruction achieved a sensor-level mean absolute relative error of 10.3% against the controlled releases, and the facility-scale annual reconstruction agreed with fuel-based carbon references within 6.8% on an annual aggregate basis (maximum monthly difference 14.7%). Residual analysis indicates that the physics-informed constraints significantly reduce sensitivity to sensor noise and measurement sparsity. The study also provides practical guidance on "observability diagnostics," defining the minimum sensor spacing required for auditable flux reconstruction. Integrating upstream operational data as a physical prior stabilizes the inverse problem, offering a scalable alternative to hardware-intensive monitoring. The framework is species-agnostic: methane deployment retains the same transport physics and process constraints while requiring methane-sensitive sensors. This offers a commercially viable pathway to auditable carbon accounting — and a validated route toward methane quantification — in production and processing assets where continuous methane sensing is logistically or economically constrained.

• semanticscholar https://doi.org/10.2118/235837-msfirst seen 2026-11-05 05:43:39