MOSセンサアレイに基づく小型有機アミンガス認識・予測のためのハイブリッド深層学習ニューラルネットワーク
Hybrid Deep Learning Neural Networks for Small-Molecule Organic Amine Gas Recognition and Prediction Based on MOS Sensor Array (原題)
Xing-Lei Zhao, Chen-Jun Ning, Wen-Qi Fan, Chen Chen, Shan-Shan Li, Jia-Le Zheng, Lei Li
🤖 gxceed AI 要約
日本語
CCUSのCO2回収で広く使われる有機アミン（MEA、MDEA、AMP）の漏洩を、金属酸化物半導体（MOS）センサアレイと深層学習で監視する手法を提案。1D-CNNとBi-GRUを5分割交差検証で訓練し、分類精度0.9969、MEA濃度予測でR2=0.9969を達成した。プラント最適化・環境保護・早期健康警告への実用性を示す。
English
A MOS sensor array combined with 1D-CNN and Bi-GRU deep learning was developed to classify and quantify small-molecule organic amines (MEA, MDEA, AMP) used in CO2 capture. Five-fold cross-validation yielded 0.9969 classification accuracy and R2=0.9969 for MEA concentration prediction. The method supports in-situ gas monitoring, process optimization, and early hazard warning in CCUS operations.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本はCCUSをGX推進の重点技術と位置づけ、製鉄・電力・化学での実装を進める。本手法はアミン漏洩の安全管理・プロセス最適化に資し、CCS事業の環境・安全ガバナンス強化に寄与しうる。
In the global GX context
CCUS is central to global net-zero pathways, and safe amine-based capture operations are a prerequisite for scaling. This work adds an AI-driven monitoring layer relevant to industrial safety and environmental compliance in carbon capture facilities, though it sits outside mainstream disclosure frameworks like TCFD/ISSB.
👥 読者別の含意
🔬研究者:CCUSプラントのガスモニタリングに深層学習を適用する際のベンチマーク（精度・RMSE）を提供する。
🏢実務担当者:アミン系CO2回収設備の漏洩検知・安全管理システムの高度化に活用できる。
🏛政策担当者:CCS/CCUSの安全規制・労働環境基準の設計において、リアルタイム監視技術の実装可能性を示す。
📄 Abstract（原文）
Carbon capture, utilization and storage (CCUS) is a critical technology for the fossil energy industry to achieve the dual carbon goals. Organic amine-based absorption methods, represented by mono-ethanolamine (MEA), methyl-diethanolamine (MDEA) and 2-amino-2-methyl-1-propanol (AMP), are currently the most widely adopted approaches for carbon dioxide capture. If the concentration of leaked organic amines exceeds the safety threshold, inhalation will cause severe respiratory irritation and even serious illnesses in humans. Accordingly, in situ monitoring of organic amine concentrations in waste gas is of great significance for process optimization, environmental protection, early health warning, energy conservation and emission reduction. In this study, a metal oxide semiconductor (MOS) sensor array was developed to identify categories and concentration variations in small-molecule organic amines. To realize effective gas classification and accurate concentration prediction, single-component gas data and mixed gas data with varying concentrations of the three amines collected in the laboratory were adopted to train a one-dimensional convolutional neural network (1D-CNN) and a bidirectional gated recurrent unit (Bi-GRU) via five-fold cross-validation. The proposed model achieved a classification accuracy of 0.9969 ± 0.0006. For MEA concentration prediction, the optimal determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) were 0.9969, 2.1306 and 1.5503, respectively. The experimental results demonstrate that the proposed method possesses promising practical application prospects in in situ gas monitoring and early hazard warning.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/s26196012first seen 2026-09-27 05:11:41 · last seen 2026-09-29 05:24:44
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