勾配ベース非線形モデル予測制御による吸着式直接空気回収のデマンドサイドマネジメント
Gradient-based nonlinear model predictive control enabling demand-side management for adsorption-based direct air carbon capture (原題)
Sebastian F. Henzler, Daniel Rezo, Patrik Postweiler, Niklas von der Aßen, Alexander Mitsos
🤖 gxceed AI 要約
日本語
吸着式DACCSの電力コスト削減のため、実時間対応の経済的モデル予測制御（eNMPC）を実装。平滑化アプローチによる直接単射で標準CPU上での実時間適用を実証し、制御解像度や予見時間が性能に与える影響を分析。勾配ベース最適化により、従来のPSOより高収益で実時間性を実現し、大規模DACCS展開への道を開く。
English
This study implements real-time capable economic nonlinear model predictive control (eNMPC) for adsorption-based direct air carbon capture (DACCS) to reduce electricity costs via demand-side management. By using a smoothing approach for direct single shooting, the method achieves real-time applicability on a standard CPU, outperforming particle-swarm optimization in profitability. The analysis explores the impact of control resolution, foresight, and optimality tolerance, concluding that gradient-based optimization enables more profitable and real-time operation, paving the way for large-scale DACCS deployment.
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
日本ではDACCSはカーボンネガティブ技術として注目され、NEDOやグリーンイノベーション基金の支援対象。本研究成果は電力コスト削減によるDACCSの経済性向上に寄与し、日本のCCUS戦略やカーボンプライシング導入時の運用最適化に示唆を与える。
In the global GX context
Globally, DACCS is recognized as a key carbon dioxide removal technology, but high energy costs hinder deployment. This research demonstrates that advanced process control can significantly reduce electricity costs, enhancing the economic viability of DACCS. It aligns with global efforts to scale up CDR technologies and provides a methodological framework applicable to other energy-intensive carbon capture processes.
👥 読者別の含意
🔬研究者:Provides a novel eNMPC framework for DACCS with real-time capability, offering insights into control strategies for energy-intensive carbon capture processes.
🏢実務担当者:Offers a method to reduce operational electricity costs for DACCS facilities, improving economic feasibility and enabling demand-side management.
🏛政策担当者:Highlights the potential of advanced control to lower DACCS costs, supporting policy incentives for carbon dioxide removal technologies.
📄 Abstract（原文）
Adsorption-based Direct Air Carbon Capture and Storage (DACCS) is a promising carbon dioxide removal technology, albeit with high electricity demand. Demand-side management (DSM), i.e., shifting electricity demand to times of low electricity prices, can reduce electricity costs. In Postweiler et al. (2025), we demonstrated the efficacy of DSM for DACCS by solving a dynamic optimization problem via the gradient-free optimization method particle-swarm optimization (PSO). However, PSO is computationally very demanding and thus not real-time capable, severely limiting the resolution of time-continuous controls like flow rates. Herein, we implement real-time capable economic nonlinear model predictive control (eNMPC). We first modify the process model to obtain nonsmooth differential–algebraic equations enabling direct single shooting using a smoothing approach. We demonstrate our method in an eNMPC case study, showing real-time applicability on a single core of a standard CPU. We study the impact of the resolution and the choice of the controls, the foresight in the eNMPC setup, and the optimality tolerance on the computational performance. We choose a trade-off to compare the optimal profit achieved with the literature. We conclude that gradient-based dynamic optimization enables real-time applicability as well as more profitable operation, paving the way for large-scale DACCS employment.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.ijggc.2026.104756first seen 2026-08-19 04:42:58
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