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Dataset for the Article: "A decomposition method for optimizing the operation of power-to-X energy systems with detailed process models"

論文「詳細プロセスモデルを用いたPower-to-Xエネルギーシステムの運用最適化のための分解手法」のデータセット (AI 翻訳)

Wang, Yifan, von der Assen, Niklas, Zhang, Qi

Zenodoデータセット2026-07-23#CCUSOrigin: EU
DOI: 10.5281/zenodo.14860488
原典: https://zenodo.org/records/14860488

🤖 gxceed AI 要約

日本語

本データセットは、直接空気回収(DAC)プラントの運転最適化のための分解手法を扱う論文に付随する。DACプラントの詳細プロセスモデルと代理モデルを用い、PSOアルゴリズムで最適化を行った結果を含む。約30GBのデータ。

English

This dataset accompanies an article on a decomposition method for optimizing power-to-X energy systems. It contains optimization data from a DAC plant using a detailed process model and a surrogate model, solved with particle swarm optimization. Requires ~30 GB storage.

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

日本でもDACはカーボンネガティブ技術として注目されており、本データセットは日本の研究機関がDAC運用最適化を研究する際の参考となる。

In the global GX context

DAC is a key negative emissions technology globally. This dataset enables reproducibility and further optimization research, supporting the development of efficient carbon removal systems.

👥 読者別の含意

🔬研究者:Provides optimization data and methodology for DAC plant operation using surrogate models, valuable for researchers in carbon capture and energy systems.

🏢実務担当者:Useful for companies deploying DAC to benchmark optimization approaches and improve operational efficiency.

📄 Abstract（原文）

This dataset accompanies the article  "A decomposition method for optimizing the operation of power-to-X energy systems with detailed process models." In the main article, a direct air capture (DAC) plant is modeled at two different levels of granularity: a detailed process model and a corresponding surrogate process model. These models are used to optimize the operational decisions of the DAC plant. The detailed process model of the DAC plant was developed by Postweiler et al. [1] and is formulated as a system of differential-algebraic equations (DAEs). In our work, we treat this model as a black-box model and optimize the operation of a stand-alone DAC plant using the DAE model to achieve a specified CO₂ capture target while minimizing the operational cost. The optimization problems are solved using a particle swarm optimization (PSO) algorithm. The resulting optimization data are collected and provided in this dataset. This dataset includes the following information: Progress of the PSO algorithm Top 1 optimization result Top 10 optimization results   More details and instructions can be found in: the accompanying article: https://doi.org/10.1016/j.apenergy.2026.128445 our Git repository: https://git-ce.rwth-aachen.de/ltt/ptg-es4 Please note that the dataset requires approximately 30 GB of storage space.   This study is co-funded by the  "Europäischer Fonds für regionale Entwicklung" (EFRE-20800350) and a fellowship from the German Academic Exchange Service (DAAD) . The support is gratefully acknowledged.   Reference [1] Postweiler, P., et al. Environmental Process Optimisation of an Adsorption-Based Direct Air Carbon Capture and Storage System. Energy & Environmental Science, 17(9), 3004–3020 (2024).

🔗 Provenance — このレコードを発見したソース

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。