AI and Digital Twin–Driven Intelligent Management and Control for Green Transformation of Process Equipment under China's Dual Carbon Goals
AIとデジタルツイン駆動によるプロセス機器のインテリジェント管理と制御：中国のダブルカーボン目標下でのグリーン変革 (AI 翻訳)
Weixuan Luo
🤖 gxceed AI 要約
日本語
本論文は、中国のダブルカーボン目標（2030年カーボンピーク、2060年カーボンニュートラル）達成に向け、高エネルギー消費・高排出プロセス産業（石油化学、電力、冶金など）におけるプロセス機器のグリーン変革にAIとデジタルツインを統合したインテリジェント管理制御手法を提案。実時間・予測・最適化・閉ループの省エネ・炭素削減を目指し、データ収集から統合エネルギー炭素管理プラットフォーム構築までの経路を示す。さらに、PINN活用、オープンなデジタルツインモデルライブラリ整備、産学連携人材育成などの政策提言を行う。
English
This paper proposes an AI and digital twin–driven intelligent management and control pathway for green transformation of process equipment in high-emission industries (petrochemical, power, metallurgy) under China's Dual Carbon Goals. It addresses real-time energy efficiency prediction, optimization, and closed-loop control by integrating digital twin health monitoring and AI-driven control. The paper also discusses challenges and policy recommendations including PINN development, open model libraries, and talent training.
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
中国のダブルカーボン目標に特化した研究であるが、AIとデジタルツインを組み合わせたプロセス機器のグリーン化手法は、日本の製造業やエネルギー産業におけるSSBJ対応や省エネ推進にも応用可能性がある。特に、データガバナンスやモデル解釈性の課題は日本の現場とも共通する。
In the global GX context
This paper presents a China-specific framework for AI–digital twin integration in industrial decarbonization, aligning with global trends in smart manufacturing and energy management. The proposed energy-carbon management platform and policy recommendations offer insights for process industries worldwide, though tailored to China's regulatory environment and dual-carbon timeline.
👥 読者別の含意
🔬研究者:Provides a comprehensive framework for AI–digital twin application in industrial green transformation, including implementation pathways and research gaps.
🏢実務担当者:Offers a reference for developing integrated energy-carbon management platforms and deploying AI for real-time energy efficiency optimization in process equipment.
🏛政策担当者:Recommends standardization of AI energy management systems, open model libraries, and university-enterprise collaboration for talent development.
📄 Abstract（原文）
The Dual Carbon goals (carbon peak by 2030, carbon neutrality by 2060) impose systematic green transformation requirements on high-energy-consumption and high-emission process industries such as petrochemicals, power, and metallurgy. However, the energy consumption and emissions of process equipment are characterized by strong operating condition disturbances, significant nonlinearity, and difficulty in cross-level data integration, making it challenging for traditional empirical control methods to achieve "real-time — predictive — optimized — closed-loop" energy saving and carbon reduction. Based on a research framework integrating AI and digital twins, this paper proposes an intelligent management and control pathway for process equipment and control engineering, from data acquisition to the establishment of an integrated energy-carbon management platform. Digital twins are applied to achieve equipment online health monitoring, while AI is used to achieve energy efficiency prediction and optimization control. In addition, a full-process traceable carbon management platform is building to analyze challenges in data governance, model interpretability and safety, engineering deployment costs, and talent. Furthermore, this paper proposes several policies and technical recommendations, including advancing industrial AI energy efficiency management system standards, developing Physics-Informed Neural Networks (PINN) to enhance model extrapolation safety, promoting open-shared digital twin model libraries and typical datasets, and improving university-enterprise collaborative talent cultivation systems. The research provides a reference for the intelligent upgrading of green factories and energy-saving equipment.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.54254/2755-2721/2026.34961first seen 2026-07-03 05:17:39
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