ADAPTIVE DIGITAL TWIN SYSTEM FOR CEMENT PRODUCTION OPTIMIZATION USING MARKOV CHAIN-BASED REACTION RATE MODELLING
マルコフ連鎖に基づく反応速度モデリングを用いたセメント生産最適化のための適応型デジタルツインシステム (AI 翻訳)
P. Dutta, Vasileios Paliktzoglou, S. Shambhavi, Vugar Abdullayev, Abhik Patra
🤖 gxceed AI 要約
日本語
本研究は、セメント製造におけるエネルギー消費と二酸化炭素排出を削減するため、リアルタイムデータとマルコフ連鎖に基づく反応速度モデルを融合した適応型デジタルツイン（ADT）フレームワークを提案する。キルン内の化学反応を確率的に予測し、強化学習コントローラで燃料噴射などを調整することで、熱エネルギー消費を12%削減し、スループットを8%向上させた。
English
This study proposes an Adaptive Digital Twin (ADT) framework for cement kilns, fusing real-time data with a Markov Chain-based reaction rate model to predict and control chemical transformations. A reinforcement learning controller optimizes fuel and air flow. Over three months, the ADT reduced thermal energy consumption by 12%, increased throughput by 8%, and decreased quality variability and downtime. The work shows a scalable approach for decarbonizing process industries.
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
日本のセメント産業はGXの重要分野であり、本論文のADTフレームワークはエネルギーロス低減の具体策として有望。日本政府が推進するSociety 5.0とも親和性が高く、国内セメントメーカーへの導入が期待される。
In the global GX context
Cement production accounts for ~8% of global CO2 emissions. This paper provides a practical, data-driven method to reduce energy intensity without major capital investment. The Markov chain-based modeling offers a novel approach for real-time optimization in heavy industry, relevant to global decarbonization efforts.
👥 読者別の含意
🔬研究者:Researchers in industrial decarbonization and digital twin modeling can benefit from the novel combination of Markov chains with reinforcement learning for process optimization.
🏢実務担当者:Corporate sustainability teams in cement and other heavy industries can use this framework to identify energy savings and reduce Scope 1 emissions.
🏛政策担当者:Policymakers can cite this as evidence that digitalization provides low-cost emissions reductions in industrial sectors.
📄 Abstract（原文）
Cement manufacturing remains one of the most energy - intensive and carbon - intensive industrial activities, largely because legacy control systems cannot accommodate the highly dynamic, multi - stage nature of clinker formation. This study proposes an Adaptive Digital Twin (ADT) framework that fuses real - time plant data with a Markov Chain - based reaction‑rate model to predict, and proactively steer, chemical transformations inside the kiln. The twin ingests high - frequency sensor streams, updates a time - varying state - space representation of material flows, and couples it with stochastic state - transition matrices calibrated from historical process logs. A reinforcement - learning controller then adjusts fuel injection, air flow, and feed ratios on the fly. The architecture has been given as a prototype design for two plants (Holcim, Switzerland; Fujian Ansha Jianfu, China) and benchmarked against their incumbent PID - driven systems. Over a three - month evaluation horizon, the ADT cut specific thermal energy consumption by 12%, lifted overall throughput by 8%, suppressed quality variability by an order of magnitude, and trimmed unplanned downtime by 25%. Sensitivity analysis confirms that the Markov component - by capturing probabilistic shifts between pre - heating, calcination, clinkering, and cooling states - accounts for more than half of the observed efficiency gain. Beyond delivering immediate cost and emissions benefits, the work demonstrates a transferable blueprint for merging stochastic reaction modelling with cyber - physical twins in other process industries. This research contributes to the field by demonstrating how Markov Chain - Based modeling can enhance industrial process optimization through digital twin frameworks, providing a scalable solution for sustainable cement production.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.55766/sujst8764first seen 2026-05-06 00:10:44
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。