Digital Twin-Enabled Predictive Maintenance for Climate-Smart Food Dehydration in Tropical Climates
熱帯気候における気候スマート食品乾燥のためのデジタルツイン活用予知保全 (AI 翻訳)
Saheed A. Adeniyi, Adeyemi A. Adefisan-Williams, Rukayat Abisola Olawale, Daniel O. Olokpo and Elizabeth I. Igbodor
🤖 gxceed AI 要約
日本語
本研究は、ナイジェリアの熱帯条件下でハイブリッド太陽電気乾燥機に、物理ベースの乾燥速度エンジンとオートエンコーダ駆動の予知保全エンジンを結合したデジタルツインを開発・評価した。42バッチの1分解像度データで、水分比をRMSE 0.031で予測し、ファンベアリングの劣化を故障5.3時間前に検出（精度94%、再現率91%）。最適化により、エネルギー31%、CO2e 36%、廃棄量約3分の2を削減した。
English
This study develops a digital twin coupling a physics-based drying-kinetics engine with an autoencoder-driven predictive maintenance engine for a hybrid solar-electric dehydrator in tropical Nigeria. It predicted moisture ratio with RMSE 0.031, detected fan-bearing degradation 5.3 hours before failure (94% precision, 91% recall), and reduced energy use by 31%, CO2e by 36%, and spoilage by two-thirds.
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
日本では食品加工分野の省エネ・脱炭素が急務であり、本成果は気候変動適応型スマート農業技術として応用可能。特に中小食品事業者向けの低コストIoT活用モデルとして、SSBJやカーボンフットプリント削減に寄与しうる。
In the global GX context
Globally, this digital twin template offers a replicable solution for climate-smart agriculture in emerging economies, directly addressing SDG 2 (zero hunger) and SDG 13 (climate action). It demonstrates how AI-driven predictive maintenance and process optimization can deliver measurable decarbonization and reliability gains in decentralized food processing.
👥 読者別の含意
🔬研究者:Provides a validated digital twin architecture integrating physics-based and data-driven models for food drying, with implications for broader agricultural process optimization.
🏢実務担当者:Shows how modestly instrumented dehydrators can achieve significant energy and emission reductions using digital twins, offering a low-cost path to climate-smart operations.
🏛政策担当者:Highlights the potential of digital twins in smallholder food supply chains, supporting policies for energy efficiency and renewable energy adoption in agri-food SMEs.
📄 Abstract（原文）
Abstract Post-harvest losses and the high energy intensity of conventional drying remain stubborn bottlenecks for decentralised food processing in tropical economies. This study develops and evaluates a digital twin (DT) that couples a physics-based drying-kinetics engine with an autoencoder-driven predictive-maintenance (PdM) engine for a hybrid solar-electric dehydrator operated under real tropical conditions in Jos, Nigeria. Streams of chamber temperature, relative humidity, airflow and power draw were synchronised with a virtual model at one-minute resolution across 42 drying batches. The twin predicted moisture ratio with a root-mean-square error of 0.031 and tracked humidity disturbances that a static recipe model missed entirely. The PdM engine detected incipient fan-bearing degradation 5.3 h before functional failure, achieving 94.0% precision and 91.2% recall while correctly rejecting weather-driven anomalies. Relative to static recipe operation, twin-optimised drying cut specific energy use by 31%, batch-level CO2e emissions by 36% and spoilage by roughly two-thirds. The results show that modestly instrumented, climate-smart dehydrators can deliver industrial-grade reliability alongside material decarbonisation gains, offering a replicable template for smallholder-linked food enterprises in emerging economies. Keywords: digital twin; predictive maintenance; food dehydration; drying kinetics; climate-smart agriculture
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21513181first seen 2026-07-26 04:16:00
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