Digital twin-enabled multi-agent control for energy-efficient wood drying in desiccant-assisted heat pump systems.
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| Title: | Digital twin-enabled multi-agent control for energy-efficient wood drying in desiccant-assisted heat pump systems. |
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| Authors: | Bhatta, Kshitij1 (AUTHOR), Waseem, Muhammad1 (AUTHOR), Liu, Mingzhe2 (AUTHOR), Yang, Zhiyao2 (AUTHOR), O'Neill, Zheng2 (AUTHOR), Chang, Qing1 (AUTHOR) qc9nq@virginia.edu |
| Source: | Drying Technology. 2026, Vol. 44 Issue 5, p639-657. 19p. |
| Subjects: | Digital twin, Lumber drying, Clean energy, Reinforcement learning, Energy consumption, Industry 4.0, Multiagent systems, Heat pumps |
| Abstract: | This article presents a digital twin-enabled framework for modeling and control of wood drying in a Desiccant-Assisted Heat Pump (DAHP) system. The digital twin integrates high-fidelity physics-based models of the heat pump, kiln chamber, and wood moisture–stress behavior, capturing coupled heat and mass transfer processes critical to drying performance. Leveraging the digital twin as the environment, the drying process is cast as a Decentralized Markov Decision Process (Dec-MDP) and addressed through Multi-Agent Reinforcement Learning (MARL). The proposed approach reduces total site energy consumption by 43.2%, shortens drying duration by approximately eight days, and decreases carbon intensity by up to 94% relative to a conventional boiler-based baseline. To enhance interpretability and support industrial deployment, a heuristic policy distilled from the MARL control achieves comparable performance. By coupling digital twin modeling with advanced learning-based control, this study establishes a deployable pathway toward sustainable, energy-efficient, and high-quality wood drying, with broader implications for next-generation smart manufacturing and energy systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Drying Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192628781 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Digital twin-enabled multi-agent control for energy-efficient wood drying in desiccant-assisted heat pump systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bhatta%2C+Kshitij%22">Bhatta, Kshitij</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Waseem%2C+Muhammad%22">Waseem, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Mingzhe%22">Liu, Mingzhe</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Zhiyao%22">Yang, Zhiyao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22O'Neill%2C+Zheng%22">O'Neill, Zheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chang%2C+Qing%22">Chang, Qing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qc9nq@virginia.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Drying+Technology%22">Drying Technology</searchLink>. 2026, Vol. 44 Issue 5, p639-657. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Lumber+drying%22">Lumber drying</searchLink><br /><searchLink fieldCode="DE" term="%22Clean+energy%22">Clean energy</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink><br /><searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+pumps%22">Heat pumps</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This article presents a digital twin-enabled framework for modeling and control of wood drying in a Desiccant-Assisted Heat Pump (DAHP) system. The digital twin integrates high-fidelity physics-based models of the heat pump, kiln chamber, and wood moisture–stress behavior, capturing coupled heat and mass transfer processes critical to drying performance. Leveraging the digital twin as the environment, the drying process is cast as a Decentralized Markov Decision Process (Dec-MDP) and addressed through Multi-Agent Reinforcement Learning (MARL). The proposed approach reduces total site energy consumption by 43.2%, shortens drying duration by approximately eight days, and decreases carbon intensity by up to 94% relative to a conventional boiler-based baseline. To enhance interpretability and support industrial deployment, a heuristic policy distilled from the MARL control achieves comparable performance. By coupling digital twin modeling with advanced learning-based control, this study establishes a deployable pathway toward sustainable, energy-efficient, and high-quality wood drying, with broader implications for next-generation smart manufacturing and energy systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Drying Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/07373937.2026.2631672 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 639 Subjects: – SubjectFull: Digital twin Type: general – SubjectFull: Lumber drying Type: general – SubjectFull: Clean energy Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Energy consumption Type: general – SubjectFull: Industry 4.0 Type: general – SubjectFull: Multiagent systems Type: general – SubjectFull: Heat pumps Type: general Titles: – TitleFull: Digital twin-enabled multi-agent control for energy-efficient wood drying in desiccant-assisted heat pump systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bhatta, Kshitij – PersonEntity: Name: NameFull: Waseem, Muhammad – PersonEntity: Name: NameFull: Liu, Mingzhe – PersonEntity: Name: NameFull: Yang, Zhiyao – PersonEntity: Name: NameFull: O'Neill, Zheng – PersonEntity: Name: NameFull: Chang, Qing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 07373937 Numbering: – Type: volume Value: 44 – Type: issue Value: 5 Titles: – TitleFull: Drying Technology Type: main |
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