From Process Digital Twins to Portfolio and Network Intelligence: Enterprise-Level Knowledge Infrastructure Driving Decision Intelligence

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ID: 332060
2026
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Abstract
Abstract Artificial intelligence has widely optimised material screening, reaction tuning and unit-level digital twins in chemical and energy industries, facilitating clean processes. Yet existing tools fail to support high-level strategic decisions on product portfolios and cross-site production networks. Fragmented cross-departmental data and static planning models delay agile responses to volatile feedstock, demand and logistics conditions. Drawing on knowledge-graph process twin frameworks, this commentary argues industrial artificial intelligence should evolve from isolated process optimisation to enterprise-wide portfolio-network intelligence. Machine-readable unified knowledge bases integrating products, assets, supply chains and carbon constraints underpin scalable business digital twins, enabling consistent, traceable cross-functional optimisation. Such infrastructure empowers artificial intelligence to evaluate technical feasibility alongside profitability, asset utilisation and carbon performance under uncertainty, guiding optimal product selection and site allocation. This paper concludes that structured corporate knowledge infrastructure, rather than standalone algorithms, constitutes the long-term foundation for artificial intelligence-driven holistic business decision-making across chemical value chains.
Reference Key
openalex_W7221356774 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muye Xiao, Ning Han
Journal journal of modern power systems and clean energy
Year 2026
DOI
10.1093/ce/zkag072
URL
https://academic.oup.com/ce/advance-article-pdf/doi/10.1093/ce/zkag072/71820488/zkag072.pdf
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