Abstract
Digital transformation is widely seen as a route to accelerate bioenergy toward sustainable energy delivery, yet digital technologies remain underutilised in bioenergy systems because of heterogeneous feedstocks, complex reaction mechanisms and variable plant configurations. This work combines three complementary evidence streams: a systematic review of 231 operationally focused peer-reviewed articles (2012–2025), thematic analysis of an academic–industrial workshop (80+ participants, Sheffield, November 2024) and a targeted survey of 22 UK-focused bioenergy professionals comprising technical and engineering staff (50%), academic and research professionals (27%), management (18%) and other specialists (5%). The literature findings are globally representative (Scopus-indexed, 30+ countries); the workshop and survey findings are UK-focused. Seven digital-tool families were identified: AI/ML (31.2%), geographic information systems (27.7%), digital twins (15.2%), model predictive control (10.8%), supervisory control and data acquisition (5.2%), decision support systems (5.2%) and multi-criteria decision analysis (4.8%). AI/ML dominates the recent literature, but the evidence base is dominated by small, single-site, offline studies with limited external validation, complicating translation to industrial deployment. Across the three streams, the most frequently cited barriers are fragmented data infrastructure, insufficient standardised protocols, skills gaps, institutional resistance and biological process complexity. The survey findings should be read as exploratory evidence from experienced UK-based bioenergy industrial experts, rather than population-level claims. By triangulating the three streams, this work offers researchers, industry and policymakers a prioritised roadmap clarifying where digital tools have proven themselves, where they remain immature and which policy, skills and investment instruments can move the sector from proof-of-concept to commercial deployment.