What question did this study set out to answer?

To evaluate the current state and challenges of digital technologies in bioenergy systems through literature review, workshops, and surveys.

May 8, 2026Biomass and BioenergyOpen Access

Digitalising bioenergy processes: Surveying literature and industrial experience to understand progress, challenges and opportunities

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Mixed-method review explores digital tools in bioenergy, highlighting barriers and opportunities for advancement.

To evaluate the current state and challenges of digital technologies in bioenergy systems through literature review, workshops, and surveys.
Conducted a systematic review of 231 peer-reviewed articles focused on operational bioenergy insights from 2012–2025.
Facilitated an academic–industrial workshop with 80+ participants to gather insights on digital tool applications.
Administered a targeted survey to 22 UK bioenergy professionals, assessing expertise and barriers in digital tool deployment.
Identified seven categories of digital tools used in bioenergy, with AI and machine learning being the most prevalent.
Barriers to digital tool adoption include fragmented data infrastructure and skills gaps, as reported by UK professionals.
Key opportunities for advancement are real-time monitoring, predictive maintenance, and process optimization.

Dekhici et al. (2026) studied this question.

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May 8, 2026Biomass and BioenergyOpen Access

View Full Paper

Ask AI

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Authors

Mixed-method review explores digital tools in bioenergy, highlighting barriers and opportunities for advancement.

To evaluate the current state and challenges of digital technologies in bioenergy systems through literature review, workshops, and surveys.
Conducted a systematic review of 231 peer-reviewed articles focused on operational bioenergy insights from 2012–2025.
Facilitated an academic–industrial workshop with 80+ participants to gather insights on digital tool applications.
Administered a targeted survey to 22 UK bioenergy professionals, assessing expertise and barriers in digital tool deployment.
Identified seven categories of digital tools used in bioenergy, with AI and machine learning being the most prevalent.
Barriers to digital tool adoption include fragmented data infrastructure and skills gaps, as reported by UK professionals.
Key opportunities for advancement are real-time monitoring, predictive maintenance, and process optimization.

Dekhici et al. (2026) studied this question.

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