Bridging Bioenergy and Artificial Intelligence for Sustainable Technological Synergies

1. Introduction

Biomass represents the world’s largest renewable energy source, providing heat, power, transportation fuels, and feedstock for chemicals and materials while also holding potential for negative emissions through bioenergy with carbon capture and storage [1]. Currently accounting for over 6% of global energy supply and almost 55% of all renewable energy (excluding traditional use of biomass) [2], its deployment varies greatly across regions due to disparities in resource availability, technological capacity, policy frameworks, and economic conditions. Europe leads in advanced biofuels and biogas development under the Renewable Energy Directive [3], while North America prioritizes lignocellulosic ethanol and biogas commercialization supported by the U.S. Renewable Fuel Standard and state-level incentives [4]. Brazil’s well-established sugarcane ethanol program continues to integrate biomass into transportation, with growing diversification into the fields of aviation fuels and anaerobic digestion [5]. In Asia, China and India are rapidly scaling biomass-to-power projects [6], while Southeast Asia emphasizes residue use, though with inconsistent governance [7]. In contrast, Africa and the Middle East face high costs, logistical barriers, and limited policy support despite considerable resource potential, although some countries are beginning to integrate residues and energy crops into their industrial strategies [1].

The role of bioenergy extends beyond climate mitigation, contributing to rural development, energy security, and the circular bioeconomy. Sustainability models highlight benefits such as employment, skills development, and energy access, but also reveal significant risks, including land competition, biodiversity loss, and water stress [1]. Transition strategies increasingly restrict first-generation biofuels in favor of waste- and residue-based pathways to balance sustainability concerns. While second- and third-generation biofuels are approaching commercial viability, they remain constrained by high costs and technological challenges [7]. Looking forward, advancements in feedstock logistics, conversion technologies, and robust governance could enable bioenergy to provide 15–25% of primary energy supply by mid-century, strengthening global energy security and reducing dependence on fossil fuels [8]. Despite these prospects, biomass valorization continues to face challenges linked to feedstock variability, complex conversion pathways, and scale-up uncertainties. In this context, emerging applications of machine learning (ML) and artificial intelligence (AI) offer powerful tools to address these barriers by enabling predictive analytics, real-time monitoring, process optimization, and system-level integration, ultimately enhancing the efficiency, sustainability, and economic feasibility of bioenergy systems [9].

The accelerated expansion of AI infrastructure, characterized by energy-intensive data centers and high-performance computing, has raised concerns about electricity demand and associated carbon emissions. Biomass-based fuels and carbon-negative materials present promising solutions by offering renewable and dispatchable energy sources that complement intermittent renewables and enhance the reliability of critical digital infrastructure [1]. Advanced biofuels can provide reliable power for AI operations, while bio-based carbon-negative materials, such as biochar and lignin-derived composites, can help reduce embodied emissions when used in cooling systems, insulation, and hardware parts. Through integrating AI-enabled optimization of bioenergy processes with innovations in carbon-negative material development, this sector can effectively address the growing energy demands of AI infrastructure, reduce fossil fuel reliance, and promote net-negative emission pathways. This synergy highlights how renewable and carbon-negative bioenergy solutions can support the growing digital economy while promoting climate and sustainability goals.

The present editorial synthesizes insights from a corpus of recent reviews and research analyses on the intersection of ML and bioenergy, emphasizing applications across the biomass supply chain and conversion processes. It proposes a roadmap for advancing bioenergy optimization not only through yield and quality prediction but also through process optimization, monitoring and control, supply chain integration, and alignment with broader energy and policy frameworks. The ultimate vision is a synergistic relationship: AI enabling bioenergy improvements and bioenergy enhancing AI infrastructure via sustainable power solutions.

2. Discussion

2.1. Biofuel Yield and Quality Prediction

One of the first and most significant uses of ML in bioenergy was the prediction of product yield and quality characteristics for various thermochemical and biochemical conversion processes. Traditional kinetic or empirical models often fail to accurately capture the nonlinear dynamics of biomass conversion because of feedstock variability and complex multiphase reactions.

Manatura et al. [10] emphasize the role of ML and statistical analysis in predicting and optimizing biomass torrefaction for bioenergy. ANNs are most used for modeling this process due to their strong predictive ability, while other algorithms like multivariate adaptive regression splines, decision trees, random forests, and support vector machines are also used successfully. These methods help capture interactions between variables such as temperature, residence time, and feedstock composition, especially when combined with factorial design, response surface methodology, and ANOVA, combining ML with statistical rigor.

Zhang et al. [11] show that ML methods effectively predict and optimize hydrothermal biomass treatment, including carbonization, liquefaction, and gasification. ML models reliably forecast yields, compositions, and properties of products (hydrochar, bio-oil, syngas, and byproducts) across biomass types and reaction parameters, often with R² > 0.85. Key inputs include biomass composition and process conditions, with temperature and carbon content often being crucial for product quality and yield. This review highlights ML’s role in prediction, mechanistic insight, process optimization, and experimental design. It calls for future efforts to improve model generalizability, interpretability, data sharing, and multi-target modeling to scale up hydrothermal treatment.

ML-driven predictive modeling has also benefited pyrolysis and co-pyrolysis. ML models (ANN, RF, SVR, XGBoost) often achieve R² > 0.9 for bio-oil, biochar, and biogas yields [12]. In microwave-assisted pyrolysis, ML outperforms traditional models in terms of yield and quality prediction by capturing nonlinear interactions [13]. Case studies show ML’s high predictive power, speed, and flexibility, but challenges include interpretability and data quality. The review urges more research on feature selection, data integration, and industrial adoption. In co-pyrolysis of biomass and plastics, ANNs, neuro-fuzzy systems, and tree algorithms predict yields and characterize synergistic effects that are less easily captured by mechanistic models [14]. However, there is a lack of studies analyzing ML for catalytic pyrolysis and co-pyrolysis.

Gasification research also emphasizes the usefulness of supervised ML for predicting hydrogen-rich syngas production. Wang et al. [15] found that models like support vector regression, random forests, neural networks, and gradient boosting accurately predict syngas from biomass gasification. Effective models depend on data quality, feature engineering, and avoiding overfitting. Boosted models, such as XGBoost and GBR, perform well with small and clean datasets, while bagged models like random forest excel with larger or noisier data. Feature importance analyses using Garson’s algorithm, Pearson correlation, SHAP values, and tree-based methods consistently identify parameters like the steam-to-biomass ratio (S/B), equivalence ratio (ER), temperature, and feedstock composition as key. The review suggests that larger datasets, better uncertainty quantification, interpretability, and further research into deep learning and robust algorithms are needed to improve accuracy and application.

In addition to fuel quality, ML has advanced biomass briquette optimization. Nakimuli et al. [16] reviewed ML’s use in predicting the quality of biomass briquettes derived from agricultural and municipal solid organic waste, emphasizing its role in sustainable, low-carbon energy. They detail how ML models like random forest and ANNs achieve high accuracy in key parameters, including deformation energy (R² = 0.9936) and impact resistance (R² = 0.8936), respectively. These models enable rapid, non-destructive, and cost-effective assessment of properties, supporting traditional testing methods. The authors identify gaps in model generalizability across feedstocks and call for broader quality parameters. They conclude that AI solutions will advance green energy and biomass use.

Taken together, these studies highlight a paradigm shift in which data-driven machine learning models enable rapid, accurate, and non-destructive yield and quality predictions, reducing experimental demands, lowering costs, and enhancing both research efficiency and industrial decision-making through optimized input–output strategies.

2.2. Process Optimization

Prediction alone is insufficient. What distinguishes ML methods in bioenergy conversion is their ability to optimize process parameters dynamically. Statistical optimization frameworks like RSM, ANOVA, and factorial design remain relevant, but ML expands optimization by balancing multiple objectives simultaneously (maximizing yield, minimizing emissions, stabilizing quality features). For example, in torrefaction, combining theoretical models with ML predictors improves interpretability and fine-tunes parameters like temperature and residence time to balance energy yield and carbon content [10]. In HTT optimization, ML facilitates optimization not only at the level of single targets (such as hydrochar yield) but also in multi-target optimization, ensuring that bio-oil quality, syngas yield, and aqueous phase treatment efficiencies are balanced against one another [11]. For gasification, the optimization problem shifts toward configurations that maximize hydrogen selectivity, reduce tar formation, and increase syngas purity. Boosted models (XGBoost, GBR) have shown remarkable value for parameter tuning with limited datasets, while ensemble methods handle process uncertainties and operational variability effectively [15]. In the co-treatment of feedstocks, ML plays a key role in optimizing synergistic reactions. By modeling the nonlinear effects of blend ratios, heating rates, and catalysts, ML supports the strategic design of co-process configurations for better target product upgrades.

Optimization also applies to pretreatment pathways, where ML can assess the effects of acid, alkali, enzymatic, or ionic liquid pretreatments on yields and energy use. By mapping multidimensional input–output spaces, ML finds the best pretreatment conditions in different situations, such as biochar production for energy storage or bio-oil production and upgrading for liquid fuels. Furthermore, optimization covers everything from process-level parameters to entire biomass energy systems. Recent reviews suggest that optimization models should not stay limited to experimental labs but should also include supply chain optimization, lifecycle analyses, and multi-factor techno-economic assessments. For example, ML methods have been integrated into predictive optimization regimes for bioenergy value chains, improving efficiency in feedstock management, transportation, and technology deployment [17,18].

2.3. Real-Time Process Monitoring and Automated Control

An essential dimension beyond prediction and optimization is process monitoring and automated control. Modern AI approaches enable digital twins, real-time sensing integration, and closed-loop process control for biomass processing. Digital convergence between ML, smart sensors, and process modeling underpins this transformation.

Research on machine learning (ML)-enabled digital twins (DTs) for bioenergy systems converges on a shared objective: the transition from static process models (functioning as passive simulators) to closed-loop, real-time optimization of biomass conversion and energy generation. Akhator and Oboirien [19] address this challenge within the context of biomass gasification power plants (BGPPs) with integrated CO2 capture. They propose a comprehensive DT-BGPP architecture intended to bridge the “digital shadow” gap and enable bidirectional data flow and control mechanisms. Extending this discourse, Sheik et al. [20] highlight the potential of algal digital twins (ADTs), illustrating how these systems can evolve from decision-support tools into active operational drivers while simultaneously tackling issues of interoperability and governance. Selwal et al. [21] expand the scope to encompass hybrid biorefineries, providing a review of AI–digital twin synergies in predictive optimization and the pursuit of circularity. In parallel, Ananda et al. [22] integrate advances in laboratory automation, reinforcement learning, and explainable AI for biomass transformation, with particular emphasis on the infrastructural and workforce challenges that must be addressed to facilitate plant-scale implementation.

Across these contributions, two complementary methodological trajectories emerge. Studies centered on digital twin frameworks [19,20,21] advocate for combining physics-informed dynamic process models with ML layers and dense sensor networks. Specifically, Akhator and Oboirien [19] define four core DT-BGPP components: high-order process models, statistical anomaly detection modules, distributed sensor arrays, and localized pre-simulations, each interconnected by a “system genome” that ensures adaptive calibration. Sheik et al. [20], employing a PRISMA-guided systematic review, map the ADT development pipeline, spanning interoperability standards, AIoT-based modeling approaches, visualization strategies, and decision-support applications. Similarly, Selwal et al. [21] present case studies demonstrating the use of surrogate models embedded within digital twins, which enable near-instantaneous setpoint optimization at the scale of industrial power plants.

The findings across these studies are convergent, indicating that hybrid approaches integrating physics-based and data-driven methods consistently outperform either framework in isolation. Akhator and Oboirien [19] illustrate that combining adaptive ML-based anomaly detection with mechanistic models strengthens fault diagnosis and enhances operational resilience. Sheik et al. [20] contend that technical sophistication alone is insufficient, underscoring the importance of transparency, governance, and stakeholder trust for successful ADT deployment. Selwal et al. [21] provide empirical evidence of efficiency improvements when ML-enabled digital twins are integrated into IoT environments, documenting retuning intervals of 3–5 min in a 200 MW biomass power plant. Ananda et al. [22] report that RL-based control strategies significantly reduce overshoot and settling time when compared to conventional control approaches, while ML models demonstrate superior generalization performance relative to mechanistic models across heterogeneous biomass feedstocks.

Despite the emerging consensus, several divergences and unresolved questions remain. The reviewed studies consistently identify critical prerequisites for digital twin deployment, namely reliable data streams, robust cyber–physical infrastructure, and flexible hybrid modeling frameworks. However, significant research gaps persist. These include (1) the formalization of ontologies and the integration of explainable AI (XAI) techniques to ensure transparency and auditability; (2) the development of digital twins that extend beyond individual processes to encompass supply chain and multi-process levels, thereby enabling optimization across energy efficiency, carbon intensity, and output quality; (3) the design of couplings between physics-based and ML models that preserve predictive accuracy and system reliability under conditions of distributional shift; and (4) the creation of economic and governance frameworks capable of supporting autonomous, optimized bioenergy plants that are both technically viable and socially acceptable. Addressing these challenges will be decisive in determining whether ML-enabled digital twins evolve into the foundational infrastructure for next-generation bioenergy systems.

Within thermochemical processing for bioenergy, ML-enhanced real-time monitoring enables adaptive control of reactor conditions, facilitating both batch-level and continuous optimization, which is particularly critical for multi-scale reactors, where single-particle models fail to capture complex heat and mass transfer dynamics on an industrial scale [23]. Similar approaches are applied in forestry and biomass logistics, where ML supports real-time monitoring of harvesting, transportation, and delivery operations, allowing supply chains to adapt dynamically to demand or weather fluctuations [24]. Model predictive control (MPC) frameworks further illustrate this integration, particularly in solar greenhouse-based smart energy hubs, where ML-guided MPC balances electricity, hydrogen, and thermal flows to stabilize grids and maximize hydrogen production from biomass gasification [25].

Collectively, these advances illustrate how AI-driven systems are transforming bioenergy into autonomous, self-correcting operations that reduce costs, downtime, and inefficiencies. More broadly, AI and ML applications span the entire bioenergy value chain, from biomass characterization, yield prediction, and conversion optimization to logistics planning, grid integration, and decision support, strengthening efficiency, sustainability, and resilience. As highlighted by Anbarasu et al. [9], AI enhances precision agriculture, predictive modeling, and process control while helping to reduce emissions and supporting compliance, positioning AI–bioenergy integration as a paradigm shift with significant implications for energy security, climate resilience, and the transition toward low-carbon energy systems.

2.4. Creating a Positive Feedback Loop: AI for Bioenergy and Bioenergy for AI

The rapid growth of AI workloads is transforming data centers into one of the fastest-growing sources of electricity demand worldwide. Global data center electricity use is expected to more than double between 2022 and 2026, exceeding 1000 TWh, with AI alone causing a tenfold rise in demand during that period [26]. This increase is driven by the computational intensity of large language models, where inference (rather than training) now makes up as much as 90% of lifecycle energy use [27]. Despite previous efficiency improvements from hyperscale cloud infrastructure, the rate of progress has slowed, and electricity demand is now growing faster than efficiency gains can offset it [28]. Regional effects are particularly severe, such as in Northern Virginia (USA), where data centers already consume nearly a quarter of the total electricity, and this could reach half under high-growth scenarios, threatening grid stability and prompting discussions about adding more fossil fuel generation [26]. These trends raise concerns about a potential “AI electricity crisis,” where computing demands exceed grid expansion, leading to economic and operational disruptions. However, empirical data remains limited, especially for inference-phase energy costs across proprietary models, leaving significant uncertainty about the actual environmental impact and complicating policymaking [27].

In response, bioenergy, combined with innovations in biomaterials, offers a promising strategy to reduce the growing energy and carbon footprint of AI infrastructures. As a dispatchable renewable resource, bioenergy can provide reliable, low-carbon power that supports grid stability during peak data center demand, complementing variable wind and solar generation. Co-locating bioenergy plants near major AI hubs could help alleviate transmission bottlenecks and decrease reliance on gas-fired generation in regions already operating near reserve margin thresholds [28]. Beyond electricity generation, carbon materials derived from biomass feedstocks can be used in construction and cooling infrastructure for data centers, reducing embodied carbon and promoting circular supply chains. Such integration could provide a dual benefit, lowering both the operational and material lifecycle emissions of AI data centers. However, scaling these solutions requires careful governance to prevent land-use conflicts, ensure sustainable feedstock sourcing, and avoid displacing food production.

In summary, a mutually beneficial AI–bioenergy relationship supports sustainable energy, with AI accelerating bioenergy innovation and bioenergy satisfying AI’s energy needs. AI improves resource assessment, yield prediction, real-time monitoring, and supply chain optimization, while advances in digital twins, nanocatalysts, and bioengineered feedstocks create more efficient, climate-resilient bioenergy options. Bioenergy offers a carbon-neutral, distributed, and resilient power source for AI infrastructures, reducing fossil fuel dependence and strengthening digital ecosystems. This relationship emphasizes that investing in AI boosts bioenergy efficiency and bioenergy breakthroughs sustain AI systems, fostering ongoing improvement.

3. Conclusions

Integrating machine learning with bioenergy represents a pivotal step in advancing sustainable energy transitions by enhancing yield prediction, process optimization, supply chain resilience, and system-level integration. Despite ongoing challenges such as fragmented data, limited transparency, and uneven global progress, ML enables scalable, efficient, and accountable solutions that strengthen bioenergy’s role as both a clean fuel and a foundation for negative-emission strategies. Realizing this potential requires interdisciplinary collaboration, open data sharing, explainable AI, and supportive policies, ensuring that the synergy between AI and bioenergy not only strengthens renewable energy systems but also advances global energy security and climate change mitigation.