AI-Enhanced Framework for Evaluating Bioenergy Material Characteristics Linked to Lifecycle Emissions

Abstract

Biomass has become a prominent renewable energy source that could play an important role in low carbon energy production, waste valorization and mitigation of greenhouse gases. This potential in Sustainable Development has been increasingly noticed, especially with global energy systems moving away from fossil fuels. But the performance and environmental impact of biomass energy is different for different feedstocks because of the small differences in composition and their nonlinear characteristics. In this work, the question is: Is it possible to create a unifying framework for biomass that can be interpreted and used to assess biomass based on energy content and life cycle emissions?

Thematic integration of assessments of bioenergy, life cycle analysis and machine learning interpretability. The research shows a correlation between the characteristics of the feedstocks (carbon, lignin, xylan, moisture, ash) and the predicted energy properties (HHV, LHV, bioenergy potential) and estimated lifecycle GHGs. Core literature suggests that it is important to assess these interactions comprehensively and not separately to gain a deeper insight into biomass sustainability and the selection of feedstock. The importance of using the interpretable machine learning SHAP, permutation importance, and sensitivity analysis methods to identify important variables that affect bioenergy and emissions.

Research methodology adopts a systematic process involving data pre-processing, feature engineering, computing of the empirical baseline (HAL), and model development using machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), XGBoost and Artificial Neural Network (ANN). It is found that nonlinear learning approaches perform better than traditional empirical predictions with SVM model showing the best results for predicting HHV and LHV. Key variables were identified to influence energy and emissions and char from these feedstocks was ranked as the most preferable in this regard due to their high energy potential and low life cycle emissions. This coordinated framework is a clear and sustainability-oriented tool to direct biomass assessment and facilitate low carbon bio-energy development.