Del Duca, Vincenzo (2025) Environmental, economic and social analysis of novel fuel production processes. [Tesi di dottorato]

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Abstract

The growing urgency to mitigate climate change and decarbonize the global economy demands the development of innovative and sustainable energy pathways. This PhD thesis investigates the environmental, economic, and social dimensions of novel fuel production processes derived from residual biomass, contributing to the understanding and advancement of the green energy transition. The research aims to support the development of decision-support tools for guiding strategic choices in the transition toward low-carbon energy systems. To this end, a comprehensive review of the state of the art on green energy transition models was carried out to identify the variables and methodological approaches most adopted to jointly analyse environmental, economic, and social aspects. The review revealed that the most widely used input variables are economic and environmental, while output variables often include environmental, economic, and increasingly social dimensions. Simulation and statistical models emerged as the predominant approaches, reflecting a growing interest in multidimensional analyses. A taxonomy of variables and a research agenda were developed, outlining open research directions and methodological needs. Building on these findings, the thesis applies three complementary methodologies. First, machine learning (ML) techniques were developed to quantitatively and qualitatively predict the outcomes of torrefaction and pyrolysis processes applied to residual biomass. Trained on a dedicated dataset of 29 scientific studies, the ML models demonstrated high predictive accuracy for key process outputs—such as torrefied biomass composition, biochar, and bio-oil yields—thus confirming the potential of data-driven tools to optimize biofuel production while reducing experimental time and cost. Second, an agent-based model (ABM) was designed to simulate a supply chain for green hydrogen and high-value co-products from sugarcane biomass. The model captures the dynamic interactions among agents, enabling the exploration of technological, socio-economic, and policy scenarios. Results show the scalability and flexibility of the system and demonstrate that ABM can effectively support decision-making in energy transition contexts. Finally, a prospective life cycle assessment (LCA) was performed for the production of bio–synthetic natural gas (bio-SNG) from potato residual biomass along a supply chain located in the Campania region (Southern Italy). The analysis quantified the environmental benefits of bio-SNG compared with fossil natural gas produced and imported into Italy, highlighting significant reductions in greenhouse gas emissions and the regional energy potential associated with potato residues. Beyond environmental gains, the valorisation of these agricultural residues also contributes to reducing dependence on imported fossil fuels, enhancing local energy security, and generating socio-economic opportunities through a regional innovative supply chain. Overall, the development of ML, ABM, and LCA provides a robust interdisciplinary framework capable of addressing the complexity of energy transition processes from multiple perspectives. The development of data-driven, simulation-based, and environmental assessment methodologies demonstrates the potential for developing unified decision-support tools to guide policies and strategies toward a sustainable, low-carbon energy future.

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