This thesis develops an integrated system for hydrogen production from fruit-based food waste using a combined dark- and photo-fermentation approach. A stochastic Python model was first created to characterize the CHON composition of the heterogeneous fruit-waste mixture through 300,000 random simulations, providing a statistically robust representation of the substrate. The full conversion chain (including pretreatment, biological hydrogen production, carbon capture, metal-hydride hydrogen storage, and fuel-cell electricity generation) was simulated in Aspen Plus and Engineering Equation Solver (EES). Hydrogen production in the dark-fermentation stage was simulated using both the theoretical acetic-pathway yield and a realistic kinetic formulation that incorporates hydrogen partial-pressure inhibition. Photo-fermentation further enhanced hydrogen recovery through VFA conversion, again producing yields consistent with literature data. The integrated carbon-capture unit demonstrated stable absorber–stripper operation with effective CO₂ removal and negligible hydrogen losses, confirming compatibility between biological gas streams and MEA-based solvent systems. The purified hydrogen was stored using magnesium-based hydrides, achieving near-complete absorption under mild conditions, and subsequently converted into electricity through a solid oxide fuel cell. Overall, the results demonstrate the technical feasibility of a fully integrated waste-to-hydrogen system that links stochastic feed characterization, multi-stage fermentation, carbon capture, solid-state hydrogen storage, and fuel-cell power generation. The modelling framework developed in this work provides a basis for designing decentralized bio-refinery systems capable of converting organic waste into clean hydrogen and renewable electricity with high process coherence.
Questa tesi sviluppa un sistema integrato per la produzione di idrogeno a partire da rifiuti alimentari di origine frutticola, utilizzando un approccio combinato di fermentazione oscura e foto-fermentazione. È stato innanzitutto elaborato un modello stocastico in Python per caratterizzare la composizione CHON della miscela eterogenea di scarti di frutta mediante 300.000 simulazioni casuali, ottenendo così una rappresentazione statisticamente robusta del substrato. L’intera catena di conversione — comprendente pre-trattamento, produzione biologica di idrogeno, cattura della CO₂, stoccaggio mediante idruri metallici e generazione elettrica tramite celle a combustibile — è stata simulata in Aspen Plus ed Engineering Equation Solver (EES). La produzione di idrogeno nella fase di fermentazione oscura è stata modellata sia tramite la resa teorica del percorso acetico, sia attraverso una formulazione cinetica realistica che include l’inibizione dovuta alla pressione parziale dell’idrogeno. La foto-fermentazione ha ulteriormente incrementato il recupero di idrogeno attraverso la conversione degli acidi grassi volatili, producendo rese coerenti con i dati di letteratura. L’unità integrata di cattura della CO₂ ha mostrato un funzionamento stabile della sezione assorbitore–stripper, con un’efficace rimozione dell’anidride carbonica e perdite trascurabili di idrogeno, confermando la compatibilità tra le correnti gassose biologiche e i sistemi a solvente MEA. L’idrogeno purificato è stato quindi stoccato mediante idruri a base di magnesio, raggiungendo un’assorbimento quasi completo in condizioni blande, e successivamente convertito in energia elettrica attraverso una cella a ossidi solidi. Complessivamente, i risultati evidenziano la fattibilità tecnica di un sistema integrato waste-to-hydrogen che collega caratterizzazione stocastica del substrato, fermentazione multi-stadio, cattura della CO₂, stoccaggio solido dell’idrogeno e generazione elettrica tramite fuel cell. Il modello sviluppato costituisce una base metodologica per la progettazione di bio-raffinerie decentralizzate in grado di trasformare rifiuti organici in idrogeno pulito ed energia rinnovabile con elevata coerenza di processo.
Simulation of hydrogen production and storage from fruit-based food waste via multi-stage fermentation and carbon capture utilization
AHMADI, MEHRAN
2024/2025
Abstract
This thesis develops an integrated system for hydrogen production from fruit-based food waste using a combined dark- and photo-fermentation approach. A stochastic Python model was first created to characterize the CHON composition of the heterogeneous fruit-waste mixture through 300,000 random simulations, providing a statistically robust representation of the substrate. The full conversion chain (including pretreatment, biological hydrogen production, carbon capture, metal-hydride hydrogen storage, and fuel-cell electricity generation) was simulated in Aspen Plus and Engineering Equation Solver (EES). Hydrogen production in the dark-fermentation stage was simulated using both the theoretical acetic-pathway yield and a realistic kinetic formulation that incorporates hydrogen partial-pressure inhibition. Photo-fermentation further enhanced hydrogen recovery through VFA conversion, again producing yields consistent with literature data. The integrated carbon-capture unit demonstrated stable absorber–stripper operation with effective CO₂ removal and negligible hydrogen losses, confirming compatibility between biological gas streams and MEA-based solvent systems. The purified hydrogen was stored using magnesium-based hydrides, achieving near-complete absorption under mild conditions, and subsequently converted into electricity through a solid oxide fuel cell. Overall, the results demonstrate the technical feasibility of a fully integrated waste-to-hydrogen system that links stochastic feed characterization, multi-stage fermentation, carbon capture, solid-state hydrogen storage, and fuel-cell power generation. The modelling framework developed in this work provides a basis for designing decentralized bio-refinery systems capable of converting organic waste into clean hydrogen and renewable electricity with high process coherence.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/101762