The recycling of waste printed circuit boards (PCBs) presents a growing challenge and opportunity within the broader context of electronic waste (WEEE) and circular economy strategies. This thesis addresses the underexplored problem of selecting optimal recycling processes for PCBs by proposing a data-oriented decision support approach. Through a multi-level literature review and gap analysis, it identifies the absence of specific information systems to support the selection of PCB processes, and a lack of a holistic perspective when evaluating different recycling techniques. In response, the thesis develops a relational database model and a decision support system structured to enable simulation-based assessments. Particular focus is placed on defining the full spectrum of data required to inform such simulations, including PCB-specific attributes, market conditions, and process-level operational parameters, along with their potential sources. To assess the model’s feasibility and relevance, interviews were conducted with academic researchers and industry professionals. Thematic analysis revealed key implementation barriers, such as the lack of component-level data, resistance to information sharing, and limited industrial recycling diversity, as well as enablers like policy-driven data transparency or recycling process technology innovation. The framework supports structured decision-making using the Analytic Hierarchy Process (AHP). Through interviews and expert input, the weights of the AHP criteria were assigned, with profitability and technology readiness emerging as the most important. Although simulation execution is outside the thesis scope, the proposed architecture creates an initial basis for future implementation. The resulting model contributes to bridging the gap between academic research and real-world decision-making in PCB recycling process selection.
Il riciclo delle schede elettroniche a fine vita (PCBs) rappresenta una sfida e un’opportunità sempre più rilevante nel contesto dei rifiuti elettronici (RAEE) e dell’economia circolare. Questa tesi affronta il problema ancora poco esplorato della selezione dei processi ottimali di riciclo delle PCB, proponendo un approccio decisionale orientato ai dati. Con una literature review su più livelli e un’analisi dei gap esistenti, viene evidenziata l’assenza di sistemi informativi specifici a supporto della selezione dei processi di trattamento dei PCB e la mancanza di una prospettiva olistica nella valutazione delle diverse tecniche di riciclo. In risposta a queste lacune, la tesi sviluppa un modello di database relazionale e un Decision Support System strutturato per consentire valutazioni basate sulla simulazione. Attenzione particolare è rivolta alla definizione dell’insieme di dati necessari peralimentare tali simulazioni, inclusi attributi specifici dei PCB, condizioni di mercato e parametri operativi a livello di processo, oltre alle fonti di questi dati. Per valutare la fattibilità e la rilevanza del modello, sono state condotte interviste con ricercatori e professionisti del settore. L’analisi tematica ha evidenziato ostacoli chiave all’implementazione, come la mancanza di dati a livello di componente, la resistenza alla condivisione delle informazioni e la scarsa diversificazione industriale nei processi di riciclo, nonché alcuni fattori abilitanti, come la maggiore trasparenza dei dati favorita dalle politiche europee o l’innovazione tecnologica nei processi di trattamento. Il framework supporta un processo decisionale strutturato tramite Analytic Hierarchy Process (AHP). Le interviste agli esperti hanno permesso di attribuire i pesi ai criteri dell’AHP, identificando la redditività e la maturità tecnologica come i criteri più rilevanti. Sebbene l’esecuzione delle simulazioni sia fuori dallo scopo di questa tesi, l’architettura proposta pone le basi per una futura implementazione. Il modello contribuisce a colmare il divario tra ricerca accademica e processi decisionali nel contesto reale della selezione dei trattamenti per il riciclo delle PCB.
A data-driven framework for selecting recycling processes of end-of-life PCBs
Notarianni, Anita Clara
2024/2025
Abstract
The recycling of waste printed circuit boards (PCBs) presents a growing challenge and opportunity within the broader context of electronic waste (WEEE) and circular economy strategies. This thesis addresses the underexplored problem of selecting optimal recycling processes for PCBs by proposing a data-oriented decision support approach. Through a multi-level literature review and gap analysis, it identifies the absence of specific information systems to support the selection of PCB processes, and a lack of a holistic perspective when evaluating different recycling techniques. In response, the thesis develops a relational database model and a decision support system structured to enable simulation-based assessments. Particular focus is placed on defining the full spectrum of data required to inform such simulations, including PCB-specific attributes, market conditions, and process-level operational parameters, along with their potential sources. To assess the model’s feasibility and relevance, interviews were conducted with academic researchers and industry professionals. Thematic analysis revealed key implementation barriers, such as the lack of component-level data, resistance to information sharing, and limited industrial recycling diversity, as well as enablers like policy-driven data transparency or recycling process technology innovation. The framework supports structured decision-making using the Analytic Hierarchy Process (AHP). Through interviews and expert input, the weights of the AHP criteria were assigned, with profitability and technology readiness emerging as the most important. Although simulation execution is outside the thesis scope, the proposed architecture creates an initial basis for future implementation. The resulting model contributes to bridging the gap between academic research and real-world decision-making in PCB recycling process selection.File | Dimensione | Formato | |
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2025_07_Notarianni_Tesi_01.pdf
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2025_07_Notarianni_Executive Summary_02.pdf
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https://hdl.handle.net/10589/239789