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Coagulation and Flocculation Optimization for Sustainable Wastewater Treatment Investigation of coagulation methods and predictive modelling to reduce chemical consumption and carbon footprint
(2026) Ljungberg, Anton; Martinez Eriksson, Anton
This thesis investigates opportunities for optimizing the primary coagulation and flocculation process at the company that treats industrial wastewater. The treatment process handles highly variable incoming raw water, where chemical dosing is currently based on fixed dosages. These variations in incoming water quality create opportunities to proactively improve resource efficiency and process stability, with a primary focus on reducing environmental impact. The aim of this work was to investigate how variations in raw water quality influence the efficiency of the coagulation and flocculation process and to examine how a data driven approach can support and improve chemical dosing. The objective was to reduce chemical consumption and its associated carbon footprint while maintaining or improving treatment performance. This thesis was based on laboratory jar tests using wastewater collected from the inlet of the treatment process following initial sedimentation. A range of experimental conditions were tested and evaluated, including the current performance of TOC, absorbance and turbidity removal, pH adjustments for ferric chloride, polymer addition, mixing strategies and dilution series. The dilution series was evaluated based on the measurement error between the instruments used at Chalmers and at the company's lab. Key water quality and performance parameters for the coagulation and flocculation process were measured, including TOC, turbidity, absorbance, conductivity and sludge production. In addition, a linear model was developed in Python to predict TOC removal from SUVA. Based on this model, an algorithm was proposed to predict the coagulation outcome. The algorithm suggests an optimal chemical dose based on these raw water quality parameters. The results show that the performance of the treatment process is influenced by several operational conditions, such as pH and mixing strategies, and by how these are managed in relation to variations in the incoming raw water quality, which directly affects the efficiency of contaminant removal. The study also demonstrates that the characteristics of the raw water influence the required treatment level, highlighting the importance of an adaptive and flexible approach to chemical dosing. Furthermore, the findings indicate that transitioning from a fixed dosing strategy to a more adaptive approach can improve both plant stability and chemical use efficiency, resulting in reduced carbon footprint. By combining experimental and modelling approaches, this thesis demonstrates how treatment performance can be better understood and managed under varying operating conditions.
Utvärdering av AI-integrering i åskskyddsdesign - Utvärdering av artificiell intelligens vid stöd för ingenjörsmässig design inom åskskyddssystem
(2026) Klingström, Oskar; Svahn, Ivar
På uppdrag av WSP syftar detta arbete till att analysera hur AI-baserade verktyg kan användas för att effektivisera och kvalitetssäkra åskskyddsdesign. Studien undersöker särskilt hur AI kan fungera som beslutsstöd, vilka delar av projekteringsprocessen som lämpar sig för AI-stöd samt hur tekniken förhåller sig till relevanta standarder. Arbetet har genomförts i tre steg: en litteraturstudie av standarder, främst SSEN 62305, en processkartläggning av nuvarande arbetsmetoder inom organisationen samt utveckling och utvärdering av AI-agenter i Copilot Studio. AI-agenterna utvecklades iterativt och utformades för att identifiera parametrar, strukturera indata samt samverka med externa beräkningsverktyg i Excel. Resultaten visar att AI kan stödja flera delar av åskskyddsdesign, särskilt inom riskbedömning, indatahantering och dokumentation. Jämförelser med tidigare projekt visar att AI-agenten kan uppnå resultat med motsvarande noggrannhet som traditionella metoder, både vid riskbedömning och tekniska beräkningar. Vidare indikerar resultaten att en AI-integrerad arbetsprocess kan minska manuella moment och därmed effektivisera arbetet. Samtidigt visar studien att AI bör användas som ett beslutsstöd och att mänsklig kontroll är nödvändig, särskilt vid säkerhetskritiska tillämpningar. Arbetet demonstrerar därmed den tekniska genomförbarheten för AI inom åskskyddsdesign, men vidare utveckling krävs innan fullskalig implementering är möjlig.
Remanufacturing and Refurbishment of Truck Spare Parts A Mapping and Environmental Assessment of Circular Processes
(2026) Jansson, Julia; Jarl Benander, Johanna
The transition towards a circular economy is considered important for reducing resource use and environmental impacts within the automotive industry. Remanufacturing and refurbishment are two circular strategies that extend the lifetime of components through a number of re-engineering processes. While this save materials, it is still important to evaluate the environmental impact from these activities. The purpose of this thesis was to map circular process steps and component flows within remanufacturing and refurbishment of three truck spare parts at Volvo Group: a diesel particulate filter (DPF), an engine, and an electric vehicle battery. This was done through a semi-quantitative Material Flow Analysis (MFA). Additionally, the study aimed to assess the environmental impacts associated with the remanufacturing of the DPF and the engine through a Life Cycle Assessment (LCA). Hotspot and sensitivity analyses were conducted to identify key environmental impact drivers and evaluate the influence of transportation modelling assumptions. The results of the semi-quantitative MFA showed that remanufacturing and refurbishment of components vary depending on the component characteristics. The LCA further showed that the environmental impact of remanufacturing is influenced by both component type and process complexity, with transportation and material replacement significantly affecting the results. These aspects should therefore be considered when assessing the environmental benefits of remanufacturing.
Explainable AI for Automatic Document Classification in Regulated Finance
(2026) Stenhammar, Zachris; Alavala, Praveen
The increasing volume of digital documents in regulated financial environments has created significant challenges related to information security, regulatory compliance, and efficient information management. Financial institutions routinely process sensitive information, including internal business data, customer records, and regulatory documents, where incorrect handling or classification may result in legal, financial, and reputational consequences. Despite the importance of information classification, the process is often performed manually, making it inconsistent, time-consuming, and difficult to scale. These challenges motivate the need for automated and trustworthy document classification systems that can support regulated organizations while maintaining transparency and accountability. This thesis investigates the use of transformer-based language models for automatic document classification in regulated financial environments, with a particular focus on explainability and auditability. The study explores how contextual and semantic information within documents can be used to distinguish between different information sensitivity levels, including Public, Internal, Confidential, and Strictly Confidential classifications. To address privacy and regulatory constraints, the work utilizes a synthetic and semi-controlled dataset .generated using a controlled template-based synthetic document generation methodology with constrained vocabulary, document structures, and contextual patterns designed to reflect the structural and linguistic characteristics of financial documents while avoiding the use of sensitive real-world data. The proposed system is based on a fine-tuned transformer architecture combined with explainable artificial intelligence (XAI) techniques. Attention-based explanations and Integrated Gradients feature attribution methods are integrated into the classification pipeline to provide insight into the model’s decision making process. The explainability analysis investigates whether the generated explanations align with meaningful contextual indicators associated with document sensitivity and whether they can support transparency, trust, and compliance requirements within regulated financial settings. The experimental results demonstrate that transformer-based models can effectively learn contextual patterns related to information sensitivity within the controlled dataset while also providing interpretable explanations of classification decisions. The study further analyzes explanation consistency, confidence behaviour, robustness against external documents, and potential shortcut learning effects. Since both the training and evaluation data were generated using the same controlled template-based document generation methodology, the results should be interpreted within the context of this experimental setting. Although separate documents were used for training and evaluation, come from the same dataset share similar linguistic and structural characteristics. Therefore, further evaluation using independent datasets is required to assess the generalizability of the proposed approach. This work contributes to the growing field of explainable AI in regulated industries by demonstrating how modern natural language processing techniques can be combined with explainability methods to support secure, transparent, and trustworthy information classification in financial organizations, while also highlighting the importance of independent evaluation when using controlled and synthetic data.
Accessibility in Motion - Developing context-specific accessibility recommendations and re-designing the Thule mobile application
(2026) Akin, Enes; Blom, Benjamin
Mobile applications are increasingly used in dynamic environments, yet existing accessibility frameworks, such as WCAG 2.2, primarily address static use cases. This Master’s thesis investigates the limitations of current guidelines when faced with Situationally Induced Impairments and Disabilities (SIIDs), including screen glare and reduced dexterity from physical multitasking. In collaboration with Thule Sweden AB, the study employs a Research through Design methodology within a Double Diamond framework. By combining contextual inquiry, general and targeted surveys, and heuristic evaluations, the research identifies critical pain points in active mobile usage. The project culminates in the formulation of 14 context-specific accessibility recommendations and the iterative development of a high-fidelity mobile prototype. This proposed final design features an "Active Mode", an interface variation that re moves non-essential visuals to prioritize cognitive clarity. The findings demonstrate that digital accessibility in high-motion environments relies heavily on functional minimalism, ergonomic adaptability, and error tolerance, ensuring usability supersedes traditional aesthetics.