Within the automotive industry, the press hardening process has been increasingly used to manufacture components. The process allows for both ultra high strength as well as good design freedom of the components. The steel is first austenitized, followed by forming and cooling. The work within this thesis was aimed at modeling this process, where variations in the initial microstructure and austenitization parameters were introduced. The steel grade 22MnB5 was utilized throughout the work, with three different initial states being studied, hot rolled, cold rolled, and cold rolled which was soft annealed. Tensile testing was performed for rapidly quenched samples, as well as for samples cooled at lower rates, where mixtures of phases were present in the final microstructures. A phase transformation model was calibrated based on estimated final phase fractions, dilatometry, and EBSD reconstruction of the prior austenite. The effect of the austenitization parameters on grain growth was studied, where a grain growth model based on the average grain size and temperature was implemented. The relationship between the austenitization conditions and mechanical properties was evaluated with bending tests according to the standard VDA 238-100. To study the relationship between the microstructure evolution during cooling and the final tensile properties, 48 samples were heat treated with unique thermal histories. The temperature histories from the heat treatments were supplied to a phase transformation model, which in turn was coupled to a non-linear regression model that estimated the tensile properties. For a martensitic microstructure, there were minor differences in tensile properties for different initial states of the steel, with similar strength, ductility, and anisotropy. For lower cooling rates, the tensile properties and phase transformation kinetics displayed differences with respect to the initial steel. The grain growth was limited for samples heated up to 930 ˚C, and could be modeled based on the average grain size. For a higher austenitization temperature, there were instances of inhomogeneous grain growth. There were only minor effects of the austenitization conditions on the bending properties, with an average bending angle over 90 ˚C for all thermal histories. The estimations of the tensile properties displayed a minor effect of the split between the training and testing data, with an acceptable agreement with the measured properties. The work presented in this thesis can aid in the modeling of the press hardening process, giving insights into the effects of process variations.

Industrial decarbonization is essential to achieving climate and net-zero emissions goals,with Carbon Capture and Storage (CCS) one the key strategies to handle on-site emissions. From 2045, Sweden aims negative goal emissions, which makes even more relevant the fast development of strategies on permanent carbon storage processes.The Swedish forestry industry, in particular the the pulp and paper sector, tops among the largest players in the world. Neverthless, it yearly produces over hundreds of thousands tonns of by-product, such as lime mud, green liquor sludge and lime grits which landfilled, bringing both economic and environmental costs.

The aim of this work was to explore the potential of enzymatic routes combined with residues from the paper-making industry for carbon dioxide capture, and to assess carbon storage using CO₂-rich media and relevant minerals within Sweden. Carbonic anhydrase (CA), a ubiquitous enzyme that catalyzes CO₂ hydration, was used to evaluate its enhancement effect on CO₂ capture. Four distinct residues tested with DvCA8.0 lysate showed a CA-boosting effect on CO₂-equivalent capture of up to 4-fold under open-air conditions and 2.2-fold in CO₂-rich experiments, reaching concentrations of up to 0.5 g/L and 0.74 g/L, respectively. Element leaching, particularly Ca²⁺ and Mg²⁺, positively correlated with CA addition, likely due to enzyme-enhanced H⁺ production promoting the dissolution of residue components such as CaCO₃ and MgO, further demonstrating the compatibility of CA with the tested materials.

Despite their high specificity, enzymes are sensitive and costly to produce, posing challenges for large-scale applications. To address this issue, DvCA8.0 was immobilized on nanomagnetic particles to improve stability, enhance productivity, and enable biocatalyst recycling. CO₂ capture experiments with lime mud (0.4%) showed that the immobilized enzyme remained active for up to ten consecutive cycles, exhibiting productivity 2.4 times higher than the free enzyme, highlighting the potential of the CA immobilization strategy.

System optimization was followed using lime mud. Variables such as gas flowrate, residue concentration, CA lysate load and type, and time were assessed to identify the most suitable condition for both increasing carbon capture and CA-effect. Tests were firstly conducted in a continous flow and ambient pressure mode, and showed a CA enhacement of up to 70% higher when compared to non-catalyzed conditions. At optimized conditions, a total of 5.1 g/L of bicarbonate was measured in the supernatant, with CA-boosting effect of 1.4-fold.

Later, tests under distinct CO₂ partial pressures and temperatures were performed to evaluate kinetics of CO₂ capture for both enzyme and non-enzyme assisted systems, which led to an increase on the rate of CO₂ uptake in 2-fold (40^oC and 14 bar).Other four enzymes were tested, and ApCA, a carbonic anhydrase from Aeribacillus pallidus, showed the best performance, with a enhancement of 4-fold when compared to non-CA added systems. Finally, a multicycle experiment was performed and revealed a CO₂ uptake of 10.3 g/L in the CA presence.

In addition, the carbon storage potential of different minerals was evaluated. Batch tests with olivine and bicarbonate-rich solutions showed a modest 6.9% increase in mineral carbonation with DvCA8.0, likely limited by the low bicarbonate concentration and mildly acidic pH. Finally, 10-bar CO₂ batch experiments with Swedish rock samples confirmed carbon storage in stable carbonate forms, such as Ca- and Fe-carbonate forms, demonstrating the feasibility of coupling enzymatic CO₂ capture with long-term mineral storage in integrated CCS processes for Sweden.

Sustainability alliances (SAs) are increasingly recognised as a relevant hybrid governance mode for exploring sustainability opportunities (SOs) – ideas that simultaneously pursue economic, environmental, and social value, resulting in sustainable innovations and circular business models (CBMs). By pooling complementary resources, knowledge, and perspectives, such alliances can generate systemic solutions that exceed the capabilities of individual organisations, making them increasingly central to sustainability transitions.   

However, such alliances often fall short of realising SOs. Alliance performance – encompassing the two intertwined dimensions of relational quality (e.g., partner commitment) and substantive opportunity realisation (e.g., financial returns, emission reductions), is inherently difficult to achieve because goal and cognitive conflicts rooted in bounded rationality and opportunism can undermine collaboration. In sustainability transition contexts, the non-excludable nature of environmental and social value, the wickedness of sustainability problems, and the emergence of novel partner constellations make these inherent alliance conflicts particularly acute. Rich traditional alliance governance research offers valuable solutions but remains incomplete because it is grounded primarily in economic logics. Emerging SA scholarship offers valuable theorising on domain-specific managerial activities relevant to SA operation. However, it rarely adopts a governance process view that examines how firms select, form, design, and continuously operate alliances while addressing these compounded goal and cognitive conflicts, including the boundary conditions under which such governance supports the realisation of SOs. 

The dissertation comprises a cover essay and five interrelated articles exploring the process of SA governance to pre-empt conflicts and support the realisation of SOs. Employing a mixed-methods design, the research includes 11 in-depth case studies of Swedish manufacturing firms, a Q-method study with 25 stakeholders engaged in Sweden's transition to net zero emissions, and a literature review of academic studies. Empirical material includes 90 interviews with company representatives and 16 interviews with regulatory, research, NGO, and financial actors.

This thesis advances SA governance research by developing a governance process framework, thereby extending existing research on SA’s domain-specific managerial activities to explicitly examine conflicts threatening SO realisation, the responding governance mechanisms, and their boundary conditions. Moreover, it contributes to transition studies by advancing understanding of actor agency and detailing the governance process through which alliances help pre-empt unstable actor relationships – a recognized source of failure in broader innovation systems. Finally, it extends CBM research by viewing CBMs not primarily as a design challenge, but as embedded in alliance governance processes in sustainability transitions. For practitioners, this framework provides practical guidance on how to steer SAs in ways that reduces conflicts and, ultimately, support the joint realisation of SOs, while also recognising the limitations of such efforts.

The era of extensive digitalization marked by the fourth industrial revolution has ushered in significant advancements in technologies like automation, artificial intelligence (AI), and the Internet of Things (IoT). These innovations are revolutionizing smart industries like manufacturing, smart energy systems (SESs), and the automotive industry. Industry 4.0 (I4.0) and the subsequent Industry 5.0 (I5.0) emerged as comprehensive representations of the physical world in the information world, with goals to establish smart factories and promote human-machine coexistence. However, the implementation of I4.0 and I5.0 applications faces challenges related to engineering efficiency, interoperability, and efficient service discovery and binding.

This thesis seeks to address these challenges by exploring potential strategies to develop an efficient System of Systems (SoS) that comprises individual, autonomous systems collaborating to achieve a shared goal. This research examines methods to enhance the efficacy of SoS by refining its engineering procedures, promoting interoperability between standardized protocols and heterogeneous systems, and employing dynamic adaptation mechanisms. It aims to achieve automatic service discovery and interoperability between diverse industrial standards and systems across the different domains within the smart industry by integrating the Eclipse Arrowhead Framework. This IoT framework facilitates secure and seamless communication and collaboration among devices, machines, and systems.

Moreover, this work delves into saving energy consumption in distributed SoS environments. This is achieved through the Demand Response (DR) mechanism in SESs combined with the Eclipse Arrowhead framework. In addition, the thesis examines challenges in automotive testing, specifically in Vehicle-in-the-Loop (VIL) testing environments, which are distributed SoS systems requiring efficient communication, diverse hardware and system interoperability, realistic simulation, and scalable system integration with minimal cost and resource demand. The research also explores flexible methods for integrating heterogeneous environment models into VIL frameworks and proposes a standardized service-oriented vehicle data communication framework to improve interoperability, operational efficiency, and scalability.

The overarching objective is to pave the way for flexible production processes characterized by minimal resource waste, optimized energy consumption, and sustainable solutions. Through this endeavor, the thesis contributes to shaping a more efficient, interoperable, and sustainable smart industrial landscape in the context of Industry 4.0 and beyond.

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants whose transport, retention, and removal are influenced by complex interactions with natural and engineered surfaces. Despite extensive research, key uncertainties remain regarding how PFAS molecular structure, sorbent chemistry, and environmental conditions jointly control adsorption behavior. This thesis addresses these challenges by systematically investigating PFAS interactions across a range of systems, from natural soil components to engineered sorbents and dynamic treatment processes. A stepwise experimental approach was applied, beginning with fundamental interactions with soil organic matter (SOM) and iron (hydr)oxide, followed by competitive adsorption on granular activated carbon (GAC) and ion exchange resin (IER), and culminating in the evaluation of PFAS removal under flow conditions.

The results demonstrate that PFAS sorption in soils is governed primarily by the chemical composition of SOM rather than its total content. In addition, PFAS exposure also affected dissolved organic matter (DOM) mobilization and composition, particularly in soils with less hydrophobic SOM. Adsorption onto ferrihydrite (Fh) was strongly pH-dependent, and the observed S-shaped isotherms, together with atomic force microscopy observations, supported the contribution of PFAS-PFAS interactions and surface-associated aggregation at higher surface coverage. In engineered systems, competitive adsorption was controlled by PFAS molecular structure, sorbent properties, and the composition of the surrounding water matrix. GAC was particularly affected by DOM-induced competition and displacement, whereas IER showed greater PFAS selectivity but was more susceptible to phosphate-induced displacement. Dynamic treatment experiments further demonstrated that reactor configuration strongly influenced treatment performance. The rotating bed reactor (RBR) substantially accelerated PFAS adsorption kinetics, particularly for short-chain PFAS, whereas the small-scale column test (SSCT) provided more sustained removal and delayed breakthrough.

Together, these findings provide a coherent framework linking molecular-scale interactions to treatment-system performance. The results demonstrate that PFAS behavior cannot be adequately understood or predicted from individual factors alone but instead reflects the combined effects of PFAS molecular structure, sorbent properties, solution chemistry, and process configuration. These findings have implications for understanding PFAS mobility in environmental systems and for designing adsorption-based remediation strategies, emphasizing the importance of mechanistic understanding and matrix-specific evaluation when addressing PFAS contamination.

Source-separated blackwater contains most of the nutrients present in domestic wastewater and therefore represents an important resource for nutrient recovery. Blackwater digestate contains nitrogen, phosphorus, potassium, and micronutrients that could be recovered and reused in agriculture, contributing to more circular sanitation systems. However, the digestate is typically too dilute for efficient transport and agricultural reuse, requiring nitrogen stabilisation and volume reduction to produce a practical fertiliser product and reduce ammonia losses during thermal treatment.

This thesis investigates the pathway towards producing a fertiliser product from blackwater digestate through two sequential treatment steps: nitrogen stabilisation and low-grade heat concentration. Nitrogen stabilisation was investigated using both biological and chemical approaches. Biological stabilisation was achieved through nitrification in pH-controlled moving bed biofilm reactors (MBBRs), where the effects of startup strategy, temperature, and pH on reactor performance were evaluated. Startup strategy and temperature strongly influenced nitrification establishment, while nitrifier abundance alone did not explain reactor performance. Stable nitrification rates up to 1.8 g N m-² d-¹ were achieved at pH 6 and 30 °C. Furthermore, complete nitrification of approximately half of the ammonium was maintained even under acidic disturbance conditions at pH 2.3 and temperatures of 20 °C and 30 °C. Chemical stabilisation was achieved through acid dosing prior to thermal concentration, using phosphoric acid in membrane distillation experiments and nitric acid in low-temperature evaporation experiments to suppress ammonia volatilisation and maintain nutrients in soluble form.

Low-grade heat concentration of blackwater digestate was evaluated using laboratory-scale air-gap membrane distillation and pilot-scale low-temperature evaporation operated at moderate temperatures (40–70 °C), suitable for waste heat utilisation. The processes were assessed with respect to nutrient retention, volume reduction, flux behaviour, and energy demand. Membrane distillation achieved up to 15-fold concentration, although permeate flux declined to approximately 15% of its initial value due to fouling and membrane wetting. Pilot-scale evaporation achieved volume reduction factors up to 85 while producing nutrient-rich concentrates. Lower pH improved nutrient retention by reducing volatilisation and precipitation losses, while potassium and sodium concentrations increased under acidic conditions.

A dynamic model describing condensate generation during evaporation was calibrated against experimental data, yielding coefficients of determination (R²) between 0.961 and 0.997. The model showed that progressive solute enrichment increased the apparent enthalpy of evaporation at high volume reduction factors. Exergy analysis demonstrated that only about 12% of the supplied thermal energy was converted into useful exergy in the produced vapor, highlighting the importance of heat recovery and waste heat integration.

The development of edge computing, Internet-of-Things (IoT), and AI-driven services involves increasing volumes of sensitive data—health records, industrial sensor readings, personal information—at the network edge. Regulatory frameworks such as the GDPR, the European Health Data Space, and the EU AI Act impose strict requirements on such processing. However, privacy-enhancing technologies face trade-offs between utility, privacy guarantees, and computational cost that limit their practical use and uptake.

This thesis investigates how these trade-offs can be addressed through encrypted machine learning (ML) inference, architecture co-design for efficient encrypted computation, and privacy-preserving model training. The six appended papers, complemented by additional publications, address these trade-offs across health data infrastructure, edge data center management, and privacy regulation.

First, fully homomorphic encryption (FHE) is applied to data processing and control pipelines to investigate practically feasible encrypted computation on health data and encrypted remote monitoring and control of edge data center systems for lightweight ML tasks. Two leading encryption schemes—CKKS and TFHE—are evaluated, showing that both data and algorithm confidentiality can be achieved simultaneously, with encrypted execution times ranging from milliseconds to seconds.

Second, the computational bottleneck of neural network inference under FHE is addressed through architecture co-design. A novel mechanism is proposed—the Inhibitor—which replaces the multiplications and softmax activations of conventional gated RNNs and Transformer attention with encryption-friendly addition and ReLU. Experiments demonstrate 3–6× speedup for encrypted inference, and knowledge distillation produces a compact Inhibitor language model competitive on corresponding NLP benchmarks.

Third, methods are developed for training on distributed or sensitive data without compromising privacy. A local conditioning approach for heterogeneous federated learning is introduced, where locally computed statistics replace cross-client coordination, enabling scalable personalization without revealing distributional information or adding communication overhead. A proactive defense against training data memorization is also proposed, reducing the log-likelihood ratio for membership identification by an order of magnitude at the chosen audit precision, while maintaining model accuracy.

Taken together, the results suggest that privacy-preserving ML can be made more feasible for resource-constrained settings, including latency-sensitive edge deployments, helping to narrow the gap between regulatory requirements and computational cost. The evaluation relies on established benchmarks and controlled numerical experiments; validation in production deployments remains future work.

Biochar demonstrates strong decarbonization potential in concrete but significantly affects workability. To address this and to better understand the effects of biochar on concrete, as well as its role in decarbonizing it, this study applied a volumetric water-to-binder ratio (w/b), replacing cement with biochar powder at 5%, 10%, and 20% by volume, and compared the results with a 5% weight-based replacement. Additionally, prewetted biochar aggregate was used to replace sand at 30%, 60%, and 100% by volume. Various properties were assessed, including rheology, mechanical strength, hydration products, fire resistance, and carbon emissions of the concrete.

For biochar powder used as a cement replacement, the results indicate that the volumetric w/b ratio effectively improved workability and enhanced internal curing, leading to increased hydration products and improved mechanical performance compared to weight-based replacement. Despite a lower cement content, the 10% volumetric biochar sample achieved higher strength than the 5% weight-based sample. Biochar used as a sand replacement in concrete can better enhance internal curing and increase the degree of hydration due to improved moisture retention from the larger replacement volumes compared with its use as a cement replacement. Furthermore, replacing sand with biochar aggregate effectively reduces total shrinkage due to improved internal curing. Total shrinkage at 28 days was reduced by 41%, 55%, and 65% for 30%, 60%, and 100% biochar aggregate replacement levels, respectively.

Regarding fire resistance, both types of biochar increased the temperature gradients in concrete during heating. However, biochar aggregate contributed to a greater increase due to its higher volume and sand replacement ratio than biochar powder, which intensified thermal damage and, at 400 °C, even offset the benefits of accelerated cement hydration.

Due to energy recovery and carbon sequestration, wood biochar replacing 20% of cement reduced concrete carbon emissions by 42%, while fruit biochar replacing 100% of crushed sand reduced emissions by 167%, indicating that the concrete can become an effective carbon sink. However, carbon emissions should not be the sole consideration. Wood biochar used as a cement replacement reduced 56-day compressive strength by 3%, 6%, and 13% at 5 vol%, 10 vol%, and 20 vol%, respectively. Similarly, when fruit biochar replaced sand, the 56-day compressive strength decreased by 7%, 21%, and 47.4% at 30 vol%, 60 vol%, and 100 vol%, respectively.