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The large-scale electrification of transport is accelerating globally, resulting i n a g rowing in flux o f end-of-first-life batteries from electric vehicles (EVs). Repurposing these batteries into second- life battery energy storage systems (SL-BESS) offers a promising pathway to extend battery lifecycles, reduce environmental impacts, and support circular value creation. Despite significant technological progress and numerous pilot projects, the adoption of SL-BESS in scalable business practice remains limited. A key challenge lies in defining viable and context-specific value propositions that can guide actors in designing circular business models. This paper presents an explorative qualitative study examining how circular value propositions for SL-BESS can be defined and contextualized within EV charging infrastructure. Empirical data was gathered through semistructured interviews and co-creative workshops involving multiple actors in the Swedish EV ecosystem. The analysis resulted in three business scenarios: resilient energy hubs within municipalities, mobility-integrated energy for flexible and bidirectional bus charging, and circular Battery-as-a-Service solutions for industrial and mining applications. The findings indicate that while SL-BESS technologies have reached a relatively high level of maturity, business model development lags due to regulatory uncertainty, fragmented market structures, and unclear revenue and ownership arrangements. By abstracting across scenarios, the study identifies recurring patterns in how second-life batteries can function as enabling infrastructure for flexible operations, shared resource use, and industrial-societal symbiosis. The paper contributes to the circular business model literature by presenting scenario-based circular value propositions, crossscenario comparison criteria, and strategic patterns that can support actors in positioning themselves within emerging markets for circular energy storage and charging infrastructure.

Thermal management is essential for safe load-following of solid oxide electrolysis cells (SOECs) under fluctuating renewable power. This study develops and validates a one-dimensional dynamic model of an anode-supported co-flow planar SOEC under steady-state and transient conditions. The maximum RMSE values for cell voltage and temperature are 0.02 V and 2.3 °C, respectively. Based on this model, regime-specific thermal management strategies are proposed. For endothermic operation, direct stack heating using embedded electric heaters is compared with PI-based air-flow control and achieves temperature regulation with a lower thermal gradient. For exothermic operation, a multi-input multi-output model predictive control strategy coordinates anode and cathode flow rates while constraining thermal gradients within safe limits. The proposed strategies are further evaluated under fluctuating renewable load profiles, demonstrating effective temperature regulation and safe thermal-gradient control for flexible SOEC operation.

The electrification of public transport has introduced new challenges in managing lithium-ion battery degradation, operational efficiency, and lifecycle sustainability. While battery digital twins (BDTs) have emerged as a promising solution for monitoring and prediction, most existing approaches remain limited to short-term state estimation and do not exploit lifecycle data for circular decisionmaking. This paper proposes a Circular Battery Digital Twin (CBDT) framework that integrates aging-aware operation, predictive lifecycle modeling, and circular value retention within a unified architecture. The framework combines realworld operational, environmental, and maintenance data to continuously calibrate a semi-empirical degradation model supporting multiple use cases, including route reassignment and second-life assessment. The framework is demonstrated using a multi-year dataset from a real electric bus operating in Sweden. The bus operates with a 60 kWh LFP battery pack and relies exclusively on pantograph charging at terminus stops. The results show that CBDT enables consistent degradation estimation, supports cost-aware operational decisions, and provides a pathway toward circular battery management. The framework successfully identified an optimal reassignment threshold at 70% load that extends operational life by 45%. The study demonstrates how integrating multi-source data and lifecycle intelligence enables the transition from reactive battery management to proactive and circular decision-making.

The development of small-scale modular gasification systems represents one of the several pathways needed to follow to reach a net-zero emission society. Currently, the research is fostering the technology readiness level of those units to achieve clean and sustainable energy production from biomass waste while decreasing the load to the centralized waste treatment facilities. The operability of those conversion systems can be improved with the co-gasification of low-grade residues, like sewage sludge, organic municipal solid waste, nut shells, in wood matrixes. Downdraft co-gasification is a promising technology that offers competitive conversion efficiencies, reliability, and capillary diffusion especially in the context of renewable energy communities. Still, there are research gaps that must be addressed especially for improving the design and process control. This work presents a modelling approach that allow for deeper understanding of the treatment and propose improvements for SUPREMAS project. A lumped, steady-state model is developed in Aspen Plus using chemical pseudo-equilibrium and empirical correlations, to replicate the real process. Then, the cleaned syngas is converted to energy with a combined heat and power / solid oxide fuel cell units.

Sewage sludge is one of the main challenges facing the management of municipal solid waste globally. Sludge is the final sink for all the wastes leaving residential, commercial and industrial sources that is conveyed with wastewater and accumulated at the wastewater treatment plants. Sewage sludge can be a good candidate for waste to energy processes like gasification due to energy content. However, tar is challenging the quality of the syngas and needs to be removed using different methods. In this research, an Aspen Plus models is used to study the efficiency of removing tar using water scrubber and thermal cracking. Results from the two techniques will be validated using laboratory experiments and the optimum efficiency will be evaluated based on technical, environmental and economical factors. An experimental procedure to produce representative samples of tar has been established. The physiochemical properties of the tar samples will give the necessary input data for the simulations of the tar removal process optimisation.

Rapid on-site assessment of wood quality can significantly enhance decision-making and operational efficiency in the pulp and paper industry. Spectroscopic techniques, including near-infrared (NIR) spectroscopy, can be used to characterise wood, but there is a need for development to use these methods in the field, especially for optimising the continuous kraft pulping process. This study focused on using a portable NIR spectrometer to measure lignin and moisture content in Nordic softwood chips, while also distinguishing between bound and free water, with particular focus on challenges related to field-based measurements and process-relevant information. Partial Least Square Regression (PLSR) effectively reduced data dimensionality and simultaneously performed the regression. The performance of portable NIR model was found to be comparable with models based on data from a traditional benchtop NIR. However, several portable NIR spectra need to be acquired and averaged to achieve predictive capability. The results confirm that robustness of benchtop systems for accurate moisture determination. Portable NIR may be useful at kraft pulp mills during disturbance situations, when rapid information about chip quality is necessary.

Solid oxide electrolysis is a promising technology for green hydrogen production. However, its wide thermal operating range, spanning endothermic, thermoneutral, and exothermic regimes, poses significant challenges for thermal management, particularly during load-following operations. Insufficient control of temperature and thermal gradients can result in substantial thermal stresses, leading to reduced durability and, in severe cases, cracking or failure of cells and stacks. This work investigates the thermal performance of counter-flow and co-flow solid oxide electrolysis cells. A dynamic multi-physics model is developed to simulate coupled electrochemical, thermal, and fluid-flow behaviors. A model predictive control strategy, regulating both cathode and anode flows, is implemented for active thermal control of solid oxide electrolysis cells. The performance of the model predictive control strategy is compared to that of a traditional proportional–integral controller. The results demonstrate that the proposed control strategy outperforms the traditional proportional–integral controller. It effectively maintains the cell temperature at the target value of 800 °C while keeping the maximum thermal gradient across the cell at a moderate level, limited to 6 °C/cm for both co-flow and counter-flow configurations.

The transition to electric vehicles (EVs) presents new challenges and opportunities for sustainable transport systems, particularly concerning battery degradation, lifecycle management, and long-term system reliability. While numerous decision-support models exist for vehicle routing, charging infrastructure planning, and investment analysis, few integrate battery aging dynamics into a comprehensive circularity-oriented decision framework. This paper proposes a novel Battery Circularity Decision Support Framework that links operational, tactical, and strategic decision-making with a semi-empirical battery degradation model. The framework enables stakeholders to evaluate the impacts of driving behavior, duty cycles, charging strategies, and thermal environments on battery state-of-health (SoH), extending into future reuse, repurposing, and recycling pathways. Drawing on recent literature and experimental data, we highlight how various decisions, ranging from energy-efficient routing to battery end-of-life planning, can be informed through degradation-aware simulations.

To demonstrate the practical utility of the framework, we apply it to a real-world use case involving an electric bus operating in Sweden. The framework enabled the evaluation of battery degradation over time under consistent operational conditions, revealing the projected timeframe during which the bus could continue to reliably perform the same route. As SoH decreased, the framework supported a strategic decision to reassign the bus to a less power-demanding route, thereby extending its operational life and reducing the risk of service interruptions. This example illustrates how our framework enables data-driven decisions that align with circular economy goals and sustainable fleet operations. By integrating battery aging into system-level planning, the framework fills a crucial gap in current EV battery management and battery circularity methodologies.

This paper presents a simulation platform developed to support battery-aware operation and energy optimization of electric off-road vehicles used in quarry environments. The platform integrates Monte Carlo simulations of hauling with battery degradation models to assess the impact of different charging strategies and duty cycles on energy consumption and battery health. By simulating various operating scenarios, the platform enables analysis of hybrid charging policies, such as cycle-based and max-based charging, for both small and large quarry operations. The results demonstrate how informed operational planning can improve energy efficiency and contribute to more resilient and sustainable industrial transport systems.

Continuous kraft cooking digesters face challenges affecting product quality, making it valuable to improve control through advanced techniques like near-infrared (NIR) spectroscopy, model predictive control, and machine learning models. The primary goal of this study was to use NIR spectra to predict the amount of lignin in hardwood and softwood samples. This study investigated the correlation of NIR derivative spectra with the amounts of lignin relative to other constituents, namely cellulose, hemicellulose, and water, in wood chip samples of varying chip sizes and shapes from six Nordic wood species. It employed partial least squares regression (PLSR) on the NIR data to construct a model that predicted the lignin fraction and the relative fraction of acid-soluble lignin. When trained on a group of five wood species, the model achieved a satisfactory predictive ability, striking a balance between a wide range of lignin content and a consistent chemical environment. The accuracy increased further when the model was restricted only to spruce and pine, reflecting the benefits of a more homogenous dataset. Additionally, the optimal number of latent variables was identified as two, indicating that three distinct chemical components - cellulose, lignin and water - can be effectively differentiated using NIR. Application: This NIR-based methodology is designed to be robust and applicable for pulp mills that alternate between softwood and hardwood campaigns, aiming to create models that perform well in industrial environments across various wood species.