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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.

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.

The growing demand for batteries, driven by the rapid growth of electric vehicles and renewable energy technologies, has led to increased focus on their lifecycle management. While batteries may reach the end of their primary life, they often have the potential for a second life, offering valuable services before their eventual recycling. This paper presents a comprehensive business model framework tailored for the second life battery business, aiming to guide stakeholders in the battery ecosystem through the complexities of this emerging field. Developed through detailed theoretical and practical industry insights, the framework is segmented into three core elements: value proposition, value creation and delivery, and value capture. Each core element further branches into sub-elements, offering a modular approach for stakeholders to construct unique business models. The paper also includes in-depth value analyses, sustainability implications, and stakeholder interests, providing a holistic understanding of the second life battery business. Practical implications for key stakeholders in the battery ecosystem, including battery OEMs, remanufacturers, repurposers, and dealers, are discussed. The paper contributes to the theory of circular business models in general, with specific relevance to battery circularity. 

Battery degradation plays a critical role in determining battery performance and in predicting the remaining useful life (RUL). Several methods exist to monitor degradation. Electrochemical Impedance Spectroscopy (EIS) is well-suited for single cells, but its application becomes more challenging when analyzing battery modules composed of multiple cells connected in series. Differential capacity analysis, expressed as either differential capacity versus voltage (dQ/dV) or differential voltage versus capacity (dV/dQ), can be applied to both single cells and modules. EIS allows tracking the deterioration of specific internal mechanisms within a cell. In contrast, the differential methods provide partial insights into these mechanisms. Typically, the dQ/dV curve exhibits three distinct peaks, which can be monitored over time to quantitatively assess the degree of degradation. This, in turn, enables the estimation of the state of health (SOH), and, to a certain extent, the remaining useful life provided that the data is correlated with results from batteries that have been cycled under controlled conditions. The paper presents examples demonstrating this approach, including comparisons between single cells and cells arranged in series.

Organic raw materials are the renewable sources of substrates for our industries and for our microbial communities. As industrial, agricultural or forestry side streams, they are usually affordable raw materials if the process entities, equipment and protocols are properly designed. The microbial communities that are used as biocatalysts take care of the process development together with the process team. Moreover, they constitute or shape the process to resemble the natural bioprocess as it takes place or occurs in nature and thus make it “Industry Like Nature®” – type of endeavor. As an ultimate result, we could make our industries increasingly 100% sustainable with the help of microbes. In case of food or forest industry side streams, this means fossil-free production of valuable chemicals, food and feed components, energy and gases, and soil improvement agents or organic fertilizers. The so-called “Finnoflag biorefinery” idea has been tested in many cases together with domestic and international colleagues and industries. In here, we attempt to share the basic thinking.

Integrating performant waste-to-energy solutions in composting facilities is essential to mitigate the pressure of increasing amounts of residual municipal solid waste. Hence, this paper investigates the integration of residual municipal solid waste gasification with a bottoming organic Rankine cycle (ORC) unit, a biomass pyrolysis reactor and a wastewater treatment plant from an energy point of view. Two layouts are compared to evaluate the system's flexibility in a district cooling and heating network with varying load demand. Results: The analyses show that pyrolysis can reduce the ORC partialisation and obtain a bio-crude oil annual production in the range of 9617 t⋅y−1 – 12718 t⋅y−1. When the wastewater treatment is decoupled from the ORC condenser (second layout), it is possible to treat all 16000 t⋅y−1 of wastewater produced. On the contrary, in the first layout, the amount of wastewater treated is affected by the ORC working fluid. Toluene (9391 t⋅y−1) treats more wastewater than cyclopentane (8762 t⋅y−1) since the operability of the treatment line is extended by 275 h. In both layouts, the electricity production is higher with toluene, ranging between 341 and 5185 kWe. However, the highest natural gas savings, 1783990 m3⋅y−1, is obtained with cyclopentane.

In this study calculation over material and energy balances for bio-refinery product upgrading using membrane filtration (MF, UF, and RO), distillation, and ion-exchanger has been performed. Tests have been made with UF filtration in a pilot plant, separation tests made at lab with ion-exchanger and simulation using ASPEN plus simulator for distillation. Rough economic analysis has been made for the different solutions/techniques.

In the research of mixed microbial cultures, the numbers and identifications of individual strains are often only partially unknown. Their metabolic capabilities are also not wholly predictable especially if the joint potential is to be understood. In these kinds of situations, deeper insight into the variable microbial communities cannot be obtained by genetic analysis only. Even more critical than the taxonomic aspect is usually the functional metabolic outcome of the mixed flora in question. The results from such studies as NMR (nucleic magnetic resonance) give a precise view from versatile angles into the biochemical activities during the multiparametric metabolic responses of the microflora as a whole. Originally, metabonomics was mainly used for the pathophysiological research of various microbes or for recording the genetic or biochemical modifications of mixed microflora. This approach offers a tool for monitoring changes in microscopic or otherwise confined ecosystems or at multiple locations from which representative specimens are difficult to obtain. It also offers repeatability in various processes. In microbiological studies, the research group can attain overall views on variable populations and their alterations in time and space. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Firms require multi-stakeholder ecosystems to successfully create and implement circular business models for electric vehicle (EV) battery second life. However, there is a notable absence of guiding instruments to assist EV battery ecosystem actors in formulating and managing strategies for battery second-life operations. This study's aim is to analyze how circular ecosystems are formed and managed, considering the interplay between firm-level and ecosystem-level interactions. The study identifies 17 prerequisites divided into four stages of circular ecosystem management, considering the interplay between firm- and ecosystem-level aspects, as well as short- and long-term perspectives. These four stages of circular ecosystem management are firm-level assessment, ecosystem formation, firm-level adaptation, and ecosystem orchestration. Based on these four stages, the study identifies three distinct pathways through which EV battery ecosystem actors can effectively form and manage second-life operations. Additionally, a decision tree model is proposed to navigate ecosystem actors through these four stages for enabling battery second-life operations. This research makes a valuable contribution to the field of ecosystem management and circular ecosystems, specifically by examining the interplay between firm-level and ecosystem-level dynamics within the context of EV battery second-life operations. The findings hold significance for both academic researchers and practitioners seeking to implement and scale-up circular business models and circular ecosystems in the realm of EV batteries.

Hydro power plants are among the most mature technologies for power production. To optimally manage possible overgeneration from non-programmable renewable energy sources, such as photovoltaic power plants and wind power plants, a Pumped Hydro Storage (PHS) plant can be employed as both a storage device (pumping mode) and a power production technology (turbine mode). To this aim, this paper deals with the optimization of the sizing and operation of a PHS plant that interacts with a power generation system consisting of different power production technologies. The national power production system and electric energy demand of Sweden are used as a case study and a PHS plant is sized and managed to fit conventional hydraulic sites as well as abandoned mines to be used as reservoirs. First, this paper develops a methodology suitable to identify the optimal size and operation strategy of the PHS plant, by means of the simultaneous use of two algorithms: surrogate modeling optimization algorithm and mixed integer linear programming algorithm. Then, this paper analyzes different present and future scenarios of electricity production, demand, and cost, in order to assess the energy and economic feasibility of PHS plants. The analyses carried out in this paper demonstrate that PHS plants are highly recommended with high overgeneration from photovoltaic power plants or wind power plants. This situation is a likely scenario thanks to green energy transition strategies. The return on investment of the most cost-effective solutions in the future scenarios ranges from 8.5 % to 11.6 %. In all the investigated scenarios, the results indicate that PHS is mainly employed to meet domestic electricity demand.