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Throughout human settlement history, the pursuit of durability has been a paramount objective in building construction. The emphasis on durability has resulted in the construction of buildings designed to outlast human lifespans. However, the lack of consideration for building demolition and disposal during the design and construction phases has created challenges for future generations. This oversight contributes to the environmental impact of structures after demolition, which is a significant concern given that the construction industry is a major contributor to energy consumption, CO2 emissions, and solid waste production. In fact, in recent decades, there has been an increasing demand for temporary constructions, driven by factors such as migration phenomena, natural disasters, and the COVID-19 pandemic, but also in sectors like agriculture, where seasonality and annual variations in activities require adaptable structures such as warehouses, barns, livestock shelters, and food storage facilities. Unlike traditional constructions, these temporary buildings must be assembled and disassembled multiple times during their lifespan. The challenge lies in ensuring the structural integrity, adaptability to varying conditions, and compliance with specific requirements to extend their usability and postpone the disposal phase. This study focuses on the design of a novel type of temporary structures intended for temporary needs such as emergencies and planned agricultural activities, resulting in a European patent. The structure is based on a glulam frame inside two OSB panels—that work as structural bracing, creating a hollow, resistant, light structure—connected with external steel connections. This work reports results of mechanical simulations and thermal transmittance calculations. Specifically, it demonstrates the building maintains structural strength through multiple usages and its thermal characteristics can be easily adapted to the context. These are the first steps for a resilient and sustainable building.

Circular economy (CE) is a new business model that is pressing manufacturing companies to think about closed loop scenarios for materials and products. Design for End-of-Life (DfEoL) and Design for Disassembly (DfD) are key enabling methods for the effective application of this model. The paper presents a time-based method for the calculation of disassembly sequences, adopting basic theories and techniques in this topic and integrating new concepts for the assessment of the disassembly time. The method consists of five steps and starts from the documentations (e.g., CAD model) generally available early in the product development process. The first three steps encompass the product analysis by including (i) the definition of target components from the general assembly, (ii) the analyses of the virtual model, and (iii) the assessment of the so-called level matrix, which is based on the concept of disassembly levels and liaisons characterization among components. The last two steps allow for the assessment of the time-based disassembly sequence by including (iv) the analysis of feasible sequences and (v) the generation of the best disassembly sequence for target components. The method mainly overcomes two issues highlighted in the literature regarding the reliability of the disassemblability analysis using a time-based approach and the quality of results accounting for the real condition of the product at the time of disassembly. The calculation of the effective disassembly time is grounded on a specific repository developed to gather knowledge about the disassembly tasks and related disassembly time. This is the main contribution and novelty of the proposed approach. By using the proposed method, different target components of a washing machine are analysed with the aim of demonstrating the robustness of the method and its consistency in the estimation of disassembly time. A maximum deviation of 10% between the estimated and measured disassembly times is noticed.

Disassembly is one of the most crucial links in the product life cycle. Disassembly sequence planning (DSP) is a combination of optimization problem and has been studied by many researchers. Asynchronous parallel disassembly planning (APDP) eliminates the requirement that manipulators must start and stop their tasks in the working process. In this paper, a new asynchronous parallel disassembly sequence planning method is proposed, which to deal with the problems of highly time-dependent on asynchronous parallel disassembly mode. The disassembly time model has been divided into execution time and preparation time. The former represents the time consumes by removing a part, and the latter means the time consumes on the disassembly tool preparation while the disassembly position switch. The disassembly matrix and the disassembly work area collision matrix are also utilized to avoid infeasible sequences. Based on the max-min ant system, a waiting strategy is proposed to solve asynchronous parallel disassembly sequence planning problem. To further shorten disassembly process, a time overlap strategy is developed to take advantage of waiting time. Finally, a bevel gear reducer is utilized as an example to discuss and analysis the value of key parameters, as well as the effectiveness of the algorithm and strategies proposed in the paper.

Various studies show that electrification, integrated into a circular economy, is crucial to reach sustainable mobility solutions. In this context, the circular use of electric vehicle batteries (EVBs) is particularly relevant because of the resource intensity during manufacturing. After reaching the end-of-life phase, EVBs can be subjected to various circular economy strategies, all of which require the previous disassembly. Today, disassembly is carried out manually and represents a bottleneck process. At the same time, extremely high return volumes have been forecast for the next few years, and manual disassembly is associated with safety risks. That is why automated disassembly is identified as being a key enabler of highly efficient circularity. However, several challenges need to be addressed to ensure secure, economic, and ecological disassembly processes. One of these is ensuring that optimal disassembly strategies are determined, considering the uncertainties during disassembly. This paper introduces our design for an adaptive disassembly planner with an integrated disassembly strategy optimizer. Furthermore, we present our optimization method for obtaining optimal disassembly strategies as a combination of three decisions: (1) the optimal disassembly sequence, (2) the optimal disassembly depth, and (3) the optimal circular economy strategy at the component level. Finally, we apply the proposed method to derive optimal disassembly strategies for one selected battery system for two condition scenarios. The results show that the optimization of disassembly strategies must also be used as a tool in the design phase of battery systems to boost the disassembly automation and thus contribute to achieving profitable circular economy solutions for EVBs.

Key Points The dynamic regulation of chromatin involves four subfamilies of ATP-dependent nucleosome-remodelling complexes: imitation switch (ISWI), chromodomain helicase DNA-binding (CHD), switch/sucrose non-fermentable (SWI/SNF) and INO80. Each subfamily is specialized to preferentially achieve particular chromatin outcomes: assembly, access or editing. Diversity in the protein composition of remodellers enables their specific interaction with particular transcription activators, repressors and histone modifications, which together specify targeting. Although diverse in protein composition, all remodellers have a similar ATPase 'motor' that translocates DNA from a common location within the nucleosome, which breaks histone–DNA contacts. The diverse specialized proteins and domains in each remodeller subfamily are also involved in detecting nucleosome epitopes, which differentially regulate the conserved ATPase–translocase motor to achieve the various chromatin-remodelling outcomes. We propose an 'hourglass' model of chromatin remodelling that involves convergence on a DNA translocation mechanism, which is preceded and followed by remodeller diversity, in terms of differential remodeller targeting and remodelling outcomes, respectively. Remodellers are emerging as 'smart' machines that are informed about whether or how to utilize DNA translocation to conduct chromatin remodelling. Nucleosome-remodelling complexes can slide or eject histones, or incorporate histone variants, but they share an ATPase–translocase 'motor' and a common DNA translocation mechanism. In a unifying 'hourglass' model of remodeller function, the different remodeller subfamilies use different modules for targeting to nucleosomes but converge on a DNA translocation mechanism and then diverge again to achieve various outcomes. Cells utilize diverse ATP-dependent nucleosome-remodelling complexes to carry out histone sliding, ejection or the incorporation of histone variants, suggesting that different mechanisms of action are used by the various chromatin-remodelling complex subfamilies. However, all chromatin-remodelling complex subfamilies contain an ATPase–translocase 'motor' that translocates DNA from a common location within the nucleosome. In this Review, we discuss (and illustrate with animations) an alternative, unifying mechanism of chromatin remodelling, which is based on the regulation of DNA translocation. We propose the 'hourglass' model of remodeller function, in which each remodeller subfamily utilizes diverse specialized proteins and protein domains to assist in nucleosome targeting or to differentially detect nucleosome epitopes. These modules converge to regulate a common DNA translocation mechanism, to inform the conserved ATPase 'motor' on whether and how to apply DNA translocation, which together achieve the various outcomes of chromatin remodelling: nucleosome assembly, chromatin access and nucleosome editing.

In the coming decades, the number of end-of-life (EoL) traction battery systems will increase sharply. The disassembly of the system to the battery module is necessary to recycle the battery modules or to be able to use them for further second-life applications. These different recovery paths are important pathways to archive a circular battery supply chain. So far, little knowledge about the disassembling of EoL batteries exists. Based on a disassembly experiment of a plug-in hybrid battery system, we present results regarding the battery set-up, including their fasteners, the necessary disassembly steps, and the sequence. Upon the experimental data, we assess the disassembly duration of the battery system under uncertainty with a fuzzy logic approach. The results indicate that a disassembling time of about 22 min is expected for the battery system in the field study if one worker conducts the process. An estimation for disassembling costs per battery system is performed for a plant in Germany. Depending on the plant capacity, the disassembling to battery module level is associated with costs between EUR 80 and 100 per battery system.

The rapidly increasing adoption of electric vehicles (EVs) globally underscores the urgent need for effective management strategies for end-of-life (EOL) EV batteries. Efficient EOL management is crucial in reducing the ecological footprint of EVs and promoting a circular economy where battery materials are sustainably reused, thereby extending the life cycle of the resources and enhancing overall environmental sustainability. In response to this pressing issue, this review presents a comprehensive analysis of the role of artificial intelligence (AI) in improving the disassembly processes for EV batteries, which is integral to the practical echelon utilization and recycling process. This paper reviews the application of AI techniques in various stages of retired battery disassembly. A significant focus is placed on estimating batteries’ state of health (SOH), which is crucial for determining the availability of retired EV batteries. AI-driven methods for planning battery disassembly sequences are examined, revealing potential efficiency gains and cost reductions. AI-driven disassembly operations are discussed, highlighting how AI can streamline processes, improve safety, and reduce environmental hazards. The review concludes with insights into the future integration of electric vehicle battery (EVB) recycling and disassembly, emphasizing the possibility of battery swapping, design for disassembly, and the optimization of charging to prolong battery life and enhance recycling efficiency. This comprehensive analysis underscores the transformative potential of AI in revolutionizing the management of retired EVBs.

Integration of disassembly operations during product design is an important issue today. As known, the number of possible disassembly sequences increases significantly with the number of parts in a product. Thus, generating proper disassembly sequences is critical. Most existing methods often require tremendous computational resources, while, at the same time, they often fail to find realistic and optimal solutions for complex product disassembly. For selective disassembly, for instance, it is important to eliminate the components which are unrelated with the target prior to sequence generation. In order to address this configuration, this paper deals with a method for generating the feasible disassembly sequences for selective disassembly. It is based on the lowest levels of a disassembly product graph. Instead of considering the geometric constraints for each pair of components, the proposed method considers the geometric contact and collision relationships among the components in order to generate the proposed disassembly geometry contacting graph (DGCG). The generation of the disassembly sequences is based on the investigated three cases called micro-units , which consider all the possible situations of relationships among the components in the DGCG . The latter is then used for disassembly sequence generation, thus allowing decreasing the number of possible disassembly sequences. For this purpose, a mixed Virtual Reality Disassembly Environment (VRDE) is developed based on Python programming language using mixed Visualization Toolkit (VTK) and Open Dynamics Engine (ODE) libraries. It is applied for automatic generation of the selective disassembly sequences and is illustrated within two examples.

Remanufacturing is an effective way to realize the reutilization of resources. Disassembly, as an essential step of remanufacturing, is usually finished by manual work which is low efficiency and high labor cost. Robotic disassembly provides an alternative way to reduce labor intensity and disassembly cost. Disassembly line is an efficient method to deal with end-of-life products on a large scale. Balancing the workload of robotic workstations is the main objective of robotic disassembly line balancing problem. In this paper, an improved multi-objective discrete bees algorithm is proposed to solve robotic disassembly line balancing problem. The feasible disassembly sequence is obtained by space interference matrix method. It is used to generate robotic disassembly line solution by robotic workstation assignment method. After that, the multi-objective robotic disassembly line balancing problem is proposed. With the help of efficient non-dominated Pareto sorting method, the improved multi-objective discrete bees algorithm is proposed to find Pareto optimal solutions. Based on a gear pump and a camera, the performance of the improved multi-objective discrete bees algorithm is analyzed under different parameters and compared with the other optimization algorithms. In addition, Pareto fronts of robotic disassembly line balancing problem are also compared with those of the other two cases. The result shows the proposed method can find better solutions using comparable running time compared with the other optimization algorithms.

With the growing requirements of retired electric vehicles (EVs), the recycling of EV batteries is being paid more and more attention to regarding its disassembly and echelon utilization to reach highly efficient resource utilization and environmental protection. In order to make full use of the retired EV batteries, we here discuss various possible application methods of echelon utilization, including hierarchical analysis methods based on various battery evaluation index. In addition, retired EV battery disassembly is also reviewed through the entire EV battery recycling based on human–robot collaboration methods. In order to improve the efficiency and reduce the cost of EV recycling, it is necessary to find a suitable recycling mode and disassembly process. This paper discusses the future possibility of echelon utilization and disassembly in retired EV battery recycling from disassembly optimization and human–robot collaboration, facing uncertain disassembly and echelon utilization.