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The extensive use of external thermal insulation composite systems (ETICS) using expanded polystyrene (EPS) for building insulation in Germany has resulted in accumulating end-of-life EPS-ETICS. To address limited waste management options and enhance EPS circularity, alternative recycling options like pyrolysis or solvent-based recycling need to be implemented. However, efficient recycling requires ex-ante analyses of the corresponding reverse logistics networks, including all necessary pre-treatment and transport processes. This study aims to develop a decision support system to determine optimal EPS-ETICS waste management by minimising the net present value of the corresponding network design. Our mixed-integer linear optimisation model considers decisions on technology, capacity, and location for pre-processing and recycling facilities as well as on material flow allocation. Applied to a German case study, our model finds pyrolysis and solvent-based recycling economically superior to current waste treatment in waste incineration plants. We present an optimal network design combining central pyrolysis with decentral pre-processing. A scenario analysis shows fluctuating styrene and polystyrene prices might favour solvent-based recycling over the optimal choice in pyrolysis, with minor overall cost differences. Further, legal recycling quotas of at least 40% would be necessary to promote solvent-based recycling and enhance EPS circularity beyond what is achievable through pyrolysis. [ABSTRACT FROM AUTHOR]

ABSTRACTThis study presents the development of an event-driven hybrid control for position and force tracking applied on a mobile robotic manipulator for metal recycling tasks. The suggested controller operates in a sequenced strategy starting from a fixed spot, moving the mobile device towards a targeted zone ([Formula: see text]) from where the i-th piece-to-be-recycled is attainable (considering the arm manipulation). Once the event of entering the zone is completed, the mobile robot is fixed at a position, and the end-effector of the robotic arm is enforced towards the piece-to-be-recycled. When the end-effector touches the piece in a given spot ([Formula: see text]), the hybrid control changes to the force tracking intending to carry the piece towards the spot ([Formula: see text]) where it ill be processed. Each piece location is identified based on a vision-based system that applies deep learning tools using convolutional neural networks. A multi-physics numerical simulation illustrated the application of the developed controller in a realistic scenario, showing all the elements of the event-driven operation. To validate the suggested controller, the comparison with a robust control that works on a wide range of carrying mass confirms the operational improvement of the event-driven hybrid position and force design.

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As the demand for silicone polymers continues to grow across various industries, the need for effective recycling methods has become increasingly important, because recycling silicone products reduces landfill waste, conserves resources, and uses less energy. Chemical recycling involves the depolymerization of silicone waste into oligomers, which can then be used to produce virgin-grade silicone. While this sector of the recycling industry is still in its infancy—we estimate that 35,000 to 45,000 metric tons of silicone waste will be chemically recycled worldwide in 2024—an increasing number of companies are beginning to explore the implementation of closed-loop systems to recycle silicones. This article examines the technical options and challenges for recycling silicone polymers, the major degradation chemistries available for depolymerizing silicones, and the current industrial reality of chemical recycling of silicones. [ABSTRACT FROM AUTHOR]

Purpose: Recycling is associated with positive social and environmental impact, but previous studies have overlooked the cost of recycling operations. Based on the dynamic capability view, the purpose of this study was to identify and evaluate risk factors and resilience strategies within the recycling industry, prioritize these factors and identify the optimal combination of resilience strategies and risk factors to improve market performance. Design/methodology/approach: The research questions were addressed in three subsequent studies. In Study 1, qualitative interviews were conducted to identify risk factors and strategies to mitigate those risks. In Study 2, quality function deployment methodologies were implemented via case studies derived from three different companies. Based on the results of Studies 1 and 2, in addition to the use of fuzzy set qualitative comparative analysis, Study 3 aimed to determine the optimal combination of risk factors and strategies impacting market performance. Findings: The results across the three studies revealed a number of risk factors as well as which risk factors and resilience strategies have the greatest impact on market performance. Specifically, it was found that higher levels of readiness, response and recovery strategies lead to greater market performance, whereas weak readiness, response and recovery strategies, along with low societal, environmental and health and safety risk factors, significantly inhibit performance. Originality/value: This research extends current understandings of market performance in relation to recycling industry management and offers insight for decision-makers toward combating significant risk factors in business-to-business settings. [ABSTRACT FROM AUTHOR]

Increasing efforts have been devoted to promoting sustainable demolition waste management (DWM) from a life cycle thinking perspective. To this end, facilitating sustainability-oriented decision-making for DWM planning requires a sustainability assessment framework for assessing the trade-offs among multifaceted criteria. This study develops a BIM-based DWM sustainability assessment approach to facilitate the life cycle sustainability assessment (LCSA) and decision-making by integrating LCSA-related properties and hybrid Multi-Criteria Decision-Aiding (MCDA) methods into a BIM environment using Dynamo visual scripting. A dynamic linkage is developed in the streamlined BIM-based LCSA process, where the enriched Industry Foundation Class (IFC) models are coupled with custom LCSA data templates to achieve seamless data exchange between the BIM platform and external LCA tools. Subsequently, hybrid MCDA methods convert the assessment results into DWM scenario ranking. A pilot study verifies the applicability of the BIM-based framework. The results unveil that the sustainability score ascended with the recycling rate. The optimal DWM alternative with the highest recycling rate yields the highest sustainability score at 91.63. Conversely, a DWM alternative reflecting the 'status quo' in China's recycling industry has the lowest score at 8.37, significantly lower than the baseline scenario with a 50% recycling rate. It is worth noting that the 'growth curve' of the sustainability score continuously flattens as the target recycling rate escalates. The increment in recycling rate from the "Australian standard" scenario to the optimal scenario is 18.4%, whereas the sustainability score merely increases by 2.3%, implying that the former scenario arrived at an optimum point for maximising the cost-efficiency of DWM under the predefined settings. [ABSTRACT FROM AUTHOR]

Lithium batteries, as an important energy storage device, are widely used in the fields of renewable vehicles and renewable energy. The related lithium battery recycling industry has also ushered in a golden period of development. However, the high cost of lithium battery recycling makes it difficult to accurately evaluate its recycling value, which seriously restricts the development of the industry. To address the above issues, machine learning will be applied in the field of economic benefit analysis for lithium battery recycling, and backpropagation neural networks will be combined with stepwise regression. On the basis of considering social and commercial values, a lithium battery recycling and utilization economic benefit analysis model based on stepwise regression backpropagation neural network was designed. The experimental results show that the mean square error of the model converges between 10–6 and 10–7, and the convergence speed is improved by 33%. In addition, in practical experiments, the model predicted the actual economic benefits of recycling a batch of lithium batteries. The results show that the predictions are basically in line with the true values. Therefore, the economic benefit analysis and prediction model for lithium battery recycling proposed in the study has the advantages of high accuracy and fast operation speed, providing new ideas and tools for promoting innovation in the field of economic benefit analysis. It has certain application potential in the evaluation of the benefits of lithium battery recycling. [ABSTRACT FROM AUTHOR]

Construction and demolition waste (C&DW) recycling products have drawn worldwide attention over the past few decades. There is a general agreement among researchers that C&DW recycling is an important means for curbing the deterioration of the environment. Previous papers mainly focused on the decision-making behavior of dual stakeholders or tripartite stakeholders, as well as the lack of cooperation among multiple stakeholders. This study explored a dynamic evolutionary game model with three different parameter conditions to research the decision-making behaviors and stable strategies of the multi-stakeholders involved in the C&DW recycling product industry, including government departments and some enterprises. This research also investigated how the government's supervision costs, penalties applied to enterprises, and resource taxes affect the dynamic evolution process of C&DW recycling. This research conducted numerical simulations using Python to analyze stakeholders' behavioral evolutionarily stable strategy (ESS) and the sensitivity to main factors in each stage of the C&DW recycling process to accelerate the development of construction waste resource utilization. Based on the results of the evolutionary game, this paper proposed that the roles of multi-stakeholders are different at different stages of industry development, and that supervision costs, penalties, and resource tax have their own impacts on the C&DW recycling product industry. The paper suggests a range of discussions and simulation studies to highlight the significance of the government's refined and moderate adjustments to the regulatory incentive system and the level of government regulation and involvement at different stages of the process. These adjustments are aimed at promoting the sustainable recycling and utilization of construction and demolition waste (C&DW) products within some countries' construction industry. [ABSTRACT FROM AUTHOR]