Our current take-make-dispose economic model faces a vital challenge as it extracts resources from the natural environment at faster rates than that the natural environment can replenish. A circular economy where businesses lower their negative impact on the natural environment by transitioning towards recycling business models (RBMs), one of the four principles of circularity, is suggested as a promising solution. For a RBM to become viable, collaboration among several stakeholders and across several industries is required. In addition, the RBM should be scalable to make a positive impact. Hence, developing RBMs is complex as organizations need to consider multiple principles imposed by the recycling, collaborative, and scalability dimensions of these business models (BMs). In addition, these principles often remain general and not actionable to the practitioners. Therefore, in this study, we researched the practical guidelines for viable RBMs that are also collaborative and scalable. The empirical setting is the reuse of textile fibers to develop biocomposite products. We studied three cases using a research-through-design approach. We contribute to the literature on RBMs by showing the six minimum practical guidelines for recyclability, collaboration, and scalability. We draw implications for within sector collaborations and advance the thought that lease constructs challenge the scalability of RBM.
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MULTIFILE
Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
