Extending multilevel statistical entropy analysis towards plastic recyclability prediction

Multilevel statistical entropy analysis (SEA) is a method that has been recently proposed to evaluate circular economy strategies on the material, component and product levels to identify critical stages of resource and functionality losses. However, the comparison of technological alternatives may be difficult, and equal entropies do not necessarily correspond with equal recyclability. A coupling with energy consumption aspects is strongly recommended but largely lacking. The aim of this paper is to improve the multilevel SEA method to reliably assess the recyclability of plastics. Therefore, the multilevel SEA method is first applied to a conceptual case study of a fictitious bag filled with plastics, and the possibilities and limitations of the method are highlighted. Subsequently, it is proposed to extend the method with the computation of the relative decomposition energies of components and products. Finally, two recyclability metrics are proposed. A plastic waste collection bag filled with plastic bottles is used as a case study to illustrate the potential of the developed extended multilevel SEA method. The proposed extension allows us to estimate the recyclability of plastics. In future work, this method will be refined and other potential extensions will be studied together with applications to real-life plastic products and plastic waste streams

Automatic Deployment Space Exploration Using Refinement Transformations

To manage the complex engineering information for real-time systems, the system under development may be modelled in a high-level architecture de- scription language. This high-level information provides a basis for deployment space exploration as it can be used to generate a low-level implementation. During this deployment mapping many platform-dependent choices have to be made whose consequences cannot be easily predicted. In this paper we present an approach to the automatic exploration of the deployment space based on platform-based design. All possible solutions of a deployment step are generated using a refinement trans- formation. Non-conforming deployment alternatives are pruned as early as possible using simulation or analytical methods. We validate the feasibility of our approach by deploying part of an automotive power window optimized for its real-time be- haviour using an AUTOSAR-like representation. First results are promising and show that the optimal solution can indeed be found efficiently with our approach