Proper waste separation is essential for recycling. However, it can be challenging to identify waste materials accurately, especially in real-world settings. In this study, a systematic literature review (SLR) was carried out to identify the physical enablers (sensors and computing devices), datasets, and machine learning (ML) algorithms used for waste identification in indirect separation systems. This review analyzed 55 studies, following the Kitchenham guidelines. The SLR identified three levels of autonomy in waste segregation systems: full, moderate, and low. Edge computing devices are the most widely used for data processing (9 of 17 studies). Five types of sensors are used for waste identification: inductive, capacitive, image-based, sound-based, and weight-based sensors. Visible-image-based sensors are the most common in the literature. Single classification is the most popular dataset type (65%), followed by bounding box detection (22.5%). Convolutional neural networks (CNNs) are the most commonly used ML technique for waste identification (24 out of 26 articles). One of the main conclusions is that waste identification faces challenges with real-world complexity, limited data in datasets, and a lack of detailed waste categorization. Future work in waste identification should focus on deployment and testing in non-controlled environments, expanding system functionalities, and exploring sensor fusion.

In multi-criteria mechanical design, it is common to support the decision-making process with algorithms that facilitate understanding the impact of modifications in design variables on the final performance and associated design objectives. This article presents the development of an interactive optimisation method for application in an operational context through the introduction of a digital twin approach. The original method incorporates uncertainty management via the CPM/PDD model to establish a traceability framework, enabling near-optimal solutions that align design objectives in a multidisciplinary context. Furthermore, it includes a graphical tool that allows users to manually adjust variables while assessing compliance with design objectives in real time. For the digital twin application, the tool examines how changes in external conditions influence product performance. By integrating sensitivity analysis with the graphical tool, engineers can improve decision-making by anticipating how external modifications may degrade performance. At the core of the method, a Monte Carlo simulation is combined with traceability graph results to evaluate the impact of variable modifications on design objectives. This innovative approach supports the development of preventive and predictive maintenance processes in mechanical systems.