Recycling and Upcycling: FENIX Validation on Three Use Cases

Within FENIX a set of three use cases has been developed in order to test in practice the selected business models. After the description of a common data repository virtually connecting all the use cases and describing the common step of PCB disassembly, this chapter presents each use case into detail, by evidencing the main findings.

A Decision-Support System for the Digitization of Circular Supply Chains

As it has been already explained, it is very important for circular economies to minimize the wasted resources, as well as maximize the utilization value of the existing ones. To that end, experts can evaluate the materials and give an accurate estimation for both aspects. In that case, one might wonder, why is a decision support system employing machine learning necessary? While a fully automated machine learning model rarely surpasses a human’s ability in such tasks, there are several advantages in employing one. For starters, human experts will be more expensive to employ, rather than use an algorithm. One could claim that research towards developing an efficient and fully automated decision support system would end up costing more than employing actual human experts. In this instance, it is paramount to think long-term. Investing in this kind of research will create systems which are reusable, extensible, and scalable. This aspect alone more than remedies the initial costs. It is also important to observe that, if the number of wastes to be processed is more than the human experts can process in a timely fashion, they will not be able to provide their services, even if employment costs were not a concern. On the contrary, a machine learning model is perfectly capable of scaling to humongous amounts of data, conducting fast data processing and decision making. For power plants with particularly fast processing needs, an automated decision support system is an important asset. Moreover, a decision support system can predict the future based on past observations. While not always entirely spot on, it can give a future estimation about aspects such as energy required, amounts of wastes produced etc. in the future. Therefore, processing plants can plan of time and adapt to specific needs. A human expert can provide this as well to some degree, but on a much smaller scale. Especially in time series forecasting, it is interesting to note that, even if a decision support model does not predict exact values, it is highly likely to predict trends of the value increasing or decreasing in certain ranges. In the next sections, we are going to describe the four machine learning models that were developed and which compose the Decision Support System of FENIX. Section 8.1 describes how we predict the quality of the extracted materials based on features such as temperature, extruder speed, etc. Section 8.2 describes the process of extracting heuristic rules based on existing results. Section 8.3 describes how FENIX provides time-series forecasting to predict the future of a variable based on past observations. Finally, Sect. 8.4 describes the process of classifying materials based on images.

An Innovative (DIW-Based) Additive Manufacturing Process

This chapter will describe the activity of Fenix project that consisted in developing the hardware, infrastructure and processes to make possible the re-use of the recycled metals through an Additive Manufacturing (AM) method called Direct Ink Writing (DIW). It will first explain what is DIW and why it is an interesting way to give added value to recycled materials specially metals. It will then focus on the working principles and the parts of a DIW machine and end with a conclusion of the adequacy of this technology to new circular business models for the recycling of Waste of Electric and Electronic Equipment (WEEE).

Semi-automated PCB Disassembly Station

The main aim of the FENIX project is the development of new business models and industrial strategies for three novel supply chains in order to enable value-added product-services. Through a set of success stories coming from the application of circular economy principles in different industrial sectors, FENIX wants to demonstrate in practice the real benefits coming from its adoption. In addition, Key Enabling Technologies (KETs) will be integrated within the selected processes to improve the efficient recovery of secondary resources. In this sense, among the available KETs, the adoption of digital and advanced automated solutions allows companies to re-thinking their business strategies, trying to cope with even more severe environmental requirements. Among these technological solutions, the paradigm of Industry 4.0 (I4.0) is the most popular. I4.0 entails the development of a new concept of economic policy based on high-tech strategies and internet-connected technologies allowing the creation of added-value for organizations and society. Unlike the activities developed in T3.1, related to the development and implementation of simulation tools and models for the smartphones’ disassembly process optimization, here the attention has been spent in managing and optimizing a new semi-automated PCBs disassembly station. The disassembly of products is a key process in the treatment of Waste Electrical and Electronic Equipment. When performed efficiently, it enables the maximization of resources re-usage and a minimization of pollution. Within the I4.0 paradigm, collaborative robots (co-bots in short) can safely interact with humans and learn from them. This flexibility makes them suitable for supporting current CE practices, especially during disassembly and remanufacturing operations. D3.2 focuses on describing the semi-automated PCB disassembly process implemented at the POLIMI’s Industry 4.0 Lab, aiming to demonstrate in practice the benefits of exploiting I4.0 technologies in PCB disassembly processes. Results highlight how a semi-automated cell where operators and cobots works together can allow a better management of both repetitive and specific activities, the safe interaction of cobots with operators and the simple management of the high variability related with different kinds of PCBs.

Circular Business Models Identification

The main objective of FENIX is demonstrating the benefits coming from the adoption of CE practices through a set of circular business models adequately configured within the project. These CBMs have been selected basing on the three use cases requirements pertaining to different industrial streams (metal powders, 3D-printed jewels and advanced filaments for 3D printing applications). The chapter starts with a literature assessment of both current CBMs and current CBM classification methods. Subsequently, existing CBMs have been mapped basing on the most common classification method (i.e. the ReSOLVE framework), evidencing the most suitable CBMs to be adopted in FENIX. In parallel, a literature assessment of industrial benefits expected from the adoption of CE practices have been implemented. Subsequently, FENIX industrial partners have been interviewed in order to select the most relevant benefits expected from the project. A final comparison of available CBMs and expected benefits allowed to select the most suitable CBMs to be demonstrated in FENIX.

User Participation and Social Integration Through ICT Technologies

User is one of the most important stakeholder cluster and its participation can link the end of life and early stages in the life cycle of each product when considering the adoption of a circular business model. This chapter presents the main elements of the customer engagement, as identified through a State-of-the-Art analysis carried out in the context of FENIX, as well as those electronic tools in which they will be integrated together with conventional tools for the conduction of commercial activities and the tools to facilitate the interaction with the other actors and activities of FENIX within a single access point digital platform (FENIX Marketplace). The SoA analysis identified the motivational factors that promote a greater customer engagement for the participation throughout all business routes (B2B, B2C but also C2C) applicable in the project. These strategies are improved and enhanced using benefits provided by the social media for the participation in the process. The customer involvement is directly linked to the motives provided within FENIX Marketplace.

The Life Cycle Performance Assessment (LCPA) Methodology

The FENIX project has started to develop future business models for the efficient recovery of secondary resources. It would not be enough just to improve business models based on traditional linear approaches. Rather, new approaches must be developed with a particular focus on environmental and climate changes. Electronic scrap is no longer scrap, but must be seen as valuable material. Using the mobile phone as an example, FENIX has developed technologies to get recyclable materials out of scrapped mobile phones and to process them into new materials and final products. The developed technological approaches are not limited to mobile phones, but can be used for all types of electronic waste. FENIX has only focused on the logistic chain from the dismantling of the cell phones to the manufacturing of new materials and products (recycling chain). This, of course, involves a lot of effort in dismantling the e-waste, as the recycling process was not yet considered when developing the products currently on the market. Such eco-design approaches would certainly reduce the disassembly effort in the future. FENIX business models should not only be based on economic success but also consider ecological effects at the same time. Therefore, an accompanying Life Cycle Performance Assessment (LCPA) has been carried out to prove the advantages of the developed business models. From the interim assessment, recommendations for further technical development directions were repeatedly given to achieve the best possible economic and ecological solutions.

Circular Economy Performance Assessment

The main aim of the FENIX project is the development of new business models and industrial strategies for three novel supply chains in order to enable value-added product-services. Through a set of success stories coming from the application of circular economy principles in different industrial sectors, FENIX wants to demonstrate in practice the real benefits coming from its adoption. In addition, Key Enabling Technologies (KETs) will be integrated within the selected processes to improve the efficient recovery of secondary resources. This chapter focuses on the definition of a novel Circular Economy Performance Assessment (CEPA) methodology to be adopted within the FENIX project. This implementation activity has been done into two steps. From one side, a state-of-the-art analysis of existing CE methodologies and related KPIs has been executed and the most common circularity assessment methods (and KPIs) have been identified. Subsequently, a totally new CEPA methodology has been developed starting from the findings coming from the literature. This methodology, together with classic LCA and LCC methods, will be exploited for the quantitative assessment of CBMs.

A Mobile Pilot Plant for the Recovery of Precious and Critical Raw Materials

In order to furtherly proceed with the recycling of raw materials from e-wastes, PCBs must be treated in a hydrometallurgical process able to extract useful materials from them. This chapter presents some details of the hydrometallurgical pilot plant developed in FENIX.

Introduction

This chapter aims at clarifying the main research contents and presenting the main objective of the FENIX project. we briefly describe some fundamental concepts, like Circular Economy (CE), Industry 4.0 (I4.0) and Product-Service Systems (PSSs). All these contents are strictly connected in FENIX, since the project aims at demonstrating the benefits coming from the adoption of CE-related practices through a set of PSS-based business models supported by I4.0-based technologies.