Graphite for lithium-ion batteries: mineral analysis and global Material Flow Analysis

Graphite is a mineral commodity used as anode for lithium-ion batteries (LIBs), and its global demand is doomed to increase significantly in the future due to the forecasted global market demand of electric vehicles. Currently, the graphite used to produce LIBs is a mix of synthetic and natural graphite. The first one is produced by the crystallization of petroleum by-products and the second comes from mining, which causes threats related to pollution, social acceptance, and health. This MSc work has the objective of determining compositional and textural characteristics of natural, synthetic, and recycled graphite by using SEM-EDS, XRF, XRD, and TEM analytical techniques and couple these data with dynamic Material Flow Analysis (MFA) models, which have the objective of predicting the future global use of graphite in order to test the hypothesis that natural graphite will no longer be used in the LIB market globally. The mineral analyses reveal that the synthetic graphite samples contain less impurities than the natural graphite, which has a rolled internal structure similar to the recycled one. However, recycled graphite shows fractures and discontinuities of the graphene layers caused by the recycling process, but its rolled internal structure can help the Li-ions’ migration through the fractures. Three dynamic MFA studies have been conducted to test distinct scenarios that include graphite recycling in the period 2022-2050 and it emerges that - irrespective of any considered scenario - there will be an increase of synthetic graphite demand, caused by the limited stocks of battery scrap available. Hence, I conclude that both natural and recycled graphite is doomed to be used in the LIB market in the future, at least until the year 2050 when the stock of recycled graphite production will be enough to supersede natural graphite. In addition, some new improvement in the dismantling and recycling processes are necessary to improve the quality of recycled graphite.

SmartForge: An event-driven architecture for simulator-based process optimization

In the metal industry, and more specifically in the forging one, scrap material is a crucial issue and reducing it would be an important goal to reach. Not only would this help the companies to be more environmentally friendly and more sustainable, but it also would reduce the use of energy and lower costs. At the same time, the techniques for Industry 4.0 and the advancements in Artificial Intelligence (AI), especially in the field of Deep Reinforcement Learning (DRL), may have an important role in helping to achieve this objective. This document presents the thesis work, a contribution to the SmartForge project, that was performed during a semester abroad at Karlstad University (Sweden). This project aims at solving the aforementioned problem with a business case of the company Bharat Forge Kilsta, located in Karlskoga (Sweden). The thesis work includes the design and later development of an event-driven architecture with microservices, to support the processing of data coming from sensors set up in the company's industrial plant, and eventually the implementation of an algorithm with DRL techniques to control the electrical power to use in it.