Artificial Intelligence Solutions for the Recycling and Utilization of Biomaterials for Industrial Applications

Abstract

The escalating threat of global warming, primarily driven by the emission of greenhouse gases and the combustion of fossil fuels, underscores the urgency for adopting more sustainable practices. In response to this need and to reduce dependence on fossil fuels, biomaterials are increasingly recognized as a viable and sustainable alternative to conventional, unsustainable materials. Concurrently, rapid advancements in technology, particularly in artificial intelligence (AI), are presenting new opportunities for innovation and offering potential solutions to address the challenges posed by climate change. The commissioner for this thesis was Vaasa University of Applied Sciences Design Center MUOVA who wanted to gain insights of the industrial applications of biomaterials and explore the potential opportunities AI could offer in this field. Given the topicality and the specific research needs identified by Muova, the research questions were formulated to investigate the utilization of biomaterials in industrial settings, their recyclability, and the potential contributions of AI to their recycling and utilization.

The methodology employed for this thesis involved conducting a comprehensive literature review with data primarily gathered from scientific articles and information obtained from companies’ websites that are actively involved in the biomaterials sector. Initially, biomaterials and their applications were examined broadly, followed by specific inquiries into industrial uses and relevant companies. Recyclability was examined both broadly and, on a case-by-case basis for previously identified biomaterials. Subsequently, utilization of AI was first searched in recycling in general and the focused specifically on the recycling of biomaterials and predictive material development.

The data gathered on biomaterials in industrial applications and their recyclability primarily focuses on various wood-based biomaterials and bio-based plastics. The recyclability of these materials is found to be dependent on their individual features, as well as factors such as quantity and geographical location. The research highlights the significant presence of companies in Finland engaged in this sector. Furthermore, results show that specific machine learning algorithms have advanced general recycling practices, indicating potential opportunities for biomaterial recycling, although more research is needed in this area. Moreover, AI has already played a pivotal role in the development and utilization of biomaterials in industrial settings. It is worth nothing that the thesis provides a broad perspective on this topic suggesting that further research is necessary to delve into each biomaterial more comprehensively for more accurate results.