| Abstract |
The circular economy and its various recirculation loops have become a major study subject over recent years, particularly in the field of agriculture, which is a significant source of waste production. There have been several studies focused on transforming agricultural lignocellulosic waste with "sustainable" processes: economically viable, socially accepted, and environmentally friendly. Thanks to "life cycle thinking", it is possible to assess these potential environmental impacts. However, these environmental analyses generally require a massive volume of specific data, the collection of which can be time-consuming and tedious, or impossible to practice. On the other hand, scientific articles describing the processes for the valorization of agricultural by-products are intriguing but rarely exploited sources of data. In this paper, a hybridization of data science techniques and environmental analysis was proposed to improve life cycle analysis (LCA) thanks to Machine Learning (ML). ML part of the proposed approach is based on unsupervised learning, which is composed of two methods: dimension reduction using the Multidimensional Scaling and clustering technique using k-means. Composed of five steps and dedicated to researchers or R&D engineers, the approach is oriented towards offering a decision on technologies and processes for waste to energy in the early eco-design step. The case study in the domain of pre-treatment processes for corn stover and rice straw is detailed. The results show that all impacts that concern the chemical pollution of soil and water are found in the same cluster. Other impacts are detected in the same cluster which is related to the land use and the land transformation. In the same vein, two purely mechanical pre-treatments have been identified and grouped by Multidimensional Scaling and k-means. |
