Integrating Machine Learning and Sustainability in Nonwoven Production: A Case Study Using the FOREST Framework

The environmental impact of industrial processes, especially regarding CO2 emissions, requires innovative tools to monitor and optimize resource consumption. This study presents a data-driven approach for a carded nonwoven production line, aiming to support integration into the sustainability framework FOREST (Framework for Resource, Energy, Sustainability Treatment). Real process data from a pilot line were pre-processed, analysed, and used to train machine learning models to predict energy consumption across multiple production stages. Using techniques such as recursive feature elimination and SHAP value interpretation, the most influential parameters for each process step were identified. Extra Trees Regression proved to be the most accurate and explainable model across all scenarios. The results allow real-time estimation of the Product Carbon Footprint (PCF) based on process parameters and provide a foundation for energy optimization in nonwoven manufacturing.