Economic and environmental life cycle assessment of spent coffee grounds valorization for bioenergy production

Abstract This study highlights the promising role of machine learning in optimizing spent coffee ground (SCG) valorization, particularly through pyrolysis-based conversion to bioenergy. With the global shift toward sustainable energy, integrating diverse technologies and renewable resources is crucial. Coffee, one of the most widely consumed beverages, produces large quantities of SCGs during brewing, posing both environmental and economic challenges. The study investigates a pyrolysis system using the Pyrolysis Interactive Simulator (PIS) V2. 0 to model key parameters such as temperature and heating rate to predict the composition of pyrolysis products (biochar, bio-oil, and syngas). Experimental validation supports the model’s biochar predictions. The environmental impact of the process is assessed using Life Cycle Assessment (LCA) tools, revealing that blending and drying at different temperatures require similar energy inputs, while pyrolysis energy demand increases by 19% as temperature rises from 300 °C to 600 °C. The model shows that pyrolysis temperature significantly influences the energy yields of bio-oil, biochar, and syngas. The economic analysis forecasts annual profits of 14–22 million USD in markets such as Qatar, the USA, the UK, and China. In particular, at 600 °C, the Chinese market demonstrates a return on investment (ROI) of 29%, with a payback period of 3. 4 years, indicating strong commercial viability. The study also reveals that higher pyrolysis temperatures increase the carbon, water, and land footprints for bio-oil and syngas, while reducing them for biochar. Overall, this work leverages simulation software and machine learning to optimize the pyrolysis process, offering valuable insights for efficient SCG valorization. Graphical Abstract