Modular model and simulation for process optimisation in advanced material recovery facilities (MRFs)

At a time when the supply of critical materials is threatened, waste recycling and reuse is an essential solution for human development. The role of Material Recovery Facilities (MRFs) to deliver efficiently high-purity material fractions as feedstock cannot be underestimated. However, MRF sorting processes need to remain adaptive with evolving smart technologies and systems that further enhance their effectiveness. For example, a re-designed MRF with AI-based robotics can improve the performance of waste recycling, leading to significant economic and environmental benefits. This study assesses the performance of potential optimisation methods for future proofing MRFs using modular simulation methods. The authors set out to review current robotics sorting technology and pointed out the challenge of efficiency analysis with multiple variables. The study develops a new conceptual model of efficiency analysis considering the identification and sorting limitations of robots, as well as the coordination requirements between robots and conveyor belts. A computational model is designed and developed by modularity program codes to help practitioners gain insight into the MRF performance by modifying the variables (composition of input waste, separation coefficients and configurations) and analysing the resulting assessment factors (purity and recovery). In the end, this study demonstrates the performance of the optimisation methods of MRF (two target materials for one robot and recirculation loops) through simulation.