Thesis title: Advanced Automation and Predictive Modeling for Urban Waste Syngas Production: A Digital Twin Approach
This thesis presents the development and implementation of a fully autonomous control system for urban waste management, aimed at optimizing syngas production through gasification. Conducted in collaboration with Nextchem/Myrechemical, the research enhances the efficiency and reliability of waste-to-energy processes by integrating predictive control algorithms, digital twin technology, and sensor-based data acquisition. A key outcome of the system is its ability to operate autonomously, thereby removing the need for human operators in hazardous or toxic environments. The digital twin developed in this work emulates the plant comprehensively, incorporating physical, kinematic, and capacity constraints, as well as advanced control logic and fully automated systems to ensure an accurate simulation of the waste feed process. The control architecture follows a dual-layer design: a predictive decision-making system that can simulate plant behavior, and local real-time control subsystems for immediate operational adjustments. A central feature is the optimal preparation of the feedstock—monitoring critical parameters such as Lower Heating Value (LHV) and chemical composition (concentration of C, H, O, and other components.)—to guarantee process stability and maximize syngas yield. The system leverages advanced analytical techniques, notably near-infrared (NIR) spectroscopy and neutron activation analysis (NAA), for real-time waste characterization. Experimental work includes the development of automated identification methods based on spectral technology and a patented custom mechatronic system for on-the-fly characterization. These technologies not only enable dynamic adjustments to the feed process, ensuring optimal operation, but also open avenues for building new fully automated gasification plants, retrofitting existing ones, and converting incineration facilities. Numerical and experimental results demonstrate the effectiveness of the proposed solution, examining different operational scenarios to assess robustness.