Efficient learning-based predictive control for acid gas abatement in waste to energy processes

Waste-to-energy plants have become a strategic resource to reduce the volume of non-recyclable solid waste in municipalities. Flue gas treatment is a key component in making these plants clean and sustainable. In particular, acid gas abatement is a fundamental process for complying with emission standards. However, developing models of the abatement process is challenging due to the complexity of the phenomena and reactions occurring inside the pollutant abatement system. In this work, a predictive control strategy is proposed to regulate the concentration of hydrogen chloride in the flue gas of a waste-to-energy plant by manipulating the reactant flow rate. Black-box models for simulation and prediction tasks are derived from experimental data from a real WtE plant in Italy. A learning strategy is proposed to update an autoregressive model of the process in real-time using Set Membership identification techniques, and a Model Predictive Controller is formulated to optimally manipulate the reactant feed rate, guaranteeing that emissions comply with regulatory constraints while minimizing the reactant dosage. The performance of the resulting control strategy is compared with a standard PI plus FeedForward controller, currently employed in this kind of process. The results show that the adaptive MPC improves the tracking performance, reducing the Mean Integrated Absolute Error by up to 57.1% and reactant consumption by 3%, while ensuring better compliance with emission regulations.