Enhancing Control Efficiency in Waste-to-Energy Thermal Power Plants: Radial Basis Function Autoregressive with Exogenous-Based Model Predictive Controller for Steam Boiler Optimization

The availability of electrical energy is essential for human progress and economic development. Renewable energy solutions, including waste-to-energy (WtE) systems, present sustainable alternatives but require advanced control strategies for optimal performance. This research aims to enhance the control of drum level, temperature, and pressure in WtE steam boilers at Ethiopia's Reppie power plant. The existing Programmable Logic Controller (PLC) system is limited in its ability to predict future states and handle nonlinear system behaviors. To overcome these challenges, a Radial Basis Function Autoregressive with Exogenous input (RBF-ARX) model was developed and integrated with a Model Predictive Controller (MPC). The results demonstrate that the MPC approach significantly surpasses the performance of the Linear Quadratic Regulator (LQR) in terms of control efficiency. For temperature control, the MPC achieves a settling time of 0.3955 seconds and a rise time of 0.0195 seconds, compared to LQR's 5.99 seconds. Similarly, for pressure control, the MPC achieves a settling time of 0.6678 seconds, outperforming the LQR's 12.507 seconds. Drum level regulation further showcases the superiority of MPC, with a settling time of 0.5223 seconds versus the LQR's 8.302 seconds. This proposed RBF-ARX-based MPC framework not only optimizes control efficiency at Reppie but also demonstrates scalability and applicability to other WtE plants, enhancing operational performance under varying conditions. MATLAB/Simulink was used for the modeling and simulation, confirming the robustness of this approach for global adoption in WtE systems.