Data-driven system identification and model predictive control of pneumatic conveying using nonlinear dynamics analysis for optimised energy consumption

Pneumatic conveying systems provide secure transportation of particulate material and a dust-free environment. These systems face high energy consumption, material degradation, and pipeline blockages. This research presents an innovative solution by integrating nonlinear dynamics analysis of electrostatic sensor data, including chaos and recurrence quantification analysis, sparse identification of nonlinear dynamics with control (SINDYc) and model predictive control (MPC). The Lyapunov exponent, approximate entropy and recurrence rate of electrostatic sensor data reveal the chaotic nature of gas-solid flows. MPC framework was tailored for real-time optimisation of a pneumatic conveying system. SINDYc system models were developed using data collected from an open-loop control pneumatic conveying process to conduct MPC simulations and select an appropriate model for real-time control. This research illustrates the potential of integrating nonlinear dynamics analysis, SINDYc integrated and MPC for enhanced system performance, showcased a significant reduction in energy consumption without compromising the system's efficiency or reliability. A state space of the pneumatic conveying system under real-time model predictive control, tracing state trajectories guided by Lyapunov exponent (LE) reference of electrostatic sensor data. The colour bar in (a) is the LE reference and (b) is the LE measured. • Developed integrated nonlinear dynamics analysis, system identification and control. • Measured electrostatic signals to capture nonlinear dynamics of gas-solid flows. • Applied sparse identification with control to predict nonlinear dynamics measures. • Evaluated model predictive control models to find an effective control strategy. • Achieved significant energy reduction with efficient real-time control optimisation.