Cross recurrence quantification analysis of gas-solid flow in pneumatic conveying of plastic pellets using electrostatic sensors

Gas-solid flow synchronisation behaviour of plastic pellets were characterised, and particle velocities were processed using cross-recurrence quantification analysis (CRQA) of electrostatic sensors at different pneumatic conveying operating conditions, including air velocity and solids mass flow rates. Time-series data were collected from arc-shaped electrostatic sensors to reconstruct phase spaces (attractors) using the time delay coordinate embedding method. Cross-recurrence plots were developed from the reconstructed phase spaces to visualise the shared topology in two arc-shaped electrostatic sensors at two locations in a horizontal pipeline. CRQA measures, such as recurrence rate and determinism, were applied to the cross-recurrence plots to quantify the synchronisation between the two sensors for different gas-solid flow patterns: stratified flow, pulsating flow, moving dunes and blowing dunes. The flow patterns were identified using high-speed video imaging sight section of a pipeline and classified at several operating conditions in a flow pattern map and state diagram. The optimal operating conditions at the minimum conveying air velocity in the state diagram are between moving and blowing dunes. The CRQA measures and particle velocity were correlated with the state diagram to understand their relationship at optimal conditions. It was found that the CRQA measures can be used to detect changes in solids mass flow rate and air velocity

Model predictive control simulation of pneumatic conveying of plastic pellets using nonlinear dynamics analysis and sparse identification of nonlinear dynamics with control

The study explores the integration of Model Predictive Control (MPC) and Sparse Identification of Nonlinear Dynamics with control (SINDYc) with nonlinear dynamics analysis to simulate the pneumatic conveying of plastic pellets. Nonlinear dynamics analysis measures were applied to data from a bottom arc-shaped electrostatic sensor of fully developed gas-solid flow in horizontal pipelines, including Lyapunov exponents, approximate entropy and recurrence rate. The study leverages SINDYc, a data-driven method, to identify sparse system models using the analysis measures. The MPC framework is then employed to optimise control inputs over a future horizon, ensuring desired nonlinear flow behaviour. The simulation framework assesses MPC's performance, using three distinct SINDYc models for each analysis measure to understand their control system's dynamics. Results showcase the ability to integrate MPC and SINDYc with the nonlinear dynamics analysis measures, highlighting improvements in system control

Experimental and numerical study on gas-solid flow system of plastic pellets using recurrence quantification analysis of pressure sensor measurements

The recurring dynamics of fully developed gas-solid flow patterns are characterised using recurrence quantification analysis measure in horizontal pneumatic conveying of plastic pellets. This measure was applied to recurrence plots, developed from phase spaces (attractors) reconstructed from pressure signals to characterise the recurring dynamics of flow patterns: stratified flow, pulsating flow, moving dunes and blowing dunes. The recurrence plot is a representation of the recurring dynamics in the attractor. Recurrence plots were analysed for flow patterns which showed different qualitative structures. The qualitative structures of recurrence plots at different operating parameters are measured using recurrence quantification analysis such as recurrence rate (RR). The RR measure was correlated with the state diagram, indicating that the recurring dynamics of pressure signals can detect the change between moving dunes and blowing dunes at a specific range of operating conditions

Application of nonlinear dynamics analysis to gas-solid flow system in horizontal pneumatic conveying of plastic pellets

Chaotic invariant and recurrence quantification analysis measures have characterised fully developed gas-solid flow in horizontal pneumatic conveying of plastic pellets. These measures describe the complexity in phase spaces (attractors) and recurrence plots, reconstructed from pressure and bottom arc-shaped electrostatic signals to characterise the behaviour of flow patterns. Different flow patterns were identified using high-speed video imaging of a transparent pipeline and classified at several operating conditions in a flow pattern map and state diagram. Recurrence plots were analysed for the identified flow patterns, which showed different qualitative structures. The chaotic invariant and recurrence quantification analysis measures were correlated with the state diagram, indicating that the fluctuations of pressure senor and electrostatic sensor signals can classify the flow patterns at different operating conditions. Combining the analysis measures for electrostatic signals can indicate whether the flow condition is above, near or below the minimum energy consumption operating conditions

Application of recurrence quantification analysis to electrostatic measurements of horizontal gas-solid flow in pneumatic conveying of plastic pellets

The recurring dynamics of fully developed gas-solid flow patterns are characterised using recurrence quantification analysis of bottom arch-shaped electrostatic sensor data in horizontal pneumatic conveying of plastic pellets. The recurrence quantification analysis is applied to recurrence plots developed from phase spaces (attractors) reconstructed from the bottom arc-shaped electrostatic sensor signals using a statistical measure, the approximate entropy (AE). The AE measure is the probability of the unpredictability between past and future dynamics in a phase space used to select a specific range of recurring dynamics in the recurrence plot to characterise different flow patterns: stratified flow, pulsating flow, moving dunes and blowing dunes. The flow patterns were identified using high-speed video imaging of a transparent pipeline and classified at several operating conditions in a flow pattern map and state diagram. It is found that optimal operating conditions at the minimum energy consumption conveying operation in the state diagram are between moving dunes and blowing dunes. Through visual observation, recurrence plots of the identified flow patterns showed different qualitative structures. The qualitative structure of recurrence plots is quantified using a recurrence quantification analysis measure, the recurrence rate (RR). The AE and RR measures are correlated with the state diagram, indicating that the complexity of electrostatic sensor signals can classify the flow patterns at different operating conditions

Application of chaos analysis to electrostatic measurements of horizontal gas-solid flow in pneumatic conveying of plastic pellets

Chaotic invariant measures have been used to characterise the instabilities of fully developed gas-solid flow patterns in horizontal pneumatic conveying of plastic pellets. These measures were applied to phase spaces (attractors) reconstructed from bottom arc-shaped electrostatic signals to characterise the behaviour of flow patterns: stratified flow, pulsating flow, moving dunes and blowing dunes. The flow patterns were identified using high-speed video imaging sight section of a pipeline and classified at several operating conditions in a flow pattern map and state diagram. It is found that optimal operating conditions at the minimum conveying air velocity in the state diagram are between moving dunes and blowing dunes. Chaotic features of electrostatic phase spaces are characterised using statistical measures capable of staying invariant at specific operating conditions, including Lyapunov exponent, approximate entropy and correlation dimension. The correlation between the chaotic invariant measures with the operating conditions is analysed through state diagrams, indicating that the fluctuations of electrostatic signals can classify the flow patterns at different solid mass flow rates