An Experimental Investigation and CFD Analysis into Pressure Drop for Pneumatic Conveying of Fine Powders through Closely-Coupled Bends

Abstract

Bends play a vital role in the transportation of bulk materials in the pneumatic conveying system. They provide flexibility to the conveying line, but may also result in some undesirable effects like pressure loss, product build up and blockage. This report presents results from an investigation into the modelling of closely-coupled bend pressure drop for pneumatic conveying. In this study, six models were evaluated and compared with the experimental results. The present experimental work has been done on 53 mm I.D. × 69 m with cement material (median particle diameter: 17 μm; particle density: 2950 kg/m3; loose-poured bulk density: 1020 kg/m3) and 69 mm I.D. × 168 m long pipeline with fly ash material (median particle diameter: 30 μm; particle density: 2300 kg/m3; loose-poured bulk density: 700 kg/m3). All the six models showed under-prediction of the pressure drop. Out of six models, Chambers and Marcus (1986), Pan and Wypych (1998), Pan(1992) showed less under-prediction than Singh and Wolfe (1972), Rossetti (1983) and Das and Meloy (2002). A new model has been introduced of the same format of Pan and Wypych (1998) with some modifications which includes the effect of material properties and radius of curvature. Modelling of solid friction factor was done by using pressure drop model of Pan and Wypych (1998) and the new model. Results show that material properties significantly affect the pressure drop through closely coupled bends. The predicted pressure drop values were found to be in reasonably good agreement with experimental pressure drop. Computational fluid dynamics (CFD) simulation has also been done on closely-coupled bends. In simulation, effects of considering single particle size and particle size distribution of the granular phase has been analysed. From results it can be concluded that, simulation with particle size distribution shows better pressure drop prediction instead using single particle size in simulation. Results also show that, by using particle size distribution % error reduces 80% to 50%.