Improving the power consumption in pneumatic conveying systems by a daptive control of the flow regime

• Autor(a) principal: Barbosa,P. R.
• Data de Publicação: 2003
• Outros Autores: Seleghim Jr.,P.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782003000400009
• topic: Pneumatic conveying; control; neural network; gas-solid flow

The pneumatic conveying of solids in a gas stream is a recurrent process in petrochemical industries. However, due to practical limitations the majority of existing systems have capacities ranging from 1 to 400 tones per hour over distances less than 1000 m, mainly because of a high power consumption per transported unit mass. More specifically, to avoid the formation of dense structures such as dunes and plugs, which, depending on the characteristics of the material and on the availability of a pressure head from the carrier phase may cause a violent pressure surge or a possible line blockage, the system is preferably operated at homogeneous dispersed flow. To sustain such a flow regime high velocities are needed and, accounting for the resulting higher pressure drops, higher power consumption is demanded. An optimized pneumatic conveying system can be conceived with the help of adaptive control techniques. In the context described above, lower transport velocities are allowed if the formation of aggregates that precedes the transition to dense phase flow regimes are automatically detected and destroyed, thus, artificially stabilizing the light phase homogeneous flow regime. This work assesses the reduction in the necessary power that the application of such adaptive control technique could produce. Experimental results are presented for a 45 mm i.d. pneumatic conveying system used to transport Setaria Italica seeds. The instrumentation used to identify the flow regime is constituted of several pressure sensors installed along the transport line. The proposed control strategy is based on processing these signals through a neural network model to assess the flow condition and to mimic an optimized gain scheduled PID algorithm. Preliminary results show that reductions in power consumption can reach 50% when compared with classical non controlled transport.