Predictive Model for the Pulse Pressure in a Pulse-Jet Fabric Filter

Pulse-jet cleaned fabric filters are being utilized to filter dust from particulate laden gas streams. The pulse-jet fabric filter consists of a reservoir, a solenoid valve, a lateral pipe and a venturi/bag. The compressed air is released periodically through a solenoid valve and fills the lateral pipe. Then high velocity air is ejected from a series of orifices on the lateral pipe. As a result, the bag is rapidly inflated and dust is dislodged from the bag.

A predictive model for the pulse pressure in a pulse-jet fabric filter has been developed. Two types of models are presented: Simplified Static Model and Dynamic Model.

The Simplified Static Model treats the system as a series of spherical tanks in which the pressure develops uniformly throughout each tank. Each tank in series is connected by a valve. The mass balance equation is applied for the system and solved numerically. Sensitivity analysis has been conducted to evaluate the effect of configurations and operating parameters.

The dynamic model is developed to predict the local pressure developed in the system. The reservoir/lateral system is considered as a simple shock tube and the theory of a simple shock tube is applied to the system to predict the maximum pressure developed in the lateral. The maximum pressure developed at the entry of the bag is predicted based on the velocity of injected air. The pressure developed at the top of the bag dissipates along the bag due to the permeation of air mass through the bag surface. The mass balance equation is applied to the mass of air traveling along the bag. Empirical corrections are applied to the dynamic model due to the complexity of the theoretical treatment. The empirical correction factors introduced are the friction coefficient in the lateral, pressure losses at the bag entrance, and the permeability of the bags.

The proposed models can be employed to assist in the design of the pulse-jet fabric filter, to understand the behavior of the system, and to predict the performance due to the change of an operating condition.