Measuring mass flow of solids in pneumatic conveying systems has been a key element in industry and a challenge for researchers for many years. Many methods have been developed in the past; however, there is no perfect method available. The aim of the present work was to obtain a mass flow measurement method that would be as close as possible to an ideal.
Four methods are presented in this work, the methods use measurements of pressure and air flow together with a mathematical model. The first method presented in this thesis uses a pressure drop model which is a modification of the Darcy-Weisbach equation and has previously been used for pressure drop prediction. The second model presented in the thesis has been developed during the course of this investigation and it is based on an air mass balance done on a pipe section. The third model presented in this work is a multivariate data analysis model. Specifically, the calibration of Partial Least Squares (PLS) regression models for mass flow calculation has been achieved. The fourth model presented is a system identification model, particularly a Deterministic Stochastic Realization (DSR) method, which is a black box model on state space form that has also been developed during the course of this research.
The four models presented have proven to calculate the mass flow of solids in dense and dilute phase conditions. They have been tested in different pipeline configurations and with different powders. Each of the models has its own strengths and weaknesses which have been compared and discussed.
In addition to the four models, a study of particle size and shape has also taken part in this investigation. Attrition of particles with pneumatic conveying was studied as well as the effect of the changes in shape and size of the particles in relation with the mass flow of solids in pneumatic conveying systems.
This doctoral program is conducted as a partnership between NTNU and Tel-Tek. The defense was held at Telemark University College in 2011.