Online Estimation of Key Parameters for Settling Slurries

  • Author / Creator
    Moon, Dana R
  • Heterogeneous slurry pipelines are found in mining, chemical, and solid transportation (such as coal pipelines) industries worldwide. One of the most important factors in the operation and design of these pipelines is bulk velocity. Solids settle when the bulk velocity is below the deposition velocity. This can cause plugging, equipment damage, and production downtime. Since the actual particle size distribution is typically unknown online, pipelines are usually operated at excessively high velocities to mitigate the risk of settling. This causes erosion and additional energy use. Specific energy consumption (SEC) is a measure of the energy required to transport one tonne of solids a distance of one kilometer. It is highly dependent on solids properties, such as average solids size and coarse solids concentration. Presently, there is no way to predict all solids properties online accurately. Solids properties and pipeline parameters are often used as inputs into steady-state slurry models to predict the pressure gradient and other slurry flow properties. The most commonly used is the two-layer model developed by the Saskatchewan Research Council (SRC). In this model, the slurry is approximated as two step functions that horizontally split the pipe into two layers. The pressure gradient is calculated by considering Coulombic and kinematic friction. In this study, an interior-point optimization algorithm is used to estimate coarse solids median particle size and concentration using the Saskatchewan Research Council two-layer model for turbulent, heterogeneous, and Newtonian slurries. The parameters were estimated using the SRC two-layer model to simulate a sand/water slurry in a 75.65 mm diameter pipe. A narrow particle size distribution was used with coarse solids concentration volume fractions of 0.1 to 0.4. The coarse solids median particle size was varied between 75 to 625 µm in 25 µm intervals. The values were chosen to most accurately reflect slurries found in the mining and oil sands industries. For the purposes of optimization studies, the decision variables were also the parameters to be estimated. The estimated coarse solids median particle size and concentration were compared with inputs used in the Saskatchewan Research Council two-layer model. The average error for coarse solids median particle size and concentration predictions were 13.6% and 4.2%, respectively. The error from the predicted values propagated through deposition velocity calculations. The predictions for the deposition velocity had 5.5% average error. The SEC prediction trends matched the known SEC trends, and the minimum SEC could be predicted graphically with close agreement to the known minimum. Future work includes experimental validation. It is necessary to use process instruments including a flowmeter, a differential pressure transducer, and a densitometer to provide inputs for the estimation method. A velocity profiler can be used to estimate the top and bottom velocities; however, further studies are required to determine the best way to determine the velocity variables from the sensors. Additionally, a system of elliptic PDEs may be able to predict velocity variables instead of using a velocity profiler. Furthermore, the same estimation method can also be applied to a new SRC model that has particle size distributions (rather than a median value only) as an input.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Chemical and Materials Engineering
  • Specialization
    • Process Control
  • Supervisor / co-supervisor and their department(s)
    • Forbes, Fraser (Chemical and Materials Engineering)
    • Sanders, Sean (Chemical and Materials Engineering)
  • Examining committee members and their departments
    • Liu, Jinfeng (Chemical and Materials Engineering)
    • Sanders, Sean (Chemical and Materials Engineering)
    • Forbes, Fraser (Chemical and Materials Engineering)