Predictive Models for Quality Variables with Regular Sensors and Images

• Author / Creator

  Salehi, Yousef

• In the process industry, certain quality variables cannot be measured regularly due
  to technical limitations or economic constraints. Consequently, the industry relies on
  laboratory analysis to measure such quality variables. However, laboratory analysis
  introduces long time-delays in obtaining measurements due to the need to collect
  representative samples, transport them, and conduct the analysis. The associated
  delay can pose challenges in terms of timely decision-making and real-time control.
  Therefore, fast-rate measurements in the process industry are crucial for real-time
  quality monitoring and control. By obtaining real-time data, industries can improve
  efficiency, meet customer expectations, and minimize risks associated with the quality
  variables.
  Data-driven predictive models, also known as soft sensors, have emerged as valuable
  tools in predicting quality variables in the process industry. The predictive models
  employ advanced data analysis techniques to predict the values of quality variables
  using different data sources including regularly measured variables by online sensors
  and visual information provided by video cameras. By leveraging historical data and
  identifying patterns, the models can provide fast-rate predictions of quality variables.
  This capability enables proactive decision-making and facilitates timely interventions
  to maintain product quality and process control, and contribute to process optimization.
  In this thesis, we develop predictive models based on both conventional sensor
  measurements and image data, each with its own advantages. The first two contributions
  are related to conventional sensors data and the following two contributions are concerned with image data as a means to develop predictive models. The main
  contributions of this thesis are listed as follows.
  The first contribution involves developing a statistical predictive model for quality
  variables with simultaneous consideration of time-varying time-delays, time-varying
  sample collection periods, and varying operating points. A non-parametric distribution
  is used to describe the distributions of the time-delays, sample collection periods,
  and switching of different operating modes, eliminating the need for prior knowledge
  about the distributions. Furthermore, the work enables online updating of the model
  parameters using a recursive Expectation-Maximization (EM) algorithm.
  Then, we extend the linear time invariant predictive model to a linear parameter
  varying (LPV) predictive model, and enhance robustness to outlying output observations
  through the use of t-distribution. Additionally, uncertainty of the unknown
  model parameters is estimated using variational Bayesian (VB) algorithm.
  Development of a computer vision model to predict quality variables is the third
  contribution. A modified Kalman filter is formulated to restore degraded images
  caused by factors like lighting conditions changes and camera noise. Additionally,
  to estimate the predictive model parameters, a robust-to-outlier EM algorithm is
  developed. The proposed model was validated on a tailing flotation process.
  In the last contribution, the development of a computer vision model that enables
  fast-rate prediction of quality variables is considered. To address the impact
  of environmental conditions like steam and lighting on images, an atuoencoder-based
  image inpainting algorithm is developed to fill in the missing regions in the images.
  The restored images, along with slow-rate sampled measurements, are then used in
  conjunction with the EM algorithm to construct an auto-regressive with eXogenous
  input (ARX) predictive model.
  All the proposed models in this thesis have been validated through experimental
  studies.

• Subjects / Keywords

  • Process control
  • multirate data
  • Soft sensor
  • Image processing
  • Image restoration and inpainting
  • Bayesian inference
  • Computer vision
  • Flotation froth

• Graduation date

  Spring 2024

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-k6x2-et21

• 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.