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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
  • 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.