Predictive Models for Quality Variables with Regular Sensors and Images
Creators: Salehi, Yousef
Publisher: name: University of Alberta Library
Year: 2024

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