New Latent Variable Methods for selection of raw materials, process monitoring and product quality control

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Nowadays, increasingly large amounts of data are routinely collected by process industries and need to be analyzed to extract useful information for making timely decisions. This trend will sustain in the future with the growing interest for the “smart factory” concept based on Industry 4.0 technologies. It promotes data collection from different devices and sources (e.g. smart process sensors and actuators, analytical instruments, images, etc.), and interconnecting them in a structured way in order to enhance process performance. Developing efficient methods to achieve this goal is the keystone of my research program. The long-term goal of my program consists of developing new data-driven and systems engineering approaches for reducing variability, improving process operation and product quality, and applying them in industrial areas that are important for the Canadian economy. The aim is to help maintain international competitiveness, accelerate process and product development, and make a more efficient use of raw materials and energy. On the short term, the research will concentrate on further developing two new latent variable methods recently devised in my group, namely the Sequential Multi-block PLS regression (SMB-PLS) and the Undecimated Wavelet Transform - Multivariate Image Analysis (UWT-MIA). These methods will be adapted and applied for solving raw materials selection problems, and developing image-based sensors and tools for process and quality control purposes. A generic framework for setting multivariate specifications for raw material properties to cope with most practical situations found in industry will be developed. Multivariate process capability metrics will also be proposed to compare the likeliness of suppliers of materials to meet customer's specifications. These tools will help ensure smooth process operation, achieve desired product quality, and reduce the amounts of off-specification materials and costs. This research also aims at developing advanced process analytical technologies (PAT) for on-line quality control of polymer films used for food packaging or gas separation applications. A new sensor based on imaging thermography will be devised to improve quality control of the films. This is important to enable early detection of product defects, rapid diagnosis of the causes, and taking appropriate remedial actions to reduce off-specs materials and production costs. In addition, live cell imaging tools will be proposed to assess cellular functionality in rapid and non-intrusive fashion. They will help researchers and the biotechnology/biopharmaceutical industry accelerate the development of new cell culture media, cell therapies, and medications using their high throughput screening platforms. Finally, the highly qualified personnel trained in this research program will take leadership roles in transferring the methods and tools in practice, and help increase the competitiveness of the Canadian industry.