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Project Title:  Data-driven robust, explainable AI techniques for process, product quality control, and security in intelligent manufacturing 

Project Description:   Quality, productivity, and security are essential elements in an industrial production plant. Even a slight improvement in productivity, e.g., 1%, can lead to gains of Millions in revenue. These elements are dependent on various factors, including process control and automation. Furthermore, these factors are derived from several probabilistic and deterministic parameters that span from raw material collection and transportation to manufacturing. In this 4-year Ph.D. research project, a student will investigate the novel methods to improve the quality (e.g., customer requirements), productivity (e.g., production time), and security of manufacturing by considering data-driven approaches and leveraging robust explainable artificial intelligence/machine learning algorithms for overall intelligent automation. This project is outcomes-oriented, so the data is collected from the actual plant of Sonac Australia Pty Ltd, an animal feed industry, and the proposed control methods and techniques will be leveraged to optimize the actual plant.  

The project will focus on the process of blood collection and delivery to the production plant. Despite its importance for the quality of the process outcome, this part of the production cycle does not allow for best control and monitoring as it takes place at slaughterhouses and on delivery tankers. The project will look at ways of automating the initial blood processing in the slaughterhouse in a way which prevents damage to the blood (e.g., premature coagulation, impurities, etc.). Also, the process of pumping blood to and from the transporting tanks will be a target for improvement as it has settings (e.g., pumping flowrate) which impact the quality and productivity of the Sonac processes. The outcome of this project will result in better controllability over the pre-plant processing.