This course provides a comprehensive introduction to process systems engineering with a strong focus on the integration of digital twins, big data analytics, and advanced data-driven approaches for process optimisation. The course is designed to equip students with both theoretical knowledge and practical skills in modern engineering methods, specifically tailored for applications in the food industry. It includes the following four sections:
Process Modeling for Food Systems: Students will learn to build mathematical and empirical models tailored to food processes, covering essential modeling techniques and considerations unique to food production environments. This section emphasizes developing models that can simulate, predict, and optimize various food processing stages.
Statistical Process Control (SPC) for Quality Control and Optimization: This segment introduces students to SPC as a tool for monitoring, controlling, and optimizing production processes. Students will explore how to use SPC techniques to ensure quality and consistency in manufacturing through models, with a focus on interpreting control charts and understanding variation in food processing operations.
Design of Experiments (DoE): The DoE section teaches students how to systematically investigate the effects of multiple independent variables on one or more dependent variables, a critical skill for optimizing process conditions. Students will learn how to plan and conduct experiments using models that yield valuable insights and enhancing process efficiency.
Machine Learning Techniques for Process Analysis: This section covers the fundamentals of machine learning as applied to process data, focusing on multivariable analysis, and pattern recognition in food processing systems. Students will gain exposure to machine learning algorithms that can identify complex relationships within data, enabling improved process performance.