Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

It is widely believed that we are at the dawn of the fourth industrial revolution that will bring a level of automation process industry has never seen before. This optimism is spurred by the rather serendipitous confluence of ubiquitous cyber-physical systems, easy access to large volumes of data, expanding computing power and major theoretical breakthroughs in data analytics. Our group's long term vision is to create self-driving processes similar to self driving cars. A truly self-driving autonomous process will operate with minimal to no human interference by generating necessary process inputs, by learning its dynamics, by automatically tuning its controller, and by detecting, isolating and predicting various faults. This proposal seeks to develop a set of algorithms and computational tools to bring this vision of automation to the process industry.

Industrial processes are characterized by complex nonlinear and stochastic dynamics, multi-rate noisy measurements and large interconnected units. This proposal addresses each of these process characteristics to build an over arching systematic approach for self driving processes. In pursuit of our vision, this project is divided into three sub-projects: 1) Model Monitoring and Active Data Generation: We will develop algorithms to actively monitor the performance of a model of the process. This will be done without injecting external inputs and therefore preventing performance degradation. Once a model is determined to be poor an automatic algorithm will generate a new set of sufficiently informative data for model re-identificaiton. 2) Adaptive Modelling and Control: Using the generated data, new models will be identified online and embedded in a control strategy. This approach will take two forms depending on the available information. When a model structure is available a maximum likelihood approach in conjunction with simulation methods and a nonlinear model predictive control strategy will be used. When a model structure is unavailable approaches such as deep reinforcement learning will be used to design the controller. 3) Fault Detection and Isolation: Process faults will be identified using simulation based methods and machine learning algorithms on large scale data sets. In particular known faults will be identified by using model based algorithms and unknown faults will be identified by learning and extracting features from large dimensional data sets. These algorithms will be tested on real processes with the help of our industrial partners.

This proposal is novel due to its unique unifying approach for processes with rather generic characteristic features. To the best of our knowledge, this is the first ever attempt to build a self driving process with the features described above. Realizing this vision will benefit Canadian industries by making them highly efficient and by giving them a global competitive advantage.