Model Predictive Control of Paper Machines using Industry 4.0 Principles

This project offers an exciting opportunity to work at the interface of Academia and Industry in order to develop an advanced automated control configuration based on model predictive control (MPC) using the Industry 4.0 framework. The MPC configuration will have predictive capabilities and will be able to ideally address disturbances, before they hit the production line. In addition, the control actions will be “exactly” (as opposed to empirically) calculated to optimise the process performance and stability. The project will develop “smart”, modular controllers that use the existing process timeline data (rather than actual process models) to predict future process data, to decide control actions for optimal performance (e.g. optimal thickness, softenss, and moisture) and importantly to update process timelines and control actions accordingly. In addition, economic criteria will be built into the optimal process performance evaluation, such as minimum cost, maximum economic potential etc. To construct these smart controllers we will use Artificial Neural Networks (ANNs) as well as the so-called “equation-free” methodologies. Both methodologies have been extensively and successfully tested in model-free applications.

The successful candidate will work under the supervision of Prof. C. Theodorpoulos at the University of Manchester, School of Chemical Engineering and Analytical Science and he/she will be expected to work closely with the plant manager and the technical manager of the industrial sponsor. Prof. Theodoropoulos is an expert at systems engineering including smart technologies for design, optimisation and control application. His group is developing novel methodologies for complex chemical and biochemical systems and follows them all the way through to applications. He has worked with a number of industries, he has performed consulting, designed in-house workshops as well as software applications.

The successful candidate should have an excellent degree in Chemical Engineering or a related field (e.g. Electrical Engineering, Applied Mathematics) evidenced by marks, position in class, if available, etc. He/she should also have excellent computational/programming skills as well as communication skills, and should value research impact and industrial input.