Systems identification and applications to advanced control of chemical processes

Nowadays the advanced controllers are based on a model of the process, so it is possible to predict the variations of the operative conditions, when the process is subject to certain inputs. This is possible only having a model of the process. Several techniques have been developed to build a model, having input-output data available. The first aim of this work is to deepen the knowledge about systems identification, presenting the most used structures of models and some techniques to develop a model, then an implementation of the proposed identification methods is made. In chemical engineering the process control is a wide field of research and ever more platforms are available to simulate the action of a system and a controller. Python is playing an important role in process control, being a programming language quite easy to use and for the possibility to install modules and packages that allow new operations to the user. Moreover, Python can be used on Windows, Linux and Mac, covering the most known operating systems. This is why the implementation of the methods is made in Python language. Chemical process modeling deals with building complicated models of processes using a software, a process simulator, that is able to take care of complex systems, interactions between different equipments, non-ideal behaviours of chemical substances and so on. A process modelled using a modeling software can be very near to describe the real one. Due to the complexity of such a software, a modelled process cannot be used as model of the controller, that requires a simple characterization of the system. However, it can be useful to test the response of a process and to see the advantages of a model-based controller over the traditional controllers (PID). A well-known process simulator is UniSim Design: this offers different possibilities, e.g. the process optimization in steady-state conditions, sizing and designing equipments, dynamic simulations. Another target of this work is to connect Python and UniSim Design R451 to collect input-output data on a UniSim-modelled process, then perform an identification and see how the MPC (Model Predictive Controller) can act on UniSim variables. This connection can be an additional assurance on a designed MPC, before installing it on the plant