Nowadays more importance is given to run chemical processes in a sustainable way, from environmental, economic and social point of view. The future shortage of primary goods and the current problems related to climate change need to be faced also in the field of process industry. In this view, process automation can play an important role in increasing the sustainability of processes, helping to run them in the best possible way. Automation of continuous processes is already well known and the theory behind this world is everywhere applied with relevant visible improvements in the control of the process. In continuous processes, Proportional-Integral-Derivative (PID) control layers are algorithms everywhere applied with success from the beginning of the automation’ s era, together with multivariable control concepts as model predictive control (MPC), also called Dynamic Matrix Control (DMC), an algorithm based on the linearization of the process behavior. However, for batch and semibatch processes, that present a more nonlinear and dynamic behaviour, linear models are not sufficiently adeguate anymore: for this reason, nonlinear model predictive control (NMPC) started being studied and implemented over the last 40 years. The general goal of this thesis is the development of a simulator of an industrial semi-batch plant controlled by a Nonlinear Model Predictive Control (NMPC) strategy, that can be used in order to study different scenarios and estimate the potential of the NMPC control strategy. Simulations have shown that the NMPC control strategy can reduce the reaction time of the semi-batch process, thus demonstrating the presence of potential for the optimization of the process under study.
Al giorno d' oggi sempre più importanza è data a controllare e operare i processi chimici in una via sostenibile dal punto di vista ambientale, economico e sociale. E' necessario che la futura carenza di beni primari e gli attuali problemi legati al cambiamento climatico vengano affrontati anche dal mondo dell' industria di processo. In questa ottica, l' automatizzazione dei processi può giocare un ruolo importante nell' aumentare la loro sostenibilità, aiutando a operare questi nel modo migliore possibile. L' automazione dei processi continui è ben conosciuta attualmente e la teoria dietro questo mondo è applicata ovunque con significativi miglioramenti nel controllo di processo. Nei processi continui, i controllori proporzionali-integrali-derivativi (PID) sono algoritmi applicati ovunque con successo dall' inizio dell' era dell' automazione, assieme al concetto di controllo multivariabile rappresentato al giorno d' oggi dal Model Predictive Control (MPC), algoritmo basato sulla linearizzazione del comportamento dei processi. Tuttavia, per i processi batch e semi-batch, i quali presentano un comportamento intrinsecamente nonlineare e dinamico, i modelli lineari non sono più adeguati: per questa ragione, Nonlinear Model Predictive Control (NMPC) è stato iniziato a essere studiato e implementato negli ultimi 40 anni. Lo scopo generale di questa tesi è lo sviluppo di un simulatore di un impianto semi-batch industriale controllato da una strategia Nonlinear Model Predictive Control (NMPC), il quale può essere usato per studiare diversi scenari e stimare il potenziale della strategia di controllo NMPC. Le simulazioni hanno mostrato che la strategia di controllo NMPC può ridurre il tempo di reazione del processo semi-batch, dimostrando perciò la presenza di potenziale per l' ottimizzazione del processo sotto studio.
Development of a closed-loop nonlinear model predictive control simulator for an industrial semi-batch plant
MORANDI, SANTIAGO
2018/2019
Abstract
Nowadays more importance is given to run chemical processes in a sustainable way, from environmental, economic and social point of view. The future shortage of primary goods and the current problems related to climate change need to be faced also in the field of process industry. In this view, process automation can play an important role in increasing the sustainability of processes, helping to run them in the best possible way. Automation of continuous processes is already well known and the theory behind this world is everywhere applied with relevant visible improvements in the control of the process. In continuous processes, Proportional-Integral-Derivative (PID) control layers are algorithms everywhere applied with success from the beginning of the automation’ s era, together with multivariable control concepts as model predictive control (MPC), also called Dynamic Matrix Control (DMC), an algorithm based on the linearization of the process behavior. However, for batch and semibatch processes, that present a more nonlinear and dynamic behaviour, linear models are not sufficiently adeguate anymore: for this reason, nonlinear model predictive control (NMPC) started being studied and implemented over the last 40 years. The general goal of this thesis is the development of a simulator of an industrial semi-batch plant controlled by a Nonlinear Model Predictive Control (NMPC) strategy, that can be used in order to study different scenarios and estimate the potential of the NMPC control strategy. Simulations have shown that the NMPC control strategy can reduce the reaction time of the semi-batch process, thus demonstrating the presence of potential for the optimization of the process under study.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/151202