This M.Sc. thesis project has as objective the definition of a control NMPC (Non Linear Predictive Control) strategy for a semi-batch process located at the BASF plant in Antwerp. This analysis has been carried out in collaboration with the SUPER (SUstainable Process Engineering Research) group of Department of Chemistry, Material, and Chemical Engineering "Giulio Natta" of Politecnico of Milan and the Advanced Process Control-ESP/TIA team in BASF Antwerp. Until the 70's, the control algorithms applied to the majority of the industrial processes were classified as PID (Proportional Integral Derivative). Later in the years, the chemical industry started to develop the first MPC (Model Predictive control) control algorithms. In this way, a new research effort started to study the possibility to apply linear and non linear models for the optimization and the control of the chemical processes. Nowadays, the analysis of the control algorithm NMPC (Nonlinear Model Predictive Control), although it has been examined in depth by the academia, is a research topic that deals with its practicable application in the chemical industry. This thesis project consists of three different parts. The first one has the goal to study the proposed chemical process through a data analysis and the implementation of mathematical correlations between the different physicochemical properties. In the second part, some mathematical models have been developed to represent the real trends of the most important physicochemical variables. In the last part, the NMPC strategy has been implemented for the process under study, with the scope to test the performance and the robustness of the control algorithm, simulating the response of the process in different conditions.
La presente tesi si occupa di determinare una strategia di controllo NMPC (Non Linear Predictive Control) applicata ad un processo semi-batch presente presso lo stabilimento BASF di Anversa. Questo studio è stato realizzato in collaborazione con il gruppo SUPER (SUsteinable Process Engineering Research) del dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta" del Politecnico di Milano ed il team di Advanced Process Control-ESP/TIA di BASF Anversa. Fino alla fine degli anni '70, le logiche di controllo adottate sulla maggior parte dei processi industriali erano di tipo PID (Proportional Integral Derivative). Negli anni successivi, l'industria chimica ha iniziato a sviluppare i primi algoritmi di controllo di tipo MPC (Model Predictive Control). In questo modo, è iniziata una nuova fase riguardante la possibilità di applicazione di modelli lineari e non lineari per l'ottimizzazione ed il controllo dei processi chimici. Lo studio della logica di controllo NMPC (Nonlinear Model Predictive Control), benchè largamente approfondita nel settore accademico, è tuttora oggetto di analisi e di ricerca come applicazione pratica nell'industria chimica. Questa tesi si compone di tre parti principali. La prima ha lo scopo di analizzare il processo industriale proposto attraverso un'analisi di dati e la derivazione di correlazioni tra le variabili chimico-fisiche presenti. Nella seconda parte sono stati sviluppati alcuni modelli matematici in grado di rappresentare il reale andamento delle caratteristiche chimico-fisiche di interesse. Nell'ultima parte la strategia NMPC è stata implementata sul processo di riferimento, testandone la robustezza e la performance simulando la risposta del processo in diverse condizioni.
Data analysis, modeling and nonlinear model predictive control of an industrial semi-batch process
Barone, Stefano
2020/2021
Abstract
This M.Sc. thesis project has as objective the definition of a control NMPC (Non Linear Predictive Control) strategy for a semi-batch process located at the BASF plant in Antwerp. This analysis has been carried out in collaboration with the SUPER (SUstainable Process Engineering Research) group of Department of Chemistry, Material, and Chemical Engineering "Giulio Natta" of Politecnico of Milan and the Advanced Process Control-ESP/TIA team in BASF Antwerp. Until the 70's, the control algorithms applied to the majority of the industrial processes were classified as PID (Proportional Integral Derivative). Later in the years, the chemical industry started to develop the first MPC (Model Predictive control) control algorithms. In this way, a new research effort started to study the possibility to apply linear and non linear models for the optimization and the control of the chemical processes. Nowadays, the analysis of the control algorithm NMPC (Nonlinear Model Predictive Control), although it has been examined in depth by the academia, is a research topic that deals with its practicable application in the chemical industry. This thesis project consists of three different parts. The first one has the goal to study the proposed chemical process through a data analysis and the implementation of mathematical correlations between the different physicochemical properties. In the second part, some mathematical models have been developed to represent the real trends of the most important physicochemical variables. In the last part, the NMPC strategy has been implemented for the process under study, with the scope to test the performance and the robustness of the control algorithm, simulating the response of the process in different conditions.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/173338