Abstract
Model-based solutions for monitoring and control of chemical batch processes have been of interest in research for many decades. However, unlike in continuous processes, in which model-based tools such as Model Predictive Control (MPC) have become a standard in the industry, the reported use of models for batch processes, either for monitoring or control, is rather scarce. This limited use is attributed partly to the inherent complexity of the batch processes (e. g., dynamic, nonlinear, multipurpose) and partly to the lack of appropriate commercial tools in the past. In recent years, algorithms and commercial tools for model-based monitoring and control of batch processes have become more mature and in the era of Industry 4.0 and digitalization they are slowly but steadily gaining more interest in real-word batch applications. This contribution provides a practical example in this application field. Specifically, the use of a grey-box modeling approach, in which a multiway Projection to Latent Structure (PLS) model is combined with a first-principles model, to monitor the evolution of a batch polymerization process and predict in real-time the final batch quality is reported. The modeling approach is described, and the experimental results obtained from an industrial batch laboratory reactor are presented.
Zusammenfassung
Modellbasierte Lösungen zur Überwachung und Steuerung chemischer Batch-Prozesse werden seit vielen Jahrzehnten in der Forschung betrachtet. Anders als in kontinuierlichen Prozessen, in denen modellbasierte Werkzeuge wie Modellprädiktive Regelung in der Branche zum Standard geworden sind, gibt es nur wenige Veröffentlichungen zum praktischen Einsatz von Modellen für Batch-Prozesse, sei es zur Überwachung oder Steuerung. Diese begrenzte Nutzung ist zum Teil auf die inhärente Komplexität der Batchprozesse zurückzuführen (z. B. dynamisch, nichtlinear, Mehrproduktanlagen) und zum Teil auf den bisherigen Mangel an geeigneten kommerziellen Werkzeugen. In den letzten Jahren haben Algorithmen und kommerzielle Tools zur modellbasierten Überwachung und Steuerung von Batch-Prozessen einen höheren Reifegrad erreicht und gewinnen im Zeitalter von Industrie 4.0 und Digitalisierung langsam aber stetig mehr Interesse bei Betreibern von Batch-Anwendungen. Dieser Beitrag ist ein praktisches Beispiel in diesem Anwendungsbereich, insbesondere für die Verwendung eines Grey-Box-Modellierungsansatzes, bei dem ein Multiway-PLS-Model (PLS=Projection to Latent Structure) mit einem First-Principles-Modell kombiniert wird, um die Entwicklung eines Batch-Polymerisationsprozesses zu überwachen und in Echtzeit die Endqualität vorherzusagen. Der Modellierungsansatz wird beschrieben, und die experimentellen Ergebnisse eines industriellen Batchlaborreaktors werden vorgestellt.
This contribution is dedicated to Prof. Dr.-Ing. Dr. h.c. Michael Zeitz on the occasion of his 80th birthday.
About the authors
Ala E. F. Bouaswaig is a senior automation engineer working at the Advanced Process Control (APC) Team in the Competence Center for Automation Technology in Ludwigshafen Germany. He holds a M.Sc. degree in Process Systems Engineering and a Dr.-Ing. Degree from TU Dortmund university. He worked 5 years as a process engineer in the cement industry in Libya and joined BASF in 2011. His main scientific interest is the use of first-principles models for online monitoring and control in the chemical industry.
Keivan Rahimi-Adli, born 1990, received his M.Sc. degree in Chemical Engineering, specialization “Process Systems Engineering”, at TU Dortmund university. He started his career at INEOS in Köln as a project engineer for the European research project CoPro and pursued his research in parallel at the Process Dynamics and Operations Group of TU Dortmund university as PhD student. His research focusses on the modelling of the resource consumption of process plants and production planning under uncertainty. Since May 2020 he is working as a Modelling and Optimisation Engineer at INEOS in Köln and is finalizing his PhD.
Matthias Roth joined BASF SE after his PhD on Fault Diagnosis of Discrete Event Systems in 2010. He is leading the Advanced Process Control (APC) Team in the Competence Center for Automation Technology in Ludwigshafen. The APC team is covering technologies from base layer control and MPC/RTO to process data analysis.
Alireza Hosseini is regional operational excellence manager at BASF SE. With the goal of continuous improvement, his main areas of activities are technical/economical evaluation of projects and supporting/consulting operations with the focus on automation, technical process optimization, and industry 4.0.
Hugo Vale is senior principal scientist and group leader in the field of polymer reaction engineering at BASF, Germany. He holds a MSc. in Chemical Engineering from the Technical University of Lisbon, Portugal, and a Ph.D. from the University Claude Bernard Lyon I, France. He worked 6 years as R&D engineer at Michelin, after which he joined BASF in 2012. His scientific interests include the development and application of mechanistic and phenomenological models to accelerate product development and support the optimization and design of polymerization processes.
Sebastian Engell received a Dipl.-Ing degree in Electrical Engineering from Ruhr-Universität Bochum, Germany in 1978 and the Dr.-Ing. Degree and the venia legendi in Automatic Control from Universität Duisburg in 1981 and 1987. 1986–1990 he was the head of an R&D group at the Fraunhofer Institut IITB in Karlsruhe, Germany. Since 1990 he has been Full Professor of Process Dynamics and Operations in the Department of Biochemical and Chemical Engineering at TU Dortmund. He was Vice-Rector for Research of TU Dortmund 2002–2006 and currently is a member of the Research Council of the Alliance of the Universities in the Ruhr Region. Prof. Engell received an IFAC Journal of Process Control Best Paper Award in 2008, and is a co-author of the 2014 and 2016 Best Papers in Computers and Chemical Engineering. He is a Fellow of IFAC and led the IFAC Fellow Selection Committee 2012–2014. In 2012, he was awarded a European Advanced Investigator Grant for the Project MOBOCON – Model-based Optimizing Control – From a Vision to Industrial Reality. His research areas are in the domains of model-based optimizing control, real-time optimization, scheduling, and optimization-based process design.
Since his PhD in 1992 on Computer-aided analysis and design of nonlinear observation problems, Prof. Dr.-Ing. Joachim Birk has been working at BASF SE, Ludwigshafen. As Vice President and Executive Expert of Automation Technology he is responsible for control system technology, regulated automation solutions, advanced process control (APC), manufacturing execution systems (MES) and automation security. In addition, he holds a honorary professor position at Stuttgart University.
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