Page 25 - ESCAPE PROGRAMME

GREEN AND SUSTAINABLE PROCESS SYSTEMS ENGINEERING IN THE DIGITAL AGE
MONDAY, JUNE 19, 2023
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A Cloud-based Collaborative Interactive Platform for Education and Research in Dynamic Process Modelling
Vinay Gautam, Alberto Rodríguez-Fernández, Heinz A. Preisig
Department of Chemical Engineering, Norwegian University of Science & Technology, Trondheim, 7491, Norway
An Online Course for Teaching Process Simulation
Daniel R. Lewina, Assaf Simona, Sapir Lifshiz Simona, Asia Matatyaho Ya’akobia, Abigail Barzilaib
a Department of Chemical Engineering, Technion. I. I. T., Haifa 32000, Israel
b Centre for the Promotion of Learning and Teaching, Technion, Haifa 32000, Israel
16:50 – 18:20 Conf. Room III
Efficient physical model building algorithm using equations extracted from documents
Shota Kato, Manabu Kano
Department of Systems Science, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501, Japan
Architecture and Design of a Modern Commercial Process Simulator
Ian Boysa, Jochen Steimelb
a AVEVA, 26561 Rancho Parkway South, Lake Forest, CA 92630, USA b AVEVA, Mainzer Landstraße 178-190, 60327 Frankfurt, Germany
Data augmentation for machine learning of chemical process flowsheets
Lukas Schulze Balhorn, Edwin Hirtreiter, Lynn Luderer, Artur M. Schweidtmann
Process Intelligence Research, Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
Sustainable Development Goals assessment of carbon capture on-board
Valentina Negri, Margarita A. Charalambous, Juan D. Medrano-García, Gonzalo Guillén-Gosálbez
Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 1, Zürich 8093, Switzerland
Extended Multiple-Curve Resolution framework for the calibration of first-principles models
Daniel Casas-Orozco, Jaron Mackey, Ilke Akturk, Gintaras V. Reklaitis, Zoltan K. Nagy
Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47907,USA
Symbolic regression-based method for developing a physics-informed surrogate model for
a manufacturing process
Utsav Awasthi, George M. Bollas
Department of Chemical and Biomolecular Engineering, Pratt and Whitney Institute of Advanced Systems Engineering, University of Connecticut, 159 Discovery Dr, Storrs, CT, 06269, USA
SESSION S2-06: Modeling and Optimization (I)
Track 6
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