1
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Linear Hybrid Models of Distillation Towers. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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2
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Kelley MT, Tsay C, Cao Y, Wang Y, Flores-Cerrillo J, Baldea M. A data-driven linear formulation of the optimal demand response scheduling problem for an industrial air separation unit. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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3
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Kender R, Rößler F, Wunderlich B, Pottmann M, Thomas I, Ecker A, Rehfeldt S, Klein H. Improving the Load Flexibility of Industrial Air Separation Units Using a
Pressure‐Driven
Digital Twin. AIChE J 2022. [DOI: 10.1002/aic.17692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Robert Kender
- Department of Energy and Process Engineering, TUM School of Engineering and Design Institute of Plant and Process Technology, Technical University of Munich Garching Germany
| | - Felix Rößler
- Department of Energy and Process Engineering, TUM School of Engineering and Design Institute of Plant and Process Technology, Technical University of Munich Garching Germany
- Linde GmbH, Linde Engineering Pullach Germany
| | | | | | - Ingo Thomas
- Linde GmbH, Linde Engineering Pullach Germany
| | | | - Sebastian Rehfeldt
- Department of Energy and Process Engineering, TUM School of Engineering and Design Institute of Plant and Process Technology, Technical University of Munich Garching Germany
| | - Harald Klein
- Department of Energy and Process Engineering, TUM School of Engineering and Design Institute of Plant and Process Technology, Technical University of Munich Garching Germany
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4
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5
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Sharma N, Liu YA. A Hybrid
Science‐Guided
Machine Learning Approach for Modeling Chemical Processes: A Review. AIChE J 2022. [DOI: 10.1002/aic.17609] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Niket Sharma
- AspenTech Center of Excellence in Process System Engineering, Department of Chemical Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia USA
| | - Y. A. Liu
- AspenTech Center of Excellence in Process System Engineering, Department of Chemical Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia USA
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6
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Tian Y, Wan Y, Zhang L, Chu G, Fisher AC, Zou H. Optimized Artificial Neural Network for Evaluation: C4 Alkylation Process Catalyzed by Concentrated Sulfuric Acid. ACS OMEGA 2022; 7:372-380. [PMID: 35036707 PMCID: PMC8756446 DOI: 10.1021/acsomega.1c04757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/10/2021] [Indexed: 06/14/2023]
Abstract
In this work, an artificial neural network was first achieved and optimized for evaluating product distribution and studying the octane number of the sulfuric acid-catalyzed C4 alkylation process in the stirred tank and rotating packed bed. The feedstock compositions, operating conditions, and reactor types were considered as input parameters into the artificial neural network model. Algorithm, transfer function, and framework were investigated to select the optimal artificial neural network model. The optimal artificial neural network model was confirmed as a network topology of 10-20-30-5 with Bayesian Regularization backpropagation and tan-sigmoid transfer function. Research octane number and product distribution were specified as output parameters. The artificial neural network model was examined, and 5.8 × 10-4 training mean square error, 8.66 × 10-3 testing mean square error, and ±22% deviation were obtained. The correlation coefficient was 0.9997, and the standard deviation of error was 0.5592. Parameter analysis of the artificial neural network model was employed to investigate the influence of operating conditions on the research octane number and product distribution. It displays a bright prospect for evaluating complex systems with an artificial neural network model in different reactors.
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Affiliation(s)
- Yuntao Tian
- Beijing
Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
- Research
Center of the Ministry of Education for High Gravity Engineering and
Technology, Beijing University of Chemical
Technology, Beijing 100029, China
| | - Yuanfang Wan
- College
of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Liangliang Zhang
- State
Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China
| | - Guangwen Chu
- Research
Center of the Ministry of Education for High Gravity Engineering and
Technology, Beijing University of Chemical
Technology, Beijing 100029, China
- State
Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China
| | - Adrian C. Fisher
- Beijing
Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Haikui Zou
- Research
Center of the Ministry of Education for High Gravity Engineering and
Technology, Beijing University of Chemical
Technology, Beijing 100029, China
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7
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Winz J, Nentwich C, Engell S. Surrogate Modeling of Thermodynamic Equilibria: Applications, Sampling and Optimization. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Joschka Winz
- TU Dortmund Process Dynamics and Operations Group, Department of Biochemical and Chemical Emil-Figge-Straße 70 44227 Dortmund Germany
| | - Corina Nentwich
- Evonik Operations GmbH Paul-Baumann-Straße 1 45772 Marl Germany
| | - Sebastian Engell
- TU Dortmund Process Dynamics and Operations Group, Department of Biochemical and Chemical Emil-Figge-Straße 70 44227 Dortmund Germany
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8
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Schweidtmann AM, Esche E, Fischer A, Kloft M, Repke J, Sager S, Mitsos A. Machine Learning in Chemical Engineering: A Perspective. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100083] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Artur M. Schweidtmann
- Delft University of Technology Department of Chemical Engineering Van der Maasweg 9 2629 HZ Delft The Netherlands
- RWTH Aachen University Aachener Verfahrenstechnik Forckenbeckstr. 51 52074 Aachen Germany
| | - Erik Esche
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
| | - Asja Fischer
- Ruhr-Universität Bochum Department of Mathematics Universitätsstraße 150 44801 Bochum Germany
| | - Marius Kloft
- Technische Universität Kaiserslautern Department of Computer Science Erwin-Schrödinger-Straße 52 67663 Kaiserslautern Germany
| | - Jens‐Uwe Repke
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
| | - Sebastian Sager
- Otto-von-Guericke-Universität Magdeburg Department of Mathematics Universitätsplatz 2 39106 Magdeburg Germany
| | - Alexander Mitsos
- RWTH Aachen University Aachener Verfahrenstechnik Forckenbeckstr. 51 52074 Aachen Germany
- JARA Center for Simulation and Data Science (CSD) Aachen Germany
- Forschungszentrum Jülich Institute for Energy and Climate Research IEK-10 Energy Systems Engineering Wilhelm-Johnen-Straße 52428 Jülich Germany
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9
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Tsay C. Sobolev trained neural network surrogate models for optimization. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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10
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11
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Göttl Q, Tönges Y, Grimm DG, Burger J. Automated Flowsheet Synthesis Using Hierarchical Reinforcement Learning: Proof of Concept. CHEM-ING-TECH 2021. [DOI: 10.1002/cite.202100086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Quirin Göttl
- Technical University of Munich Campus Straubing for Biotechnology and Sustainability Laboratory of Chemical Process Engineering Schulgasse 16 94315 Straubing Germany
| | - Yannic Tönges
- Technical University of Munich Campus Straubing for Biotechnology and Sustainability Laboratory of Chemical Process Engineering Schulgasse 16 94315 Straubing Germany
| | - Dominik G. Grimm
- Technical University of Munich Campus Straubing for Biotechnology and Sustainability Bioinformatics Schulgasse 22 94315 Straubing Germany
- Weihenstephan-Triesdorf University of Applied Sciences Petersgasse 18 94315 Straubing Germany
- Technical University of Munich Department of Informatics Boltzmannstraße 3 85748 Garching Germany
| | - Jakob Burger
- Technical University of Munich Campus Straubing for Biotechnology and Sustainability Laboratory of Chemical Process Engineering Schulgasse 16 94315 Straubing Germany
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12
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Sansana J, Joswiak MN, Castillo I, Wang Z, Rendall R, Chiang LH, Reis MS. Recent trends on hybrid modeling for Industry 4.0. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107365] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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13
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Wei J, Yuan Z. A generalized benders decomposition-based global optimization approach to symbolic regression for explicit surrogate modeling from limited data information. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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14
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Schäfer P, Caspari A, Schweidtmann AM, Vaupel Y, Mhamdi A, Mitsos A. The Potential of Hybrid Mechanistic/Data‐Driven Approaches for Reduced Dynamic Modeling: Application to Distillation Columns. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000048] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Pascal Schäfer
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Adrian Caspari
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Artur M. Schweidtmann
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Yannic Vaupel
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Adel Mhamdi
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
| | - Alexander Mitsos
- RWTH Aachen University Process Systems Engineering (AVT.SVT) Forckenbeckstraße 51 52074 Aachen Germany
- JARA-Energy Templergraben 55 52056 Aachen Germany
- Forschungszentrum Jülich GmbH Institute of Energy and Climate Research: Energy Systems Engineering (IEK-10) Wilhelm-Johnen-Straße 52425 Jülich Germany
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15
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Chen Y, Ierapetritou M. A framework of hybrid model development with identification of plant‐model mismatch. AIChE J 2020. [DOI: 10.1002/aic.16996] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Yingjie Chen
- Department of Chemical and Biomolecular Engineering University of Delaware Newark Delaware USA
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering University of Delaware Newark Delaware USA
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16
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Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning. J Memb Sci 2020. [DOI: 10.1016/j.memsci.2020.118208] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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17
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Affiliation(s)
- Ganzhou Wang
- RWTH Aachen UniversityProcess Systems Engineering Aachen Germany
| | - Alexander Mitsos
- RWTH Aachen UniversityProcess Systems Engineering Aachen Germany
- JARA‐ENERGY Jülich Germany
- Institute of Energy and Climate Research—Energy Systems Engineering (IEK‐10)Forschungszentrum Jülich GmbH Jülich Germany
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18
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Caspari A, Offermanns C, Schäfer P, Mhamdi A, Mitsos A. A flexible air separation process: 1. Design and steady‐state optimizations. AIChE J 2019. [DOI: 10.1002/aic.16705] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Adrian Caspari
- AVT—Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
| | - Christoph Offermanns
- AVT—Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
| | - Pascal Schäfer
- AVT—Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
| | - Adel Mhamdi
- AVT—Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
| | - Alexander Mitsos
- AVT—Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
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19
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Caspari A, Offermanns C, Schäfer P, Mhamdi A, Mitsos A. A flexible air separation process: 2. Optimal operation using economic model predictive control. AIChE J 2019. [DOI: 10.1002/aic.16721] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Adrian Caspari
- AVT ‐ Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
| | - Christoph Offermanns
- AVT ‐ Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
| | - Pascal Schäfer
- AVT ‐ Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
| | - Adel Mhamdi
- AVT ‐ Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
| | - Alexander Mitsos
- JARA‐ENERGY 52056 Aachen Germany
- AVT ‐ Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany
- Energy Systems Engineering (IEK‐10), Forschungszentrum Jülich 52425 Jülich Germany
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20
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Tsay C, Kumar A, Flores-Cerrillo J, Baldea M. Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.03.022] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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