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Ma K, Sahinidis NV, Bindlish R, Bury SJ, Haghpanah R, Rajagopalan S. Data-driven strategies for extractive distillation unit optimization. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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2
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Vasilas N, Papadopoulos AI, Papadopoulos L, Salamanis A, Kazepidis P, Soudris D, Kehagias D, Seferlis P. Approximate computing, skeleton programming and run-time scheduling in an algorithm for process design and controllability in distributed and heterogeneous infrastructures. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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3
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Langiu M, Dahmen M, Mitsos A. Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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4
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Thebelt A, Wiebe J, Kronqvist J, Tsay C, Misener R. Maximizing information from chemical engineering data sets: Applications to machine learning. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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5
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Iftakher A, Aras CM, Monjur MS, Hasan MMF. Data‐driven Approximation of Thermodynamic Phase Equilibria. AIChE J 2022. [DOI: 10.1002/aic.17624] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Ashfaq Iftakher
- Artie McFerrin Department of Chemical Engineering Texas A& University College Station Texas USA
| | - Chinmay M. Aras
- Artie McFerrin Department of Chemical Engineering Texas A& University College Station Texas USA
| | - Mohammed Sadaf Monjur
- Artie McFerrin Department of Chemical Engineering Texas A& University College Station Texas USA
| | - M. M. Faruque Hasan
- Artie McFerrin Department of Chemical Engineering Texas A& University College Station Texas USA
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6
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Engle MR, Sahinidis NV. Deterministic symbolic regression with derivative information: General methodology and application to equations of state. AIChE J 2021. [DOI: 10.1002/aic.17457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Marissa R. Engle
- Department of Chemical Engineering Carnegie Mellon University Pittsburgh Pennsylvania USA
| | - Nikolaos V. Sahinidis
- H. Milton Stewart School of Industrial and Systems Engineering, School of Chemical and Biomolecular Engineering, Georgia Institute of Technology Atlanta Georgia USA
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8
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Savage TR, Almeida‐Trasvina F, del‐Rio Chanona EA, Smith R, Zhang D. An integrated dimensionality reduction and surrogate optimization approach for plant‐wide chemical process operation. AIChE J 2021. [DOI: 10.1002/aic.17358] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Thomas R. Savage
- Centre for Process Integration, The Mill, University of Manchester Manchester UK
- Department of Chemical Engineering and Biotechnology West Cambridge Site, University of Cambridge Cambridge UK
| | | | | | - Robin Smith
- Centre for Process Integration, The Mill, University of Manchester Manchester UK
| | - Dondga Zhang
- Centre for Process Integration, The Mill, University of Manchester Manchester UK
- Centre for Process Systems Engineering, Roderic Hill Building South Kensington Campus London London UK
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9
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Zhang X, Ding X, Song Z, Zhou T, Sundmacher K. Integrated ionic liquid and
rate‐based
absorption process design for gas separation: Global optimization using hybrid models. AIChE J 2021. [DOI: 10.1002/aic.17340] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Xiang Zhang
- Process Systems Engineering Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
| | - Xuechong Ding
- Process Systems Engineering Otto‐von‐Guericke University Magdeburg Magdeburg Germany
| | - Zhen Song
- Process Systems Engineering Otto‐von‐Guericke University Magdeburg Magdeburg Germany
| | - Teng Zhou
- Process Systems Engineering Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
- Process Systems Engineering Otto‐von‐Guericke University Magdeburg Magdeburg Germany
| | - Kai Sundmacher
- Process Systems Engineering Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
- Process Systems Engineering Otto‐von‐Guericke University Magdeburg Magdeburg Germany
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Fang H, Zhou J, Wang Z, Qiu Z, Sun Y, Lin Y, Chen K, Zhou X, Pan M. Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations. Front Chem Sci Eng 2021. [DOI: 10.1007/s11705-021-2043-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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11
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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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12
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Huster WR, Schweidtmann AM, Lüthje JT, Mitsos A. Deterministic global superstructure-based optimization of an organic Rankine cycle. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106996] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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13
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Zhang X, Zhou T, Ng KM. Optimization‐based cosmetic formulation: Integration of mechanistic model, surrogate model, and heuristics. AIChE J 2020. [DOI: 10.1002/aic.17064] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Xiang Zhang
- Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology, Clear Water Bay Hong Kong China
| | - Teng Zhou
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany
- Process Systems Engineering Otto‐von‐Guericke University Magdeburg Magdeburg Germany
| | - Ka Ming Ng
- Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology, Clear Water Bay Hong Kong China
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14
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Comparison of a Response Surface Method and Artificial Neural Network in Predicting the Aerodynamic Performance of a Wind Turbine Airfoil and Its Optimization. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186277] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
To find the optimal design for an engineering object, thousands of (or even more) simulations should be implemented to obtain the outcome data for the variously designed objects. However, repeating simulations this many times is impossible because a typical simulation is a computationally expensive task. Instead of conducting all the required simulations, a more efficient way is predicting the outcome from the approximation model, called the surrogate model. The response surface method (RSM) with polynomials and artificial neural network (ANN) are the most prominent methods in constructing a surrogate model in the engineering fields. In this study, the prediction accuracy of the surrogate models computed by using an RSM and ANN is compared with several datasets showing different complexities. This comparison is investigated by constructing the surrogate models in predicting aerodynamic performance of a wind turbine airfoil. In the current paper, it is verified that the prediction accuracy of the ANN-computed surrogate model is higher than the RSM-computed one when the datasets have a high level of complexity, but the opposite phenomenon is observed if the datasets have a low level of complexity. When the surrogate models with different accuracies are used to enhance the performance of a wind turbine airfoil, the surrogate model with a high level of accuracy produces the optimal design, showing a high performance improvement. The current study is expected to give guidance on how to properly choose between an RSM and ANN to construct a highly accurate surrogate model that can help in finding a design with a high performance improvement during the optimization process.
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17
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Song Z, Shi H, Zhang X, Zhou T. Prediction of CO2 solubility in ionic liquids using machine learning methods. Chem Eng Sci 2020. [DOI: 10.1016/j.ces.2020.115752] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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18
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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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19
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Katz J, Pappas I, Avraamidou S, Pistikopoulos EN. Integrating deep learning models and multiparametric programming. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106801] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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20
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21
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Rall D, Schweidtmann AM, Aumeier BM, Kamp J, Karwe J, Ostendorf K, Mitsos A, Wessling M. Simultaneous rational design of ion separation membranes and processes. J Memb Sci 2020. [DOI: 10.1016/j.memsci.2020.117860] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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22
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Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106598] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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23
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Tsay C, Baldea M. 110th Anniversary: Using Data to Bridge the Time and Length Scales of Process Systems. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b02282] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Calvin Tsay
- McKetta Department of Chemical Engineering The University of Texas at Austin, Austin, Texas 78712, United States
| | - Michael Baldea
- McKetta Department of Chemical Engineering The University of Texas at Austin, Austin, Texas 78712, United States
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24
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Schäfer P, Caspari A, Kleinhans K, Mhamdi A, Mitsos A. Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks. AIChE J 2019. [DOI: 10.1002/aic.16568] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Pascal Schäfer
- AVT ‐ Aachener Verfahrenstechnik, Process Systems EngineeringRWTH Aachen University Aachen Germany
| | - Adrian Caspari
- AVT ‐ Aachener Verfahrenstechnik, Process Systems EngineeringRWTH Aachen University Aachen Germany
| | - Kerstin Kleinhans
- AVT ‐ Aachener Verfahrenstechnik, Process Systems EngineeringRWTH Aachen University Aachen Germany
| | | | - Alexander Mitsos
- AVT ‐ Aachener Verfahrenstechnik, Process Systems EngineeringRWTH Aachen University Aachen Germany
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