1
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Pistikopoulos EN, Tian Y. Advanced Modeling and Optimization Strategies for Process Synthesis. Annu Rev Chem Biomol Eng 2024; 15:81-103. [PMID: 38594946 DOI: 10.1146/annurev-chembioeng-100522-112139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation-based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning-and quantum computing-assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies.
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Affiliation(s)
- Efstratios N Pistikopoulos
- Texas A&M Energy Institute and Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA;
| | - Yuhe Tian
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, West Virginia, USA;
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2
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Amusat O, Atia AA, Dudchenko AV, Bartholomew TV. Modeling Framework for Cost Optimization of Process-Scale Desalination Systems with Mineral Scaling and Precipitation. ACS ES&T ENGINEERING 2024; 4:1028-1047. [PMID: 38751651 PMCID: PMC11091887 DOI: 10.1021/acsestengg.3c00537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 05/18/2024]
Abstract
Cost-optimization models are powerful tools for evaluating emerging water treatment processes. However, to date, optimization models do not incorporate detailed chemical reaction phenomena, limiting the assessment of pretreatment and mineral scaling. Moreover, novel approaches for high-salinity and high-recovery desalination are typically proposed without direct quantification of pretreatment needs or mineral scaling. This work addresses a critical gap in the literature by presenting a modeling framework that includes complex water chemistry predictions with process-scale optimization. We use this approach to conduct a technoeconomic assessment on a conceptual high-recovery treatment train that includes chemical pretreatment (i.e., soda ash softening and recarbonation) and membrane-based desalination (i.e., standard and high-pressure reverse osmosis). We demonstrate how to develop and integrate accurate multidimensional surrogate models for predicting precipitation, pH, and mineral scaling tendencies. Our findings show that cost-optimal results balance the costs of pretreatment with reverse osmosis system design. Optimizing across a range of water recoveries (i.e., 50-90%) reveals multiple cost-optimal schemas that vary the chemical dosing in pretreatment and the design and operation of reverse osmosis. Our results reveal that pretreatment costs can be more than double the cost of the primary desalination process at high recoveries due to the extensive pretreatment required to control scaling. This work emphasizes the importance of and provides a framework for including chemistry and mineral scaling predictions in the evaluation of emerging technologies in high-recovery desalination.
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Affiliation(s)
- Oluwamayowa
O. Amusat
- Lawrence
Berkeley National Laboratory (LBNL), 1 Cyclotron Road, Berkeley, California 94720, United States
| | - Adam A. Atia
- National
Energy Technology Laboratory (NETL), Pittsburgh, Pennsylvania 15236, United States
- NETL
Support Contractor, Pittsburgh, Pennsylvania 15236, United States
| | - Alexander V. Dudchenko
- SLAC
National Accelerator Laboratory, 2575 Sand Hill Road, Menlo
Park, California 94025, United States
| | - Timothy V. Bartholomew
- National
Energy Technology Laboratory (NETL), Pittsburgh, Pennsylvania 15236, United States
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3
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Martín-Hernández E, Montero-Rueda C, Ruiz-Mercado GJ, Vaneeckhaute C, Martín M. Multi-scale techno-economic assessment of nitrogen recovery systems for livestock operations. SUSTAINABLE PRODUCTION AND CONSUMPTION 2023; 41:49-63. [PMID: 37986715 PMCID: PMC10659086 DOI: 10.1016/j.spc.2023.07.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Intensive livestock farming generates vast amounts of organic materials, which are an important source of nitrogen releases. These anthropogenic nitrogen releases contribute to multiple environmental problems, including eutrophication of water systems, contamination of drinking water sources, and greenhouse gas emissions. Nitrogen recovery and recycling are technically feasible, and there exists a number of processes for nitrogen recovery from livestock material in the form of different products. In this work, a multi-scale techno-economic assessment of techniques for nitrogen recovery and recycling is performed. The assessment includes a material flow analysis of each process, from material collection to final treatment, to determine nitrogen recovery efficiency, losses, and recovery cost, as well as an environmental cost-benefit analysis to compare the nitrogen recovery cost versus the economic losses derived from its uncontrolled release into the environment. The results show that transmembrane chemisorption process results in the lowest recovery cost, 3.4-10.4 USD per kilogram of nitrogen recovered in the range of studied processing scales. The recovery of nitrogen from livestock material through three technologies, i.e., transmembrane chemisorption, MAPHEX, and stripping in packed bed, reveales to be cost-effective. Since the economic losses due to the harmful effects of nitrogen into the environment are estimated at 32-35 USD per kilogram of nitrogen released, nitrogen recycling is an environmentally and economically beneficial approach to reduce nutrient pollution caused by livestock operations.
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Affiliation(s)
- Edgar Martín-Hernández
- Department of Chemical Engineering, University of Salamanca, Plza. Caídos 1-5, 37008 Salamanca, Spain
- BioEngine - Research Team on Green Process Engineering and Biorefineries, Chemical Engineering Department, Université Laval, 1065 Ave. de la Médecine, Québec, QC, G1V 0A6, Canada
- CentrEau, Centre de recherche sur l’eau, Université Laval, 1065 Avenue de la Médecine, Québec, QC, G1V 0A6, Canada
| | - Clara Montero-Rueda
- Department of Chemical Engineering, University of Salamanca, Plza. Caídos 1-5, 37008 Salamanca, Spain
| | - Gerardo J. Ruiz-Mercado
- Center for Environmental Solutions and Emergency Response (CESER), US Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, OH 45268, United States
- Chemical Engineering Graduate Program, Universidad del Atlántico, Puerto Colombia 080007, Colombia
| | - Céline Vaneeckhaute
- BioEngine - Research Team on Green Process Engineering and Biorefineries, Chemical Engineering Department, Université Laval, 1065 Ave. de la Médecine, Québec, QC, G1V 0A6, Canada
- CentrEau, Centre de recherche sur l’eau, Université Laval, 1065 Avenue de la Médecine, Québec, QC, G1V 0A6, Canada
| | - Mariano Martín
- Department of Chemical Engineering, University of Salamanca, Plza. Caídos 1-5, 37008 Salamanca, Spain
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4
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Srinivas SV, Karimi IA. Zone-wise Surrogate Modelling (ZSM) of Univariate Systems. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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5
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Abdullah F, Christofides PD. Data-based modeling and control of nonlinear process systems using sparse identification: An overview of recent results. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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6
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On generalization error of neural network models and its application to predictive control of nonlinear processes. Chem Eng Res Des 2023. [DOI: 10.1016/j.cherd.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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7
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Modeling of Bioprocesses via MINLP-based Symbolic Regression of S-system Formalisms. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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8
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Abdullah F, Alhajeri MS, Christofides PD. Modeling and Control of Nonlinear Processes Using Sparse Identification: Using Dropout to Handle Noisy Data. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- Fahim Abdullah
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California90095, United States
| | - Mohammed S. Alhajeri
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California90095, United States
- Department of Chemical Engineering, Kuwait University, P.O.Box 5969, Safat13060, Kuwait
| | - Panagiotis D. Christofides
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California90095, United States
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California90095, United States
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9
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Negri V, Vázquez D, Sales-Pardo M, Guimerà R, Guillén-Gosálbez G. Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO 2 Capture Technologies. ACS OMEGA 2022; 7:41147-41164. [PMID: 36406548 PMCID: PMC9670717 DOI: 10.1021/acsomega.2c04736] [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: 07/27/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
Process modeling has become a fundamental tool to guide experimental work. Unfortunately, process models based on first principles can be expensive to develop and evaluate, and hard to use, particularly when convergence issues arise. This work proves that Bayesian symbolic learning can be applied to derive simple closed-form expressions from rigorous process simulations, streamlining the process modeling task and making process models more accessible to experimental groups. Compared to conventional surrogate models, our approach provides analytical expressions that are easier to communicate and manipulate algebraically to get insights into the process. We apply this method to synthetic data obtained from two basic CO2 capture processes simulated in Aspen HYSYS, identifying accurate simplified interpretable equations for key variables dictating the process economic and environmental performance. We then use these expressions to analyze the process variables' elasticities and benchmark an emerging CO2 capture process against the business as usual technology.
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Affiliation(s)
- Valentina Negri
- Institute
for Chemical and Bioengineering, Department of Chemistry and Applied
Biosciences, ETH Zürich, Vladimir-Prelog-Weg 1, 8093Zürich, Switzerland
| | - Daniel Vázquez
- Institute
for Chemical and Bioengineering, Department of Chemistry and Applied
Biosciences, ETH Zürich, Vladimir-Prelog-Weg 1, 8093Zürich, Switzerland
| | - Marta Sales-Pardo
- Department
of Chemical Engineering, Universitat Rovira
i Virgili, Tarragona43007, Catalonia, Spain
| | - Roger Guimerà
- Department
of Chemical Engineering, Universitat Rovira
i Virgili, Tarragona43007, Catalonia, Spain
- ICREA, Barcelona08010, Catalonia, Spain
| | - Gonzalo Guillén-Gosálbez
- Institute
for Chemical and Bioengineering, Department of Chemistry and Applied
Biosciences, ETH Zürich, Vladimir-Prelog-Weg 1, 8093Zürich, Switzerland
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10
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Mitra A, Jain A, Kishore A, Kumar P. A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach. OPERATIONS RESEARCH FORUM 2022. [PMCID: PMC9514716 DOI: 10.1007/s43069-022-00166-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Demand forecasting has been a major concern of operational strategy to manage the inventory and optimize the customer satisfaction level. The researchers have proposed many conventional and advanced forecasting techniques, but no one leads to complete accuracy. Forecasting is equally important in manufacturing as well as retail companies. In this study, the performances of five regression techniques of machine learning, viz. random forest (RF), extreme gradient boosting (XGBoost), gradient boosting, adaptive boosting (AdaBoost), and artificial neural network (ANN) algorithms, are compared with a proposed hybrid (RF-XGBoost-LR) model for sales forecasting of a retail chain considering the various parameters of forecasting accuracy. The weekly sales data of a US-based retail company is considered in the analysis of the forecasts undertaking the attributes affecting the sale such as the temperature of the region and the size of the store. It is observed that the hybrid RF-XGBoost-LR outperformed the other models measured against various metrics of performance. This study may help the industry decision-maker to understand and improve the methods of forecasting.
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Affiliation(s)
- Arnab Mitra
- Department of Mechanical Engineering, Delhi Technological University, Delhi, 110042 India
| | - Arnav Jain
- Department of Mechanical Engineering, Delhi Technological University, Delhi, 110042 India
| | - Avinash Kishore
- Department of Mechanical Engineering, Delhi Technological University, Delhi, 110042 India
| | - Pravin Kumar
- Department of Mechanical Engineering, Delhi Technological University, Delhi, 110042 India
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11
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12
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Kudva A, Sorourifar F, Paulson JA. Constrained Robust Bayesian Optimization of Expensive Noisy Black‐Box Functions with Guaranteed Regret Bounds. AIChE J 2022. [DOI: 10.1002/aic.17857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Akshay Kudva
- Department of Chemical and Biomolecular Engineering at The Ohio State University Columbus OH USA
| | - Farshud Sorourifar
- Department of Chemical and Biomolecular Engineering at The Ohio State University Columbus OH USA
| | - Joel A. Paulson
- Department of Chemical and Biomolecular Engineering at The Ohio State University Columbus OH USA
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13
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Boucheikhchoukh AA, Swartz CLE, Bouveresse E, Lutran P, Robert A. Optimization of a Multiperiod Refinery Planning Problem under Uncertainty. AIChE J 2022. [DOI: 10.1002/aic.17799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Ariel A. Boucheikhchoukh
- Department of Chemical Engineering McMaster University, 1280 Main Street West Hamilton Ontario Canada
| | - Christopher L. E. Swartz
- Department of Chemical Engineering McMaster University, 1280 Main Street West Hamilton Ontario Canada
| | | | - Pierre Lutran
- TotalEnergies SE, 2 Pl. Jean Millier Courbevoie France
| | - Anna Robert
- TotalEnergies SE, 2 Pl. Jean Millier Courbevoie France
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14
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Bradley W, Kim J, Kilwein Z, Blakely L, Eydenberg M, Jalvin J, Laird C, Boukouvala F. Perspectives on the Integration between First-Principles and Data-Driven Modeling. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107898] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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15
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Multilevel surrogate modeling of an amine scrubbing process for
CO
2
capture. AIChE J 2022. [DOI: 10.1002/aic.17705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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16
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Affiliation(s)
- Leo H. Chiang
- Core R&D The Dow Chemical Company Lake Jackson Texas 77566 USA
| | - Birgit Braun
- Core R&D The Dow Chemical Company Lake Jackson Texas 77566 USA
| | - Zhenyu Wang
- Chemometrics, AI & Statistics The Dow Chemical Company Lake Jackson Texas 77566 USA
| | - Ivan Castillo
- Chemometrics, AI & Statistics The Dow Chemical Company Lake Jackson Texas 77566 USA
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17
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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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18
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19
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Hao Z, Zhang C, Lapkin AA. Efficient Surrogates Construction of Chemical Processes: Case studies on Pressure Swing Adsorption and
Gas‐to‐Liquids. AIChE J 2022. [DOI: 10.1002/aic.17616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Zhimian Hao
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Chonghuan Zhang
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd Singapore
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20
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Mowbray M, Vallerio M, Perez-Galvan C, Zhang D, Del Rio Chanona A, Navarro-Brull FJ. Industrial data science – a review of machine learning applications for chemical and process industries. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00541c] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Understand and optimize industrial processes via machine learning and chemical engineering principles.
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Affiliation(s)
- Max Mowbray
- The University of Manchester, Manchester, M13 9PL, UK
| | | | | | - Dongda Zhang
- The University of Manchester, Manchester, M13 9PL, UK
- Imperial College London, London, SW7 2AZ, UK
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21
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Esche E, Weigert J, Brand Rihm G, Göbel J, Repke JU. Architectures for neural networks as surrogates for dynamic systems in chemical engineering. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2021.10.042] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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22
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23
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Abstract
Systematic decision making in engineering requires appropriate models. In this article, we introduce a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is particularly useful when the available datasets are small or do not cover the entire input space, as is often the case in manufacturing applications. We set up the regression subject to the considered monotonicity constraints as a semi-infinite optimization problem, and propose an adaptive solution algorithm. The method is applicable in multiple dimensions and can be extended to more general shape constraints. It was tested and validated on two real-world manufacturing processes, namely, laser glass bending and press hardening of sheet metal. It was found that the resulting models both complied well with the expert’s monotonicity knowledge and predicted the training data accurately. The suggested approach led to lower root-mean-squared errors than comparative methods from the literature for the sparse datasets considered in this work.
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24
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Pedrozo H, Rodriguez Reartes S, Bernal D, Vecchietti A, Diaz M, Grossmann I. Hybrid model generation for superstructure optimization with Generalized Disjunctive Programming. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107473] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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25
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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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26
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Franzoi RE, Kelly JD, Menezes BC, Swartz CL. An adaptive sampling surrogate model building framework for the optimization of reaction systems. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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27
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Statistical Machine Learning in Model Predictive Control of Nonlinear Processes. MATHEMATICS 2021. [DOI: 10.3390/math9161912] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Recurrent neural networks (RNNs) have been widely used to model nonlinear dynamic systems using time-series data. While the training error of neural networks can be rendered sufficiently small in many cases, there is a lack of a general framework to guide construction and determine the generalization accuracy of RNN models to be used in model predictive control systems. In this work, we employ statistical machine learning theory to develop a methodological framework of generalization error bounds for RNNs. The RNN models are then utilized to predict state evolution in model predictive controllers (MPC), under which closed-loop stability is established in a probabilistic manner. A nonlinear chemical process example is used to investigate the impact of training sample size, RNN depth, width, and input time length on the generalization error, along with the analyses of probabilistic closed-loop stability through the closed-loop simulations under Lyapunov-based MPC.
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28
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Na J, Bak JH, Sahinidis NV. Efficient Bayesian inference using adversarial machine learning and low-complexity surrogate models. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107322] [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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29
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Thebelt A, Kronqvist J, Mistry M, Lee RM, Sudermann-Merx N, Misener R. ENTMOOT: A framework for optimization over ensemble tree models. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107343] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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30
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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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31
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Vollmer NI, Al R, Gernaey KV, Sin G. Synergistic optimization framework for the process synthesis and design of biorefineries. Front Chem Sci Eng 2021. [DOI: 10.1007/s11705-021-2071-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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32
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Pistikopoulos EN, Barbosa-Povoa A, Lee JH, Misener R, Mitsos A, Reklaitis GV, Venkatasubramanian V, You F, Gani R. Process systems engineering – The generation next? Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107252] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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33
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Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107249] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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34
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Cao L, Russo D, Lapkin AA. Automated robotic platforms in design and development of formulations. AIChE J 2021. [DOI: 10.1002/aic.17248] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Liwei Cao
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore
| | - Danilo Russo
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore
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35
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Scheffold L, Finkler T, Piechottka U. Gray-Box system modeling using symbolic regression and nonlinear model predictive control of a semibatch polymerization. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2020.107204] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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36
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Kämper A, Leenders L, Bahl B, Bardow A. AutoMoG: Automated data-driven Model Generation of multi-energy systems using piecewise-linear regression. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2020.107162] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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37
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Kumaran K, Papageorgiou DJ, Takac M, Lueg L, Sahinidis NV. Active metric learning for supervised classification. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2020.107132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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38
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Gargalo CL, de Las Heras SC, Jones MN, Udugama I, Mansouri SS, Krühne U, Gernaey KV. Towards the Development of Digital Twins for the Bio-manufacturing Industry. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2020; 176:1-34. [PMID: 33349908 DOI: 10.1007/10_2020_142] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The bio-manufacturing industry, along with other process industries, now has the opportunity to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully accomplish this, a physical-to-digital-to-physical information loop should be carefully developed. One way to achieve this is, for example, through the implementation of digital twins (DTs), which are virtual copies of the processes. Therefore, in this paper, the focus is on understanding the needs and challenges faced by the bio-manufacturing industry when dealing with this digitalized paradigm. To do so, two major building blocks of a DT, data and models, are highlighted and discussed. Hence, firstly, data and their characteristics and collection strategies are examined as well as new methods and tools for data processing. Secondly, modelling approaches and their potential of being used in DTs are reviewed. Finally, we share our vision with regard to the use of DTs in the bio-manufacturing industry aiming at bringing the DT a step closer to its full potential and realization.
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Affiliation(s)
- Carina L Gargalo
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
| | | | - Mark Nicholas Jones
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark.,Molecular Quantum Solutions ApS, Copenhagen, Denmark
| | - Isuru Udugama
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Seyed Soheil Mansouri
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Ulrich Krühne
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Krist V Gernaey
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark.
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Sun W, Braatz RD. ALVEN: Algebraic learning via elastic net for static and dynamic nonlinear model identification. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107103] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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40
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Esche E, Repke J. Dynamic Process Operation Under Demand Response – A Review of Methods and Tools. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000091] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Erik Esche
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
| | - Jens‐Uwe Repke
- Technische Universität Berlin Fachgebiet Dynamik und Betrieb technischer Anlagen Straße des 17. Juni 135 10623 Berlin Germany
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41
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Franzoi RE, Menezes BC, Kelly JD, Gut JAW, Grossmann IE. Cutpoint Temperature Surrogate Modeling for Distillation Yields and Properties. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c02868] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Robert E. Franzoi
- Department of Chemical Engineering, University of São Paulo, São Paulo, Brazil
| | - Brenno C. Menezes
- Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Jeffrey D. Kelly
- Industrial Algorithms Ltd., 15 St. Andrews Road, Toronto, Canada
| | - Jorge A. W. Gut
- Department of Chemical Engineering, University of São Paulo, São Paulo, Brazil
| | - Ignacio E. Grossmann
- Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213 United States
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42
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Beykal B, Avraamidou S, Pistikopoulos IPE, Onel M, Pistikopoulos EN. DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems. JOURNAL OF GLOBAL OPTIMIZATION : AN INTERNATIONAL JOURNAL DEALING WITH THEORETICAL AND COMPUTATIONAL ASPECTS OF SEEKING GLOBAL OPTIMA AND THEIR APPLICATIONS IN SCIENCE, MANAGEMENT AND ENGINEERING 2020; 78:1-36. [PMID: 32753792 PMCID: PMC7402589 DOI: 10.1007/s10898-020-00890-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 02/12/2020] [Indexed: 05/21/2023]
Abstract
The Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework is presented for addressing the optimization of bi-level mixed-integer nonlinear programming problems. In this framework, bi-level optimization problems are approximated as single-level optimization problems by collecting samples of the upper-level objective and solving the lower-level problem to global optimality at those sampling points. This process is done through the integration of the DOMINO framework with a grey-box optimization solver to perform design of experiments on the upper-level objective, and to consecutively approximate and optimize bi-level mixed-integer nonlinear programming problems that are challenging to solve using exact methods. The performance of DOMINO is assessed through solving numerous bi-level benchmark problems, a land allocation problem in Food-Energy-Water Nexus, and through employing different data-driven optimization methodologies, including both local and global methods. Although this data-driven approach cannot provide a theoretical guarantee to global optimality, we present an algorithmic advancement that can guarantee feasibility to large-scale bi-level optimization problems when the lower-level problem is solved to global optimality at convergence.
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Affiliation(s)
- Burcu Beykal
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA
| | - Styliani Avraamidou
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA
| | - Ioannis P. E. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA
| | - Melis Onel
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA
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Harjunkoski I, Ikonen T, Mostafaei H, Deneke T, Heljanko K. Synergistic and Intelligent Process Optimization: First Results and Open Challenges. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c02032] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Iiro Harjunkoski
- Aalto University, Department of Chemical and Metallurgical Engineering, P.O. Box
16100, 00076 Aalto, Finland
- Hitachi ABB Power Grids Research, Kallstadter Straße 1, 68309 Mannheim, Germany
| | - Teemu Ikonen
- Aalto University, Department of Chemical and Metallurgical Engineering, P.O. Box
16100, 00076 Aalto, Finland
| | - Hossein Mostafaei
- Aalto University, Department of Chemical and Metallurgical Engineering, P.O. Box
16100, 00076 Aalto, Finland
| | - Tewodros Deneke
- University of Helsinki, Department of Computer Science, P.O. Box 68, 00014 Helsinki, Finland
| | - Keijo Heljanko
- University of Helsinki, Department of Computer Science, P.O. Box 68, 00014 Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), 00014 Helsinki, Finland
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McBride K, Sanchez Medina EI, Sundmacher K. Hybrid Semi‐parametric Modeling in Separation Processes: A Review. CHEM-ING-TECH 2020. [DOI: 10.1002/cite.202000025] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Kevin McBride
- Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstraße 1 39106 Magdeburg Germany
| | - Edgar Ivan Sanchez Medina
- Otto-von-Guericke University Magdeburg Chair for Process Systems Engineering Universitätsplatz 2 39106 Magdeburg Germany
| | - Kai Sundmacher
- Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstraße 1 39106 Magdeburg Germany
- Otto-von-Guericke University Magdeburg Chair for Process Systems Engineering Universitätsplatz 2 39106 Magdeburg Germany
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Merino A, Garcia-Alvarez D, Sainz-Palmero GI, Acebes LF, Fuente MJ. Knowledge based recursive non-linear partial least squares (RNPLS). ISA TRANSACTIONS 2020; 100:481-494. [PMID: 31952793 DOI: 10.1016/j.isatra.2020.01.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 11/21/2019] [Accepted: 01/03/2020] [Indexed: 06/10/2023]
Abstract
Soft sensors driven by data are very common in industrial plants to perform indirect measurements of difficult to measure critical variables by using other variables that are relatively easier to obtain. The use of soft sensors implies some challenges, such as the colinearity of the predictor variables, the time-varying and possible non-linear nature of the industrial process. To deal with the first challenge, the partial least square (PLS) regression has been employed in many applications to model the linear relations between process variables, with noisy and highly correlated data. However, the PLS model needs to deal with the other two issues: the non-linear and time-varying characteristics of the processes. In this work, a new knowledge-based methodology for a recursive non-linear PLS algorithm (RNPLS) is systematized to deal with these issues. Here, the non-linear PLS algorithm is set up by carrying out the PLS regression over the augmented input matrix, which includes knowledge based non-linear transformations of some of the variables. This transformation depends on the system's nature, and takes into account the available knowledge about the process, which is provided by expert knowledge or emulated using software tools. Then, the recursive exponential weighted PLS is used to modify and adapt the model according to the process changes. This RNPLS algorithm has been tested using two case studies according to the available knowledge, a real industrial evaporation station of the sugar industry, where the expert knowledge about the process permits the formulation of the relationships, and a simulated wastewater treatment plant, where the necessary knowledge about the process is obtained by a software tool. The results show that the methodology involving knowledge regarding the process is able to adjust the process changes, providing highly accurate predictions.
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Affiliation(s)
- A Merino
- Department of Electromechanic Engineering, University of Burgos, Burgos, Spain.
| | - D Garcia-Alvarez
- Empresarios Agrupados Internacional, 47151 - Parque Tecnológico Boecillo, Valladolid, Spain
| | - G I Sainz-Palmero
- Department of Systems Engineering and Automatic Control, University of Valladolid, Valladolid, Spain
| | - L F Acebes
- Department of Systems Engineering and Automatic Control, University of Valladolid, Valladolid, Spain
| | - M J Fuente
- Department of Systems Engineering and Automatic Control, University of Valladolid, Valladolid, Spain
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Mencarelli L, Pagot A, Duchêne P. Surrogate-based modeling techniques with application to catalytic reforming and isomerization processes. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106772] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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49
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Degeling K, IJzerman MJ, Lavieri MS, Strong M, Koffijberg H. Introduction to Metamodeling for Reducing Computational Burden of Advanced Analyses with Health Economic Models: A Structured Overview of Metamodeling Methods in a 6-Step Application Process. Med Decis Making 2020; 40:348-363. [PMID: 32428428 PMCID: PMC7754830 DOI: 10.1177/0272989x20912233] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 02/14/2020] [Indexed: 01/24/2023]
Abstract
Metamodels can be used to reduce the computational burden associated with computationally demanding analyses of simulation models, although applications within health economics are still scarce. Besides a lack of awareness of their potential within health economics, the absence of guidance on the conceivably complex and time-consuming process of developing and validating metamodels may contribute to their limited uptake. To address these issues, this article introduces metamodeling to the wider health economic audience and presents a process for applying metamodeling in this context, including suitable methods and directions for their selection and use. General (i.e., non-health economic specific) metamodeling literature, clinical prediction modeling literature, and a previously published literature review were exploited to consolidate a process and to identify candidate metamodeling methods. Methods were considered applicable to health economics if they are able to account for mixed (i.e., continuous and discrete) input parameters and continuous outcomes. Six steps were identified as relevant for applying metamodeling methods within health economics: 1) the identification of a suitable metamodeling technique, 2) simulation of data sets according to a design of experiments, 3) fitting of the metamodel, 4) assessment of metamodel performance, 5) conducting the required analysis using the metamodel, and 6) verification of the results. Different methods are discussed to support each step, including their characteristics, directions for use, key references, and relevant R and Python packages. To address challenges regarding metamodeling methods selection, a first guide was developed toward using metamodels to reduce the computational burden of analyses of health economic models. This guidance may increase applications of metamodeling in health economics, enabling increased use of state-of-the-art analyses (e.g., value of information analysis) with computationally burdensome simulation models.
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Affiliation(s)
- Koen Degeling
- />Health Technology and Services Research Department, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, Overijssel, the Netherlands
- />Cancer Health Services Research Unit, School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Maarten J. IJzerman
- />Victorian Comprehensive Cancer Centre, Melbourne, Australia
- />Health Technology and Services Research Department, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, Overijssel, the Netherlands
- />Cancer Health Services Research Unit, School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Mariel S. Lavieri
- Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Mark Strong
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, England, UK
| | - Hendrik Koffijberg
- Health Technology and Services Research Department, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, Overijssel, the Netherlands
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Dias LS, Ierapetritou MG. Integration of planning, scheduling and control problems using data-driven feasibility analysis and surrogate models. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106714] [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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