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Hee Kim J, Bae Rhim G, Choi N, Hye Youn M, Hyun Chun D, Heo S. A hybrid modeling framework for efficient development of Fischer-Tropsch kinetic models. J IND ENG CHEM 2022. [DOI: 10.1016/j.jiec.2022.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Data Augmentation to Support Biopharmaceutical Process Development through Digital Models—A Proof of Concept. Processes (Basel) 2022. [DOI: 10.3390/pr10091796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In recent years, monoclonal antibodies (mAbs) are gaining a wide market share as the most impactful bioproducts. The development of mAbs requires extensive experimental campaigns which may last several years and cost billions of dollars. Following the paradigm of Industry 4.0 digitalization, data-driven methodologies are now used to accelerate the development of new biopharmaceutical products. For instance, predictive models can be built to forecast the productivity of the cell lines in the culture in such a way as to anticipate the identification of the cell lines to be progressed in the scale-up exercise. However, the number of experiments that can be performed decreases dramatically as the process scale increases, due to the resources required for each experimental run. This limits the availability of experimental data and, accordingly, the applicability of data-driven methodologies to support the process development. To address this issue in this work we propose the use of digital models to generate in silico data and augment the amount of data available from real (i.e., in vivo) experimental runs, accordingly. In particular, we propose two strategies for in silico data generation to estimate the endpoint product titer in mAbs manufacturing: one based on a first principles model and one on a hybrid semi-parametric model. As a proof of concept, the effect of in silico data generation was investigated on a simulated biopharmaceutical process for the production of mAbs. We obtained very promising results: the digital model effectively supports the identification of high-productive cell lines (i.e., high mAb titer) even when a very low number of real experimental batches (two or three) is available.
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Process Model Inversion in the Data-Driven Engineering Context for Improved Parameter Sensitivities. Processes (Basel) 2022. [DOI: 10.3390/pr10091764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Industry 4.0 has embraced process models in recent years, and the use of model-based digital twins has become even more critical in process systems engineering, monitoring, and control. However, the reliability of these models depends on the model parameters available. The accuracy of the estimated parameters is, in turn, determined by the amount and quality of the measurement data and the algorithm used for parameter identification. For the definition of the parameter identification problem, the ordinary least squares framework is still state-of-the-art in the literature, and better parameter estimates are only possible with additional data. In this work, we present an alternative strategy to identify model parameters by incorporating differential flatness for model inversion and neural ordinary differential equations for surrogate modeling. The novel concept results in an input-least-squares-based parameter identification problem with significant parameter sensitivity changes. To study these sensitivity effects, we use a classic one-dimensional diffusion-type problem, i.e., an omnipresent equation in process systems engineering and transport phenomena. As shown, the proposed concept ensures higher parameter sensitivities for two relevant scenarios. Based on the results derived, we also discuss general implications for data-driven engineering concepts used to identify process model parameters in the recent literature.
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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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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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Mädler J, Richter B, Wolz DSJ, Behnisch T, Böhm R, Jäger H, Gude M, Urbas L. Hybride semi‐parametrische Modellierung der thermooxidativen Stabilisierung von PAN‐Precursorfasern. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202100072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Jonathan Mädler
- Technische Universität Dresden Professur für Prozessleittechnik & Arbeitsgruppe Systemverfahrenstechnik Georg-Schumann-Straße 18 01069 Dresden Deutschland
| | - Benjamin Richter
- Technische Universität Dresden Institut für Leichtbau und Kunststofftechnik 01062 Dresden Deutschland
- Technische Universität Dresden Research Center for Carbon Fibers Saxony Holbeinstraße 3 01307 Dresden Deutschland
| | - Daniel S. J. Wolz
- Technische Universität Dresden Institut für Leichtbau und Kunststofftechnik 01062 Dresden Deutschland
- Technische Universität Dresden Research Center for Carbon Fibers Saxony Holbeinstraße 3 01307 Dresden Deutschland
| | - Thomas Behnisch
- Technische Universität Dresden Institut für Leichtbau und Kunststofftechnik 01062 Dresden Deutschland
- Technische Universität Dresden Research Center for Carbon Fibers Saxony Holbeinstraße 3 01307 Dresden Deutschland
| | - Robert Böhm
- Technische Universität Dresden Research Center for Carbon Fibers Saxony Holbeinstraße 3 01307 Dresden Deutschland
- Hochschule für Technik, Wirtschaft und Kultur Leipzig Fakultät Ingenieurwissenschaften PF 30 11 66 04251 Leipzig Deutschland
| | - Hubert Jäger
- Technische Universität Dresden Institut für Leichtbau und Kunststofftechnik 01062 Dresden Deutschland
- Technische Universität Dresden Research Center for Carbon Fibers Saxony Holbeinstraße 3 01307 Dresden Deutschland
| | - Maik Gude
- Technische Universität Dresden Institut für Leichtbau und Kunststofftechnik 01062 Dresden Deutschland
- Technische Universität Dresden Research Center for Carbon Fibers Saxony Holbeinstraße 3 01307 Dresden Deutschland
| | - Leon Urbas
- Technische Universität Dresden Professur für Prozessleittechnik & Arbeitsgruppe Systemverfahrenstechnik Georg-Schumann-Straße 18 01069 Dresden Deutschland
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Galvanin F, Hartman RL, Kulkarni AA, Nieves-Remacha MJ. Introduction to the themed collection on digitalization in reaction engineering. REACT CHEM ENG 2022. [DOI: 10.1039/d2re90011d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Federico Galvanin, Ryan Hartman, Amol Kulkarni and María José Nieves-Remacha introduce the Reaction Chemistry & Engineering themed collection on digitalization in reaction engineering.
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Affiliation(s)
- Federico Galvanin
- Department of Chemical Engineering, University College London, London, UK
| | - Ryan L. Hartman
- Department of Chemical and Biomolecular Engineering, New York University, 6 MetroTech Center, Brooklyn, NY, USA
| | - Amol A. Kulkarni
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory (NCL) Campus, Pune-411008, India
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Machalek D, Quah T, Powell KM. A novel implicit hybrid machine learning model and its application for reinforcement learning. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107496] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Generic Design Methodology for Smart Manufacturing Systems from a Practical Perspective, Part I—Digital Triad Concept and Its Application as a System Reference Model. MACHINES 2021. [DOI: 10.3390/machines9100207] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Rapidly developed information technologies (IT) have continuously empowered manufacturing systems and accelerated the evolution of manufacturing system paradigms, and smart manufacturing (SM) has become one of the most promising paradigms. The study of SM has attracted a great deal of attention for researchers in academia and practitioners in industry. However, an obvious fact is that people with different backgrounds have different expectations for SM, and this has led to high diversity, ambiguity, and inconsistency in terms of definitions, reference models, performance matrices, and system design methodologies. It has been found that the state of the art SM research is limited in two aspects: (1) the highly diversified understandings of SM may lead to overlapped, missed, and non-systematic research efforts in advancing the theory and methodologies in the field of SM; (2) few works have been found that focus on the development of generic design methodologies for smart manufacturing systems from the practice perspective. The novelty of this paper consists of two main aspects which are reported in two parts respectively. In the first part, a simplified definition of SM is proposed to unify the existing diversified expectations, and a newly developed concept named digital triad (DT-II) is adopted to define a reference model for SM. The common features of smart manufacturing systems in various applications are identified as functional requirements (FRs) in systems design. To model a system that is capable of reconfiguring itself to adapt to changes, the concept of IoDTT is proposed as a reference model for smart manufacturing systems. In the second part, these two concepts are used to formulate a system design problem, and a generic methodology, based on axiomatic design theory (ADT), is proposed for the design of smart manufacturing systems.
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Predicting the behavior of granules of complex shapes using coarse-grained particles and artificial neural networks. POWDER TECHNOL 2021. [DOI: 10.1016/j.powtec.2021.01.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Song W, Du W, Fan C, Yang M, Qian F. Adaptive Weighted Hybrid Modeling of Hydrocracking Process and Its Operational Optimization. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.0c05416] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Wenjiang Song
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Wei Du
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
- Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
| | - Chen Fan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Minglei Yang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Feng Qian
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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