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Hybrid Modeling in Process Industries

https://doi.org/10.1201/9781351184373
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inferred← citeshttps://doi.org/10.2139/ssrn.4684199openalex-extracted
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inferred← citeshttps://doi.org/10.1002/9781119634140.ch3openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/9783527843831.ch11openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/aic.16996openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/aic.17609openalex-snapshot
A hybrid science‐guided machine learning approach for modeling chemical processes: A review
inferred← citeshttps://doi.org/10.1002/aic.17644openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/biot.201900172openalex-snapshot
Bioprocessing in the Digital Age: The Role of Process Models
inferred← citeshttps://doi.org/10.1002/bit.28346openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/btpr.3122openalex-snapshot
Model‐based workflow for scale‐up of process strategies developed in miniaturized bioreactor systems
inferred← citeshttps://doi.org/10.1002/cite.202000025openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/cite.202100072openalex-snapshot
Hybride semi‐parametrische Modellierung der thermooxidativen Stabilisierung von PAN‐Precursorfasern
inferred← citeshttps://doi.org/10.1002/cjce.25416openalex-snapshot
Modelling a chemical plant using grey‐box models employing the support vector regression and artificial neural network
inferred← citeshttps://doi.org/10.1002/rnc.7275openalex-snapshot
Flexible development and evaluation of machine‐learning‐supported optimal control and estimation methods via HILO‐MPC
inferred← citeshttps://doi.org/10.1007/10_2020_142openalex-snapshot
Towards the Development of Digital Twins for the Bio-manufacturing Industry
inferred← citeshttps://doi.org/10.1007/978-3-030-78424-9_59openalex-snapshot
Hybrid Modelling in Production: Approach and Evaluation
inferred← citeshttps://doi.org/10.1007/978-3-031-75698-6_1openalex-snapshot
Trends der Entwicklung von digitalen Zwillingen für die Bioproduktionsindustrie
inferred← citeshttps://doi.org/10.1007/s11081-021-09608-0openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s12532-021-00204-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s43393-021-00024-0openalex-snapshot
Digital Twin in biomanufacturing: challenges and opportunities towards its implementation
inferred← citeshttps://doi.org/10.1016/b978-0-323-88506-5.50041-3openalex-snapshot
A Digital Twin for Process Optimisation in Pharmaceutical Manufacturing
inferred← citeshttps://doi.org/10.1016/b978-0-323-88506-5.50094-2openalex-snapshot
An uncertainty-aware hybrid modelling approach using probabilistic machine learning
inferred← citeshttps://doi.org/10.1016/b978-0-443-28824-1.50484-1openalex-snapshot
Delving into machine learning modeling of catalytic reactor system: a case study of steam methane reforming
inferred← citeshttps://doi.org/10.1016/j.bej.2023.108813openalex-snapshot
Comparison of strategies for iterative model-based upstream bioprocess development with single and parallel reactor set-ups
inferred← citeshttps://doi.org/10.1016/j.cej.2022.139487openalex-snapshot
Generalized reactor neural ODE for dynamic reaction process modeling with physical interpretability
inferred← citeshttps://doi.org/10.1016/j.cep.2025.110496openalex-snapshot
Hybrid modelling approaches in process intensification: A thorough review
inferred← citeshttps://doi.org/10.1016/j.cherd.2020.03.030openalex-snapshot
Discrete-continuous dynamic simulation of plantwide batch process systems in MATLAB
inferred← citeshttps://doi.org/10.1016/j.cherd.2022.12.036openalex-snapshot
Deeppipe: A hybrid intelligent framework for real-time batch tracking of multi-product pipelines
inferred← citeshttps://doi.org/10.1016/j.coche.2021.100715openalex-snapshot
Hybrid modeling — a key enabler towards realizing digital twins in biopharma?
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2020.106916openalex-snapshot
Hybrid machine learning assisted modelling framework for particle processes
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2022.107736openalex-snapshot
HybridML: Open source platform for hybrid modeling
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2022.107898openalex-snapshot
Perspectives on the integration between first-principles and data-driven modeling
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2023.108319openalex-snapshot
Hybrid modelling of a batch separation process
inferred← citeshttps://doi.org/10.1016/j.conengprac.2024.105841openalex-snapshot
Machine learning for industrial sensing and control: A survey and practical perspective
inferred← citeshttps://doi.org/10.1016/j.ifacol.2020.12.126openalex-snapshot
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
inferred← citeshttps://doi.org/10.1016/j.ifacol.2023.10.1380openalex-snapshot
Regularization when modeling with biased simulation data as a prior
inferred← citeshttps://doi.org/10.1016/j.inffus.2018.10.005openalex-snapshot
Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0
inferred← citeshttps://doi.org/10.1016/j.rineng.2024.103548openalex-snapshot
Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach
inferred← citeshttps://doi.org/10.1016/j.seppur.2020.117064openalex-snapshot
Hybrid modeling of cross-flow filtration: Predicting the flux evolution and duration of ultrafiltration processes
inferred← citeshttps://doi.org/10.1016/j.seppur.2021.119277openalex-snapshot
Hybrid modeling reduces experimental effort to predict performance of serial and parallel single-pass tangential flow filtration
inferred← citeshttps://doi.org/10.1016/j.tibtech.2021.02.004openalex-snapshot
Towards in silico Process Modeling for Vaccines
inferred← citeshttps://doi.org/10.1017/9781108847209openalex-snapshot
Perfusion Cell Culture Processes for Biopharmaceuticals
inferred← citeshttps://doi.org/10.1017/9781108847209.001openalex-snapshot
Perfusion Mammalian Cell Culture for Recombinant Protein Manufacturing
inferred← citeshttps://doi.org/10.1017/9781108847209.002openalex-snapshot
Perfusion Bioreactors:The Set-Up and Process Characterisation
inferred← citeshttps://doi.org/10.1017/9781108847209.003openalex-snapshot
Scale-Down Models and Sensors for Process Development
inferred← citeshttps://doi.org/10.1017/9781108847209.004openalex-snapshot
Design and Optimisation of Mammalian Cell Perfusion Cultures
inferred← citeshttps://doi.org/10.1017/9781108847209.005openalex-snapshot
Clinical- and Commercial-Scale Reactors
inferred← citeshttps://doi.org/10.1017/9781108847209.006openalex-snapshot
Mechanistic and Statistical Modelling of Bioprocesses
inferred← citeshttps://doi.org/10.1017/9781108847209.007openalex-snapshot
Index
inferred← citeshttps://doi.org/10.1021/acs.iecr.0c02720openalex-snapshot
Construction of a Valid Domain for a Hybrid Model and Its Application to Dynamic Optimization with Controlled Exploration
inferred← citeshttps://doi.org/10.1021/acs.iecr.1c01384openalex-snapshot
Data-Driven Ionic Liquid Design for CO2 Capture: Molecular Structure Optimization and DFT Verification
inferred← citeshttps://doi.org/10.1021/acs.iecr.1c04507openalex-snapshot
Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes
inferred← citeshttps://doi.org/10.1038/s41598-024-55577-6openalex-snapshot
A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals
inferred← citeshttps://doi.org/10.1039/bk9781837670178-00001openalex-snapshot
Part I: Model Construction Theory
inferred← citeshttps://doi.org/10.1039/bk9781837670178-00056openalex-snapshot
Hybrid Model Construction
inferred← citeshttps://doi.org/10.1039/bk9781837670178-00133openalex-snapshot
Part II: Applications in Reaction Engineering
inferred← citeshttps://doi.org/10.1039/bk9781837670178-00208openalex-snapshot
Hybrid Modelling Under Uncertainty: Effects of Model Greyness, Data Quality and Data Quantity
inferred← citeshttps://doi.org/10.1039/d1dd00037copenalex-snapshot
Graph neural networks for the prediction of infinite dilution activity coefficients
inferred← citeshttps://doi.org/10.1080/21693277.2024.2305358openalex-snapshot
Physics-based and data-driven hybrid modeling in manufacturing: a review
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1013681openalex-snapshot
A Bayesian neural ordinary differential equations framework to study the effects of chemical mixtures on survival
inferred← citeshttps://doi.org/10.1515/auto-2020-0038openalex-snapshot
Application of a grey-box modelling approach for the online monitoring of batch production in the chemical industry
inferred← citeshttps://doi.org/10.1590/s1413-4152esp00001openalex-snapshot
My adsorption journey: Ups and downs and other stories
inferred← citeshttps://doi.org/10.22541/au.167465887.70993839/v1openalex-snapshot
Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review
inferred← citeshttps://doi.org/10.25369/ll.v3i1.68openalex-snapshot
Opportunities of digital teaching in process control: flip the classroom, provide a level playing field, and integrate external experts
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The use of predictive models to develop chromatography-based purification processes
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From Physics to Bioengineering: Microbial Cultivation Process Design and Feeding Rate Control Based on Relative Entropy Using Nuisance Time
inferred← citeshttps://doi.org/10.3390/fermentation11070411openalex-snapshot
Leveraging Historical Process Data for Recombinant P. pastoris Fermentation Hybrid Deep Modeling and Model Predictive Control Development
inferred← citeshttps://doi.org/10.3390/fermentation9100922openalex-snapshot
From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives
inferred← citeshttps://doi.org/10.3390/met16020142openalex-snapshot
Parallel Hybrid Modeling of Al–Mg–Si Tensile Properties Using Density-Based Weighting
inferred← citeshttps://doi.org/10.3390/min16080781openalex-snapshot
An Integrated Grey System Theory Approach for Operational Risk Assessment and Interdependency Analysis in Mineral Processing Plants: A Case of Gohar Zamin Iron Ore Complex (Sirjan, Iran)
inferred← citeshttps://doi.org/10.3390/pharmaceutics12060562openalex-snapshot
Working within the Design Space: Do Our Static Process Characterization Methods Suffice?
inferred← citeshttps://doi.org/10.3390/pr8121625openalex-snapshot
Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration
inferred← citeshttps://doi.org/10.35376/10324/55185openalex-snapshot
Advanced decision support through real-time optimization in the process industry
inferred← citeshttps://doi.org/10.69997/sct.129325openalex-snapshot
Development of a hybrid, semi-parametric Simulation Model of an AEM Electrolysis Stack Unit for large-scale System Simulations
inferred← citeshttps://doi.org/10.69997/sct.187062openalex-snapshot
Predicting Surface Tension of Organic Molecules using COSMO-RS Theory and Machine Learning
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