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Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies among parameters

Parth Shah, M. Ziyan Sheriff, Mohammed Saad Faizan Bangi, Costas Kravaris, Joseph Sang‐Il Kwon, Chiranjivi Botre, Junichi Hirota

2022Chemical Engineering Journal136 citationsDOI

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Robustness (evolution)Artificial neural networkProcess (computing)Computer scienceProcess modelingExperimental dataSystem identificationData-drivenScale (ratio)Identification (biology)Data miningWork in processMachine learningBiological systemArtificial intelligenceProcess engineeringEngineeringMathematicsMeasure (data warehouse)ChemistryBotanyStatisticsBiologyGeneBiochemistryQuantum mechanicsOperating systemOperations managementPhysicsViral Infectious Diseases and Gene Expression in InsectsSpectroscopy and Chemometric AnalysesFuel Cells and Related Materials
Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies among parameters | Litcius