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In this paper we apply bayesian optimization to identify the most efficient neural network structure to predict mass-transfer coefficients CO2 capture spray columns
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In this paper we compare the performance of artificial neural networks and polynomial models optimized with warm intelligence in predicting process behaviours under different data scenario
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In this paper we investigate the effect of MEA concentration on the overall mass transfer coefficient in spray column on CO2 capture
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In this paper we apply hybrid modeling to predict mass-transfer coefficients in spray columns for CO2 capture
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In this paper we apply reinforcement learning techniques to improve the efficiency of solvent switch process.
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In this paper we investigate the application of hybrid modelling technique to predict the solvent switch process behaviour.
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In this paper we investigate the effect of MEA concentration on the overall CO2 capture process efficiency
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In this paper we investigate the application of reinforcement learning techniques to improve the efficiency of continous reaction processes
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In this paper we investigate the application of hybrid modelling to predict poorly specified reaction system. After the modelling effot, we propose a method to optimize the reaction system.
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In this paper we investigate the application of hybrid modelling to predict activity coefficients
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In this paper we propose the structure teaching machine learning application for Chemical Engineering Master’s students
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In this paper we present an autoencoder-based methodology that simulates Raman spectra from process variables and predicts the concentrations of different chemicals.
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This study proposes a hybrid model correlating the partitioning of organic molecules in octanol/water with the partitioning in ionic-liquid/water systems.
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In this paper we develop an hybrid model for CO₂ solubility prediction differentiating physical and chemical absorption
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In this paper we apply evolutionary algorithm for the automatic generation of hybrid models for chemical processes starting from mechanistic model description
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In this paper we apply bayesian optimisation algorithm to analyse the effect of shapes and dimensions of monolith channels in photochemical reactor and including multiobjective optimisation to improve its design
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In this paper we apply machine learning technique to improve the accuracy of COSMO-RS model in predicting surface tension of liquids employing molecular descriptors
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In this paper we review the capabilities of AI in generating novel reactor designs employing CFD simulations
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In this paper we review the capabilities of hybrid modelling in process intensification domain
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In this paper we propose a MILP formulation for the automatic identification and training of hybrid models starting from mechanistic information and data
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Bachelor's course, KU Leuven, Faculty of Engineering Technology, 2020
Master's course, KU Leuven, Faculty of Engineering Technology, 2023
Workshop, 5th Hybrid Modeling Summer School, 2023
Master's course, KU Leuven, Faculty of Engineering Science, 2025